2025-09-07T06:38:45.1083254Z Current runner version: '2.328.0' 2025-09-07T06:38:45.1087709Z Runner name: 'linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b' 2025-09-07T06:38:45.1088249Z Runner group name: 'default' 2025-09-07T06:38:45.1088800Z Machine name: 'linux' 2025-09-07T06:38:45.1090393Z ##[group]GITHUB_TOKEN Permissions 2025-09-07T06:38:45.1091879Z Contents: read 2025-09-07T06:38:45.1092192Z Metadata: read 2025-09-07T06:38:45.1092504Z ##[endgroup] 2025-09-07T06:38:45.1093838Z Secret source: Actions 2025-09-07T06:38:45.1094147Z Prepare workflow directory 2025-09-07T06:38:45.1352487Z Prepare all required actions 2025-09-07T06:38:45.1374628Z Getting action download info 2025-09-07T06:38:45.4361395Z Download action repository 'pytorch/pytorch@main' (SHA:93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:38:47.8043967Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-09-07T06:38:48.1380744Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-09-07T06:38:48.4136157Z Download action repository 'pytorch/test-infra@main' (SHA:548a4bc624d43a01cdf165a63b041f0ae014ddbd) 2025-09-07T06:38:48.9316571Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-07T06:38:49.3458020Z Getting action download info 2025-09-07T06:38:49.4562126Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955) 2025-09-07T06:38:49.7866133Z Getting action download info 2025-09-07T06:38:49.9085781Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-07T06:38:50.2278752Z Getting action download info 2025-09-07T06:38:50.3594804Z Uses: pytorch/pytorch/.github/workflows/_rocm-test.yml@refs/heads/main (93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:38:50.3596985Z ##[group] Inputs 2025-09-07T06:38:50.3597135Z build-environment: linux-noble-rocm-py3.12-mi300 2025-09-07T06:38:50.3597764Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}]} 2025-09-07T06:38:50.3598522Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:38:50.3598794Z sync-tag: 2025-09-07T06:38:50.3602314Z timeout-minutes: 300 2025-09-07T06:38:50.3602417Z tests-to-include: 2025-09-07T06:38:50.3602506Z dashboard-tag: 2025-09-07T06:38:50.3602734Z disable-monitor: true 2025-09-07T06:38:50.3602838Z monitor-log-interval: 5 2025-09-07T06:38:50.3602950Z monitor-data-collect-interval: 1 2025-09-07T06:38:50.3603066Z ##[endgroup] 2025-09-07T06:38:50.3603231Z Complete job name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:38:50.3886433Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-09-07T06:38:50.3886672Z with: 2025-09-07T06:38:50.3886754Z no-sudo: true 2025-09-07T06:38:50.3886838Z submodules: recursive 2025-09-07T06:38:50.3886927Z fetch-depth: 0 2025-09-07T06:38:50.3887043Z env: 2025-09-07T06:38:50.3887126Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:38:50.3887228Z ##[endgroup] 2025-09-07T06:38:50.3940684Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:38:50.3941032Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:38:50.3948984Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:38:50.3949125Z env: 2025-09-07T06:38:50.3949208Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:38:50.3949301Z ##[endgroup] 2025-09-07T06:38:50.4081649Z ##[group]Run # Use all available CPUs for fetching 2025-09-07T06:38:50.4081861Z # Use all available CPUs for fetching 2025-09-07T06:38:50.4081998Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:38:50.4082135Z git config --global fetch.parallel 0 2025-09-07T06:38:50.4082278Z git config --global submodule.fetchJobs 0 2025-09-07T06:38:50.4082500Z  2025-09-07T06:38:50.4087926Z # Clean workspace. The default checkout action should also do this, but 2025-09-07T06:38:50.4088098Z # do it here as well just in case 2025-09-07T06:38:50.4088217Z if [[ -d .git ]]; then 2025-09-07T06:38:50.4088330Z  if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:38:50.4088458Z  sudo git clean -ffdx 2025-09-07T06:38:50.4088559Z  else 2025-09-07T06:38:50.4088645Z  git clean -ffdx 2025-09-07T06:38:50.4088737Z  fi 2025-09-07T06:38:50.4088814Z fi 2025-09-07T06:38:50.4094154Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:38:50.4094287Z env: 2025-09-07T06:38:50.4094451Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:38:50.4094544Z NO_SUDO: true 2025-09-07T06:38:50.4094626Z ##[endgroup] 2025-09-07T06:38:50.4221688Z ##[group]Run actions/checkout@v4 2025-09-07T06:38:50.4221829Z with: 2025-09-07T06:38:50.4221928Z ref: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:38:50.4222048Z fetch-depth: 0 2025-09-07T06:38:50.4222132Z submodules: recursive 2025-09-07T06:38:50.4222226Z show-progress: false 2025-09-07T06:38:50.4222327Z repository: pytorch/pytorch 2025-09-07T06:38:50.4222505Z token: *** 2025-09-07T06:38:50.4222592Z ssh-strict: true 2025-09-07T06:38:50.4225595Z ssh-user: git 2025-09-07T06:38:50.4225703Z persist-credentials: true 2025-09-07T06:38:50.4225801Z clean: true 2025-09-07T06:38:50.4225894Z sparse-checkout-cone-mode: true 2025-09-07T06:38:50.4225998Z fetch-tags: false 2025-09-07T06:38:50.4226079Z lfs: false 2025-09-07T06:38:50.4226160Z set-safe-directory: true 2025-09-07T06:38:50.4226265Z env: 2025-09-07T06:38:50.4226345Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:38:50.4226438Z ##[endgroup] 2025-09-07T06:38:50.4832304Z Syncing repository: pytorch/pytorch 2025-09-07T06:38:50.4832907Z ##[group]Getting Git version info 2025-09-07T06:38:50.4833068Z Working directory is '/home/runner/_work/pytorch/pytorch' 2025-09-07T06:38:50.4833306Z [command]/usr/bin/git version 2025-09-07T06:38:50.4833405Z git version 2.51.0 2025-09-07T06:38:50.4841158Z ##[endgroup] 2025-09-07T06:38:50.4846401Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/4bfd55b0-ce9c-4e8c-9371-47d651f87c7e/.gitconfig' 2025-09-07T06:38:50.4854655Z Temporarily overriding HOME='/home/runner/_work/_temp/4bfd55b0-ce9c-4e8c-9371-47d651f87c7e' before making global git config changes 2025-09-07T06:38:50.4854993Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T06:38:50.4862833Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch 2025-09-07T06:38:50.4881365Z Deleting the contents of '/home/runner/_work/pytorch/pytorch' 2025-09-07T06:38:50.4883875Z ##[group]Initializing the repository 2025-09-07T06:38:50.4886480Z [command]/usr/bin/git init /home/runner/_work/pytorch/pytorch 2025-09-07T06:38:50.4912360Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-07T06:38:50.4912657Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-07T06:38:50.4912920Z hint: of your new repositories, which will suppress this warning, call: 2025-09-07T06:38:50.4913112Z hint: 2025-09-07T06:38:50.4913269Z hint: git config --global init.defaultBranch 2025-09-07T06:38:50.4913669Z hint: 2025-09-07T06:38:50.4913830Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-07T06:38:50.4914092Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-07T06:38:50.4922031Z hint: 2025-09-07T06:38:50.4922198Z hint: git branch -m 2025-09-07T06:38:50.4922357Z hint: 2025-09-07T06:38:50.4922513Z hint: Disable this message with "git config set advice.defaultBranchName false" 2025-09-07T06:38:50.4922768Z Initialized empty Git repository in /home/runner/_work/pytorch/pytorch/.git/ 2025-09-07T06:38:50.4923180Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-09-07T06:38:50.4952288Z ##[endgroup] 2025-09-07T06:38:50.4952461Z ##[group]Disabling automatic garbage collection 2025-09-07T06:38:50.4955535Z [command]/usr/bin/git config --local gc.auto 0 2025-09-07T06:38:50.4984256Z ##[endgroup] 2025-09-07T06:38:50.4984527Z ##[group]Setting up auth 2025-09-07T06:38:50.4988938Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T06:38:50.5011861Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2025-09-07T06:38:50.5253584Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T06:38:50.5276609Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2025-09-07T06:38:50.5482185Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:38:50.5505730Z ##[endgroup] 2025-09-07T06:38:50.5505942Z ##[group]Fetching the repository 2025-09-07T06:38:50.5514717Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-09-07T06:39:16.0718393Z From https://github.com/pytorch/pytorch 2025-09-07T06:39:16.0719445Z * [new branch] 160583 -> origin/160583 2025-09-07T06:39:16.0719996Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-09-07T06:39:16.0720505Z * [new branch] 5addvllmbuild -> origin/5addvllmbuild 2025-09-07T06:39:16.0721081Z * [new branch] AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest 2025-09-07T06:39:16.0721683Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-09-07T06:39:16.0732508Z * [new branch] ISSUE-154849 -> origin/ISSUE-154849 2025-09-07T06:39:16.0732820Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-09-07T06:39:16.0733123Z * [new branch] NicoshevSVE128 -> origin/NicoshevSVE128 2025-09-07T06:39:16.0733390Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-09-07T06:39:16.0733670Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-09-07T06:39:16.0734599Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-09-07T06:39:16.0734869Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-09-07T06:39:16.0735116Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-09-07T06:39:16.0735371Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-09-07T06:39:16.0735624Z * [new branch] VLA_exp -> origin/VLA_exp 2025-09-07T06:39:16.0735883Z * [new branch] actually-run-mps-aot-inductor -> origin/actually-run-mps-aot-inductor 2025-09-07T06:39:16.0736334Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-09-07T06:39:16.0736702Z * [new branch] add-user-guide-structure -> origin/add-user-guide-structure 2025-09-07T06:39:16.0736982Z * [new branch] add-vllm-nightly-build -> origin/add-vllm-nightly-build 2025-09-07T06:39:16.0737242Z * [new branch] add_compile_benchmarking -> origin/add_compile_benchmarking 2025-09-07T06:39:16.0737500Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-09-07T06:39:16.0737730Z * [new branch] addsimde -> origin/addsimde 2025-09-07T06:39:16.0737940Z * [new branch] addvllmtest -> origin/addvllmtest 2025-09-07T06:39:16.0738173Z * [new branch] adi/acl_upgrade -> origin/adi/acl_upgrade 2025-09-07T06:39:16.0738390Z * [new branch] adi/test -> origin/adi/test 2025-09-07T06:39:16.0738605Z * [new branch] adi/test_bgemm -> origin/adi/test_bgemm 2025-09-07T06:39:16.0738832Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-09-07T06:39:16.0739085Z * [new branch] adi/test_onednn_v3.9 -> origin/adi/test_onednn_v3.9 2025-09-07T06:39:16.0739713Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-09-07T06:39:16.0739993Z * [new branch] adi/test_timm -> origin/adi/test_timm 2025-09-07T06:39:16.0740221Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-09-07T06:39:16.0740421Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-09-07T06:39:16.0740755Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-09-07T06:39:16.0740948Z * [new branch] alt-disable -> origin/alt-disable 2025-09-07T06:39:16.0741157Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-09-07T06:39:16.0741381Z * [new branch] angelayi/aoti_inductor_fx -> origin/angelayi/aoti_inductor_fx 2025-09-07T06:39:16.0741589Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-09-07T06:39:16.0741785Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-09-07T06:39:16.0742018Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-09-07T06:39:16.0742252Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-09-07T06:39:16.0742460Z * [new branch] angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph 2025-09-07T06:39:16.0742667Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-09-07T06:39:16.0742874Z * [new branch] angelayi/fake_cache_empty -> origin/angelayi/fake_cache_empty 2025-09-07T06:39:16.0743097Z * [new branch] angelayi/is_symbolic_tracing -> origin/angelayi/is_symbolic_tracing 2025-09-07T06:39:16.0743297Z * [new branch] angelayi/item -> origin/angelayi/item 2025-09-07T06:39:16.0743538Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-09-07T06:39:16.0743738Z * [new branch] angelayi/opoverload -> origin/angelayi/opoverload 2025-09-07T06:39:16.0743932Z * [new branch] angelayi/pattern -> origin/angelayi/pattern 2025-09-07T06:39:16.0744121Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-09-07T06:39:16.0744311Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-09-07T06:39:16.0744622Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-09-07T06:39:16.0744875Z * [new branch] angelayi/test_cpp -> origin/angelayi/test_cpp 2025-09-07T06:39:16.0745063Z * [new branch] angelayi/torch_size -> origin/angelayi/torch_size 2025-09-07T06:39:16.0745259Z * [new branch] aoti-cuda-alloc -> origin/aoti-cuda-alloc 2025-09-07T06:39:16.0745448Z * [new branch] aoti_target_windows -> origin/aoti_target_windows 2025-09-07T06:39:16.0745632Z * [new branch] aoti_weight_sharing -> origin/aoti_weight_sharing 2025-09-07T06:39:16.0745846Z * [new branch] atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124 2025-09-07T06:39:16.0746088Z * [new branch] atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1 2025-09-07T06:39:16.0746308Z * [new branch] atalman-patch-1 -> origin/atalman-patch-1 2025-09-07T06:39:16.0746492Z * [new branch] atalman-patch-3 -> origin/atalman-patch-3 2025-09-07T06:39:16.0746677Z * [new branch] atalman-patch-4 -> origin/atalman-patch-4 2025-09-07T06:39:16.0746859Z * [new branch] atalman-patch-5 -> origin/atalman-patch-5 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2025-09-07T06:39:16.1537447Z * [new branch] xmfan/ca_fix_polyfills -> origin/xmfan/ca_fix_polyfills 2025-09-07T06:39:16.1537511Z * [new branch] xmfan/ca_jan3 -> origin/xmfan/ca_jan3 2025-09-07T06:39:16.1537580Z * [new branch] xmfan/ca_jun18 -> origin/xmfan/ca_jun18 2025-09-07T06:39:16.1537646Z * [new branch] xmfan/ca_jun24 -> origin/xmfan/ca_jun24 2025-09-07T06:39:16.1537716Z * [new branch] xmfan/ca_mem_base -> origin/xmfan/ca_mem_base 2025-09-07T06:39:16.1537786Z * [new branch] xmfan/ca_mem_fix -> origin/xmfan/ca_mem_fix 2025-09-07T06:39:16.1537858Z * [new branch] xmfan/ca_memory_fix -> origin/xmfan/ca_memory_fix 2025-09-07T06:39:16.1539176Z * [new branch] xmfan/ca_memory_fix_rebased -> origin/xmfan/ca_memory_fix_rebased 2025-09-07T06:39:16.1539309Z * [new branch] xmfan/ca_memory_fix_rebased2 -> origin/xmfan/ca_memory_fix_rebased2 2025-09-07T06:39:16.1539384Z * [new branch] xmfan/ca_move_to_cuda -> origin/xmfan/ca_move_to_cuda 2025-09-07T06:39:16.1539454Z * [new branch] xmfan/ca_nested -> origin/xmfan/ca_nested 2025-09-07T06:39:16.1539524Z * [new branch] xmfan/ca_overhead -> origin/xmfan/ca_overhead 2025-09-07T06:39:16.1539612Z * [new branch] xmfan/ca_overhead_0eba7e5451 -> origin/xmfan/ca_overhead_0eba7e5451 2025-09-07T06:39:16.1539681Z * [new branch] xmfan/ca_scalar -> origin/xmfan/ca_scalar 2025-09-07T06:39:16.1539805Z * [new branch] xmfan/ca_subclass_mem_fix -> origin/xmfan/ca_subclass_mem_fix 2025-09-07T06:39:16.1539875Z * [new branch] xmfan/ca_warm_mem -> origin/xmfan/ca_warm_mem 2025-09-07T06:39:16.1539954Z * [new branch] xmfan/ca_warm_mem_base -> origin/xmfan/ca_warm_mem_base 2025-09-07T06:39:16.1540023Z * [new branch] xmfan/cacu_jun18 -> origin/xmfan/cacu_jun18 2025-09-07T06:39:16.1540091Z * [new branch] xmfan/cacu_jun19 -> origin/xmfan/cacu_jun19 2025-09-07T06:39:16.1540159Z * [new branch] xmfan/cacu_jun4 -> origin/xmfan/cacu_jun4 2025-09-07T06:39:16.1540226Z * [new branch] xmfan/cacu_may27 -> origin/xmfan/cacu_may27 2025-09-07T06:39:16.1540311Z * [new branch] xmfan/disable_duck_shape -> origin/xmfan/disable_duck_shape 2025-09-07T06:39:16.1540408Z * [new branch] xmfan/fca_cpp_node_passthrough -> origin/xmfan/fca_cpp_node_passthrough 2025-09-07T06:39:16.1540484Z * [new branch] xmfan/issue_123374 -> origin/xmfan/issue_123374 2025-09-07T06:39:16.1540635Z * [new branch] xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:39:16.1540781Z * [new branch] xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:39:16.1540857Z * [new branch] xmfan/segfault_test -> origin/xmfan/segfault_test 2025-09-07T06:39:16.1540929Z * [new branch] xmfan/single_step -> origin/xmfan/single_step 2025-09-07T06:39:16.1540995Z * [new branch] xmfan/sth_0829 -> origin/xmfan/sth_0829 2025-09-07T06:39:16.1541061Z * [new branch] xmfan/test -> origin/xmfan/test 2025-09-07T06:39:16.1541149Z * [new branch] yguo/debug-0226-constexpr -> origin/yguo/debug-0226-constexpr 2025-09-07T06:39:16.1541231Z * [new branch] yguo/new_latest_changes -> origin/yguo/new_latest_changes 2025-09-07T06:39:16.1541325Z * [new branch] yguo/patch_constexpr_changes -> origin/yguo/patch_constexpr_changes 2025-09-07T06:39:16.1541400Z * [new branch] yihan_quantization -> origin/yihan_quantization 2025-09-07T06:39:16.1541493Z * [new branch] yiming/add_jit_trace_benchmark -> origin/yiming/add_jit_trace_benchmark 2025-09-07T06:39:16.1541588Z * [new branch] yiming/add_nativert_benchmark -> origin/yiming/add_nativert_benchmark 2025-09-07T06:39:16.1541660Z * [new branch] yiming/bootcamp -> origin/yiming/bootcamp 2025-09-07T06:39:16.1541732Z * [new branch] zainr/canary-test -> origin/zainr/canary-test 2025-09-07T06:39:16.1541825Z * [new branch] zainr/cleanup-gh-runners -> origin/zainr/cleanup-gh-runners 2025-09-07T06:39:16.1541899Z * [new branch] zainr/git-push-v2 -> origin/zainr/git-push-v2 2025-09-07T06:39:16.1541982Z * [new branch] zainr/pull-migration-c -> origin/zainr/pull-migration-c 2025-09-07T06:39:16.1542047Z * [new branch] zainr/test -> origin/zainr/test 2025-09-07T06:39:16.1542139Z * [new branch] zainr/test2 -> origin/zainr/test2 2025-09-07T06:39:16.1542208Z * [new branch] zainr/unstable -> origin/zainr/unstable 2025-09-07T06:39:16.1542284Z * [new branch] zainr/unstable-xla -> origin/zainr/unstable-xla 2025-09-07T06:39:16.1542360Z * [new branch] zasdfgbnm-patch-3 -> origin/zasdfgbnm-patch-3 2025-09-07T06:39:16.1542421Z * [new branch] zb2p -> origin/zb2p 2025-09-07T06:39:16.1542498Z * [new branch] zero_grad_optimization -> origin/zero_grad_optimization 2025-09-07T06:39:16.1542617Z * [new branch] zeros-and-scatter-part2 -> origin/zeros-and-scatter-part2 2025-09-07T06:39:16.1542694Z * [new branch] zhxchen17/scratch/0 -> origin/zhxchen17/scratch/0 2025-09-07T06:39:16.1542773Z * [new branch] zhxhcen17/moodycamel -> origin/zhxhcen17/moodycamel 2025-09-07T06:39:16.1542841Z * [new branch] zxiiro/main -> origin/zxiiro/main 2025-09-07T06:39:16.1543006Z * [new tag] bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug -> bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug 2025-09-07T06:39:16.1544290Z * [new tag] ci/binaries/77164 -> ci/binaries/77164 2025-09-07T06:39:16.1544359Z * [new tag] ciflow/binaries/156049 -> ciflow/binaries/156049 2025-09-07T06:39:16.1544489Z * [new tag] ciflow/binaries/156712 -> ciflow/binaries/156712 2025-09-07T06:39:16.1544557Z * [new tag] ciflow/binaries/157432 -> ciflow/binaries/157432 2025-09-07T06:39:16.1544624Z * [new tag] ciflow/binaries/157685 -> ciflow/binaries/157685 2025-09-07T06:39:16.1544688Z * [new tag] ciflow/binaries/157689 -> ciflow/binaries/157689 2025-09-07T06:39:16.1544756Z * [new tag] ciflow/binaries/158104 -> ciflow/binaries/158104 2025-09-07T06:39:16.1544819Z * [new tag] ciflow/binaries/160229 -> ciflow/binaries/160229 2025-09-07T06:39:16.1544882Z * [new tag] ciflow/binaries/160720 -> ciflow/binaries/160720 2025-09-07T06:39:16.1544945Z * [new tag] ciflow/binaries/162080 -> ciflow/binaries/162080 2025-09-07T06:39:16.1545010Z * [new tag] ciflow/binaries/162329 -> ciflow/binaries/162329 2025-09-07T06:39:16.1545097Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-09-07T06:39:16.1545182Z * [new tag] ciflow/binaries_libtorch/156711 -> ciflow/binaries_libtorch/156711 2025-09-07T06:39:16.1545270Z * [new tag] ciflow/binaries_libtorch/157432 -> ciflow/binaries_libtorch/157432 2025-09-07T06:39:16.1545345Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-09-07T06:39:16.1545420Z * [new tag] ciflow/binaries_wheel/156711 -> ciflow/binaries_wheel/156711 2025-09-07T06:39:16.1545494Z * [new tag] ciflow/binaries_wheel/157432 -> ciflow/binaries_wheel/157432 2025-09-07T06:39:16.1545567Z * [new tag] ciflow/binaries_wheel/162136 -> ciflow/binaries_wheel/162136 2025-09-07T06:39:16.1545639Z * [new tag] ciflow/binaries_wheel/162252 -> ciflow/binaries_wheel/162252 2025-09-07T06:39:16.1545713Z * [new tag] ciflow/binaries_wheel/162325 -> ciflow/binaries_wheel/162325 2025-09-07T06:39:16.1545795Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-09-07T06:39:16.1545871Z * [new tag] ciflow/h100-symm-mem/157635 -> ciflow/h100-symm-mem/157635 2025-09-07T06:39:16.1545941Z * [new tag] ciflow/h100-symm-mem/161984 -> ciflow/h100-symm-mem/161984 2025-09-07T06:39:16.1546010Z * [new tag] ciflow/h100-symm-mem/162003 -> ciflow/h100-symm-mem/162003 2025-09-07T06:39:16.1546129Z * [new tag] ciflow/h100-symm-mem/162011 -> ciflow/h100-symm-mem/162011 2025-09-07T06:39:16.1546199Z * [new tag] ciflow/h100-symm-mem/162026 -> ciflow/h100-symm-mem/162026 2025-09-07T06:39:16.1546267Z * [new tag] ciflow/h100-symm-mem/162033 -> ciflow/h100-symm-mem/162033 2025-09-07T06:39:16.1546336Z * [new tag] ciflow/h100-symm-mem/162040 -> ciflow/h100-symm-mem/162040 2025-09-07T06:39:16.1546405Z * [new tag] ciflow/h100-symm-mem/162041 -> ciflow/h100-symm-mem/162041 2025-09-07T06:39:16.1546472Z * [new tag] ciflow/h100-symm-mem/162142 -> ciflow/h100-symm-mem/162142 2025-09-07T06:39:16.1546607Z * [new tag] ciflow/h100-symm-mem/162150 -> ciflow/h100-symm-mem/162150 2025-09-07T06:39:16.1546678Z * [new tag] ciflow/h100-symm-mem/162243 -> ciflow/h100-symm-mem/162243 2025-09-07T06:39:16.1546750Z * [new tag] ciflow/h100-symm-mem/162320 -> ciflow/h100-symm-mem/162320 2025-09-07T06:39:16.1546810Z * [new tag] ciflow/h100/159158 -> ciflow/h100/159158 2025-09-07T06:39:16.1546869Z * [new tag] ciflow/h100/160480 -> ciflow/h100/160480 2025-09-07T06:39:16.1546928Z * [new tag] ciflow/h100/161749 -> ciflow/h100/161749 2025-09-07T06:39:16.1546985Z * [new tag] ciflow/h100/162022 -> ciflow/h100/162022 2025-09-07T06:39:16.1547042Z * [new tag] ciflow/h100/162278 -> ciflow/h100/162278 2025-09-07T06:39:16.1547182Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/156592 -> ciflow/inductor-perf-test-nightly-rocm/156592 2025-09-07T06:39:16.1547301Z * [new tag] ciflow/inductor-perf-test-nightly/156592 -> ciflow/inductor-perf-test-nightly/156592 2025-09-07T06:39:16.1547391Z * [new tag] ciflow/inductor-periodic/162063 -> ciflow/inductor-periodic/162063 2025-09-07T06:39:16.1547481Z * [new tag] ciflow/inductor-periodic/162227 -> ciflow/inductor-periodic/162227 2025-09-07T06:39:16.1547567Z * [new tag] ciflow/inductor-periodic/162323 -> ciflow/inductor-periodic/162323 2025-09-07T06:39:16.1547639Z * [new tag] ciflow/inductor-rocm/154170 -> ciflow/inductor-rocm/154170 2025-09-07T06:39:16.1547712Z * [new tag] ciflow/inductor-rocm/159146 -> ciflow/inductor-rocm/159146 2025-09-07T06:39:16.1547783Z * [new tag] ciflow/inductor-rocm/159158 -> ciflow/inductor-rocm/159158 2025-09-07T06:39:16.1549090Z * [new tag] ciflow/inductor-rocm/161715 -> ciflow/inductor-rocm/161715 2025-09-07T06:39:16.1549166Z * [new tag] ciflow/inductor-rocm/162053 -> ciflow/inductor-rocm/162053 2025-09-07T06:39:16.1549238Z * [new tag] ciflow/inductor-rocm/162056 -> ciflow/inductor-rocm/162056 2025-09-07T06:39:16.1549306Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-09-07T06:39:16.1549371Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-09-07T06:39:16.1549436Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-09-07T06:39:16.1549502Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-09-07T06:39:16.1549566Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-09-07T06:39:16.1549629Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-09-07T06:39:16.1549694Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-09-07T06:39:16.1549758Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-09-07T06:39:16.1549822Z * [new tag] ciflow/inductor/156592 -> ciflow/inductor/156592 2025-09-07T06:39:16.1549886Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-09-07T06:39:16.1549988Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-09-07T06:39:16.1550052Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-09-07T06:39:16.1550115Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-09-07T06:39:16.1550180Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-09-07T06:39:16.1550243Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-09-07T06:39:16.1550306Z * [new tag] ciflow/inductor/157994 -> ciflow/inductor/157994 2025-09-07T06:39:16.1550403Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-09-07T06:39:16.1550466Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-09-07T06:39:16.1550530Z * [new tag] ciflow/inductor/158404 -> ciflow/inductor/158404 2025-09-07T06:39:16.1550596Z * [new tag] ciflow/inductor/158647 -> ciflow/inductor/158647 2025-09-07T06:39:16.1550659Z * [new tag] ciflow/inductor/158932 -> ciflow/inductor/158932 2025-09-07T06:39:16.1550722Z * [new tag] ciflow/inductor/159146 -> ciflow/inductor/159146 2025-09-07T06:39:16.1550786Z * [new tag] ciflow/inductor/159158 -> ciflow/inductor/159158 2025-09-07T06:39:16.1550849Z * [new tag] ciflow/inductor/159274 -> ciflow/inductor/159274 2025-09-07T06:39:16.1550912Z * [new tag] ciflow/inductor/159664 -> ciflow/inductor/159664 2025-09-07T06:39:16.1550977Z * [new tag] ciflow/inductor/159778 -> ciflow/inductor/159778 2025-09-07T06:39:16.1551042Z * [new tag] ciflow/inductor/159835 -> ciflow/inductor/159835 2025-09-07T06:39:16.1551105Z * [new tag] ciflow/inductor/159944 -> ciflow/inductor/159944 2025-09-07T06:39:16.1551171Z * [new tag] ciflow/inductor/160161 -> ciflow/inductor/160161 2025-09-07T06:39:16.1551234Z * [new tag] ciflow/inductor/160174 -> ciflow/inductor/160174 2025-09-07T06:39:16.1551297Z * [new tag] ciflow/inductor/160323 -> ciflow/inductor/160323 2025-09-07T06:39:16.1551359Z * [new tag] ciflow/inductor/160324 -> ciflow/inductor/160324 2025-09-07T06:39:16.1551423Z * [new tag] ciflow/inductor/160325 -> ciflow/inductor/160325 2025-09-07T06:39:16.1551487Z * [new tag] ciflow/inductor/160326 -> ciflow/inductor/160326 2025-09-07T06:39:16.1551552Z * [new tag] ciflow/inductor/160327 -> ciflow/inductor/160327 2025-09-07T06:39:16.1551618Z * [new tag] ciflow/inductor/160328 -> ciflow/inductor/160328 2025-09-07T06:39:16.1551681Z * [new tag] ciflow/inductor/160329 -> ciflow/inductor/160329 2025-09-07T06:39:16.1551746Z * [new tag] ciflow/inductor/160480 -> ciflow/inductor/160480 2025-09-07T06:39:16.1551810Z * [new tag] ciflow/inductor/160532 -> ciflow/inductor/160532 2025-09-07T06:39:16.1551873Z * [new tag] ciflow/inductor/160539 -> ciflow/inductor/160539 2025-09-07T06:39:16.1551985Z * [new tag] ciflow/inductor/160580 -> ciflow/inductor/160580 2025-09-07T06:39:16.1552050Z * [new tag] ciflow/inductor/160685 -> ciflow/inductor/160685 2025-09-07T06:39:16.1552113Z * [new tag] ciflow/inductor/160686 -> ciflow/inductor/160686 2025-09-07T06:39:16.1553390Z * [new tag] ciflow/inductor/160687 -> ciflow/inductor/160687 2025-09-07T06:39:16.1553458Z * [new tag] ciflow/inductor/160688 -> ciflow/inductor/160688 2025-09-07T06:39:16.1553521Z * [new tag] ciflow/inductor/160690 -> ciflow/inductor/160690 2025-09-07T06:39:16.1553632Z * [new tag] ciflow/inductor/160706 -> ciflow/inductor/160706 2025-09-07T06:39:16.1553698Z * [new tag] ciflow/inductor/160729 -> ciflow/inductor/160729 2025-09-07T06:39:16.1553761Z * [new tag] ciflow/inductor/160798 -> ciflow/inductor/160798 2025-09-07T06:39:16.1553825Z * [new tag] ciflow/inductor/160836 -> ciflow/inductor/160836 2025-09-07T06:39:16.1553889Z * [new tag] ciflow/inductor/160843 -> ciflow/inductor/160843 2025-09-07T06:39:16.1553952Z * [new tag] ciflow/inductor/160869 -> ciflow/inductor/160869 2025-09-07T06:39:16.1554015Z * [new tag] ciflow/inductor/160920 -> ciflow/inductor/160920 2025-09-07T06:39:16.1554112Z * [new tag] ciflow/inductor/160943 -> ciflow/inductor/160943 2025-09-07T06:39:16.1554176Z * [new tag] ciflow/inductor/161092 -> ciflow/inductor/161092 2025-09-07T06:39:16.1554239Z * [new tag] ciflow/inductor/161093 -> ciflow/inductor/161093 2025-09-07T06:39:16.1554307Z * [new tag] ciflow/inductor/161109 -> ciflow/inductor/161109 2025-09-07T06:39:16.1554462Z * [new tag] ciflow/inductor/161118 -> ciflow/inductor/161118 2025-09-07T06:39:16.1554528Z * [new tag] ciflow/inductor/161178 -> ciflow/inductor/161178 2025-09-07T06:39:16.1554592Z * [new tag] ciflow/inductor/161246 -> ciflow/inductor/161246 2025-09-07T06:39:16.1554656Z * [new tag] ciflow/inductor/161349 -> ciflow/inductor/161349 2025-09-07T06:39:16.1554719Z * [new tag] ciflow/inductor/161350 -> ciflow/inductor/161350 2025-09-07T06:39:16.1554787Z * [new tag] ciflow/inductor/161351 -> ciflow/inductor/161351 2025-09-07T06:39:16.1554850Z * [new tag] ciflow/inductor/161397 -> ciflow/inductor/161397 2025-09-07T06:39:16.1554913Z * [new tag] ciflow/inductor/161404 -> ciflow/inductor/161404 2025-09-07T06:39:16.1554980Z * [new tag] ciflow/inductor/161405 -> ciflow/inductor/161405 2025-09-07T06:39:16.1555045Z * [new tag] ciflow/inductor/161406 -> ciflow/inductor/161406 2025-09-07T06:39:16.1555109Z * [new tag] ciflow/inductor/161410 -> ciflow/inductor/161410 2025-09-07T06:39:16.1555173Z * [new tag] ciflow/inductor/161414 -> ciflow/inductor/161414 2025-09-07T06:39:16.1555238Z * [new tag] ciflow/inductor/161442 -> ciflow/inductor/161442 2025-09-07T06:39:16.1555301Z * [new tag] ciflow/inductor/161458 -> ciflow/inductor/161458 2025-09-07T06:39:16.1555367Z * [new tag] ciflow/inductor/161468 -> ciflow/inductor/161468 2025-09-07T06:39:16.1555432Z * [new tag] ciflow/inductor/161469 -> ciflow/inductor/161469 2025-09-07T06:39:16.1555495Z * [new tag] ciflow/inductor/161485 -> ciflow/inductor/161485 2025-09-07T06:39:16.1555559Z * [new tag] ciflow/inductor/161499 -> ciflow/inductor/161499 2025-09-07T06:39:16.1555623Z * [new tag] ciflow/inductor/161534 -> ciflow/inductor/161534 2025-09-07T06:39:16.1555687Z * [new tag] ciflow/inductor/161595 -> ciflow/inductor/161595 2025-09-07T06:39:16.1555750Z * [new tag] ciflow/inductor/161596 -> ciflow/inductor/161596 2025-09-07T06:39:16.1555814Z * [new tag] ciflow/inductor/161630 -> ciflow/inductor/161630 2025-09-07T06:39:16.1555876Z * [new tag] ciflow/inductor/161667 -> ciflow/inductor/161667 2025-09-07T06:39:16.1555941Z * [new tag] ciflow/inductor/161670 -> ciflow/inductor/161670 2025-09-07T06:39:16.1556005Z * [new tag] ciflow/inductor/161673 -> ciflow/inductor/161673 2025-09-07T06:39:16.1556068Z * [new tag] ciflow/inductor/161674 -> ciflow/inductor/161674 2025-09-07T06:39:16.1556178Z * [new tag] ciflow/inductor/161675 -> ciflow/inductor/161675 2025-09-07T06:39:16.1556242Z * [new tag] ciflow/inductor/161693 -> ciflow/inductor/161693 2025-09-07T06:39:16.1556306Z * [new tag] ciflow/inductor/161695 -> ciflow/inductor/161695 2025-09-07T06:39:16.1556368Z * [new tag] ciflow/inductor/161715 -> ciflow/inductor/161715 2025-09-07T06:39:16.1556432Z * [new tag] ciflow/inductor/161730 -> ciflow/inductor/161730 2025-09-07T06:39:16.1556496Z * [new tag] ciflow/inductor/161732 -> ciflow/inductor/161732 2025-09-07T06:39:16.1556560Z * [new tag] ciflow/inductor/161744 -> ciflow/inductor/161744 2025-09-07T06:39:16.1557909Z * [new tag] ciflow/inductor/161746 -> ciflow/inductor/161746 2025-09-07T06:39:16.1557973Z * [new tag] ciflow/inductor/161747 -> ciflow/inductor/161747 2025-09-07T06:39:16.1558039Z * [new tag] ciflow/inductor/161819 -> ciflow/inductor/161819 2025-09-07T06:39:16.1558104Z * [new tag] ciflow/inductor/161821 -> ciflow/inductor/161821 2025-09-07T06:39:16.1558168Z * [new tag] ciflow/inductor/161828 -> ciflow/inductor/161828 2025-09-07T06:39:16.1558231Z * [new tag] ciflow/inductor/161879 -> ciflow/inductor/161879 2025-09-07T06:39:16.1558295Z * [new tag] ciflow/inductor/161880 -> ciflow/inductor/161880 2025-09-07T06:39:16.1558359Z * [new tag] ciflow/inductor/161881 -> ciflow/inductor/161881 2025-09-07T06:39:16.1558422Z * [new tag] ciflow/inductor/161907 -> ciflow/inductor/161907 2025-09-07T06:39:16.1558488Z * [new tag] ciflow/inductor/161914 -> ciflow/inductor/161914 2025-09-07T06:39:16.1558551Z * [new tag] ciflow/inductor/161924 -> ciflow/inductor/161924 2025-09-07T06:39:16.1558616Z * [new tag] ciflow/inductor/161936 -> ciflow/inductor/161936 2025-09-07T06:39:16.1558681Z * [new tag] ciflow/inductor/161938 -> ciflow/inductor/161938 2025-09-07T06:39:16.1558744Z * [new tag] ciflow/inductor/161939 -> ciflow/inductor/161939 2025-09-07T06:39:16.1558807Z * [new tag] ciflow/inductor/161940 -> ciflow/inductor/161940 2025-09-07T06:39:16.1558871Z * [new tag] ciflow/inductor/161955 -> ciflow/inductor/161955 2025-09-07T06:39:16.1558934Z * [new tag] ciflow/inductor/161957 -> ciflow/inductor/161957 2025-09-07T06:39:16.1558998Z * [new tag] ciflow/inductor/161975 -> ciflow/inductor/161975 2025-09-07T06:39:16.1559063Z * [new tag] ciflow/inductor/161977 -> ciflow/inductor/161977 2025-09-07T06:39:16.1559129Z * [new tag] ciflow/inductor/161978 -> ciflow/inductor/161978 2025-09-07T06:39:16.1559195Z * [new tag] ciflow/inductor/161979 -> ciflow/inductor/161979 2025-09-07T06:39:16.1559258Z * [new tag] ciflow/inductor/161980 -> ciflow/inductor/161980 2025-09-07T06:39:16.1559322Z * [new tag] ciflow/inductor/161988 -> ciflow/inductor/161988 2025-09-07T06:39:16.1559385Z * [new tag] ciflow/inductor/161994 -> ciflow/inductor/161994 2025-09-07T06:39:16.1559449Z * [new tag] ciflow/inductor/162013 -> ciflow/inductor/162013 2025-09-07T06:39:16.1559514Z * [new tag] ciflow/inductor/162014 -> ciflow/inductor/162014 2025-09-07T06:39:16.1559577Z * [new tag] ciflow/inductor/162017 -> ciflow/inductor/162017 2025-09-07T06:39:16.1559642Z * [new tag] ciflow/inductor/162021 -> ciflow/inductor/162021 2025-09-07T06:39:16.1559706Z * [new tag] ciflow/inductor/162023 -> ciflow/inductor/162023 2025-09-07T06:39:16.1559769Z * [new tag] ciflow/inductor/162027 -> ciflow/inductor/162027 2025-09-07T06:39:16.1559862Z * [new tag] ciflow/inductor/162029 -> ciflow/inductor/162029 2025-09-07T06:39:16.1559927Z * [new tag] ciflow/inductor/162030 -> ciflow/inductor/162030 2025-09-07T06:39:16.1559990Z * [new tag] ciflow/inductor/162031 -> ciflow/inductor/162031 2025-09-07T06:39:16.1560053Z * [new tag] ciflow/inductor/162033 -> ciflow/inductor/162033 2025-09-07T06:39:16.1560117Z * [new tag] ciflow/inductor/162052 -> ciflow/inductor/162052 2025-09-07T06:39:16.1560180Z * [new tag] ciflow/inductor/162053 -> ciflow/inductor/162053 2025-09-07T06:39:16.1560273Z * [new tag] ciflow/inductor/162056 -> ciflow/inductor/162056 2025-09-07T06:39:16.1560337Z * [new tag] ciflow/inductor/162063 -> ciflow/inductor/162063 2025-09-07T06:39:16.1560401Z * [new tag] ciflow/inductor/162066 -> ciflow/inductor/162066 2025-09-07T06:39:16.1560465Z * [new tag] ciflow/inductor/162068 -> ciflow/inductor/162068 2025-09-07T06:39:16.1560528Z * [new tag] ciflow/inductor/162081 -> ciflow/inductor/162081 2025-09-07T06:39:16.1560592Z * [new tag] ciflow/inductor/162088 -> ciflow/inductor/162088 2025-09-07T06:39:16.1560655Z * [new tag] ciflow/inductor/162089 -> ciflow/inductor/162089 2025-09-07T06:39:16.1560718Z * [new tag] ciflow/inductor/162094 -> ciflow/inductor/162094 2025-09-07T06:39:16.1560782Z * [new tag] ciflow/inductor/162098 -> ciflow/inductor/162098 2025-09-07T06:39:16.1560846Z * [new tag] ciflow/inductor/162101 -> ciflow/inductor/162101 2025-09-07T06:39:16.1562120Z * [new tag] ciflow/inductor/162102 -> ciflow/inductor/162102 2025-09-07T06:39:16.1562187Z * [new tag] ciflow/inductor/162104 -> ciflow/inductor/162104 2025-09-07T06:39:16.1562252Z * [new tag] ciflow/inductor/162106 -> ciflow/inductor/162106 2025-09-07T06:39:16.1562315Z * [new tag] ciflow/inductor/162108 -> ciflow/inductor/162108 2025-09-07T06:39:16.1562379Z * [new tag] ciflow/inductor/162126 -> ciflow/inductor/162126 2025-09-07T06:39:16.1562442Z * [new tag] ciflow/inductor/162149 -> ciflow/inductor/162149 2025-09-07T06:39:16.1562505Z * [new tag] ciflow/inductor/162164 -> ciflow/inductor/162164 2025-09-07T06:39:16.1562569Z * [new tag] ciflow/inductor/162166 -> ciflow/inductor/162166 2025-09-07T06:39:16.1562635Z * [new tag] ciflow/inductor/162169 -> ciflow/inductor/162169 2025-09-07T06:39:16.1562698Z * [new tag] ciflow/inductor/162170 -> ciflow/inductor/162170 2025-09-07T06:39:16.1562762Z * [new tag] ciflow/inductor/162171 -> ciflow/inductor/162171 2025-09-07T06:39:16.1562826Z * [new tag] ciflow/inductor/162183 -> ciflow/inductor/162183 2025-09-07T06:39:16.1562890Z * [new tag] ciflow/inductor/162189 -> ciflow/inductor/162189 2025-09-07T06:39:16.1562954Z * [new tag] ciflow/inductor/162190 -> ciflow/inductor/162190 2025-09-07T06:39:16.1563017Z * [new tag] ciflow/inductor/162191 -> ciflow/inductor/162191 2025-09-07T06:39:16.1563080Z * [new tag] ciflow/inductor/162194 -> ciflow/inductor/162194 2025-09-07T06:39:16.1563144Z * [new tag] ciflow/inductor/162200 -> ciflow/inductor/162200 2025-09-07T06:39:16.1563208Z * [new tag] ciflow/inductor/162201 -> ciflow/inductor/162201 2025-09-07T06:39:16.1563274Z * [new tag] ciflow/inductor/162208 -> ciflow/inductor/162208 2025-09-07T06:39:16.1563337Z * [new tag] ciflow/inductor/162211 -> ciflow/inductor/162211 2025-09-07T06:39:16.1563443Z * [new tag] ciflow/inductor/162216 -> ciflow/inductor/162216 2025-09-07T06:39:16.1563507Z * [new tag] ciflow/inductor/162220 -> ciflow/inductor/162220 2025-09-07T06:39:16.1563569Z * [new tag] ciflow/inductor/162222 -> ciflow/inductor/162222 2025-09-07T06:39:16.1563634Z * [new tag] ciflow/inductor/162227 -> ciflow/inductor/162227 2025-09-07T06:39:16.1563697Z * [new tag] ciflow/inductor/162238 -> ciflow/inductor/162238 2025-09-07T06:39:16.1563759Z * [new tag] ciflow/inductor/162239 -> ciflow/inductor/162239 2025-09-07T06:39:16.1563823Z * [new tag] ciflow/inductor/162240 -> ciflow/inductor/162240 2025-09-07T06:39:16.1563925Z * [new tag] ciflow/inductor/162244 -> ciflow/inductor/162244 2025-09-07T06:39:16.1563987Z * [new tag] ciflow/inductor/162245 -> ciflow/inductor/162245 2025-09-07T06:39:16.1564053Z * [new tag] ciflow/inductor/162262 -> ciflow/inductor/162262 2025-09-07T06:39:16.1564116Z * [new tag] ciflow/inductor/162275 -> ciflow/inductor/162275 2025-09-07T06:39:16.1564179Z * [new tag] ciflow/inductor/162278 -> ciflow/inductor/162278 2025-09-07T06:39:16.1564242Z * [new tag] ciflow/inductor/162284 -> ciflow/inductor/162284 2025-09-07T06:39:16.1564305Z * [new tag] ciflow/inductor/162286 -> ciflow/inductor/162286 2025-09-07T06:39:16.1564437Z * [new tag] ciflow/inductor/162288 -> ciflow/inductor/162288 2025-09-07T06:39:16.1564503Z * [new tag] ciflow/inductor/162293 -> ciflow/inductor/162293 2025-09-07T06:39:16.1564568Z * [new tag] ciflow/inductor/162294 -> ciflow/inductor/162294 2025-09-07T06:39:16.1564632Z * [new tag] ciflow/inductor/162295 -> ciflow/inductor/162295 2025-09-07T06:39:16.1564698Z * [new tag] ciflow/inductor/162296 -> ciflow/inductor/162296 2025-09-07T06:39:16.1564762Z * [new tag] ciflow/inductor/162298 -> ciflow/inductor/162298 2025-09-07T06:39:16.1564825Z * [new tag] ciflow/inductor/162307 -> ciflow/inductor/162307 2025-09-07T06:39:16.1564888Z * [new tag] ciflow/inductor/162309 -> ciflow/inductor/162309 2025-09-07T06:39:16.1564951Z * [new tag] ciflow/inductor/162311 -> ciflow/inductor/162311 2025-09-07T06:39:16.1565014Z * [new tag] ciflow/inductor/162312 -> ciflow/inductor/162312 2025-09-07T06:39:16.1565077Z * [new tag] ciflow/inductor/162315 -> ciflow/inductor/162315 2025-09-07T06:39:16.1565143Z * [new tag] ciflow/inductor/162316 -> ciflow/inductor/162316 2025-09-07T06:39:16.1566443Z * [new tag] ciflow/inductor/162318 -> ciflow/inductor/162318 2025-09-07T06:39:16.1566510Z * [new tag] ciflow/inductor/162323 -> ciflow/inductor/162323 2025-09-07T06:39:16.1566577Z * [new tag] ciflow/inductor/162341 -> ciflow/inductor/162341 2025-09-07T06:39:16.1566640Z * [new tag] ciflow/inductor/162345 -> ciflow/inductor/162345 2025-09-07T06:39:16.1566711Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-09-07T06:39:16.1566782Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-09-07T06:39:16.1566848Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-09-07T06:39:16.1566922Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-09-07T06:39:16.1566996Z * [new tag] ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737 2025-09-07T06:39:16.1567067Z * [new tag] ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078 2025-09-07T06:39:16.1567128Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-09-07T06:39:16.1567249Z * [new tag] ciflow/mps/157635 -> ciflow/mps/157635 2025-09-07T06:39:16.1567308Z * [new tag] ciflow/mps/161988 -> ciflow/mps/161988 2025-09-07T06:39:16.1567368Z * [new tag] ciflow/mps/162108 -> ciflow/mps/162108 2025-09-07T06:39:16.1567425Z * [new tag] ciflow/mps/162153 -> ciflow/mps/162153 2025-09-07T06:39:16.1567482Z * [new tag] ciflow/mps/162281 -> ciflow/mps/162281 2025-09-07T06:39:16.1567550Z * [new tag] ciflow/nightly/156049 -> ciflow/nightly/156049 2025-09-07T06:39:16.1567673Z * [new tag] ciflow/nightly/158104 -> ciflow/nightly/158104 2025-09-07T06:39:16.1567746Z * [new tag] ciflow/op-benchmark/157994 -> ciflow/op-benchmark/157994 2025-09-07T06:39:16.1567845Z * [new tag] ciflow/periodic-rocm-mi300/161529 -> ciflow/periodic-rocm-mi300/161529 2025-09-07T06:39:16.1567941Z * [new tag] ciflow/periodic-rocm-mi300/161715 -> ciflow/periodic-rocm-mi300/161715 2025-09-07T06:39:16.1568011Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-09-07T06:39:16.1568078Z * [new tag] ciflow/periodic/156703 -> ciflow/periodic/156703 2025-09-07T06:39:16.1568143Z * [new tag] ciflow/periodic/161715 -> ciflow/periodic/161715 2025-09-07T06:39:16.1568208Z * [new tag] ciflow/periodic/162021 -> ciflow/periodic/162021 2025-09-07T06:39:16.1568273Z * [new tag] ciflow/periodic/162323 -> ciflow/periodic/162323 2025-09-07T06:39:16.1568342Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-09-07T06:39:16.1568409Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-09-07T06:39:16.1568475Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-09-07T06:39:16.1568545Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-09-07T06:39:16.1568619Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-09-07T06:39:16.1568702Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-09-07T06:39:16.1568783Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-09-07T06:39:16.1568860Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-09-07T06:39:16.1568936Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-09-07T06:39:16.1569023Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-09-07T06:39:16.1569098Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-09-07T06:39:16.1569168Z * [new tag] ciflow/rocm-mi300/154170 -> ciflow/rocm-mi300/154170 2025-09-07T06:39:16.1569236Z * [new tag] ciflow/rocm-mi300/158747 -> ciflow/rocm-mi300/158747 2025-09-07T06:39:16.1569301Z * [new tag] ciflow/rocm-mi300/159146 -> ciflow/rocm-mi300/159146 2025-09-07T06:39:16.1569366Z * [new tag] ciflow/rocm-mi300/159158 -> ciflow/rocm-mi300/159158 2025-09-07T06:39:16.1569430Z * [new tag] ciflow/rocm-mi300/161715 -> ciflow/rocm-mi300/161715 2025-09-07T06:39:16.1569494Z * [new tag] ciflow/rocm-mi300/161957 -> ciflow/rocm-mi300/161957 2025-09-07T06:39:16.1569558Z * [new tag] ciflow/rocm-mi300/162053 -> ciflow/rocm-mi300/162053 2025-09-07T06:39:16.1569624Z * [new tag] ciflow/rocm-mi300/162056 -> ciflow/rocm-mi300/162056 2025-09-07T06:39:16.1569689Z * [new tag] ciflow/rocm-mi300/162112 -> ciflow/rocm-mi300/162112 2025-09-07T06:39:16.1571137Z * [new tag] ciflow/rocm-mi300/162245 -> ciflow/rocm-mi300/162245 2025-09-07T06:39:16.1571206Z * [new tag] ciflow/rocm-mi300/162278 -> ciflow/rocm-mi300/162278 2025-09-07T06:39:16.1571272Z * [new tag] ciflow/rocm-mi300/162288 -> ciflow/rocm-mi300/162288 2025-09-07T06:39:16.1571335Z * [new tag] ciflow/rocm-mi355/162053 -> ciflow/rocm-mi355/162053 2025-09-07T06:39:16.1571400Z * [new tag] ciflow/rocm-mi355/162056 -> ciflow/rocm-mi355/162056 2025-09-07T06:39:16.1571460Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-09-07T06:39:16.1571521Z * [new tag] ciflow/rocm/154170 -> ciflow/rocm/154170 2025-09-07T06:39:16.1571613Z * [new tag] ciflow/rocm/156491 -> ciflow/rocm/156491 2025-09-07T06:39:16.1571673Z * [new tag] ciflow/rocm/156592 -> ciflow/rocm/156592 2025-09-07T06:39:16.1571730Z * [new tag] ciflow/rocm/158747 -> ciflow/rocm/158747 2025-09-07T06:39:16.1571790Z * [new tag] ciflow/rocm/159146 -> ciflow/rocm/159146 2025-09-07T06:39:16.1571848Z * [new tag] ciflow/rocm/159158 -> ciflow/rocm/159158 2025-09-07T06:39:16.1571906Z * [new tag] ciflow/rocm/161715 -> ciflow/rocm/161715 2025-09-07T06:39:16.1571963Z * [new tag] ciflow/rocm/161972 -> ciflow/rocm/161972 2025-09-07T06:39:16.1572021Z * [new tag] ciflow/rocm/162052 -> ciflow/rocm/162052 2025-09-07T06:39:16.1572078Z * [new tag] ciflow/rocm/162053 -> ciflow/rocm/162053 2025-09-07T06:39:16.1572135Z * [new tag] ciflow/rocm/162056 -> ciflow/rocm/162056 2025-09-07T06:39:16.1572195Z * [new tag] ciflow/rocm/162112 -> ciflow/rocm/162112 2025-09-07T06:39:16.1572252Z * [new tag] ciflow/rocm/162278 -> ciflow/rocm/162278 2025-09-07T06:39:16.1572313Z * [new tag] ciflow/rocm/162288 -> ciflow/rocm/162288 2025-09-07T06:39:16.1572372Z * [new tag] ciflow/rocm/162305 -> ciflow/rocm/162305 2025-09-07T06:39:16.1572434Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-09-07T06:39:16.1572493Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-09-07T06:39:16.1572675Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-09-07T06:39:16.1572736Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-09-07T06:39:16.1572796Z * [new tag] ciflow/slow/161395 -> ciflow/slow/161395 2025-09-07T06:39:16.1572855Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-09-07T06:39:16.1572916Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-09-07T06:39:16.1572977Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-09-07T06:39:16.1573036Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-09-07T06:39:16.1573096Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-09-07T06:39:16.1573155Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-09-07T06:39:16.1573214Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-09-07T06:39:16.1573274Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-09-07T06:39:16.1573435Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-09-07T06:39:16.1573496Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-09-07T06:39:16.1573557Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-09-07T06:39:16.1573647Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-09-07T06:39:16.1573707Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-09-07T06:39:16.1573766Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-09-07T06:39:16.1573934Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-09-07T06:39:16.1574015Z * [new tag] ciflow/triton_binaries/162329 -> ciflow/triton_binaries/162329 2025-09-07T06:39:16.1574076Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-09-07T06:39:16.1574165Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-09-07T06:39:16.1574224Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-09-07T06:39:16.1574284Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-09-07T06:39:16.1575630Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-09-07T06:39:16.1575695Z * [new tag] ciflow/trunk/148919 -> ciflow/trunk/148919 2025-09-07T06:39:16.1575754Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-09-07T06:39:16.1575812Z * [new tag] ciflow/trunk/154170 -> ciflow/trunk/154170 2025-09-07T06:39:16.1575873Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-09-07T06:39:16.1575933Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-09-07T06:39:16.1575995Z * [new tag] ciflow/trunk/156703 -> ciflow/trunk/156703 2025-09-07T06:39:16.1576055Z * [new tag] ciflow/trunk/156711 -> ciflow/trunk/156711 2025-09-07T06:39:16.1576113Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-09-07T06:39:16.1576175Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-09-07T06:39:16.1576233Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-09-07T06:39:16.1576292Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-09-07T06:39:16.1576351Z * [new tag] ciflow/trunk/157813 -> ciflow/trunk/157813 2025-09-07T06:39:16.1576409Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-09-07T06:39:16.1576469Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-09-07T06:39:16.1576528Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-09-07T06:39:16.1576589Z * [new tag] ciflow/trunk/158404 -> ciflow/trunk/158404 2025-09-07T06:39:16.1576649Z * [new tag] ciflow/trunk/158647 -> ciflow/trunk/158647 2025-09-07T06:39:16.1576710Z * [new tag] ciflow/trunk/158846 -> ciflow/trunk/158846 2025-09-07T06:39:16.1576768Z * [new tag] ciflow/trunk/159158 -> ciflow/trunk/159158 2025-09-07T06:39:16.1576828Z * [new tag] ciflow/trunk/159682 -> ciflow/trunk/159682 2025-09-07T06:39:16.1576888Z * [new tag] ciflow/trunk/159835 -> ciflow/trunk/159835 2025-09-07T06:39:16.1576947Z * [new tag] ciflow/trunk/160161 -> ciflow/trunk/160161 2025-09-07T06:39:16.1577006Z * [new tag] ciflow/trunk/160236 -> ciflow/trunk/160236 2025-09-07T06:39:16.1577065Z * [new tag] ciflow/trunk/160329 -> ciflow/trunk/160329 2025-09-07T06:39:16.1577125Z * [new tag] ciflow/trunk/160480 -> ciflow/trunk/160480 2025-09-07T06:39:16.1577185Z * [new tag] ciflow/trunk/160532 -> ciflow/trunk/160532 2025-09-07T06:39:16.1577243Z * [new tag] ciflow/trunk/160836 -> ciflow/trunk/160836 2025-09-07T06:39:16.1577350Z * [new tag] ciflow/trunk/160843 -> ciflow/trunk/160843 2025-09-07T06:39:16.1577410Z * [new tag] ciflow/trunk/160869 -> ciflow/trunk/160869 2025-09-07T06:39:16.1577470Z * [new tag] ciflow/trunk/160940 -> ciflow/trunk/160940 2025-09-07T06:39:16.1577528Z * [new tag] ciflow/trunk/160943 -> ciflow/trunk/160943 2025-09-07T06:39:16.1577587Z * [new tag] ciflow/trunk/160953 -> ciflow/trunk/160953 2025-09-07T06:39:16.1577647Z * [new tag] ciflow/trunk/161035 -> ciflow/trunk/161035 2025-09-07T06:39:16.1577745Z * [new tag] ciflow/trunk/161178 -> ciflow/trunk/161178 2025-09-07T06:39:16.1577804Z * [new tag] ciflow/trunk/161349 -> ciflow/trunk/161349 2025-09-07T06:39:16.1577864Z * [new tag] ciflow/trunk/161350 -> ciflow/trunk/161350 2025-09-07T06:39:16.1577924Z * [new tag] ciflow/trunk/161351 -> ciflow/trunk/161351 2025-09-07T06:39:16.1577982Z * [new tag] ciflow/trunk/161395 -> ciflow/trunk/161395 2025-09-07T06:39:16.1578042Z * [new tag] ciflow/trunk/161405 -> ciflow/trunk/161405 2025-09-07T06:39:16.1578101Z * [new tag] ciflow/trunk/161406 -> ciflow/trunk/161406 2025-09-07T06:39:16.1578159Z * [new tag] ciflow/trunk/161410 -> ciflow/trunk/161410 2025-09-07T06:39:16.1578218Z * [new tag] ciflow/trunk/161468 -> ciflow/trunk/161468 2025-09-07T06:39:16.1578277Z * [new tag] ciflow/trunk/161499 -> ciflow/trunk/161499 2025-09-07T06:39:16.1578337Z * [new tag] ciflow/trunk/161527 -> ciflow/trunk/161527 2025-09-07T06:39:16.1578395Z * [new tag] ciflow/trunk/161534 -> ciflow/trunk/161534 2025-09-07T06:39:16.1579655Z * [new tag] ciflow/trunk/161591 -> ciflow/trunk/161591 2025-09-07T06:39:16.1579716Z * [new tag] ciflow/trunk/161595 -> ciflow/trunk/161595 2025-09-07T06:39:16.1579777Z * [new tag] ciflow/trunk/161596 -> ciflow/trunk/161596 2025-09-07T06:39:16.1579837Z * [new tag] ciflow/trunk/161633 -> ciflow/trunk/161633 2025-09-07T06:39:16.1579895Z * [new tag] ciflow/trunk/161634 -> ciflow/trunk/161634 2025-09-07T06:39:16.1579953Z * [new tag] ciflow/trunk/161635 -> ciflow/trunk/161635 2025-09-07T06:39:16.1580012Z * [new tag] ciflow/trunk/161667 -> ciflow/trunk/161667 2025-09-07T06:39:16.1580073Z * [new tag] ciflow/trunk/161670 -> ciflow/trunk/161670 2025-09-07T06:39:16.1580132Z * [new tag] ciflow/trunk/161692 -> ciflow/trunk/161692 2025-09-07T06:39:16.1580190Z * [new tag] ciflow/trunk/161693 -> ciflow/trunk/161693 2025-09-07T06:39:16.1580251Z * [new tag] ciflow/trunk/161695 -> ciflow/trunk/161695 2025-09-07T06:39:16.1580309Z * [new tag] ciflow/trunk/161730 -> ciflow/trunk/161730 2025-09-07T06:39:16.1580368Z * [new tag] ciflow/trunk/161744 -> ciflow/trunk/161744 2025-09-07T06:39:16.1580427Z * [new tag] ciflow/trunk/161749 -> ciflow/trunk/161749 2025-09-07T06:39:16.1580487Z * [new tag] ciflow/trunk/161881 -> ciflow/trunk/161881 2025-09-07T06:39:16.1580546Z * [new tag] ciflow/trunk/161924 -> ciflow/trunk/161924 2025-09-07T06:39:16.1580605Z * [new tag] ciflow/trunk/161926 -> ciflow/trunk/161926 2025-09-07T06:39:16.1580666Z * [new tag] ciflow/trunk/161936 -> ciflow/trunk/161936 2025-09-07T06:39:16.1580725Z * [new tag] ciflow/trunk/161952 -> ciflow/trunk/161952 2025-09-07T06:39:16.1580824Z * [new tag] ciflow/trunk/161955 -> ciflow/trunk/161955 2025-09-07T06:39:16.1580883Z * [new tag] ciflow/trunk/161957 -> ciflow/trunk/161957 2025-09-07T06:39:16.1580943Z * [new tag] ciflow/trunk/161959 -> ciflow/trunk/161959 2025-09-07T06:39:16.1581002Z * [new tag] ciflow/trunk/161977 -> ciflow/trunk/161977 2025-09-07T06:39:16.1581060Z * [new tag] ciflow/trunk/161988 -> ciflow/trunk/161988 2025-09-07T06:39:16.1581120Z * [new tag] ciflow/trunk/161994 -> ciflow/trunk/161994 2025-09-07T06:39:16.1581178Z * [new tag] ciflow/trunk/162007 -> ciflow/trunk/162007 2025-09-07T06:39:16.1581268Z * [new tag] ciflow/trunk/162013 -> ciflow/trunk/162013 2025-09-07T06:39:16.1581328Z * [new tag] ciflow/trunk/162017 -> ciflow/trunk/162017 2025-09-07T06:39:16.1581390Z * [new tag] ciflow/trunk/162021 -> ciflow/trunk/162021 2025-09-07T06:39:16.1581449Z * [new tag] ciflow/trunk/162022 -> ciflow/trunk/162022 2025-09-07T06:39:16.1581507Z * [new tag] ciflow/trunk/162040 -> ciflow/trunk/162040 2025-09-07T06:39:16.1581567Z * [new tag] ciflow/trunk/162041 -> ciflow/trunk/162041 2025-09-07T06:39:16.1581626Z * [new tag] ciflow/trunk/162062 -> ciflow/trunk/162062 2025-09-07T06:39:16.1581684Z * [new tag] ciflow/trunk/162066 -> ciflow/trunk/162066 2025-09-07T06:39:16.1581744Z * [new tag] ciflow/trunk/162089 -> ciflow/trunk/162089 2025-09-07T06:39:16.1581804Z * [new tag] ciflow/trunk/162099 -> ciflow/trunk/162099 2025-09-07T06:39:16.1581862Z * [new tag] ciflow/trunk/162104 -> ciflow/trunk/162104 2025-09-07T06:39:16.1581922Z * [new tag] ciflow/trunk/162106 -> ciflow/trunk/162106 2025-09-07T06:39:16.1581982Z * [new tag] ciflow/trunk/162112 -> ciflow/trunk/162112 2025-09-07T06:39:16.1582041Z * [new tag] ciflow/trunk/162119 -> ciflow/trunk/162119 2025-09-07T06:39:16.1582100Z * [new tag] ciflow/trunk/162142 -> ciflow/trunk/162142 2025-09-07T06:39:16.1582159Z * [new tag] ciflow/trunk/162169 -> ciflow/trunk/162169 2025-09-07T06:39:16.1582218Z * [new tag] ciflow/trunk/162183 -> ciflow/trunk/162183 2025-09-07T06:39:16.1582277Z * [new tag] ciflow/trunk/162190 -> ciflow/trunk/162190 2025-09-07T06:39:16.1582337Z * [new tag] ciflow/trunk/162194 -> ciflow/trunk/162194 2025-09-07T06:39:16.1582397Z * [new tag] ciflow/trunk/162200 -> ciflow/trunk/162200 2025-09-07T06:39:16.1583660Z * [new tag] ciflow/trunk/162206 -> ciflow/trunk/162206 2025-09-07T06:39:16.1583725Z * [new tag] ciflow/trunk/162208 -> ciflow/trunk/162208 2025-09-07T06:39:16.1583785Z * [new tag] ciflow/trunk/162222 -> ciflow/trunk/162222 2025-09-07T06:39:16.1583844Z * [new tag] ciflow/trunk/162238 -> ciflow/trunk/162238 2025-09-07T06:39:16.1583902Z * [new tag] ciflow/trunk/162244 -> ciflow/trunk/162244 2025-09-07T06:39:16.1583961Z * [new tag] ciflow/trunk/162267 -> ciflow/trunk/162267 2025-09-07T06:39:16.1584021Z * [new tag] ciflow/trunk/162269 -> ciflow/trunk/162269 2025-09-07T06:39:16.1584080Z * [new tag] ciflow/trunk/162278 -> ciflow/trunk/162278 2025-09-07T06:39:16.1584140Z * [new tag] ciflow/trunk/162286 -> ciflow/trunk/162286 2025-09-07T06:39:16.1584200Z * [new tag] ciflow/trunk/162288 -> ciflow/trunk/162288 2025-09-07T06:39:16.1584258Z * [new tag] ciflow/trunk/162293 -> ciflow/trunk/162293 2025-09-07T06:39:16.1584359Z * [new tag] ciflow/trunk/162310 -> ciflow/trunk/162310 2025-09-07T06:39:16.1584476Z * [new tag] ciflow/trunk/162311 -> ciflow/trunk/162311 2025-09-07T06:39:16.1584538Z * [new tag] ciflow/trunk/162315 -> ciflow/trunk/162315 2025-09-07T06:39:16.1584596Z * [new tag] ciflow/trunk/162325 -> ciflow/trunk/162325 2025-09-07T06:39:16.1584656Z * [new tag] ciflow/trunk/162328 -> ciflow/trunk/162328 2025-09-07T06:39:16.1584714Z * [new tag] ciflow/trunk/162329 -> ciflow/trunk/162329 2025-09-07T06:39:16.1584819Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-09-07T06:39:16.1584879Z * [new tag] ciflow/vllm/162292 -> ciflow/vllm/162292 2025-09-07T06:39:16.1584948Z * [new tag] ciflow/win-arm64/156049 -> ciflow/win-arm64/156049 2025-09-07T06:39:16.1585016Z * [new tag] ciflow/win-arm64/158104 -> ciflow/win-arm64/158104 2025-09-07T06:39:16.1585079Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-09-07T06:39:16.1585138Z * [new tag] ciflow/xpu/157994 -> ciflow/xpu/157994 2025-09-07T06:39:16.1585195Z * [new tag] ciflow/xpu/159459 -> ciflow/xpu/159459 2025-09-07T06:39:16.1585253Z * [new tag] ciflow/xpu/159718 -> ciflow/xpu/159718 2025-09-07T06:39:16.1585312Z * [new tag] ciflow/xpu/159944 -> ciflow/xpu/159944 2025-09-07T06:39:16.1585369Z * [new tag] ciflow/xpu/160867 -> ciflow/xpu/160867 2025-09-07T06:39:16.1585429Z * [new tag] ciflow/xpu/160938 -> ciflow/xpu/160938 2025-09-07T06:39:16.1585487Z * [new tag] ciflow/xpu/160940 -> ciflow/xpu/160940 2025-09-07T06:39:16.1585545Z * [new tag] ciflow/xpu/160953 -> ciflow/xpu/160953 2025-09-07T06:39:16.1585604Z * [new tag] ciflow/xpu/161045 -> ciflow/xpu/161045 2025-09-07T06:39:16.1585662Z * [new tag] ciflow/xpu/161058 -> ciflow/xpu/161058 2025-09-07T06:39:16.1585719Z * [new tag] ciflow/xpu/161246 -> ciflow/xpu/161246 2025-09-07T06:39:16.1585776Z * [new tag] ciflow/xpu/161397 -> ciflow/xpu/161397 2025-09-07T06:39:16.1585834Z * [new tag] ciflow/xpu/161485 -> ciflow/xpu/161485 2025-09-07T06:39:16.1585891Z * [new tag] ciflow/xpu/161988 -> ciflow/xpu/161988 2025-09-07T06:39:16.1585949Z * [new tag] ciflow/xpu/162062 -> ciflow/xpu/162062 2025-09-07T06:39:16.1586008Z * [new tag] cslpull75 -> cslpull75 2025-09-07T06:39:16.1586064Z * [new tag] cslpull76 -> cslpull76 2025-09-07T06:39:16.1586119Z * [new tag] cslpull77 -> cslpull77 2025-09-07T06:39:16.1586170Z * [new tag] cslpull78 -> cslpull78 2025-09-07T06:39:16.1586222Z * [new tag] cslpull79 -> cslpull79 2025-09-07T06:39:16.1586273Z * [new tag] cslpull80 -> cslpull80 2025-09-07T06:39:16.1586324Z * [new tag] cslpull81 -> cslpull81 2025-09-07T06:39:16.1586375Z * [new tag] cslpull82 -> cslpull82 2025-09-07T06:39:16.1586426Z * [new tag] cslpull83 -> cslpull83 2025-09-07T06:39:16.1587694Z * [new tag] cslpull84 -> cslpull84 2025-09-07T06:39:16.1587750Z * [new tag] cslpull85 -> cslpull85 2025-09-07T06:39:16.1587802Z * [new tag] cslpull86 -> cslpull86 2025-09-07T06:39:16.1587854Z * [new tag] cslpull87 -> cslpull87 2025-09-07T06:39:16.1587952Z * [new tag] cslpull88 -> cslpull88 2025-09-07T06:39:16.1588003Z * [new tag] cslpull89 -> cslpull89 2025-09-07T06:39:16.1588054Z * [new tag] cslpull90 -> cslpull90 2025-09-07T06:39:16.1588104Z * [new tag] cslpull91 -> cslpull91 2025-09-07T06:39:16.1588155Z * [new tag] cslpull92 -> cslpull92 2025-09-07T06:39:16.1588210Z * [new tag] flight_5 -> flight_5 2025-09-07T06:39:16.1588266Z * [new tag] flight_5.1 -> flight_5.1 2025-09-07T06:39:16.1588354Z * [new tag] flight_5.2 -> flight_5.2 2025-09-07T06:39:16.1588407Z * [new tag] flight_5.3 -> flight_5.3 2025-09-07T06:39:16.1588462Z * [new tag] forpull1 -> forpull1 2025-09-07T06:39:16.1588527Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-09-07T06:39:16.1588589Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-09-07T06:39:16.1588654Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-09-07T06:39:16.1588717Z * [new tag] nightly-binary -> nightly-binary 2025-09-07T06:39:16.1588782Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-09-07T06:39:16.1588842Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-09-07T06:39:16.1588971Z * [new tag] trunk/00636e0171e7e733628c408084805442270cf608 -> trunk/00636e0171e7e733628c408084805442270cf608 2025-09-07T06:39:16.1589104Z * [new tag] trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 -> trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 2025-09-07T06:39:16.1589236Z * [new tag] trunk/01ab325cc2e0dc221af4d710974e1b9175066544 -> trunk/01ab325cc2e0dc221af4d710974e1b9175066544 2025-09-07T06:39:16.1589370Z * [new tag] trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b -> trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b 2025-09-07T06:39:16.1589497Z * [new tag] trunk/040d00af048967dde7938d358d7f5988cbd18388 -> trunk/040d00af048967dde7938d358d7f5988cbd18388 2025-09-07T06:39:16.1589625Z * [new tag] trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 -> trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 2025-09-07T06:39:16.1589748Z * [new tag] trunk/047603d35bdc70046216384838d6340feab79bf4 -> trunk/047603d35bdc70046216384838d6340feab79bf4 2025-09-07T06:39:16.1589879Z * [new tag] trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 -> trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 2025-09-07T06:39:16.1590003Z * [new tag] trunk/081cab045472ce045634548cc6c14a4870641e23 -> trunk/081cab045472ce045634548cc6c14a4870641e23 2025-09-07T06:39:16.1590131Z * [new tag] trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 -> trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 2025-09-07T06:39:16.1590258Z * [new tag] trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 -> trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 2025-09-07T06:39:16.1590383Z * [new tag] trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 -> trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 2025-09-07T06:39:16.1590512Z * [new tag] trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 -> trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 2025-09-07T06:39:16.1590639Z * [new tag] trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 -> trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 2025-09-07T06:39:16.1590773Z * [new tag] trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 -> trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 2025-09-07T06:39:16.1590936Z * [new tag] trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c -> trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c 2025-09-07T06:39:16.1591068Z * [new tag] trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 -> trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 2025-09-07T06:39:16.1591194Z * [new tag] trunk/0d84ff3b78f55492d3d4708458c92d776274939e -> trunk/0d84ff3b78f55492d3d4708458c92d776274939e 2025-09-07T06:39:16.1591320Z * [new tag] trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 -> trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 2025-09-07T06:39:16.1591451Z * [new tag] trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f -> trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f 2025-09-07T06:39:16.1591577Z * [new tag] trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f -> trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f 2025-09-07T06:39:16.1591737Z * [new tag] trunk/12814701555d3e41dfcdf8f9273af5821e322df0 -> trunk/12814701555d3e41dfcdf8f9273af5821e322df0 2025-09-07T06:39:16.1591867Z * [new tag] trunk/13b65196db422bdb394cb482e208c61ed448898c -> trunk/13b65196db422bdb394cb482e208c61ed448898c 2025-09-07T06:39:16.1592067Z * [new tag] trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 -> trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 2025-09-07T06:39:16.1592197Z * [new tag] trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 -> trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 2025-09-07T06:39:16.1592321Z * [new tag] trunk/146371483318e17929daefd37c8e459d9d6d47bb -> trunk/146371483318e17929daefd37c8e459d9d6d47bb 2025-09-07T06:39:16.1593669Z * [new tag] trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 -> trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 2025-09-07T06:39:16.1593806Z * [new tag] trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 -> trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 2025-09-07T06:39:16.1593935Z * [new tag] trunk/190c391a28845a14df26abb228d26aa813efb20c -> trunk/190c391a28845a14df26abb228d26aa813efb20c 2025-09-07T06:39:16.1594068Z * [new tag] trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 -> trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 2025-09-07T06:39:16.1594198Z * [new tag] trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 -> trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 2025-09-07T06:39:16.1594326Z * [new tag] trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 -> trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 2025-09-07T06:39:16.1594642Z * [new tag] trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a -> trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a 2025-09-07T06:39:16.1594775Z * [new tag] trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 -> trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 2025-09-07T06:39:16.1594908Z * [new tag] trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 -> trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 2025-09-07T06:39:16.1595038Z * [new tag] trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a -> trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a 2025-09-07T06:39:16.1595164Z * [new tag] trunk/1f820de639c75a1562d3fb03f160439f853ae07b -> trunk/1f820de639c75a1562d3fb03f160439f853ae07b 2025-09-07T06:39:16.1595288Z * [new tag] trunk/204697f0e695d82894c5010fbec664c4391f90cc -> trunk/204697f0e695d82894c5010fbec664c4391f90cc 2025-09-07T06:39:16.1595413Z * [new tag] trunk/20629b1619fe636227d01fc85ba221daa7185a05 -> trunk/20629b1619fe636227d01fc85ba221daa7185a05 2025-09-07T06:39:16.1595540Z * [new tag] trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 -> trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 2025-09-07T06:39:16.1595670Z * [new tag] trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd -> trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd 2025-09-07T06:39:16.1595798Z * [new tag] trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 -> trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 2025-09-07T06:39:16.1595984Z * [new tag] trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f -> trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f 2025-09-07T06:39:16.1596113Z * [new tag] trunk/25f4aaed9ec26f39c13862323ff8582006473d23 -> trunk/25f4aaed9ec26f39c13862323ff8582006473d23 2025-09-07T06:39:16.1596238Z * [new tag] trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 -> trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 2025-09-07T06:39:16.1596363Z * [new tag] trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f -> trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f 2025-09-07T06:39:16.1596493Z * [new tag] trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 -> trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 2025-09-07T06:39:16.1596664Z * [new tag] trunk/29280864d941e6108ab57f7298f520c0cf9696e9 -> trunk/29280864d941e6108ab57f7298f520c0cf9696e9 2025-09-07T06:39:16.1596790Z * [new tag] trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 -> trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 2025-09-07T06:39:16.1596921Z * [new tag] trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef -> trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef 2025-09-07T06:39:16.1597049Z * [new tag] trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c -> trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c 2025-09-07T06:39:16.1597172Z * [new tag] trunk/2ba65472dd54488a86a50326ea990195fc6732d6 -> trunk/2ba65472dd54488a86a50326ea990195fc6732d6 2025-09-07T06:39:16.1597302Z * [new tag] trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 -> trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 2025-09-07T06:39:16.1597428Z * [new tag] trunk/2dd529df0092799f68ee7afcf52338276906706a -> trunk/2dd529df0092799f68ee7afcf52338276906706a 2025-09-07T06:39:16.1597560Z * [new tag] trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 -> trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 2025-09-07T06:39:16.1597690Z * [new tag] trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 -> trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 2025-09-07T06:39:16.1597817Z * [new tag] trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 -> trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 2025-09-07T06:39:16.1597943Z * [new tag] trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 -> trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 2025-09-07T06:39:16.1598065Z * [new tag] trunk/34aa78274d6770086025a967fa63a86830e08176 -> trunk/34aa78274d6770086025a967fa63a86830e08176 2025-09-07T06:39:16.1598193Z * [new tag] trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 -> trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 2025-09-07T06:39:16.1598324Z * [new tag] trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b -> trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b 2025-09-07T06:39:16.1598452Z * [new tag] trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 -> trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 2025-09-07T06:39:16.1598579Z * [new tag] trunk/3771380f83fcac154a7c89ad679311d8c4818287 -> trunk/3771380f83fcac154a7c89ad679311d8c4818287 2025-09-07T06:39:16.1598703Z * [new tag] trunk/3a207816cc569f78863d86c01f2a3d265350e39f -> trunk/3a207816cc569f78863d86c01f2a3d265350e39f 2025-09-07T06:39:16.1598832Z * [new tag] trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 -> trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 2025-09-07T06:39:16.1598963Z * [new tag] trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 -> trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 2025-09-07T06:39:16.1599093Z * [new tag] trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f -> trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f 2025-09-07T06:39:16.1599224Z * [new tag] trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf -> trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf 2025-09-07T06:39:16.1599386Z * [new tag] trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 -> trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 2025-09-07T06:39:16.1599514Z * [new tag] trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d -> trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d 2025-09-07T06:39:16.1599641Z * [new tag] trunk/420c52ecf36f86d32da0853bfbe074b682b070aa -> trunk/420c52ecf36f86d32da0853bfbe074b682b070aa 2025-09-07T06:39:16.1599768Z * [new tag] trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 -> trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 2025-09-07T06:39:16.1601149Z * [new tag] trunk/451ed931562ec8b46d1f7e6c266a68132a119336 -> trunk/451ed931562ec8b46d1f7e6c266a68132a119336 2025-09-07T06:39:16.1601316Z * [new tag] trunk/480c7391126656154318fabf1d57ebc01e196e63 -> trunk/480c7391126656154318fabf1d57ebc01e196e63 2025-09-07T06:39:16.1601446Z * [new tag] trunk/48bedd753da22634aa94fbafeb731e82025404f3 -> trunk/48bedd753da22634aa94fbafeb731e82025404f3 2025-09-07T06:39:16.1601574Z * [new tag] trunk/494878a11b79071ada0b98f34042d47155be6d1c -> trunk/494878a11b79071ada0b98f34042d47155be6d1c 2025-09-07T06:39:16.1601704Z * [new tag] trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 -> trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 2025-09-07T06:39:16.1601831Z * [new tag] trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf -> trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf 2025-09-07T06:39:16.1601965Z * [new tag] trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e -> trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e 2025-09-07T06:39:16.1602097Z * [new tag] trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 -> trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 2025-09-07T06:39:16.1602226Z * [new tag] trunk/4f72d932feee0749397fec876dcd43994f50b215 -> trunk/4f72d932feee0749397fec876dcd43994f50b215 2025-09-07T06:39:16.1602356Z * [new tag] trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d -> trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d 2025-09-07T06:39:16.1602484Z * [new tag] trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 -> trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 2025-09-07T06:39:16.1602611Z * [new tag] trunk/524b78d4f67045b83bb69edc56ab16efe282971c -> trunk/524b78d4f67045b83bb69edc56ab16efe282971c 2025-09-07T06:39:16.1602743Z * [new tag] trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 -> trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 2025-09-07T06:39:16.1602866Z * [new tag] trunk/5561e45758d59c94605873d5db48ed459c004c3b -> trunk/5561e45758d59c94605873d5db48ed459c004c3b 2025-09-07T06:39:16.1602992Z * [new tag] trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 -> trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 2025-09-07T06:39:16.1603119Z * [new tag] trunk/5927a70934ccf7b70182d364c23245a7dd685503 -> trunk/5927a70934ccf7b70182d364c23245a7dd685503 2025-09-07T06:39:16.1603249Z * [new tag] trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 -> trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 2025-09-07T06:39:16.1603380Z * [new tag] trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 -> trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 2025-09-07T06:39:16.1603511Z * [new tag] trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 -> trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 2025-09-07T06:39:16.1603635Z * [new tag] trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 -> trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 2025-09-07T06:39:16.1603764Z * [new tag] trunk/5da573c42c332bc68d4b7946c69f690a876d951a -> trunk/5da573c42c332bc68d4b7946c69f690a876d951a 2025-09-07T06:39:16.1603889Z * [new tag] trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 -> trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 2025-09-07T06:39:16.1604019Z * [new tag] trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 -> trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 2025-09-07T06:39:16.1604184Z * [new tag] trunk/600c25e9a17fe56e3dee872be8854db08916ba0c -> trunk/600c25e9a17fe56e3dee872be8854db08916ba0c 2025-09-07T06:39:16.1604313Z * [new tag] trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 -> trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 2025-09-07T06:39:16.1604500Z * [new tag] trunk/6087ef41e54c2494b117ffd923faf20f515a6806 -> trunk/6087ef41e54c2494b117ffd923faf20f515a6806 2025-09-07T06:39:16.1604632Z * [new tag] trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 -> trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 2025-09-07T06:39:16.1604759Z * [new tag] trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 -> trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 2025-09-07T06:39:16.1604936Z * [new tag] trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 -> trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 2025-09-07T06:39:16.1605061Z * [new tag] trunk/65985937d97505f648b6ed852c3129f2dd08b251 -> trunk/65985937d97505f648b6ed852c3129f2dd08b251 2025-09-07T06:39:16.1605186Z * [new tag] trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 -> trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 2025-09-07T06:39:16.1605308Z * [new tag] trunk/6737e2c996990024187ba620d2764f3b6f6add2c -> trunk/6737e2c996990024187ba620d2764f3b6f6add2c 2025-09-07T06:39:16.1605436Z * [new tag] trunk/67c31dcd364f10072a55f4a30ffd1151c686283a -> trunk/67c31dcd364f10072a55f4a30ffd1151c686283a 2025-09-07T06:39:16.1605563Z * [new tag] trunk/68738beff73e9c3512e18b4edea811a897ce42db -> trunk/68738beff73e9c3512e18b4edea811a897ce42db 2025-09-07T06:39:16.1605687Z * [new tag] trunk/69a25f68884a168550695fdb1a7c310c54d29536 -> trunk/69a25f68884a168550695fdb1a7c310c54d29536 2025-09-07T06:39:16.1605813Z * [new tag] trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f -> trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f 2025-09-07T06:39:16.1605939Z * [new tag] trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 -> trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 2025-09-07T06:39:16.1606064Z * [new tag] trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b -> trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b 2025-09-07T06:39:16.1606189Z * [new tag] trunk/70d36e047dfb3488fd6335016711a784d810ebda -> trunk/70d36e047dfb3488fd6335016711a784d810ebda 2025-09-07T06:39:16.1606315Z * [new tag] trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b -> trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b 2025-09-07T06:39:16.1606442Z * [new tag] trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 -> trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 2025-09-07T06:39:16.1606569Z * [new tag] trunk/73eb4511fb863a37944342b7e92aae706de603c8 -> trunk/73eb4511fb863a37944342b7e92aae706de603c8 2025-09-07T06:39:16.1606699Z * [new tag] trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b -> trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b 2025-09-07T06:39:16.1606823Z * [new tag] trunk/771f369448321a387f2018535bc8b8b6e5f12fab -> trunk/771f369448321a387f2018535bc8b8b6e5f12fab 2025-09-07T06:39:16.1606947Z * [new tag] trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 -> trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 2025-09-07T06:39:16.1608317Z * [new tag] trunk/791eff96c85678c950888f9da24650083ee673fe -> trunk/791eff96c85678c950888f9da24650083ee673fe 2025-09-07T06:39:16.1608451Z * [new tag] trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 -> trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 2025-09-07T06:39:16.1608584Z * [new tag] trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 -> trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 2025-09-07T06:39:16.1608714Z * [new tag] trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 -> trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 2025-09-07T06:39:16.1608910Z * [new tag] trunk/8076a185c85112be62be292eb47409c88a585b1c -> trunk/8076a185c85112be62be292eb47409c88a585b1c 2025-09-07T06:39:16.1609034Z * [new tag] trunk/80dd397f1979371a5583fa3d5c7352029522a78d -> trunk/80dd397f1979371a5583fa3d5c7352029522a78d 2025-09-07T06:39:16.1609158Z * [new tag] trunk/8171d6052ec12628eb67e0040839314056014429 -> trunk/8171d6052ec12628eb67e0040839314056014429 2025-09-07T06:39:16.1609286Z * [new tag] trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 -> trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 2025-09-07T06:39:16.1609412Z * [new tag] trunk/81b7b16618bda250ce55982894a83dc0805eb64c -> trunk/81b7b16618bda250ce55982894a83dc0805eb64c 2025-09-07T06:39:16.1609576Z * [new tag] trunk/827f0d405448de31f79d1089f7d7fceab2f87895 -> trunk/827f0d405448de31f79d1089f7d7fceab2f87895 2025-09-07T06:39:16.1609706Z * [new tag] trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 -> trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 2025-09-07T06:39:16.1609835Z * [new tag] trunk/850e1382a9c56bfde18af09d3e72352d775e9435 -> trunk/850e1382a9c56bfde18af09d3e72352d775e9435 2025-09-07T06:39:16.1609960Z * [new tag] trunk/8678d831c48e616b717bff50f2d03141d2e9f965 -> trunk/8678d831c48e616b717bff50f2d03141d2e9f965 2025-09-07T06:39:16.1610088Z * [new tag] trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 -> trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 2025-09-07T06:39:16.1610215Z * [new tag] trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 -> trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 2025-09-07T06:39:16.1610346Z * [new tag] trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 -> trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 2025-09-07T06:39:16.1610476Z * [new tag] trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 -> trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 2025-09-07T06:39:16.1610607Z * [new tag] trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 -> trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 2025-09-07T06:39:16.1610735Z * [new tag] trunk/890626632def7e0ef95a2d01e87a0e4627824a9f -> trunk/890626632def7e0ef95a2d01e87a0e4627824a9f 2025-09-07T06:39:16.1610864Z * [new tag] trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 -> trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 2025-09-07T06:39:16.1610989Z * [new tag] trunk/89d41d3f61d04f14730ec26f008a59bef6624610 -> trunk/89d41d3f61d04f14730ec26f008a59bef6624610 2025-09-07T06:39:16.1611118Z * [new tag] trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 -> trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 2025-09-07T06:39:16.1611247Z * [new tag] trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af -> trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af 2025-09-07T06:39:16.1611378Z * [new tag] trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 -> trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 2025-09-07T06:39:16.1611510Z * [new tag] trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d -> trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d 2025-09-07T06:39:16.1611635Z * [new tag] trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 -> trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 2025-09-07T06:39:16.1611763Z * [new tag] trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 -> trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 2025-09-07T06:39:16.1611888Z * [new tag] trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab -> trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab 2025-09-07T06:39:16.1612015Z * [new tag] trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d -> trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d 2025-09-07T06:39:16.1612143Z * [new tag] trunk/93fb23d6fae7c4e82c4239a1033e522088742634 -> trunk/93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:39:16.1612273Z * [new tag] trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c -> trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c 2025-09-07T06:39:16.1612439Z * [new tag] trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e -> trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e 2025-09-07T06:39:16.1612565Z * [new tag] trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 -> trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 2025-09-07T06:39:16.1612691Z * [new tag] trunk/9499c8761cd2067feb9877414e818f6fd00290f1 -> trunk/9499c8761cd2067feb9877414e818f6fd00290f1 2025-09-07T06:39:16.1612819Z * [new tag] trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 -> trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 2025-09-07T06:39:16.1612946Z * [new tag] trunk/98374612fc2febd686be20761e56bdc2424bc36a -> trunk/98374612fc2febd686be20761e56bdc2424bc36a 2025-09-07T06:39:16.1613104Z * [new tag] trunk/98efc9e93d8fc61eb53cb91378443617cb550500 -> trunk/98efc9e93d8fc61eb53cb91378443617cb550500 2025-09-07T06:39:16.1613237Z * [new tag] trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 -> trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 2025-09-07T06:39:16.1613364Z * [new tag] trunk/99f356fa58c8d726cef022d8710f5491291158f6 -> trunk/99f356fa58c8d726cef022d8710f5491291158f6 2025-09-07T06:39:16.1613495Z * [new tag] trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 -> trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 2025-09-07T06:39:16.1613622Z * [new tag] trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd -> trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd 2025-09-07T06:39:16.1613747Z * [new tag] trunk/9aedb3cd87b52160872173c177f61053d97bed57 -> trunk/9aedb3cd87b52160872173c177f61053d97bed57 2025-09-07T06:39:16.1613873Z * [new tag] trunk/9b81fe281da41f2421506339d26b027a468902f4 -> trunk/9b81fe281da41f2421506339d26b027a468902f4 2025-09-07T06:39:16.1614005Z * [new tag] trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e -> trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e 2025-09-07T06:39:16.1614134Z * [new tag] trunk/9c03d6be87eedc06e524e202e07a7e776551a839 -> trunk/9c03d6be87eedc06e524e202e07a7e776551a839 2025-09-07T06:39:16.1615568Z * [new tag] trunk/9c957723a0fedd9c637e63e023a613019e2cab60 -> trunk/9c957723a0fedd9c637e63e023a613019e2cab60 2025-09-07T06:39:16.1615700Z * [new tag] trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 -> trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 2025-09-07T06:39:16.1615832Z * [new tag] trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 -> trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 2025-09-07T06:39:16.1615962Z * [new tag] trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 -> trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 2025-09-07T06:39:16.1616093Z * [new tag] trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 -> trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 2025-09-07T06:39:16.1616220Z * [new tag] trunk/a3c7f77e50f900721817934120d60c2361b3c40d -> trunk/a3c7f77e50f900721817934120d60c2361b3c40d 2025-09-07T06:39:16.1616347Z * [new tag] trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 -> trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 2025-09-07T06:39:16.1616471Z * [new tag] trunk/a3e5466002791da609fcb069155d8ee347baee92 -> trunk/a3e5466002791da609fcb069155d8ee347baee92 2025-09-07T06:39:16.1616601Z * [new tag] trunk/a714437093ed196eee28f7de454cf4c41badc098 -> trunk/a714437093ed196eee28f7de454cf4c41badc098 2025-09-07T06:39:16.1616726Z * [new tag] trunk/a75e8cd27098f290de0b7439685d05ce02e91356 -> trunk/a75e8cd27098f290de0b7439685d05ce02e91356 2025-09-07T06:39:16.1616852Z * [new tag] trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae -> trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae 2025-09-07T06:39:16.1616986Z * [new tag] trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 -> trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 2025-09-07T06:39:16.1617166Z * [new tag] trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e -> trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e 2025-09-07T06:39:16.1617294Z * [new tag] trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 -> trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 2025-09-07T06:39:16.1617423Z * [new tag] trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 -> trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 2025-09-07T06:39:16.1617553Z * [new tag] trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c -> trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c 2025-09-07T06:39:16.1617681Z * [new tag] trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 -> trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 2025-09-07T06:39:16.1617856Z * [new tag] trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d -> trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d 2025-09-07T06:39:16.1617987Z * [new tag] trunk/adae7f66aacf3f248c3101b858cf98d5809119fa -> trunk/adae7f66aacf3f248c3101b858cf98d5809119fa 2025-09-07T06:39:16.1618120Z * [new tag] trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c -> trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c 2025-09-07T06:39:16.1618248Z * [new tag] trunk/aed33a8fcbd60b052d4559d261390c5797129c6d -> trunk/aed33a8fcbd60b052d4559d261390c5797129c6d 2025-09-07T06:39:16.1618372Z * [new tag] trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 -> trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 2025-09-07T06:39:16.1618500Z * [new tag] trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f -> trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f 2025-09-07T06:39:16.1618629Z * [new tag] trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 -> trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 2025-09-07T06:39:16.1618756Z * [new tag] trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 -> trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 2025-09-07T06:39:16.1618888Z * [new tag] trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de -> trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de 2025-09-07T06:39:16.1619014Z * [new tag] trunk/b2b4add0e754411372060e1d7b4057a66439172b -> trunk/b2b4add0e754411372060e1d7b4057a66439172b 2025-09-07T06:39:16.1619144Z * [new tag] trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 -> trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 2025-09-07T06:39:16.1619269Z * [new tag] trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 -> trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 2025-09-07T06:39:16.1619395Z * [new tag] trunk/b4ad38279b178b7bd14355123c1101e2e853e77b -> trunk/b4ad38279b178b7bd14355123c1101e2e853e77b 2025-09-07T06:39:16.1619523Z * [new tag] trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde -> trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde 2025-09-07T06:39:16.1619656Z * [new tag] trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c -> trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c 2025-09-07T06:39:16.1619788Z * [new tag] trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 -> trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 2025-09-07T06:39:16.1619915Z * [new tag] trunk/b7e207ca9f046ddd716076965a0cce403ba99052 -> trunk/b7e207ca9f046ddd716076965a0cce403ba99052 2025-09-07T06:39:16.1620040Z * [new tag] trunk/b919560c4a7010e2d89facee25586269a994746e -> trunk/b919560c4a7010e2d89facee25586269a994746e 2025-09-07T06:39:16.1620168Z * [new tag] trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 -> trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 2025-09-07T06:39:16.1620297Z * [new tag] trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 -> trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 2025-09-07T06:39:16.1620423Z * [new tag] trunk/bb950284c7e72905994bc25dd436c10e48088d85 -> trunk/bb950284c7e72905994bc25dd436c10e48088d85 2025-09-07T06:39:16.1620589Z * [new tag] trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d -> trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d 2025-09-07T06:39:16.1620723Z * [new tag] trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 -> trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 2025-09-07T06:39:16.1620846Z * [new tag] trunk/bc505977fb66677a09c31155c987330fbb18a865 -> trunk/bc505977fb66677a09c31155c987330fbb18a865 2025-09-07T06:39:16.1620980Z * [new tag] trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 -> trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 2025-09-07T06:39:16.1621110Z * [new tag] trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 -> trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 2025-09-07T06:39:16.1621240Z * [new tag] trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 -> trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 2025-09-07T06:39:16.1621407Z * [new tag] trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf -> trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf 2025-09-07T06:39:16.1622770Z * [new tag] trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 -> trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 2025-09-07T06:39:16.1622905Z * [new tag] trunk/c10195e723eeeedd099ed8b73eda7184ca618fad -> trunk/c10195e723eeeedd099ed8b73eda7184ca618fad 2025-09-07T06:39:16.1623035Z * [new tag] trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 -> trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 2025-09-07T06:39:16.1623160Z * [new tag] trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 -> trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 2025-09-07T06:39:16.1623282Z * [new tag] trunk/c32111149921b48bfef909293f1049e21619ed76 -> trunk/c32111149921b48bfef909293f1049e21619ed76 2025-09-07T06:39:16.1623409Z * [new tag] trunk/c37103234afc832dcad307e9016230810957c9d5 -> trunk/c37103234afc832dcad307e9016230810957c9d5 2025-09-07T06:39:16.1623538Z * [new tag] trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 -> trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 2025-09-07T06:39:16.1623670Z * [new tag] trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd -> trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd 2025-09-07T06:39:16.1623795Z * [new tag] trunk/c465b3d52c5687fe910d35a5c75341b77f821741 -> trunk/c465b3d52c5687fe910d35a5c75341b77f821741 2025-09-07T06:39:16.1623923Z * [new tag] trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b -> trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b 2025-09-07T06:39:16.1624047Z * [new tag] trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 -> trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 2025-09-07T06:39:16.1624181Z * [new tag] trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 -> trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 2025-09-07T06:39:16.1624312Z * [new tag] trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b -> trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b 2025-09-07T06:39:16.1624496Z * [new tag] trunk/cbfb005f7cce79974795b148e265f594f59477c8 -> trunk/cbfb005f7cce79974795b148e265f594f59477c8 2025-09-07T06:39:16.1624627Z * [new tag] trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 -> trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 2025-09-07T06:39:16.1624755Z * [new tag] trunk/cd529b686d54bbaa443f5b310140de48422d96c7 -> trunk/cd529b686d54bbaa443f5b310140de48422d96c7 2025-09-07T06:39:16.1624880Z * [new tag] trunk/cec0ff122815582af5302360aff03676558c5c87 -> trunk/cec0ff122815582af5302360aff03676558c5c87 2025-09-07T06:39:16.1625008Z * [new tag] trunk/d11720efdb563d02cf4f7d324311fb15a755268e -> trunk/d11720efdb563d02cf4f7d324311fb15a755268e 2025-09-07T06:39:16.1625135Z * [new tag] trunk/d1706d9128ae24d9048167e80d3fe5196d19035e -> trunk/d1706d9128ae24d9048167e80d3fe5196d19035e 2025-09-07T06:39:16.1625266Z * [new tag] trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d -> trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d 2025-09-07T06:39:16.1625469Z * [new tag] trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 -> trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 2025-09-07T06:39:16.1625598Z * [new tag] trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e -> trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e 2025-09-07T06:39:16.1625723Z * [new tag] trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 -> trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 2025-09-07T06:39:16.1625849Z * [new tag] trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 -> trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 2025-09-07T06:39:16.1625974Z * [new tag] trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 -> trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 2025-09-07T06:39:16.1626157Z * [new tag] trunk/d5e0f4202ba14632e4d14862ace096609e763462 -> trunk/d5e0f4202ba14632e4d14862ace096609e763462 2025-09-07T06:39:16.1626283Z * [new tag] trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 -> trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 2025-09-07T06:39:16.1626409Z * [new tag] trunk/d64718503728001a1e78168fd7f2d4ff23e57285 -> trunk/d64718503728001a1e78168fd7f2d4ff23e57285 2025-09-07T06:39:16.1626532Z * [new tag] trunk/d67c29ad22670320d676b02e394274af34e8e643 -> trunk/d67c29ad22670320d676b02e394274af34e8e643 2025-09-07T06:39:16.1626661Z * [new tag] trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 -> trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 2025-09-07T06:39:16.1626787Z * [new tag] trunk/d711f27845abd45007ccab6076649ebd896c2661 -> trunk/d711f27845abd45007ccab6076649ebd896c2661 2025-09-07T06:39:16.1626917Z * [new tag] trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab -> trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab 2025-09-07T06:39:16.1627052Z * [new tag] trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 -> trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 2025-09-07T06:39:16.1627188Z * [new tag] trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 -> trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 2025-09-07T06:39:16.1627315Z * [new tag] trunk/dbec08729fb9848bebed6048c63831b87170d061 -> trunk/dbec08729fb9848bebed6048c63831b87170d061 2025-09-07T06:39:16.1627441Z * [new tag] trunk/dcf385395d838f38c8dca25913578230dd43099a -> trunk/dcf385395d838f38c8dca25913578230dd43099a 2025-09-07T06:39:16.1627569Z * [new tag] trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 -> trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 2025-09-07T06:39:16.1627702Z * [new tag] trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d -> trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d 2025-09-07T06:39:16.1627830Z * [new tag] trunk/e0a62b266c021b910ce6dc02a6c9429210487717 -> trunk/e0a62b266c021b910ce6dc02a6c9429210487717 2025-09-07T06:39:16.1627958Z * [new tag] trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 -> trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 2025-09-07T06:39:16.1628088Z * [new tag] trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 -> trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 2025-09-07T06:39:16.1628215Z * [new tag] trunk/e3068cdb446adefb5a875616ba37a60235391439 -> trunk/e3068cdb446adefb5a875616ba37a60235391439 2025-09-07T06:39:16.1628343Z * [new tag] trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 -> trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 2025-09-07T06:39:16.1628471Z * [new tag] trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 -> trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 2025-09-07T06:39:16.1628600Z * [new tag] trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 -> trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 2025-09-07T06:39:16.1628725Z * [new tag] trunk/e92cd9415377403b6e90585e764639e2e0b5973b -> trunk/e92cd9415377403b6e90585e764639e2e0b5973b 2025-09-07T06:39:16.1630121Z * [new tag] trunk/e9481b6617b5576b099d8ca5798111592e9ad090 -> trunk/e9481b6617b5576b099d8ca5798111592e9ad090 2025-09-07T06:39:16.1630257Z * [new tag] trunk/ea1883dfd3e42defe37b11202b878bb76defa087 -> trunk/ea1883dfd3e42defe37b11202b878bb76defa087 2025-09-07T06:39:16.1630393Z * [new tag] trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 -> trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 2025-09-07T06:39:16.1630521Z * [new tag] trunk/eb18d32bda75189494d955aa001ade15f10333de -> trunk/eb18d32bda75189494d955aa001ade15f10333de 2025-09-07T06:39:16.1630651Z * [new tag] trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 -> trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 2025-09-07T06:39:16.1630816Z * [new tag] trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 -> trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 2025-09-07T06:39:16.1630942Z * [new tag] trunk/f00445b43eee57e20bb9316fa796ca23bf73373b -> trunk/f00445b43eee57e20bb9316fa796ca23bf73373b 2025-09-07T06:39:16.1631070Z * [new tag] trunk/f0c391102b754e3b145e8c59231d2df563487e37 -> trunk/f0c391102b754e3b145e8c59231d2df563487e37 2025-09-07T06:39:16.1631196Z * [new tag] trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 -> trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 2025-09-07T06:39:16.1631320Z * [new tag] trunk/f36f285953700f971552083a5da9d0ceacb63bbd -> trunk/f36f285953700f971552083a5da9d0ceacb63bbd 2025-09-07T06:39:16.1631450Z * [new tag] trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb -> trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb 2025-09-07T06:39:16.1631580Z * [new tag] trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c -> trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c 2025-09-07T06:39:16.1631707Z * [new tag] trunk/f612045ce105f008b2b675e2fc870163babeb2e8 -> trunk/f612045ce105f008b2b675e2fc870163babeb2e8 2025-09-07T06:39:16.1631835Z * [new tag] trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c -> trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c 2025-09-07T06:39:16.1632011Z * [new tag] trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c -> trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c 2025-09-07T06:39:16.1632137Z * [new tag] trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 -> trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 2025-09-07T06:39:16.1632271Z * [new tag] trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 -> trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 2025-09-07T06:39:16.1632399Z * [new tag] trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa -> trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa 2025-09-07T06:39:16.1632527Z * [new tag] trunk/fea20775ad96bdca972a1811d7d3372f368614ab -> trunk/fea20775ad96bdca972a1811d7d3372f368614ab 2025-09-07T06:39:16.1632655Z * [new tag] trunk/fefee081642f87419a21dc852f7167d4640443cd -> trunk/fefee081642f87419a21dc852f7167d4640443cd 2025-09-07T06:39:16.1632712Z * [new tag] v0.1.1 -> v0.1.1 2025-09-07T06:39:16.1632772Z * [new tag] v0.1.10 -> v0.1.10 2025-09-07T06:39:16.1632824Z * [new tag] v0.1.11 -> v0.1.11 2025-09-07T06:39:16.1632875Z * [new tag] v0.1.12 -> v0.1.12 2025-09-07T06:39:16.1632927Z * [new tag] v0.1.2 -> v0.1.2 2025-09-07T06:39:16.1632978Z * [new tag] v0.1.3 -> v0.1.3 2025-09-07T06:39:16.1633027Z * [new tag] v0.1.4 -> v0.1.4 2025-09-07T06:39:16.1633075Z * [new tag] v0.1.5 -> v0.1.5 2025-09-07T06:39:16.1633125Z * [new tag] v0.1.6 -> v0.1.6 2025-09-07T06:39:16.1633174Z * [new tag] v0.1.7 -> v0.1.7 2025-09-07T06:39:16.1633221Z * [new tag] v0.1.8 -> v0.1.8 2025-09-07T06:39:16.1633297Z * [new tag] v0.1.9 -> v0.1.9 2025-09-07T06:39:16.1633346Z * [new tag] v0.2.0 -> v0.2.0 2025-09-07T06:39:16.1633394Z * [new tag] v0.3.0 -> v0.3.0 2025-09-07T06:39:16.1633441Z * [new tag] v0.3.1 -> v0.3.1 2025-09-07T06:39:16.1633490Z * [new tag] v0.4.0 -> v0.4.0 2025-09-07T06:39:16.1633538Z * [new tag] v0.4.1 -> v0.4.1 2025-09-07T06:39:16.1633587Z * [new tag] v1.0.0 -> v1.0.0 2025-09-07T06:39:16.1633644Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-09-07T06:39:16.1633723Z * [new tag] v1.0.1 -> v1.0.1 2025-09-07T06:39:16.1633775Z * [new tag] v1.0rc0 -> v1.0rc0 2025-09-07T06:39:16.1633826Z * [new tag] v1.0rc1 -> v1.0rc1 2025-09-07T06:39:16.1633877Z * [new tag] v1.1.0 -> v1.1.0 2025-09-07T06:39:16.1633931Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-09-07T06:39:16.1633980Z * [new tag] v1.10.0 -> v1.10.0 2025-09-07T06:39:16.1634040Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-09-07T06:39:16.1635399Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-09-07T06:39:16.1635459Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-09-07T06:39:16.1635510Z * [new tag] v1.10.1 -> v1.10.1 2025-09-07T06:39:16.1635566Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-09-07T06:39:16.1635617Z * [new tag] v1.10.2 -> v1.10.2 2025-09-07T06:39:16.1635670Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-09-07T06:39:16.1635724Z * [new tag] v1.11.0 -> v1.11.0 2025-09-07T06:39:16.1635777Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-09-07T06:39:16.1635829Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-09-07T06:39:16.1635880Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-09-07T06:39:16.1635931Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-09-07T06:39:16.1635982Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-09-07T06:39:16.1636033Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-09-07T06:39:16.1636088Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-09-07T06:39:16.1636137Z * [new tag] v1.12.0 -> v1.12.0 2025-09-07T06:39:16.1636188Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-09-07T06:39:16.1636240Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-09-07T06:39:16.1636292Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-09-07T06:39:16.1636343Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-09-07T06:39:16.1636395Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-09-07T06:39:16.1636447Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-09-07T06:39:16.1636498Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-09-07T06:39:16.1636549Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-09-07T06:39:16.1636600Z * [new tag] v1.12.1 -> v1.12.1 2025-09-07T06:39:16.1636653Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-09-07T06:39:16.1636704Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-09-07T06:39:16.1636756Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-09-07T06:39:16.1636866Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-09-07T06:39:16.1636918Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-09-07T06:39:16.1636967Z * [new tag] v1.13.0 -> v1.13.0 2025-09-07T06:39:16.1637019Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-09-07T06:39:16.1637070Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-09-07T06:39:16.1637120Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-09-07T06:39:16.1637172Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-09-07T06:39:16.1637275Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-09-07T06:39:16.1637326Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-09-07T06:39:16.1637377Z * [new tag] v1.13.1 -> v1.13.1 2025-09-07T06:39:16.1637431Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-09-07T06:39:16.1637482Z * [new tag] v1.2.0 -> v1.2.0 2025-09-07T06:39:16.1637535Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-09-07T06:39:16.1637586Z * [new tag] v1.3.0 -> v1.3.0 2025-09-07T06:39:16.1637639Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-09-07T06:39:16.1637689Z * [new tag] v1.3.1 -> v1.3.1 2025-09-07T06:39:16.1637738Z * [new tag] v1.4.0 -> v1.4.0 2025-09-07T06:39:16.1637790Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-09-07T06:39:16.1637839Z * [new tag] v1.4.1 -> v1.4.1 2025-09-07T06:39:16.1639098Z * [new tag] v1.5.0 -> v1.5.0 2025-09-07T06:39:16.1639159Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-09-07T06:39:16.1639214Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-09-07T06:39:16.1639267Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-09-07T06:39:16.1639318Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-09-07T06:39:16.1639371Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-09-07T06:39:16.1639422Z * [new tag] v1.5.1 -> v1.5.1 2025-09-07T06:39:16.1639474Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-09-07T06:39:16.1639524Z * [new tag] v1.6.0 -> v1.6.0 2025-09-07T06:39:16.1639579Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-09-07T06:39:16.1639629Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-09-07T06:39:16.1639680Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-09-07T06:39:16.1639734Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-09-07T06:39:16.1639785Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-09-07T06:39:16.1639836Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-09-07T06:39:16.1639887Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-09-07T06:39:16.1639937Z * [new tag] v1.7.0 -> v1.7.0 2025-09-07T06:39:16.1639988Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-09-07T06:39:16.1640040Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-09-07T06:39:16.1640093Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-09-07T06:39:16.1640144Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-09-07T06:39:16.1640194Z * [new tag] v1.7.1 -> v1.7.1 2025-09-07T06:39:16.1640288Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-09-07T06:39:16.1640340Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-09-07T06:39:16.1640390Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-09-07T06:39:16.1640441Z * [new tag] v1.8.0 -> v1.8.0 2025-09-07T06:39:16.1640492Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-09-07T06:39:16.1640543Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-09-07T06:39:16.1640594Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-09-07T06:39:16.1640676Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-09-07T06:39:16.1640727Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-09-07T06:39:16.1640778Z * [new tag] v1.8.1 -> v1.8.1 2025-09-07T06:39:16.1640832Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-09-07T06:39:16.1640882Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-09-07T06:39:16.1640934Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-09-07T06:39:16.1640984Z * [new tag] v1.8.2 -> v1.8.2 2025-09-07T06:39:16.1641036Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-09-07T06:39:16.1641085Z * [new tag] v1.9.0 -> v1.9.0 2025-09-07T06:39:16.1641138Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-09-07T06:39:16.1641189Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-09-07T06:39:16.1641242Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-09-07T06:39:16.1641294Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-09-07T06:39:16.1641344Z * [new tag] v1.9.1 -> v1.9.1 2025-09-07T06:39:16.1641396Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-09-07T06:39:16.1641448Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-09-07T06:39:16.1641498Z * [new tag] v2.0.0 -> v2.0.0 2025-09-07T06:39:16.1642743Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-09-07T06:39:16.1642796Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-09-07T06:39:16.1642848Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-09-07T06:39:16.1642900Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-09-07T06:39:16.1642953Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-09-07T06:39:16.1643004Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-09-07T06:39:16.1643055Z * [new tag] v2.0.1 -> v2.0.1 2025-09-07T06:39:16.1643109Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-09-07T06:39:16.1643160Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-09-07T06:39:16.1643211Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-09-07T06:39:16.1643262Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-09-07T06:39:16.1643312Z * [new tag] v2.1.0 -> v2.1.0 2025-09-07T06:39:16.1643363Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-09-07T06:39:16.1643415Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-09-07T06:39:16.1643466Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-09-07T06:39:16.1643517Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-09-07T06:39:16.1643569Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-09-07T06:39:16.1643657Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-09-07T06:39:16.1643708Z * [new tag] v2.1.1 -> v2.1.1 2025-09-07T06:39:16.1643760Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-09-07T06:39:16.1643811Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-09-07T06:39:16.1643861Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-09-07T06:39:16.1643912Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-09-07T06:39:16.1643964Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-09-07T06:39:16.1644014Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-09-07T06:39:16.1644096Z * [new tag] v2.1.2 -> v2.1.2 2025-09-07T06:39:16.1644148Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-09-07T06:39:16.1644199Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-09-07T06:39:16.1644252Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-09-07T06:39:16.1644303Z * [new tag] v2.2.0 -> v2.2.0 2025-09-07T06:39:16.1644354Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-09-07T06:39:16.1644457Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-09-07T06:39:16.1644512Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-09-07T06:39:16.1644563Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-09-07T06:39:16.1644614Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-09-07T06:39:16.1644668Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-09-07T06:39:16.1644719Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-09-07T06:39:16.1644770Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-09-07T06:39:16.1644822Z * [new tag] v2.2.1 -> v2.2.1 2025-09-07T06:39:16.1644874Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-09-07T06:39:16.1644925Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-09-07T06:39:16.1644975Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-09-07T06:39:16.1645026Z * [new tag] v2.2.2 -> v2.2.2 2025-09-07T06:39:16.1645076Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-09-07T06:39:16.1645127Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-09-07T06:39:16.1645180Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-09-07T06:39:16.1645229Z * [new tag] v2.3.0 -> v2.3.0 2025-09-07T06:39:16.1646495Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-09-07T06:39:16.1646556Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-09-07T06:39:16.1646610Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-09-07T06:39:16.1646662Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-09-07T06:39:16.1646715Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-09-07T06:39:16.1646766Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-09-07T06:39:16.1646817Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-09-07T06:39:16.1646869Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-09-07T06:39:16.1646923Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-09-07T06:39:16.1646973Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-09-07T06:39:16.1647025Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-09-07T06:39:16.1647141Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-09-07T06:39:16.1647191Z * [new tag] v2.3.1 -> v2.3.1 2025-09-07T06:39:16.1647244Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-09-07T06:39:16.1647296Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-09-07T06:39:16.1647346Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-09-07T06:39:16.1647395Z * [new tag] v2.4.0 -> v2.4.0 2025-09-07T06:39:16.1647448Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-09-07T06:39:16.1647498Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-09-07T06:39:16.1647595Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-09-07T06:39:16.1647647Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-09-07T06:39:16.1647697Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-09-07T06:39:16.1647751Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-09-07T06:39:16.1647802Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-09-07T06:39:16.1647853Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-09-07T06:39:16.1647904Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-09-07T06:39:16.1647955Z * [new tag] v2.4.1 -> v2.4.1 2025-09-07T06:39:16.1648006Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-09-07T06:39:16.1648056Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-09-07T06:39:16.1648109Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-09-07T06:39:16.1648160Z * [new tag] v2.5.0 -> v2.5.0 2025-09-07T06:39:16.1648211Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-09-07T06:39:16.1648266Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-09-07T06:39:16.1648318Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-09-07T06:39:16.1648368Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-09-07T06:39:16.1648419Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-09-07T06:39:16.1648470Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-09-07T06:39:16.1648521Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-09-07T06:39:16.1648573Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-09-07T06:39:16.1648627Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-09-07T06:39:16.1648677Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-09-07T06:39:16.1648726Z * [new tag] v2.5.1 -> v2.5.1 2025-09-07T06:39:16.1648779Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-09-07T06:39:16.1648829Z * [new tag] v2.6.0 -> v2.6.0 2025-09-07T06:39:16.1648880Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-09-07T06:39:16.1648931Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-09-07T06:39:16.1650185Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-09-07T06:39:16.1650239Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-09-07T06:39:16.1650292Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-09-07T06:39:16.1650344Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-09-07T06:39:16.1650397Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-09-07T06:39:16.1650448Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-09-07T06:39:16.1650498Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-09-07T06:39:16.1650587Z * [new tag] v2.7.0 -> v2.7.0 2025-09-07T06:39:16.1650639Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-09-07T06:39:16.1650692Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-09-07T06:39:16.1650743Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-09-07T06:39:16.1650795Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-09-07T06:39:16.1650845Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-09-07T06:39:16.1650896Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-09-07T06:39:16.1650999Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-09-07T06:39:16.1651050Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-09-07T06:39:16.1651101Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-09-07T06:39:16.1651154Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-09-07T06:39:16.1651206Z * [new tag] v2.7.1 -> v2.7.1 2025-09-07T06:39:16.1651257Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-09-07T06:39:16.1651308Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-09-07T06:39:16.1651359Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-09-07T06:39:16.1651410Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-09-07T06:39:16.1651461Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-09-07T06:39:16.1651513Z * [new tag] v2.8.0 -> v2.8.0 2025-09-07T06:39:16.1651564Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-09-07T06:39:16.1651614Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-09-07T06:39:16.1651668Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-09-07T06:39:16.1651718Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-09-07T06:39:16.1651769Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-09-07T06:39:16.1651819Z * [new tag] v2.8.0-rc6 -> v2.8.0-rc6 2025-09-07T06:39:16.1651872Z * [new tag] v2.8.0-rc7 -> v2.8.0-rc7 2025-09-07T06:39:16.1651924Z * [new tag] v2.8.0-rc8 -> v2.8.0-rc8 2025-09-07T06:39:16.1651983Z * [new tag] whc_flight_1 -> whc_flight_1 2025-09-07T06:39:16.1652042Z * [new tag] whc_flight_2 -> whc_flight_2 2025-09-07T06:39:16.1652100Z * [new tag] whc_flight_4 -> whc_flight_4 2025-09-07T06:39:16.5227677Z [command]/usr/bin/git rev-parse --verify --quiet 93fb23d6fae7c4e82c4239a1033e522088742634^{object} 2025-09-07T06:39:16.5264696Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:39:16.5267634Z ##[endgroup] 2025-09-07T06:39:16.5267872Z ##[group]Determining the checkout info 2025-09-07T06:39:16.5268107Z ##[endgroup] 2025-09-07T06:39:16.5269961Z [command]/usr/bin/git sparse-checkout disable 2025-09-07T06:39:16.5304913Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-07T06:39:16.5333085Z ##[group]Checking out the ref 2025-09-07T06:39:16.5333643Z [command]/usr/bin/git checkout --progress --force 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:39:17.3998445Z Note: switching to '93fb23d6fae7c4e82c4239a1033e522088742634'. 2025-09-07T06:39:17.3998704Z 2025-09-07T06:39:17.4007482Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-07T06:39:17.4007927Z changes and commit them, and you can discard any commits you make in this 2025-09-07T06:39:17.4008319Z state without impacting any branches by switching back to a branch. 2025-09-07T06:39:17.4008537Z 2025-09-07T06:39:17.4009005Z If you want to create a new branch to retain commits you create, you may 2025-09-07T06:39:17.4009343Z do so (now or later) by using -c with the switch command. Example: 2025-09-07T06:39:17.4009544Z 2025-09-07T06:39:17.4009638Z git switch -c 2025-09-07T06:39:17.4009775Z 2025-09-07T06:39:17.4009863Z Or undo this operation with: 2025-09-07T06:39:17.4010029Z 2025-09-07T06:39:17.4010098Z git switch - 2025-09-07T06:39:17.4010200Z 2025-09-07T06:39:17.4010366Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-07T06:39:17.4010599Z 2025-09-07T06:39:17.4010727Z HEAD is now at 93fb23d6fae Build vLLM nightly wheels (#162000) 2025-09-07T06:39:17.4087479Z ##[endgroup] 2025-09-07T06:39:17.4087776Z ##[group]Setting up auth for fetching submodules 2025-09-07T06:39:17.4093168Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:39:17.4128766Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-07T06:39:17.4150236Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-07T06:39:17.4474661Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-07T06:39:17.4502161Z ##[endgroup] 2025-09-07T06:39:17.4502332Z ##[group]Fetching submodules 2025-09-07T06:39:17.4504355Z [command]/usr/bin/git submodule sync --recursive 2025-09-07T06:39:17.4751749Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-09-07T06:39:17.4967376Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-09-07T06:39:17.4969997Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-09-07T06:39:17.4976236Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-09-07T06:39:17.4976743Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-09-07T06:39:17.4977284Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-09-07T06:39:17.4978997Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-09-07T06:39:17.4980603Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-09-07T06:39:17.4982571Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-09-07T06:39:17.4984562Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-09-07T06:39:17.4986367Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-09-07T06:39:17.4988079Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-09-07T06:39:17.4989921Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-09-07T06:39:17.4991750Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-09-07T06:39:17.4995775Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-09-07T06:39:17.4996126Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-09-07T06:39:17.4997567Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-09-07T06:39:17.4999514Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-09-07T06:39:17.5001302Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-09-07T06:39:17.5003306Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:39:17.5005458Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-09-07T06:39:17.5007571Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-09-07T06:39:17.5009386Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-09-07T06:39:17.5011483Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-09-07T06:39:17.5013812Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-09-07T06:39:17.5016052Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-09-07T06:39:17.5019885Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-09-07T06:39:17.5020222Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-09-07T06:39:17.5022311Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-09-07T06:39:17.5024706Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-09-07T06:39:17.5026998Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-09-07T06:39:17.5029211Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-09-07T06:39:17.5031433Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-09-07T06:39:17.5034042Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-09-07T06:39:17.5036440Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-09-07T06:39:17.5038908Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-09-07T06:39:17.5041224Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-09-07T06:39:17.5043680Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-09-07T06:39:17.5073026Z Cloning into '/home/runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-09-07T06:39:17.8867291Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/psimd'... 2025-09-07T06:39:17.8867641Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-09-07T06:39:17.8867930Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FP16'... 2025-09-07T06:39:17.8868224Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-09-07T06:39:17.8868521Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-09-07T06:39:17.8952987Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-09-07T06:39:17.9277187Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-09-07T06:39:18.0492032Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-09-07T06:39:18.0498384Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-09-07T06:39:18.0499006Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep'... 2025-09-07T06:39:18.0499306Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gloo'... 2025-09-07T06:39:18.0593211Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-09-07T06:39:19.3297980Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-09-07T06:39:19.3298344Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-09-07T06:39:19.3298668Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-09-07T06:39:19.3298983Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-09-07T06:39:19.3299650Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-09-07T06:39:19.3299984Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-09-07T06:39:19.3300313Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-09-07T06:39:19.3308047Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/sleef'... 2025-09-07T06:39:19.3308398Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-09-07T06:39:19.3308708Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/googletest'... 2025-09-07T06:39:19.3309009Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-09-07T06:39:19.3309317Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fmt'... 2025-09-07T06:39:19.4274714Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-09-07T06:39:27.0439722Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-09-07T06:39:27.0440451Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto'... 2025-09-07T06:39:27.0450885Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-09-07T06:39:27.0451456Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-09-07T06:39:27.0451960Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-09-07T06:39:27.0452291Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx'... 2025-09-07T06:39:27.0452638Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-09-07T06:39:27.0452989Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter'... 2025-09-07T06:39:27.0453341Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-09-07T06:39:27.0453703Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-09-07T06:39:27.0454046Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-09-07T06:39:27.0539169Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-09-07T06:39:27.0656161Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-09-07T06:39:27.0745414Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-09-07T06:39:27.0895574Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-09-07T06:39:27.1343802Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-09-07T06:39:27.1632703Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-09-07T06:39:27.5143399Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-09-07T06:39:27.5950194Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-09-07T06:39:27.5991528Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:39:27.6019217Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-09-07T06:39:30.6701832Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-09-07T06:39:30.7218342Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-09-07T06:39:30.8743471Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977' 2025-09-07T06:39:30.9207158Z Submodule path 'third_party/cpp-httplib': checked out '89c932f313c6437c38f2982869beacc89c2f2246' 2025-09-07T06:39:31.0111635Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-09-07T06:39:31.0570380Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-09-07T06:39:31.3646492Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-07T06:39:31.4396925Z Submodule path 'third_party/fbgemm': checked out '4b39c551efe15e6bbade20565b0ceb2d8ce3352d' 2025-09-07T06:39:31.4418476Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-09-07T06:39:31.4419162Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:39:31.4419648Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:39:31.4420052Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-09-07T06:39:31.4422777Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-09-07T06:39:31.4427023Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:39:31.4428974Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-09-07T06:39:31.4482934Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-09-07T06:39:32.5277717Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-09-07T06:39:32.5278196Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-09-07T06:39:32.5286248Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-09-07T06:39:32.5286759Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-09-07T06:39:32.6277067Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-09-07T06:39:32.8896532Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-09-07T06:39:34.8829760Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea' 2025-09-07T06:39:35.0014306Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9' 2025-09-07T06:39:35.0580034Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-09-07T06:39:35.3717255Z Submodule path 'third_party/fbgemm/external/cutlass': checked out '311f3c8e51dc0eb56310cfc6980bf63d0fbd7917' 2025-09-07T06:39:35.3978804Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:39:35.4053277Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out '63b6a7b541fa7f08f8475ca7d74054db36ff2691' 2025-09-07T06:39:35.4611151Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-09-07T06:39:35.5026989Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-09-07T06:39:35.5049451Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:39:35.5051206Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:39:35.5095354Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-09-07T06:39:39.1130128Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-09-07T06:39:39.2265447Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-09-07T06:39:39.5030374Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-09-07T06:39:39.5728490Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-09-07T06:39:39.5928439Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-09-07T06:39:39.6146232Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-09-07T06:39:39.6301646Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-09-07T06:39:39.6558682Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:39:39.6647177Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-09-07T06:39:39.6665023Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-09-07T06:39:39.6701455Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-09-07T06:39:48.1174229Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-09-07T06:39:48.1335394Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-09-07T06:39:48.1882617Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-09-07T06:39:48.1901769Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:39:48.1902795Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:39:48.1905975Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:39:48.1941611Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-09-07T06:39:48.8677696Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-09-07T06:39:49.1021723Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-09-07T06:39:49.1499999Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-09-07T06:39:49.1544757Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:39:49.1555479Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:39:49.1566619Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:39:49.1576717Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:39:49.1587438Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:39:49.1606329Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:39:49.1646990Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:39:49.1700614Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:39:49.1743768Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-09-07T06:39:50.3086184Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-09-07T06:39:50.3087146Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-09-07T06:39:50.3087581Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-09-07T06:39:50.3087996Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-09-07T06:39:50.3088419Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-09-07T06:39:50.3088841Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-09-07T06:39:50.4075569Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-09-07T06:39:52.5982014Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-09-07T06:39:52.6090642Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-09-07T06:39:52.6319894Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-09-07T06:39:52.6404854Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-09-07T06:39:52.6422504Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:39:52.6440172Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-09-07T06:39:53.0370594Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-09-07T06:39:53.0521211Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-09-07T06:39:53.0768557Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-09-07T06:39:53.1302433Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-09-07T06:39:53.1423709Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-09-07T06:39:53.1690171Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-09-07T06:39:53.2037243Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-09-07T06:39:53.2277913Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-09-07T06:39:53.2510703Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-09-07T06:39:53.3126283Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-09-07T06:39:53.4957515Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-09-07T06:39:53.4987661Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-09-07T06:39:53.5012602Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-09-07T06:39:54.3633853Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-09-07T06:39:54.4000498Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-09-07T06:39:54.4025607Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:39:54.4026516Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:39:54.4027165Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:39:54.4027781Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:39:54.4028563Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:39:54.4029385Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:39:54.4031827Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:39:54.4034134Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:39:54.4058770Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-09-07T06:39:54.8977350Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-09-07T06:39:54.8982672Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-09-07T06:39:54.8983022Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-09-07T06:39:54.8983319Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-09-07T06:39:54.9977873Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-09-07T06:39:55.3382558Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-09-07T06:39:57.8814861Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-09-07T06:39:59.8900286Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-09-07T06:39:59.9138926Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-09-07T06:39:59.9257440Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2025-09-07T06:39:59.9839285Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2025-09-07T06:39:59.9917385Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2025-09-07T06:40:00.0005650Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2025-09-07T06:40:00.0108345Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2025-09-07T06:40:00.0123677Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:40:00.0129514Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:40:00.0151360Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-09-07T06:40:01.3183385Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-09-07T06:40:01.4545557Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-09-07T06:40:01.4814266Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2025-09-07T06:40:01.6844129Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2025-09-07T06:40:01.6933809Z Submodule path 'third_party/pocketfft': checked out '0fa0ef591e38c2758e3184c6c23e497b9f732ffa' 2025-09-07T06:40:01.8491811Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a' 2025-09-07T06:40:01.8515823Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:40:01.8520701Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest' 2025-09-07T06:40:01.8556520Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'... 2025-09-07T06:40:02.4580104Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'... 2025-09-07T06:40:02.6313878Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8' 2025-09-07T06:40:02.6733560Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081' 2025-09-07T06:40:02.6827048Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900' 2025-09-07T06:40:02.6915644Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8' 2025-09-07T06:40:02.7128019Z Submodule path 'third_party/pybind11': checked out 'f5fbe867d2d26e4a0a9177a51f6e568868ad3dc8' 2025-09-07T06:40:02.7297874Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67' 2025-09-07T06:40:02.7546020Z Submodule path 'third_party/sleef': checked out '5a1d179df9cf652951b59010a2d2075372d67f68' 2025-09-07T06:40:02.7703844Z Submodule path 'third_party/tensorpipe': checked out 'af0118d13e52f5a08841464a768e01a0bf3e3075' 2025-09-07T06:40:02.7716766Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:40:02.7718732Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:40:02.7720463Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:40:02.7725422Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:40:02.7757312Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'... 2025-09-07T06:40:03.5482228Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'... 2025-09-07T06:40:03.6123309Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'... 2025-09-07T06:40:03.7789089Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'... 2025-09-07T06:40:03.8105119Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e' 2025-09-07T06:40:03.8206998Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281' 2025-09-07T06:40:03.8615324Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '5152db2cbfeb5582e9c27c5ea1dba2cd9e10759b' 2025-09-07T06:40:03.8790865Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef' 2025-09-07T06:40:03.8806219Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:40:03.8828177Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'... 2025-09-07T06:40:04.2080991Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5' 2025-09-07T06:40:04.2124329Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-07T06:40:04.2330101Z Entering 'android/libs/fbjni' 2025-09-07T06:40:04.2370761Z Entering 'third_party/FP16' 2025-09-07T06:40:04.2401563Z Entering 'third_party/FXdiv' 2025-09-07T06:40:04.2423931Z Entering 'third_party/NNPACK' 2025-09-07T06:40:04.2457991Z Entering 'third_party/NVTX' 2025-09-07T06:40:04.2498914Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:40:04.2529959Z Entering 'third_party/XNNPACK' 2025-09-07T06:40:04.2565508Z Entering 'third_party/aiter' 2025-09-07T06:40:04.2595339Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:40:04.2632684Z Entering 'third_party/benchmark' 2025-09-07T06:40:04.2660859Z Entering 'third_party/composable_kernel' 2025-09-07T06:40:04.2690855Z Entering 'third_party/cpp-httplib' 2025-09-07T06:40:04.2719380Z Entering 'third_party/cpuinfo' 2025-09-07T06:40:04.2743380Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:40:04.2768873Z 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file:/home/runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2025-09-07T06:40:04.7957235Z Entering 'third_party/FP16' 2025-09-07T06:40:04.7984948Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2025-09-07T06:40:04.7997827Z Entering 'third_party/FXdiv' 2025-09-07T06:40:04.8034760Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2025-09-07T06:40:04.8049281Z Entering 'third_party/NNPACK' 2025-09-07T06:40:04.8087935Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config remote.origin.url 2025-09-07T06:40:04.8098579Z Entering 'third_party/NVTX' 2025-09-07T06:40:04.8131743Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config remote.origin.url 2025-09-07T06:40:04.8146694Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:40:04.8170458Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config remote.origin.url 2025-09-07T06:40:04.8180311Z Entering 'third_party/XNNPACK' 2025-09-07T06:40:04.8207004Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config remote.origin.url 2025-09-07T06:40:04.8224176Z Entering 'third_party/aiter' 2025-09-07T06:40:04.8256430Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/config remote.origin.url 2025-09-07T06:40:04.8275414Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:40:04.8297236Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/modules/3rdparty/composable_kernel/config remote.origin.url 2025-09-07T06:40:04.8322008Z Entering 'third_party/benchmark' 2025-09-07T06:40:04.8338202Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:40:04.8348801Z Entering 'third_party/composable_kernel' 2025-09-07T06:40:04.8382989Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config remote.origin.url 2025-09-07T06:40:04.8398405Z Entering 'third_party/cpp-httplib' 2025-09-07T06:40:04.8427468Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config remote.origin.url 2025-09-07T06:40:04.8439404Z Entering 'third_party/cpuinfo' 2025-09-07T06:40:04.8467557Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2025-09-07T06:40:04.8478997Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:40:04.8506519Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2025-09-07T06:40:04.8523219Z Entering 'third_party/cutlass' 2025-09-07T06:40:04.8555361Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2025-09-07T06:40:04.8576016Z Entering 'third_party/fbgemm' 2025-09-07T06:40:04.8609498Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 2025-09-07T06:40:04.8617807Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:40:04.8638218Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/asmjit/config remote.origin.url 2025-09-07T06:40:04.8654203Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:40:04.8684687Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/composable_kernel/config remote.origin.url 2025-09-07T06:40:04.8700541Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:40:04.8726420Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/cpuinfo/config remote.origin.url 2025-09-07T06:40:04.8736486Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:40:04.8758939Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/cutlass/config remote.origin.url 2025-09-07T06:40:04.8773547Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:40:04.8796834Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/googletest/config remote.origin.url 2025-09-07T06:40:04.8806547Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:40:04.8831527Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/hipify_torch/config remote.origin.url 2025-09-07T06:40:04.8841042Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:40:04.8861896Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/json/config remote.origin.url 2025-09-07T06:40:04.8876212Z Entering 'third_party/flash-attention' 2025-09-07T06:40:04.8899569Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config remote.origin.url 2025-09-07T06:40:04.8918020Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:40:04.8960650Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config remote.origin.url 2025-09-07T06:40:04.8979449Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:40:04.9011643Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config remote.origin.url 2025-09-07T06:40:04.9028425Z Entering 'third_party/flatbuffers' 2025-09-07T06:40:04.9073129Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config remote.origin.url 2025-09-07T06:40:04.9097017Z Entering 'third_party/fmt' 2025-09-07T06:40:04.9128931Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config remote.origin.url 2025-09-07T06:40:04.9143657Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:40:04.9182460Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config remote.origin.url 2025-09-07T06:40:04.9199286Z Entering 'third_party/gloo' 2025-09-07T06:40:04.9229332Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2025-09-07T06:40:04.9242616Z Entering 'third_party/googletest' 2025-09-07T06:40:04.9282542Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:40:04.9300321Z Entering 'third_party/ideep' 2025-09-07T06:40:04.9331686Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2025-09-07T06:40:04.9345693Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:40:04.9373484Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2025-09-07T06:40:04.9394812Z Entering 'third_party/ittapi' 2025-09-07T06:40:04.9422454Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2025-09-07T06:40:04.9436953Z Entering 'third_party/kineto' 2025-09-07T06:40:04.9471077Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2025-09-07T06:40:04.9484844Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:40:04.9520954Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config remote.origin.url 2025-09-07T06:40:04.9534328Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:40:04.9569084Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config remote.origin.url 2025-09-07T06:40:04.9581359Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:40:04.9616923Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config remote.origin.url 2025-09-07T06:40:04.9631641Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:40:04.9662355Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2025-09-07T06:40:04.9675225Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:40:04.9705338Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config remote.origin.url 2025-09-07T06:40:04.9720581Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:40:04.9756344Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2025-09-07T06:40:04.9770518Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:40:04.9796387Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2025-09-07T06:40:04.9806581Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:40:04.9835499Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:40:04.9845086Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:40:04.9872646Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config remote.origin.url 2025-09-07T06:40:04.9883527Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:40:04.9913295Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config remote.origin.url 2025-09-07T06:40:04.9926623Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:40:04.9954163Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config remote.origin.url 2025-09-07T06:40:04.9966678Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:40:04.9994864Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2025-09-07T06:40:05.0007926Z Entering 'third_party/kleidiai' 2025-09-07T06:40:05.0042979Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config remote.origin.url 2025-09-07T06:40:05.0053935Z Entering 'third_party/mimalloc' 2025-09-07T06:40:05.0084481Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2025-09-07T06:40:05.0097762Z Entering 'third_party/nlohmann' 2025-09-07T06:40:05.0130395Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2025-09-07T06:40:05.0142694Z Entering 'third_party/onnx' 2025-09-07T06:40:05.0169070Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2025-09-07T06:40:05.0187368Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:40:05.0220042Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:40:05.0241199Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:40:05.0268164Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2025-09-07T06:40:05.0279701Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:40:05.0305188Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:40:05.0317807Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:40:05.0341768Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:40:05.0351506Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:40:05.0373430Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2025-09-07T06:40:05.0385837Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:40:05.0406911Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2025-09-07T06:40:05.0417959Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:40:05.0436579Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2025-09-07T06:40:05.0445401Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:40:05.0471112Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2025-09-07T06:40:05.0481324Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:40:05.0503119Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2025-09-07T06:40:05.0512411Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:40:05.0544258Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2025-09-07T06:40:05.0555097Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:40:05.0580921Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2025-09-07T06:40:05.0592527Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:40:05.0613562Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2025-09-07T06:40:05.0632657Z Entering 'third_party/pocketfft' 2025-09-07T06:40:05.0661570Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2025-09-07T06:40:05.0673196Z Entering 'third_party/protobuf' 2025-09-07T06:40:05.0694491Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2025-09-07T06:40:05.0708923Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:40:05.0730833Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:40:05.0740415Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:40:05.0759738Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:40:05.0771085Z Entering 'third_party/psimd' 2025-09-07T06:40:05.0792867Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2025-09-07T06:40:05.0803383Z Entering 'third_party/pthreadpool' 2025-09-07T06:40:05.0823906Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2025-09-07T06:40:05.0833778Z Entering 'third_party/pybind11' 2025-09-07T06:40:05.0854911Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:40:05.0865837Z Entering 'third_party/python-peachpy' 2025-09-07T06:40:05.0899058Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2025-09-07T06:40:05.0910328Z Entering 'third_party/sleef' 2025-09-07T06:40:05.0932157Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2025-09-07T06:40:05.0943491Z Entering 'third_party/tensorpipe' 2025-09-07T06:40:05.0966004Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2025-09-07T06:40:05.0976056Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:40:05.1004230Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:40:05.1013832Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:40:05.1034847Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2025-09-07T06:40:05.1045603Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:40:05.1071026Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2025-09-07T06:40:05.1081044Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:40:05.1113912Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:40:05.1123129Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:40:05.1143699Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2025-09-07T06:40:05.1321492Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-07T06:40:05.1485561Z Entering 'android/libs/fbjni' 2025-09-07T06:40:05.1514069Z Entering 'third_party/FP16' 2025-09-07T06:40:05.1534234Z Entering 'third_party/FXdiv' 2025-09-07T06:40:05.1558461Z Entering 'third_party/NNPACK' 2025-09-07T06:40:05.1579258Z Entering 'third_party/NVTX' 2025-09-07T06:40:05.1601858Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:40:05.1626028Z Entering 'third_party/XNNPACK' 2025-09-07T06:40:05.1662403Z Entering 'third_party/aiter' 2025-09-07T06:40:05.1688596Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:40:05.1712547Z Entering 'third_party/benchmark' 2025-09-07T06:40:05.1733553Z Entering 'third_party/composable_kernel' 2025-09-07T06:40:05.1765305Z Entering 'third_party/cpp-httplib' 2025-09-07T06:40:05.1786055Z Entering 'third_party/cpuinfo' 2025-09-07T06:40:05.1808810Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:40:05.1824593Z Entering 'third_party/cutlass' 2025-09-07T06:40:05.1849993Z Entering 'third_party/fbgemm' 2025-09-07T06:40:05.1871484Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:40:05.1895372Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:40:05.1921477Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:40:05.1947833Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:40:05.1971829Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:40:05.1991227Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:40:05.2016084Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:40:05.2038164Z Entering 'third_party/flash-attention' 2025-09-07T06:40:05.2059967Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:40:05.2083980Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:40:05.2107612Z Entering 'third_party/flatbuffers' 2025-09-07T06:40:05.2130274Z Entering 'third_party/fmt' 2025-09-07T06:40:05.2150679Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:40:05.2172406Z Entering 'third_party/gloo' 2025-09-07T06:40:05.2198567Z Entering 'third_party/googletest' 2025-09-07T06:40:05.2221215Z Entering 'third_party/ideep' 2025-09-07T06:40:05.2247127Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:40:05.2279792Z Entering 'third_party/ittapi' 2025-09-07T06:40:05.2301115Z Entering 'third_party/kineto' 2025-09-07T06:40:05.2324230Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:40:05.2351958Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:40:05.2387686Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:40:05.2412080Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:40:05.2438138Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:40:05.2460684Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:40:05.2487044Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:40:05.2512675Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:40:05.2533327Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:40:05.2568374Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:40:05.2592091Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:40:05.2614164Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:40:05.2636903Z Entering 'third_party/kleidiai' 2025-09-07T06:40:05.2667367Z Entering 'third_party/mimalloc' 2025-09-07T06:40:05.2693286Z Entering 'third_party/nlohmann' 2025-09-07T06:40:05.2726734Z Entering 'third_party/onnx' 2025-09-07T06:40:05.2762280Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:40:05.2796273Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:40:05.2825638Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:40:05.2856845Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:40:05.2883721Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:40:05.2907504Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:40:05.2937075Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:40:05.2964506Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:40:05.2983260Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:40:05.3012184Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:40:05.3038929Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:40:05.3069004Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:40:05.3107323Z Entering 'third_party/pocketfft' 2025-09-07T06:40:05.3128093Z Entering 'third_party/protobuf' 2025-09-07T06:40:05.3156409Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:40:05.3179976Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:40:05.3204203Z Entering 'third_party/psimd' 2025-09-07T06:40:05.3229473Z Entering 'third_party/pthreadpool' 2025-09-07T06:40:05.3256951Z Entering 'third_party/pybind11' 2025-09-07T06:40:05.3279079Z Entering 'third_party/python-peachpy' 2025-09-07T06:40:05.3308360Z Entering 'third_party/sleef' 2025-09-07T06:40:05.3337132Z Entering 'third_party/tensorpipe' 2025-09-07T06:40:05.3365639Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:40:05.3391378Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:40:05.3423282Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:40:05.3453886Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:40:05.3485228Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:40:05.3521515Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-07T06:40:05.3770691Z Entering 'android/libs/fbjni' 2025-09-07T06:40:05.3794301Z Entering 'third_party/FP16' 2025-09-07T06:40:05.3822198Z Entering 'third_party/FXdiv' 2025-09-07T06:40:05.3847194Z Entering 'third_party/NNPACK' 2025-09-07T06:40:05.3873367Z Entering 'third_party/NVTX' 2025-09-07T06:40:05.3971862Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:40:05.4003595Z Entering 'third_party/XNNPACK' 2025-09-07T06:40:05.4034873Z Entering 'third_party/aiter' 2025-09-07T06:40:05.4059435Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:40:05.4089328Z Entering 'third_party/benchmark' 2025-09-07T06:40:05.4120951Z Entering 'third_party/composable_kernel' 2025-09-07T06:40:05.4149967Z Entering 'third_party/cpp-httplib' 2025-09-07T06:40:05.4187732Z Entering 'third_party/cpuinfo' 2025-09-07T06:40:05.4216632Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:40:05.4240966Z Entering 'third_party/cutlass' 2025-09-07T06:40:05.4278180Z Entering 'third_party/fbgemm' 2025-09-07T06:40:05.4309696Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:40:05.4332444Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:40:05.4361949Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:40:05.4386438Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:40:05.4422981Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:40:05.4446016Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:40:05.4474777Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:40:05.4505662Z Entering 'third_party/flash-attention' 2025-09-07T06:40:05.4528414Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:40:05.4574558Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:40:05.4613424Z Entering 'third_party/flatbuffers' 2025-09-07T06:40:05.4649141Z Entering 'third_party/fmt' 2025-09-07T06:40:05.4676650Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:40:05.4712514Z Entering 'third_party/gloo' 2025-09-07T06:40:05.4742754Z Entering 'third_party/googletest' 2025-09-07T06:40:05.4777220Z Entering 'third_party/ideep' 2025-09-07T06:40:05.4803997Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:40:05.4845137Z Entering 'third_party/ittapi' 2025-09-07T06:40:05.4881895Z Entering 'third_party/kineto' 2025-09-07T06:40:05.4926182Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:40:05.4955588Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:40:05.4985296Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:40:05.5055459Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:40:05.5094940Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:40:05.5132339Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:40:05.5168243Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:40:05.5192510Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:40:05.5221217Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:40:05.5254449Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:40:05.5294126Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:40:05.5330687Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:40:05.5364535Z Entering 'third_party/kleidiai' 2025-09-07T06:40:05.5501226Z Entering 'third_party/mimalloc' 2025-09-07T06:40:05.5535144Z Entering 'third_party/nlohmann' 2025-09-07T06:40:05.5566850Z Entering 'third_party/onnx' 2025-09-07T06:40:05.5599577Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:40:05.5632587Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:40:05.5669977Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:40:05.5694860Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:40:05.5723047Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:40:05.5755232Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:40:05.5786907Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:40:05.5810823Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:40:05.5834232Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:40:05.5861101Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:40:05.5884891Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:40:05.5914308Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:40:05.5949595Z Entering 'third_party/pocketfft' 2025-09-07T06:40:05.5982518Z Entering 'third_party/protobuf' 2025-09-07T06:40:05.6008744Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:40:05.6040466Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:40:05.6073502Z Entering 'third_party/psimd' 2025-09-07T06:40:05.6118182Z Entering 'third_party/pthreadpool' 2025-09-07T06:40:05.6144723Z Entering 'third_party/pybind11' 2025-09-07T06:40:05.6177344Z Entering 'third_party/python-peachpy' 2025-09-07T06:40:05.6204505Z Entering 'third_party/sleef' 2025-09-07T06:40:05.6228831Z Entering 'third_party/tensorpipe' 2025-09-07T06:40:05.6260088Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:40:05.6284696Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:40:05.6320119Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:40:05.6342997Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:40:05.6371968Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:40:05.6407967Z ##[endgroup] 2025-09-07T06:40:05.6437268Z [command]/usr/bin/git log -1 --format=%H 2025-09-07T06:40:05.6458502Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:40:05.6553490Z ##[group]Run cd "${GITHUB_WORKSPACE}" 2025-09-07T06:40:05.6553761Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:40:05.6555698Z # Clean stale submodule dirs 2025-09-07T06:40:05.6555826Z if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:40:05.6555964Z  sudo git submodule foreach --recursive git clean -ffdx 2025-09-07T06:40:05.6556098Z else 2025-09-07T06:40:05.6556205Z  git submodule foreach --recursive git clean -ffdx 2025-09-07T06:40:05.6556327Z fi 2025-09-07T06:40:05.6561866Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:05.6561998Z env: 2025-09-07T06:40:05.6562080Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:05.6562169Z NO_SUDO: true 2025-09-07T06:40:05.6562421Z ##[endgroup] 2025-09-07T06:40:05.6752362Z Entering 'android/libs/fbjni' 2025-09-07T06:40:05.6774612Z Entering 'third_party/FP16' 2025-09-07T06:40:05.6793916Z Entering 'third_party/FXdiv' 2025-09-07T06:40:05.6821017Z Entering 'third_party/NNPACK' 2025-09-07T06:40:05.6842162Z Entering 'third_party/NVTX' 2025-09-07T06:40:05.6871001Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:40:05.6899695Z Entering 'third_party/XNNPACK' 2025-09-07T06:40:05.6984097Z Entering 'third_party/aiter' 2025-09-07T06:40:05.7014018Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:40:05.7089112Z Entering 'third_party/benchmark' 2025-09-07T06:40:05.7113071Z Entering 'third_party/composable_kernel' 2025-09-07T06:40:05.7204751Z Entering 'third_party/cpp-httplib' 2025-09-07T06:40:05.7230203Z Entering 'third_party/cpuinfo' 2025-09-07T06:40:05.7257015Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:40:05.7282358Z Entering 'third_party/cutlass' 2025-09-07T06:40:05.7354935Z Entering 'third_party/fbgemm' 2025-09-07T06:40:05.7401378Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:40:05.7421805Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:40:05.7506449Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:40:05.7523837Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:40:05.7599402Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:40:05.7623776Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:40:05.7647152Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:40:05.7673450Z Entering 'third_party/flash-attention' 2025-09-07T06:40:05.7704534Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:40:05.7776267Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:40:05.7838408Z Entering 'third_party/flatbuffers' 2025-09-07T06:40:05.7883232Z Entering 'third_party/fmt' 2025-09-07T06:40:05.7908799Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:40:05.7938741Z Entering 'third_party/gloo' 2025-09-07T06:40:05.7966839Z Entering 'third_party/googletest' 2025-09-07T06:40:05.7994209Z Entering 'third_party/ideep' 2025-09-07T06:40:05.8014000Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:40:05.8067812Z Entering 'third_party/ittapi' 2025-09-07T06:40:05.8088977Z Entering 'third_party/kineto' 2025-09-07T06:40:05.8108522Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:40:05.8132181Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:40:05.8166924Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:40:05.8191740Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:40:05.8219889Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:40:05.8237690Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:40:05.8264544Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:40:05.8285175Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:40:05.8305636Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:40:05.8336438Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:40:05.8356764Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:40:05.8381948Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:40:05.8411484Z Entering 'third_party/kleidiai' 2025-09-07T06:40:05.8440082Z Entering 'third_party/mimalloc' 2025-09-07T06:40:05.8464857Z Entering 'third_party/nlohmann' 2025-09-07T06:40:05.8491829Z Entering 'third_party/onnx' 2025-09-07T06:40:05.8726038Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:40:05.8754564Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:40:05.8788382Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:40:05.8814128Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:40:05.8840670Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:40:05.8863000Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:40:05.8901364Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:40:05.8928139Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:40:05.8946358Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:40:05.8972870Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:40:05.9002725Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:40:05.9024964Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:40:05.9357085Z Entering 'third_party/pocketfft' 2025-09-07T06:40:05.9374812Z Entering 'third_party/protobuf' 2025-09-07T06:40:05.9425344Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:40:05.9452526Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:40:05.9473853Z Entering 'third_party/psimd' 2025-09-07T06:40:05.9496078Z Entering 'third_party/pthreadpool' 2025-09-07T06:40:05.9520135Z Entering 'third_party/pybind11' 2025-09-07T06:40:05.9543030Z Entering 'third_party/python-peachpy' 2025-09-07T06:40:05.9572655Z Entering 'third_party/sleef' 2025-09-07T06:40:05.9594511Z Entering 'third_party/tensorpipe' 2025-09-07T06:40:05.9616891Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:40:05.9640956Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:40:05.9665081Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:40:05.9694994Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:40:05.9726958Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:40:05.9862953Z Prepare all required actions 2025-09-07T06:40:05.9863295Z Getting action download info 2025-09-07T06:40:06.1748684Z ##[group]Run ./.github/actions/setup-rocm 2025-09-07T06:40:06.1748818Z env: 2025-09-07T06:40:06.1748906Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.1749005Z ##[endgroup] 2025-09-07T06:40:06.1768904Z ##[group]Run dpkg -l | grep -E " rocm" 2025-09-07T06:40:06.1769035Z dpkg -l | grep -E " rocm" 2025-09-07T06:40:06.1774279Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.1774503Z env: 2025-09-07T06:40:06.1774585Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.1774678Z ##[endgroup] 2025-09-07T06:40:06.1848019Z ii rocm-cmake 0.14.0.60401-83~22.04 amd64 rocm-cmake built using CMake 2025-09-07T06:40:06.1848914Z ii rocm-core 6.4.1.60401-83~22.04 amd64 ROCm Runtime software stack 2025-09-07T06:40:06.1850443Z ii rocm-dbgapi 0.77.2.60401-83~22.04 amd64 Library to provide AMD GPU debugger API 2025-09-07T06:40:06.1851082Z ii rocm-debug-agent 2.0.4.60401-83~22.04 amd64 Radeon Open Compute Debug Agent (ROCdebug-agent) 2025-09-07T06:40:06.1851514Z ii rocm-dev 6.4.1.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime software stack 2025-09-07T06:40:06.1851921Z ii rocm-device-libs 1.0.0.60401-83~22.04 amd64 Radeon Open Compute - device libraries 2025-09-07T06:40:06.1852261Z ii rocm-gdb 15.2.60401-83~22.04 amd64 ROCgdb 2025-09-07T06:40:06.1852577Z ii rocm-llvm 19.0.0.25184.60401-83~22.04 amd64 ROCm core compiler 2025-09-07T06:40:06.1852909Z ii rocm-opencl 2.0.0.60401-83~22.04 amd64 clr built using CMake 2025-09-07T06:40:06.1853253Z ii rocm-opencl-dev 2.0.0.60401-83~22.04 amd64 clr built using CMake 2025-09-07T06:40:06.1854108Z ii rocm-smi-lib 7.5.0.60401-83~22.04 amd64 AMD System Management libraries 2025-09-07T06:40:06.1854614Z ii rocm-utils 6.4.1.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime software stack 2025-09-07T06:40:06.1854993Z ii rocminfo 1.0.0.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime rocminfo tool 2025-09-07T06:40:06.1881883Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:40:06.1882229Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:40:06.1882469Z # shellcheck disable=SC2046 2025-09-07T06:40:06.1882669Z docker stop $(docker ps -q) || true 2025-09-07T06:40:06.1882859Z # Prune all stopped containers. 2025-09-07T06:40:06.1883061Z docker container prune -f 2025-09-07T06:40:06.1890325Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.1890554Z env: 2025-09-07T06:40:06.1890693Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.1890847Z ##[endgroup] 2025-09-07T06:40:06.2146516Z docker: 'docker stop' requires at least 1 argument 2025-09-07T06:40:06.2146644Z 2025-09-07T06:40:06.2147217Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-07T06:40:06.2147533Z 2025-09-07T06:40:06.2147693Z See 'docker stop --help' for more information 2025-09-07T06:40:06.2255239Z Total reclaimed space: 0B 2025-09-07T06:40:06.2286911Z ##[group]Run cat /etc/os-release || true 2025-09-07T06:40:06.2287163Z cat /etc/os-release || true 2025-09-07T06:40:06.2287393Z cat /etc/apt/sources.list.d/rocm.list || true 2025-09-07T06:40:06.2287655Z cat /opt/rocm/.info/version || true 2025-09-07T06:40:06.2287845Z whoami 2025-09-07T06:40:06.2293410Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.2293570Z env: 2025-09-07T06:40:06.2293717Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.2293856Z ##[endgroup] 2025-09-07T06:40:06.2326255Z PRETTY_NAME="Ubuntu 22.04.5 LTS" 2025-09-07T06:40:06.2333438Z NAME="Ubuntu" 2025-09-07T06:40:06.2333561Z VERSION_ID="22.04" 2025-09-07T06:40:06.2333671Z VERSION="22.04.5 LTS (Jammy Jellyfish)" 2025-09-07T06:40:06.2333807Z VERSION_CODENAME=jammy 2025-09-07T06:40:06.2333906Z ID=ubuntu 2025-09-07T06:40:06.2333998Z ID_LIKE=debian 2025-09-07T06:40:06.2334123Z HOME_URL="https://www.ubuntu.com/" 2025-09-07T06:40:06.2334266Z SUPPORT_URL="https://help.ubuntu.com/" 2025-09-07T06:40:06.2334535Z BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/" 2025-09-07T06:40:06.2334754Z PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy" 2025-09-07T06:40:06.2334946Z UBUNTU_CODENAME=jammy 2025-09-07T06:40:06.2335142Z deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.4.1 jammy main 2025-09-07T06:40:06.2336856Z 6.4.1-83 2025-09-07T06:40:06.2342828Z runner 2025-09-07T06:40:06.2360339Z ##[group]Run dpkg -l | grep -E " amdgpu" 2025-09-07T06:40:06.2360521Z dpkg -l | grep -E " amdgpu" 2025-09-07T06:40:06.2365715Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.2365854Z env: 2025-09-07T06:40:06.2365935Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.2366031Z ##[endgroup] 2025-09-07T06:40:06.2419980Z ii amdgpu-core 1:6.4.60401-2164967.22.04 all Core meta package for unified amdgpu driver. 2025-09-07T06:40:06.2420237Z ii amdgpu-install 6.4.60401-2164967.22.04 all AMDGPU driver repository and installer 2025-09-07T06:40:06.2440082Z ##[group]Run rocm-smi 2025-09-07T06:40:06.2440183Z rocm-smi 2025-09-07T06:40:06.2444217Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.2444464Z env: 2025-09-07T06:40:06.2444545Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.2444643Z ##[endgroup] 2025-09-07T06:40:06.2915470Z 2025-09-07T06:40:06.2915647Z 2025-09-07T06:40:06.2916344Z ============================================ ROCm System Management Interface ============================================ 2025-09-07T06:40:06.2916908Z ====================================================== Concise Info ====================================================== 2025-09-07T06:40:06.2917477Z Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU% 2025-09-07T06:40:06.2918473Z  (DID, GUID) (Junction) (Socket) (Mem, Compute, ID)  2025-09-07T06:40:06.2918963Z ========================================================================================================================== 2025-09-07T06:40:06.2919807Z 0 8 0x74b9, 27976 48.0°C 138.0W NPS1, SPX, 0 174Mhz 900Mhz 0% auto 1000.0W 0% 0% 2025-09-07T06:40:06.2920325Z ========================================================================================================================== 2025-09-07T06:40:06.2920763Z ================================================== End of ROCm SMI Log =================================================== 2025-09-07T06:40:06.2975037Z ##[group]Run rocminfo 2025-09-07T06:40:06.2975159Z rocminfo 2025-09-07T06:40:06.2979583Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.2979721Z env: 2025-09-07T06:40:06.2979805Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.2979904Z ##[endgroup] 2025-09-07T06:40:06.3669814Z ROCk module version 6.12.12 is loaded 2025-09-07T06:40:06.3669965Z ===================== 2025-09-07T06:40:06.3670102Z HSA System Attributes 2025-09-07T06:40:06.3670211Z ===================== 2025-09-07T06:40:06.3670321Z Runtime Version: 1.15 2025-09-07T06:40:06.3670433Z Runtime Ext Version: 1.7 2025-09-07T06:40:06.3670748Z System Timestamp Freq.: 1000.000000MHz 2025-09-07T06:40:06.3670931Z Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count) 2025-09-07T06:40:06.3671156Z Machine Model: LARGE 2025-09-07T06:40:06.3676771Z System Endianness: LITTLE 2025-09-07T06:40:06.3676920Z Mwaitx: DISABLED 2025-09-07T06:40:06.3677033Z XNACK enabled: NO 2025-09-07T06:40:06.3677163Z DMAbuf Support: YES 2025-09-07T06:40:06.3677265Z VMM Support: YES 2025-09-07T06:40:06.3677330Z 2025-09-07T06:40:06.3677375Z ========== 2025-09-07T06:40:06.3677475Z HSA Agents 2025-09-07T06:40:06.3677561Z ========== 2025-09-07T06:40:06.3677641Z ******* 2025-09-07T06:40:06.3677727Z Agent 1 2025-09-07T06:40:06.3677810Z ******* 2025-09-07T06:40:06.3677933Z Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:40:06.3678126Z Uuid: CPU-XX 2025-09-07T06:40:06.3678267Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:40:06.3678423Z Vendor Name: CPU 2025-09-07T06:40:06.3678565Z Feature: None specified 2025-09-07T06:40:06.3678701Z Profile: FULL_PROFILE 2025-09-07T06:40:06.3678844Z Float Round Mode: NEAR 2025-09-07T06:40:06.3679052Z Max Queue Number: 0(0x0) 2025-09-07T06:40:06.3679194Z Queue Min Size: 0(0x0) 2025-09-07T06:40:06.3679330Z Queue Max Size: 0(0x0) 2025-09-07T06:40:06.3679467Z Queue Type: MULTI 2025-09-07T06:40:06.3679608Z Node: 0 2025-09-07T06:40:06.3679747Z Device Type: CPU 2025-09-07T06:40:06.3679880Z Cache Info: 2025-09-07T06:40:06.3680131Z L1: 65536(0x10000) KB 2025-09-07T06:40:06.3680267Z Chip ID: 0(0x0) 2025-09-07T06:40:06.3680412Z ASIC Revision: 0(0x0) 2025-09-07T06:40:06.3680558Z Cacheline Size: 64(0x40) 2025-09-07T06:40:06.3680709Z Max Clock Freq. (MHz): 0 2025-09-07T06:40:06.3680846Z BDFID: 0 2025-09-07T06:40:06.3680990Z Internal Node ID: 0 2025-09-07T06:40:06.3681133Z Compute Unit: 80 2025-09-07T06:40:06.3681276Z SIMDs per CU: 0 2025-09-07T06:40:06.3681424Z Shader Engines: 0 2025-09-07T06:40:06.3681574Z Shader Arrs. per Eng.: 0 2025-09-07T06:40:06.3681735Z WatchPts on Addr. Ranges:1 2025-09-07T06:40:06.3681870Z Memory Properties: 2025-09-07T06:40:06.3681981Z Features: None 2025-09-07T06:40:06.3682084Z Pool Info: 2025-09-07T06:40:06.3682187Z Pool 1 2025-09-07T06:40:06.3682314Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:40:06.3682468Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:40:06.3682616Z Allocatable: TRUE 2025-09-07T06:40:06.3682763Z Alloc Granule: 4KB 2025-09-07T06:40:06.3682923Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3683079Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3683270Z Accessible by all: TRUE 2025-09-07T06:40:06.3683408Z Pool 2 2025-09-07T06:40:06.3683537Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:40:06.3683683Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:40:06.3683822Z Allocatable: TRUE 2025-09-07T06:40:06.3683971Z Alloc Granule: 4KB 2025-09-07T06:40:06.3684122Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3684281Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3684503Z Accessible by all: TRUE 2025-09-07T06:40:06.3684633Z Pool 3 2025-09-07T06:40:06.3684757Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-09-07T06:40:06.3684902Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:40:06.3685043Z Allocatable: TRUE 2025-09-07T06:40:06.3685194Z Alloc Granule: 4KB 2025-09-07T06:40:06.3685350Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3685503Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3685656Z Accessible by all: TRUE 2025-09-07T06:40:06.3685789Z Pool 4 2025-09-07T06:40:06.3685908Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:40:06.3686050Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:40:06.3686186Z Allocatable: TRUE 2025-09-07T06:40:06.3686336Z Alloc Granule: 4KB 2025-09-07T06:40:06.3686491Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3686645Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3686837Z Accessible by all: TRUE 2025-09-07T06:40:06.3686970Z ISA Info: 2025-09-07T06:40:06.3687068Z ******* 2025-09-07T06:40:06.3687162Z Agent 2 2025-09-07T06:40:06.3687254Z ******* 2025-09-07T06:40:06.3687364Z Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:40:06.3687505Z Uuid: CPU-XX 2025-09-07T06:40:06.3687650Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:40:06.3690076Z Vendor Name: CPU 2025-09-07T06:40:06.3690223Z Feature: None specified 2025-09-07T06:40:06.3690362Z Profile: FULL_PROFILE 2025-09-07T06:40:06.3690514Z Float Round Mode: NEAR 2025-09-07T06:40:06.3690659Z Max Queue Number: 0(0x0) 2025-09-07T06:40:06.3690801Z Queue Min Size: 0(0x0) 2025-09-07T06:40:06.3690940Z Queue Max Size: 0(0x0) 2025-09-07T06:40:06.3691075Z Queue Type: MULTI 2025-09-07T06:40:06.3691201Z Node: 1 2025-09-07T06:40:06.3691328Z Device Type: CPU 2025-09-07T06:40:06.3691449Z Cache Info: 2025-09-07T06:40:06.3691553Z L1: 65536(0x10000) KB 2025-09-07T06:40:06.3691682Z Chip ID: 0(0x0) 2025-09-07T06:40:06.3691823Z ASIC Revision: 0(0x0) 2025-09-07T06:40:06.3692019Z Cacheline Size: 64(0x40) 2025-09-07T06:40:06.3692169Z Max Clock Freq. (MHz): 0 2025-09-07T06:40:06.3692303Z BDFID: 0 2025-09-07T06:40:06.3692434Z Internal Node ID: 1 2025-09-07T06:40:06.3693839Z Compute Unit: 80 2025-09-07T06:40:06.3693982Z SIMDs per CU: 0 2025-09-07T06:40:06.3694119Z Shader Engines: 0 2025-09-07T06:40:06.3694260Z Shader Arrs. per Eng.: 0 2025-09-07T06:40:06.3694462Z WatchPts on Addr. Ranges:1 2025-09-07T06:40:06.3694591Z Memory Properties: 2025-09-07T06:40:06.3694686Z Features: None 2025-09-07T06:40:06.3694782Z Pool Info: 2025-09-07T06:40:06.3694872Z Pool 1 2025-09-07T06:40:06.3694995Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:40:06.3695135Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:40:06.3695274Z Allocatable: TRUE 2025-09-07T06:40:06.3695416Z Alloc Granule: 4KB 2025-09-07T06:40:06.3696664Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3696814Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3696958Z Accessible by all: TRUE 2025-09-07T06:40:06.3697081Z Pool 2 2025-09-07T06:40:06.3697199Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:40:06.3697333Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:40:06.3697465Z Allocatable: TRUE 2025-09-07T06:40:06.3697606Z Alloc Granule: 4KB 2025-09-07T06:40:06.3697753Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3697950Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3698093Z Accessible by all: TRUE 2025-09-07T06:40:06.3698218Z Pool 3 2025-09-07T06:40:06.3699402Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-09-07T06:40:06.3699537Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:40:06.3699668Z Allocatable: TRUE 2025-09-07T06:40:06.3699808Z Alloc Granule: 4KB 2025-09-07T06:40:06.3699954Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3700100Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3700246Z Accessible by all: TRUE 2025-09-07T06:40:06.3700369Z Pool 4 2025-09-07T06:40:06.3700490Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:40:06.3700624Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:40:06.3700755Z Allocatable: TRUE 2025-09-07T06:40:06.3700896Z Alloc Granule: 4KB 2025-09-07T06:40:06.3702068Z Alloc Recommended Granule:4KB 2025-09-07T06:40:06.3702220Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3702366Z Accessible by all: TRUE 2025-09-07T06:40:06.3702489Z ISA Info: 2025-09-07T06:40:06.3702578Z ******* 2025-09-07T06:40:06.3702664Z Agent 3 2025-09-07T06:40:06.3702749Z ******* 2025-09-07T06:40:06.3702889Z Name: gfx942 2025-09-07T06:40:06.3703026Z Uuid: GPU-d6ad5c386168b0b9 2025-09-07T06:40:06.3703167Z Marketing Name: AMD Instinct Mi325X VF 2025-09-07T06:40:06.3703309Z Vendor Name: AMD 2025-09-07T06:40:06.3703446Z Feature: KERNEL_DISPATCH 2025-09-07T06:40:06.3703583Z Profile: BASE_PROFILE 2025-09-07T06:40:06.3704834Z Float Round Mode: NEAR 2025-09-07T06:40:06.3704977Z Max Queue Number: 128(0x80) 2025-09-07T06:40:06.3705113Z Queue Min Size: 64(0x40) 2025-09-07T06:40:06.3705247Z Queue Max Size: 131072(0x20000) 2025-09-07T06:40:06.3705380Z Queue Type: MULTI 2025-09-07T06:40:06.3705511Z Node: 2 2025-09-07T06:40:06.3705643Z Device Type: GPU 2025-09-07T06:40:06.3705764Z Cache Info: 2025-09-07T06:40:06.3705867Z L1: 32(0x20) KB 2025-09-07T06:40:06.3705985Z L2: 4096(0x1000) KB 2025-09-07T06:40:06.3706101Z L3: 262144(0x40000) KB 2025-09-07T06:40:06.3706222Z Chip ID: 29881(0x74b9) 2025-09-07T06:40:06.3707378Z ASIC Revision: 1(0x1) 2025-09-07T06:40:06.3707525Z Cacheline Size: 128(0x80) 2025-09-07T06:40:06.3707665Z Max Clock Freq. (MHz): 2100 2025-09-07T06:40:06.3707795Z BDFID: 35584 2025-09-07T06:40:06.3707928Z Internal Node ID: 2 2025-09-07T06:40:06.3708066Z Compute Unit: 304 2025-09-07T06:40:06.3708259Z SIMDs per CU: 4 2025-09-07T06:40:06.3708395Z Shader Engines: 32 2025-09-07T06:40:06.3708536Z Shader Arrs. per Eng.: 1 2025-09-07T06:40:06.3708680Z WatchPts on Addr. Ranges:4 2025-09-07T06:40:06.3708827Z Coherent Host Access: FALSE 2025-09-07T06:40:06.3708954Z Memory Properties: 2025-09-07T06:40:06.3709058Z Features: KERNEL_DISPATCH 2025-09-07T06:40:06.3710219Z Fast F16 Operation: TRUE 2025-09-07T06:40:06.3710362Z Wavefront Size: 64(0x40) 2025-09-07T06:40:06.3710505Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:40:06.3710642Z Workgroup Max Size per Dimension: 2025-09-07T06:40:06.3710755Z x 1024(0x400) 2025-09-07T06:40:06.3710875Z y 1024(0x400) 2025-09-07T06:40:06.3710987Z z 1024(0x400) 2025-09-07T06:40:06.3711112Z Max Waves Per CU: 32(0x20) 2025-09-07T06:40:06.3711252Z Max Work-item Per CU: 2048(0x800) 2025-09-07T06:40:06.3711393Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:40:06.3711517Z Grid Max Size per Dimension: 2025-09-07T06:40:06.3711620Z x 4294967295(0xffffffff) 2025-09-07T06:40:06.3712813Z y 4294967295(0xffffffff) 2025-09-07T06:40:06.3712931Z z 4294967295(0xffffffff) 2025-09-07T06:40:06.3713064Z Max fbarriers/Workgrp: 32 2025-09-07T06:40:06.3718351Z Packet Processor uCode:: 177 2025-09-07T06:40:06.3718515Z SDMA engine uCode:: 24 2025-09-07T06:40:06.3718657Z IOMMU Support:: None 2025-09-07T06:40:06.3718783Z Pool Info: 2025-09-07T06:40:06.3718877Z Pool 1 2025-09-07T06:40:06.3718995Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:40:06.3719136Z Size: 268107776(0xffb0000) KB 2025-09-07T06:40:06.3719276Z Allocatable: TRUE 2025-09-07T06:40:06.3719419Z Alloc Granule: 4KB 2025-09-07T06:40:06.3719567Z Alloc Recommended Granule:2048KB 2025-09-07T06:40:06.3721271Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3721424Z Accessible by all: FALSE 2025-09-07T06:40:06.3721548Z Pool 2 2025-09-07T06:40:06.3721670Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:40:06.3721805Z Size: 268107776(0xffb0000) KB 2025-09-07T06:40:06.3721939Z Allocatable: TRUE 2025-09-07T06:40:06.3722078Z Alloc Granule: 4KB 2025-09-07T06:40:06.3722223Z Alloc Recommended Granule:2048KB 2025-09-07T06:40:06.3722368Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3722510Z Accessible by all: FALSE 2025-09-07T06:40:06.3722634Z Pool 3 2025-09-07T06:40:06.3722746Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:40:06.3723979Z Size: 268107776(0xffb0000) KB 2025-09-07T06:40:06.3724117Z Allocatable: TRUE 2025-09-07T06:40:06.3724313Z Alloc Granule: 4KB 2025-09-07T06:40:06.3724563Z Alloc Recommended Granule:2048KB 2025-09-07T06:40:06.3724708Z Alloc Alignment: 4KB 2025-09-07T06:40:06.3724850Z Accessible by all: FALSE 2025-09-07T06:40:06.3724974Z Pool 4 2025-09-07T06:40:06.3725083Z Segment: GROUP 2025-09-07T06:40:06.3725210Z Size: 64(0x40) KB 2025-09-07T06:40:06.3725340Z Allocatable: FALSE 2025-09-07T06:40:06.3725480Z Alloc Granule: 0KB 2025-09-07T06:40:06.3725626Z Alloc Recommended Granule:0KB 2025-09-07T06:40:06.3726858Z Alloc Alignment: 0KB 2025-09-07T06:40:06.3727014Z Accessible by all: FALSE 2025-09-07T06:40:06.3727137Z ISA Info: 2025-09-07T06:40:06.3727229Z ISA 1 2025-09-07T06:40:06.3727347Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-09-07T06:40:06.3727496Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-09-07T06:40:06.3727643Z Profiles: HSA_PROFILE_BASE 2025-09-07T06:40:06.3727786Z Default Rounding Mode: NEAR 2025-09-07T06:40:06.3727933Z Default Rounding Mode: NEAR 2025-09-07T06:40:06.3728069Z Fast f16: TRUE 2025-09-07T06:40:06.3728206Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:40:06.3728386Z Workgroup Max Size per Dimension: 2025-09-07T06:40:06.3728503Z x 1024(0x400) 2025-09-07T06:40:06.3729675Z y 1024(0x400) 2025-09-07T06:40:06.3729790Z z 1024(0x400) 2025-09-07T06:40:06.3729917Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:40:06.3730043Z Grid Max Size per Dimension: 2025-09-07T06:40:06.3730150Z x 4294967295(0xffffffff) 2025-09-07T06:40:06.3730266Z y 4294967295(0xffffffff) 2025-09-07T06:40:06.3730380Z z 4294967295(0xffffffff) 2025-09-07T06:40:06.3730510Z FBarrier Max Size: 32 2025-09-07T06:40:06.3730629Z ISA 2 2025-09-07T06:40:06.3730762Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-09-07T06:40:06.3730921Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-09-07T06:40:06.3731071Z Profiles: HSA_PROFILE_BASE 2025-09-07T06:40:06.3732263Z Default Rounding Mode: NEAR 2025-09-07T06:40:06.3732597Z Default Rounding Mode: NEAR 2025-09-07T06:40:06.3732784Z Fast f16: TRUE 2025-09-07T06:40:06.3732927Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:40:06.3733064Z Workgroup Max Size per Dimension: 2025-09-07T06:40:06.3733182Z x 1024(0x400) 2025-09-07T06:40:06.3733298Z y 1024(0x400) 2025-09-07T06:40:06.3733410Z z 1024(0x400) 2025-09-07T06:40:06.3733537Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:40:06.3733664Z Grid Max Size per Dimension: 2025-09-07T06:40:06.3733827Z x 4294967295(0xffffffff) 2025-09-07T06:40:06.3735338Z y 4294967295(0xffffffff) 2025-09-07T06:40:06.3735466Z z 4294967295(0xffffffff) 2025-09-07T06:40:06.3735598Z FBarrier Max Size: 32 2025-09-07T06:40:06.3735727Z *** Done *** 2025-09-07T06:40:06.3754221Z ##[group]Run ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx') 2025-09-07T06:40:06.3754505Z ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx') 2025-09-07T06:40:06.3754771Z 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" 2025-09-07T06:40:06.3755019Z if [[ $ngpu -eq 0 ]]; then 2025-09-07T06:40:06.3755160Z  echo "Error: Failed to detect any GPUs on the runner" 2025-09-07T06:40:06.3757000Z  echo "$msg" 2025-09-07T06:40:06.3757097Z  exit 1 2025-09-07T06:40:06.3757178Z fi 2025-09-07T06:40:06.3762718Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.3762849Z env: 2025-09-07T06:40:06.3762930Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.3763022Z ##[endgroup] 2025-09-07T06:40:06.4490317Z ##[group]Run pytorch/pytorch/.github/actions/diskspace-cleanup@main 2025-09-07T06:40:06.4490490Z with: 2025-09-07T06:40:06.4490587Z diskspace-cutoff: 70 2025-09-07T06:40:06.4490678Z env: 2025-09-07T06:40:06.4495948Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.4496055Z ##[endgroup] 2025-09-07T06:40:06.4518743Z ##[group]Run set -ex 2025-09-07T06:40:06.4518863Z set -ex 2025-09-07T06:40:06.4521561Z diskspace_cutoff=70 2025-09-07T06:40:06.4521705Z docker_root_dir=$(docker info -f '{{.DockerRootDir}}') 2025-09-07T06:40:06.4522016Z if [ ! -d "$docker_root_dir" ]; then 2025-09-07T06:40:06.4522202Z  echo "Docker root directory ($docker_root_dir) does not exist. Skipping disk space check." 2025-09-07T06:40:06.4522386Z  exit 0 2025-09-07T06:40:06.4522468Z fi 2025-09-07T06:40:06.4522618Z diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-09-07T06:40:06.4522924Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified" 2025-09-07T06:40:06.4523190Z if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then 2025-09-07T06:40:06.4523327Z  docker system prune -af 2025-09-07T06:40:06.4523504Z  diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-09-07T06:40:06.4523702Z  if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then 2025-09-07T06:40:06.4526096Z  echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace." 2025-09-07T06:40:06.4526286Z  echo "$msg" 2025-09-07T06:40:06.4526382Z  exit 1 2025-09-07T06:40:06.4526466Z  else 2025-09-07T06:40:06.4526570Z  difference=$((diskspace - diskspace_new)) 2025-09-07T06:40:06.4526714Z  echo "Diskspace saved: $difference percent" 2025-09-07T06:40:06.4526829Z  fi 2025-09-07T06:40:06.4526904Z fi 2025-09-07T06:40:06.4531320Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.4531450Z env: 2025-09-07T06:40:06.4531528Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.4531620Z ##[endgroup] 2025-09-07T06:40:06.4553148Z + diskspace_cutoff=70 2025-09-07T06:40:06.4553494Z ++ docker info -f '{{.DockerRootDir}}' 2025-09-07T06:40:06.4946302Z + docker_root_dir=/home/runner/docker-data 2025-09-07T06:40:06.4946607Z + '[' '!' -d /home/runner/docker-data ']' 2025-09-07T06:40:06.4952616Z ++ df -H --output=pcent /home/runner/docker-data 2025-09-07T06:40:06.4952854Z ++ sed -n 2p 2025-09-07T06:40:06.4953261Z ++ sed s/%// 2025-09-07T06:40:06.4953750Z ++ sed 's/ //' 2025-09-07T06:40:06.4968892Z + diskspace=12 2025-09-07T06:40:06.4969396Z + msg='Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified' 2025-09-07T06:40:06.4969664Z + [[ 12 -ge 70 ]] 2025-09-07T06:40:06.4994788Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-07T06:40:06.4994975Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-07T06:40:06.4995118Z rm -rf "${RUNNER_ARTIFACT_DIR}" 2025-09-07T06:40:06.4997323Z mkdir -p "${RUNNER_ARTIFACT_DIR}" 2025-09-07T06:40:06.4997494Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}" 2025-09-07T06:40:06.4997639Z  2025-09-07T06:40:06.4997754Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results" 2025-09-07T06:40:06.4997917Z rm -rf "${RUNNER_TEST_RESULTS_DIR}" 2025-09-07T06:40:06.4998041Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}" 2025-09-07T06:40:06.4998217Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}" 2025-09-07T06:40:06.4998371Z  2025-09-07T06:40:06.4998456Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs" 2025-09-07T06:40:06.4998571Z rm -rf "${RUNNER_DOCS_DIR}" 2025-09-07T06:40:06.4998681Z mkdir -p "${RUNNER_DOCS_DIR}" 2025-09-07T06:40:06.4998821Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}" 2025-09-07T06:40:06.5005432Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.5005562Z env: 2025-09-07T06:40:06.5005642Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.5005735Z ##[endgroup] 2025-09-07T06:40:06.5086153Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:40:06.5086605Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:40:06.5086787Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:40:06.5090580Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.5090713Z env: 2025-09-07T06:40:06.5090800Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.5090925Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:06.5091085Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:06.5091236Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:06.5091353Z ##[endgroup] 2025-09-07T06:40:06.5143342Z ##[group]Run # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py. 2025-09-07T06:40:06.5143603Z # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py. 2025-09-07T06:40:06.5143788Z # Add render group for container creation. 2025-09-07T06:40:06.5143950Z render_gid=`cat /etc/group | grep render | cut -d: -f3` 2025-09-07T06:40:06.5144137Z # Ensure GPU isolation if pod is part of kubernetes setup with DEVICE_FLAG. 2025-09-07T06:40:06.5144326Z if [ -f "/etc/podinfo/gha-render-devices" ]; then 2025-09-07T06:40:06.5144590Z  DEVICE_FLAG=$(cat /etc/podinfo/gha-render-devices) 2025-09-07T06:40:06.5144715Z else 2025-09-07T06:40:06.5144806Z  DEVICE_FLAG="--device /dev/dri" 2025-09-07T06:40:06.5144917Z fi 2025-09-07T06:40:06.5145083Z # The --group-add daemon and --group-add bin are needed in the Ubuntu 24.04 and Almalinux OSs respectively. 2025-09-07T06:40:06.5145340Z # This is due to the device files (/dev/kfd & /dev/dri) being owned by video group on bare metal. 2025-09-07T06:40:06.5145577Z # This video group ID maps to subgid 1 inside the docker image due to the /etc/subgid entries. 2025-09-07T06:40:06.5145831Z # The group name corresponding to group ID 1 can change depending on the OS, so both are necessary. 2025-09-07T06:40:06.5146252Z 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}" 2025-09-07T06:40:06.5152148Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:06.5152285Z env: 2025-09-07T06:40:06.5152366Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.5152485Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:06.5152642Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:06.5152788Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:06.5152902Z ##[endgroup] 2025-09-07T06:40:06.5242123Z ##[group]Run aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 2025-09-07T06:40:06.5242322Z with: 2025-09-07T06:40:06.5242460Z role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only 2025-09-07T06:40:06.5242628Z aws-region: us-east-1 2025-09-07T06:40:06.5242735Z role-duration-seconds: 18000 2025-09-07T06:40:06.5242849Z audience: sts.amazonaws.com 2025-09-07T06:40:06.5242948Z env: 2025-09-07T06:40:06.5243037Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.5243162Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:06.5243325Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:06.5243483Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:06.5243868Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:06.5244213Z ##[endgroup] 2025-09-07T06:40:06.7129572Z Assuming role with OIDC 2025-09-07T06:40:06.8427168Z Authenticated as assumedRoleId AROAUPVRELQNLLCOPFEJR:GitHubActions 2025-09-07T06:40:06.8869557Z ##[group]Run aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 2025-09-07T06:40:06.8869743Z with: 2025-09-07T06:40:06.8869832Z mask-password: true 2025-09-07T06:40:06.8869932Z registry-type: private 2025-09-07T06:40:06.8870034Z skip-logout: false 2025-09-07T06:40:06.8870123Z env: 2025-09-07T06:40:06.8870207Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:06.8870335Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:06.8870502Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:06.8870660Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:06.8871031Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:06.8871390Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:06.8871513Z AWS_REGION: us-east-1 2025-09-07T06:40:06.8871866Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:06.8872102Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:06.8874011Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:06.8874109Z ##[endgroup] 2025-09-07T06:40:07.1027791Z Logging into registry 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3170098Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-09-07T06:40:07.3170319Z with: 2025-09-07T06:40:07.3170634Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3170970Z use-custom-docker-registry: true 2025-09-07T06:40:07.3171126Z docker-build-dir: .ci/docker 2025-09-07T06:40:07.3171293Z docker-build-script: ./build.sh 2025-09-07T06:40:07.3171441Z working-directory: . 2025-09-07T06:40:07.3171609Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3171806Z force-push: false 2025-09-07T06:40:07.3171926Z env: 2025-09-07T06:40:07.3172058Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:07.3172215Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:07.3172538Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:07.3172777Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:07.3173191Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:07.3173591Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:07.3173740Z AWS_REGION: us-east-1 2025-09-07T06:40:07.3173989Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:07.3174185Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:07.3176501Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:07.3176631Z ##[endgroup] 2025-09-07T06:40:07.3194926Z ##[group]Run set -ex 2025-09-07T06:40:07.3195049Z set -ex 2025-09-07T06:40:07.3195136Z  2025-09-07T06:40:07.3195284Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-07T06:40:07.3195524Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-07T06:40:07.3195731Z # job could then download the pre-built image as usual 2025-09-07T06:40:07.3195978Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-07T06:40:07.3196202Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3196323Z else 2025-09-07T06:40:07.3196422Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3196585Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3196728Z  2025-09-07T06:40:07.3196926Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-07T06:40:07.3197144Z  exit 0 2025-09-07T06:40:07.3197233Z fi 2025-09-07T06:40:07.3197315Z  2025-09-07T06:40:07.3197444Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-07T06:40:07.3197657Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-07T06:40:07.3197846Z  # use it as it is, but first let's extract the tag 2025-09-07T06:40:07.3198021Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-07T06:40:07.3198204Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3198378Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3198519Z else 2025-09-07T06:40:07.3198624Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-07T06:40:07.3198768Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-07T06:40:07.3198910Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-07T06:40:07.3199033Z  fi 2025-09-07T06:40:07.3199287Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-07T06:40:07.3199505Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3199730Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3199976Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3200127Z fi 2025-09-07T06:40:07.3205455Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:07.3205592Z env: 2025-09-07T06:40:07.3205680Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:07.3205806Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:07.3205972Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:07.3206133Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:07.3206561Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:07.3206916Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:07.3207025Z AWS_REGION: us-east-1 2025-09-07T06:40:07.3207183Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:07.3207328Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:07.3209198Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:07.3209299Z REPO_NAME: pytorch 2025-09-07T06:40:07.3209565Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3209845Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:40:07.3209957Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-07T06:40:07.3210103Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3210257Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-07T06:40:07.3210375Z CUSTOM_TAG_PREFIX: 2025-09-07T06:40:07.3210470Z ##[endgroup] 2025-09-07T06:40:07.3230304Z + [[ -d .ci/docker ]] 2025-09-07T06:40:07.3230451Z + [[ -f .ci/docker/./build.sh ]] 2025-09-07T06:40:07.3230572Z + [[ true == \t\r\u\e ]] 2025-09-07T06:40:07.3230672Z + echo skip=false 2025-09-07T06:40:07.3231034Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2025-09-07T06:40:07.3234494Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3238062Z ++ awk -F '[:,]' '{print $2}' 2025-09-07T06:40:07.3244337Z + DOCKER_TAG=pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3244717Z + echo docker-tag=pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3245190Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3271921Z ##[group]Run set +e 2025-09-07T06:40:07.3272153Z set +e 2025-09-07T06:40:07.3272250Z set -x 2025-09-07T06:40:07.3272340Z  2025-09-07T06:40:07.3272432Z login() { 2025-09-07T06:40:07.3272630Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:40:07.3272829Z } 2025-09-07T06:40:07.3272919Z  2025-09-07T06:40:07.3273003Z retry () { 2025-09-07T06:40:07.3273120Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:40:07.3273243Z } 2025-09-07T06:40:07.3273330Z  2025-09-07T06:40:07.3273423Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:40:07.3273541Z  2025-09-07T06:40:07.3273645Z START_TIME=$(date +%s) 2025-09-07T06:40:07.3273767Z # Wait up to 120 minutes 2025-09-07T06:40:07.3274047Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-09-07T06:40:07.3274240Z  # Check if image already exists, if it does then skip building it 2025-09-07T06:40:07.3274581Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-09-07T06:40:07.3274726Z  exit 0 2025-09-07T06:40:07.3274822Z  fi 2025-09-07T06:40:07.3274908Z  2025-09-07T06:40:07.3275060Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-09-07T06:40:07.3275325Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-09-07T06:40:07.3275568Z  # latter, it will wait for the Docker images to become available before continuing 2025-09-07T06:40:07.3275761Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-09-07T06:40:07.3276011Z  # It's a Docker build job, let's build the image 2025-09-07T06:40:07.3276142Z  break 2025-09-07T06:40:07.3276238Z  else 2025-09-07T06:40:07.3276368Z  # It's a regular build job, wait for the image to become available 2025-09-07T06:40:07.3276521Z  sleep 300 2025-09-07T06:40:07.3276619Z  fi 2025-09-07T06:40:07.3276707Z done 2025-09-07T06:40:07.3276792Z  2025-09-07T06:40:07.3276928Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-09-07T06:40:07.3277134Z # be empty. The default action would be to continue rebuild the image 2025-09-07T06:40:07.3277320Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-09-07T06:40:07.3277488Z  # if we're on the base branch then use the parent commit 2025-09-07T06:40:07.3277641Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-09-07T06:40:07.3277759Z else 2025-09-07T06:40:07.3277888Z  # otherwise we're on a PR, so use the most recent base commit 2025-09-07T06:40:07.3278063Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-09-07T06:40:07.3278196Z fi 2025-09-07T06:40:07.3278282Z  2025-09-07T06:40:07.3278376Z if [[ -z "${MERGE_BASE}" ]]; then 2025-09-07T06:40:07.3278514Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3278639Z  2025-09-07T06:40:07.3278807Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-09-07T06:40:07.3279002Z  exit 0 2025-09-07T06:40:07.3279093Z fi 2025-09-07T06:40:07.3279178Z  2025-09-07T06:40:07.3279298Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-09-07T06:40:07.3279542Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-09-07T06:40:07.3279752Z  exit 1 2025-09-07T06:40:07.3279838Z fi 2025-09-07T06:40:07.3282464Z  2025-09-07T06:40:07.3282613Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-09-07T06:40:07.3282845Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-09-07T06:40:07.3283053Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-09-07T06:40:07.3283289Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-09-07T06:40:07.3283552Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-09-07T06:40:07.3283710Z fi 2025-09-07T06:40:07.3283788Z  2025-09-07T06:40:07.3283886Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:40:07.3288835Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:07.3288975Z env: 2025-09-07T06:40:07.3289062Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:07.3289191Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:07.3289406Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:07.3289563Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:07.3289932Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:07.3290287Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:07.3292441Z AWS_REGION: us-east-1 2025-09-07T06:40:07.3292639Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:07.3292799Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:07.3294769Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:07.3294872Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:40:07.3295001Z BASE_REVISION: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:40:07.3295362Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3295701Z DOCKER_TAG: pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:07.3295918Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3296059Z DOCKER_PUSH: 2025-09-07T06:40:07.3297885Z ##[endgroup] 2025-09-07T06:40:07.3323130Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3323303Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3327120Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:07.3327970Z /home/runner/_work/_temp/eb4a70ea-4cf6-4be0-8b7b-281e1a21ce31.sh: line 5: aws: command not found 2025-09-07T06:40:07.3330857Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:07.3429917Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:07.3439482Z + sleep 1 2025-09-07T06:40:08.3452478Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:08.3453444Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:08.3464191Z /home/runner/_work/_temp/eb4a70ea-4cf6-4be0-8b7b-281e1a21ce31.sh: line 5: aws: command not found 2025-09-07T06:40:08.3464595Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:08.3543717Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:08.3554322Z + sleep 2 2025-09-07T06:40:10.3565343Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:10.3569101Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:10.3570796Z /home/runner/_work/_temp/eb4a70ea-4cf6-4be0-8b7b-281e1a21ce31.sh: line 5: aws: command not found 2025-09-07T06:40:10.3571650Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:10.3676798Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:10.3689376Z ++ date +%s 2025-09-07T06:40:10.3697170Z + START_TIME=1757227210 2025-09-07T06:40:10.3701588Z ++ date +%s 2025-09-07T06:40:10.3711749Z + [[ 1757220010 -lt 1757227210 ]] 2025-09-07T06:40:10.3712239Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:11.0135259Z { 2025-09-07T06:40:11.0135393Z "schemaVersion": 2, 2025-09-07T06:40:11.0135602Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-09-07T06:40:11.0135786Z "config": { 2025-09-07T06:40:11.0135927Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-09-07T06:40:11.0136095Z "size": 28673, 2025-09-07T06:40:11.0136276Z 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"sha256:aa3590d8b0d480b1152679e07759b95d93710df177747ef8685d3c64dd968a80" 2025-09-07T06:40:11.0177942Z }, 2025-09-07T06:40:11.0178010Z { 2025-09-07T06:40:11.0178123Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0178266Z "size": 19279569, 2025-09-07T06:40:11.0178417Z "digest": "sha256:b790e385f849ac4fe803ffe765d93d9e52dfaf532dc3d7e77560d9361d25e4c4" 2025-09-07T06:40:11.0178579Z }, 2025-09-07T06:40:11.0178647Z { 2025-09-07T06:40:11.0179817Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0179965Z "size": 639, 2025-09-07T06:40:11.0180109Z "digest": "sha256:7f422bd7611be8b204f81a16d84217dc35125fb6ae4ab3912f7ac883afb9d143" 2025-09-07T06:40:11.0180271Z }, 2025-09-07T06:40:11.0180340Z { 2025-09-07T06:40:11.0180452Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0180593Z "size": 724, 2025-09-07T06:40:11.0180739Z "digest": "sha256:11edb6ea0bca3be307ef836b0bd07999ff562bcb7a807f5e6c9f7d4d5f976b5d" 2025-09-07T06:40:11.0180903Z }, 2025-09-07T06:40:11.0180972Z { 2025-09-07T06:40:11.0181083Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0182281Z "size": 148, 2025-09-07T06:40:11.0182429Z "digest": "sha256:8e1167399aca20f7e0ac2500fb735ae2974a9f062a74e024b8f1d5f8b4faf6bd" 2025-09-07T06:40:11.0182592Z }, 2025-09-07T06:40:11.0182661Z { 2025-09-07T06:40:11.0182772Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0182912Z "size": 136, 2025-09-07T06:40:11.0183060Z "digest": "sha256:d157c63b23dc8bf7981fbe850c79a1eb2960e26ebc0344757c7f98665b7d686d" 2025-09-07T06:40:11.0183220Z }, 2025-09-07T06:40:11.0183334Z { 2025-09-07T06:40:11.0183447Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0183588Z "size": 140, 2025-09-07T06:40:11.0185083Z "digest": "sha256:ec0c7fb1e2871ad72728e420724306dd5bd0c969056666bdb0cf8c299197ac60" 2025-09-07T06:40:11.0185247Z }, 2025-09-07T06:40:11.0185316Z { 2025-09-07T06:40:11.0185428Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0185569Z "size": 32, 2025-09-07T06:40:11.0185716Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:40:11.0185878Z }, 2025-09-07T06:40:11.0185947Z { 2025-09-07T06:40:11.0186060Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0186200Z "size": 223, 2025-09-07T06:40:11.0186346Z "digest": "sha256:1e33591be1b2caebfdb79544377eb56d04ffc3f9859ae7bff1d0d319078a9440" 2025-09-07T06:40:11.0187660Z }, 2025-09-07T06:40:11.0187731Z { 2025-09-07T06:40:11.0187847Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0187989Z "size": 347, 2025-09-07T06:40:11.0188134Z "digest": "sha256:7988968c85d36793ccaf1bf4382d8df2dced38c1c0eddab511d0d4770f1d4b0a" 2025-09-07T06:40:11.0188296Z }, 2025-09-07T06:40:11.0188362Z { 2025-09-07T06:40:11.0188475Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0188618Z "size": 88301, 2025-09-07T06:40:11.0188768Z "digest": "sha256:91a6ca8580eebcfcf0791ff40d3862a199ec745c737eee389bcd2988e2d8ade2" 2025-09-07T06:40:11.0188931Z }, 2025-09-07T06:40:11.0190079Z { 2025-09-07T06:40:11.0190194Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0190336Z "size": 106, 2025-09-07T06:40:11.0190481Z "digest": "sha256:b14b67d48e1dbd915a6da5b1d21a7b6eda25f453044993909b2f4843ab3fe279" 2025-09-07T06:40:11.0190646Z }, 2025-09-07T06:40:11.0190714Z { 2025-09-07T06:40:11.0190831Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0190972Z "size": 1665, 2025-09-07T06:40:11.0191119Z "digest": "sha256:b192db73bfe1da14e42a568d19255310ed73477fcc8608b344f4b5e6baf6d8ac" 2025-09-07T06:40:11.0191281Z }, 2025-09-07T06:40:11.0191349Z { 2025-09-07T06:40:11.0192629Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0192771Z "size": 724, 2025-09-07T06:40:11.0192920Z "digest": "sha256:11edb6ea0bca3be307ef836b0bd07999ff562bcb7a807f5e6c9f7d4d5f976b5d" 2025-09-07T06:40:11.0193085Z }, 2025-09-07T06:40:11.0193153Z { 2025-09-07T06:40:11.0193266Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0193409Z "size": 138, 2025-09-07T06:40:11.0193553Z "digest": "sha256:2150bf3310869a53ad8f3c61553cf421e8166d06bac5badf027d5a8c8c27293d" 2025-09-07T06:40:11.0193715Z }, 2025-09-07T06:40:11.0193784Z { 2025-09-07T06:40:11.0193898Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0194042Z "size": 120, 2025-09-07T06:40:11.0195498Z "digest": "sha256:733c737688573b91b5f7356eb0e1841ec926e30ea6dc7b6c947d3fe38037f4ba" 2025-09-07T06:40:11.0195658Z }, 2025-09-07T06:40:11.0195726Z { 2025-09-07T06:40:11.0195839Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0195983Z "size": 5387023681, 2025-09-07T06:40:11.0196135Z "digest": "sha256:b61655c9140ac745bb4ede98af919309736dfcf4205c1a8d8e741481940d93fd" 2025-09-07T06:40:11.0196296Z }, 2025-09-07T06:40:11.0196364Z { 2025-09-07T06:40:11.0196477Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0196619Z "size": 174, 2025-09-07T06:40:11.0196765Z "digest": "sha256:e86fed99150a751abc2fc621813b3c422f30ef2b69140ed8ecf6dac1d67a9e51" 2025-09-07T06:40:11.0201115Z }, 2025-09-07T06:40:11.0201196Z { 2025-09-07T06:40:11.0201318Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0201464Z "size": 1897, 2025-09-07T06:40:11.0201673Z "digest": "sha256:81d56f8c0e9e96289a7250e45d9cc29663b554c56251c1fee391de9bebf0c201" 2025-09-07T06:40:11.0201836Z }, 2025-09-07T06:40:11.0201906Z { 2025-09-07T06:40:11.0202023Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0202172Z "size": 162692045, 2025-09-07T06:40:11.0202325Z "digest": "sha256:c3379e965cca765342286c4f615b0ee640287447f7077d915d485ee09f5e0567" 2025-09-07T06:40:11.0202482Z }, 2025-09-07T06:40:11.0203583Z { 2025-09-07T06:40:11.0203702Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0203846Z "size": 302, 2025-09-07T06:40:11.0203990Z "digest": "sha256:ca8d286566c546411a1541f9e11d22786fae23a1d8004f8e69e283ce89ad2916" 2025-09-07T06:40:11.0204147Z }, 2025-09-07T06:40:11.0204214Z { 2025-09-07T06:40:11.0204532Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0204678Z "size": 32, 2025-09-07T06:40:11.0204832Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:40:11.0204994Z }, 2025-09-07T06:40:11.0205061Z { 2025-09-07T06:40:11.0206258Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0206406Z "size": 108, 2025-09-07T06:40:11.0206554Z "digest": "sha256:ac22ab96541095de08a583bdf8af502c383a8dd4f25afa1712460ffc31c81514" 2025-09-07T06:40:11.0206714Z }, 2025-09-07T06:40:11.0206782Z { 2025-09-07T06:40:11.0206897Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:40:11.0207040Z "size": 54145699, 2025-09-07T06:40:11.0207194Z "digest": "sha256:6fd45892b6e79a95321086f1d7842aeaf6fd2adaa82c3c8439efef9a5d79fb8d" 2025-09-07T06:40:11.0207355Z } 2025-09-07T06:40:11.0207424Z ] 2025-09-07T06:40:11.0207496Z } 2025-09-07T06:40:11.0208506Z + exit 0 2025-09-07T06:40:11.0241364Z ##[group]Run set -eux 2025-09-07T06:40:11.0241491Z set -eux 2025-09-07T06:40:11.0241648Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-07T06:40:11.0242051Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin || true 2025-09-07T06:40:11.0247545Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:11.0247685Z env: 2025-09-07T06:40:11.0247775Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:11.0247901Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:11.0248065Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:11.0248221Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:11.0248588Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:11.0251582Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:11.0251704Z AWS_REGION: us-east-1 2025-09-07T06:40:11.0251903Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:11.0252049Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:11.0253938Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:11.0254037Z ##[endgroup] 2025-09-07T06:40:11.0278049Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-07T06:40:11.0278623Z /home/runner/_work/_temp/729566f9-60da-486d-a4d6-1694ced9ccd6.sh: line 3: aws: command not found 2025-09-07T06:40:11.0279023Z + jq --raw-output .SecretString 2025-09-07T06:40:11.0279256Z + jq -r .docker_hub_readonly_token 2025-09-07T06:40:11.0279820Z + docker login --username pytorchbot --password-stdin 2025-09-07T06:40:11.0383253Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:11.0390945Z + true 2025-09-07T06:40:11.0447401Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-09-07T06:40:11.0447566Z with: 2025-09-07T06:40:11.0447820Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:11.0448131Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:11.0448273Z env: 2025-09-07T06:40:11.0448358Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:11.0448486Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:11.0448655Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:11.0448810Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:11.0449184Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:11.0451999Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:11.0452112Z AWS_REGION: us-east-1 2025-09-07T06:40:11.0452246Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:11.0452391Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:11.0454264Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:11.0454361Z ##[endgroup] 2025-09-07T06:40:11.0463167Z ##[group]Run set -x 2025-09-07T06:40:11.0463274Z set -x 2025-09-07T06:40:11.0463358Z set +e 2025-09-07T06:40:11.0463439Z  2025-09-07T06:40:11.0466362Z login() { 2025-09-07T06:40:11.0466548Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:40:11.0466734Z } 2025-09-07T06:40:11.0466813Z  2025-09-07T06:40:11.0466892Z retry () { 2025-09-07T06:40:11.0466996Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:40:11.0467112Z } 2025-09-07T06:40:11.0467189Z  2025-09-07T06:40:11.0467284Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:40:11.0467394Z  2025-09-07T06:40:11.0467573Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-07T06:40:11.0469979Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-07T06:40:11.0470115Z  2025-09-07T06:40:11.0470194Z set -e 2025-09-07T06:40:11.0470322Z # ignore output since only exit code is used for conditional 2025-09-07T06:40:11.0470497Z # only pull docker image if it's not available locally 2025-09-07T06:40:11.0470692Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-07T06:40:11.0470871Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-07T06:40:11.0470986Z fi 2025-09-07T06:40:11.0475033Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:40:11.0475168Z env: 2025-09-07T06:40:11.0477634Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:40:11.0477771Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:40:11.0477938Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:40:11.0478093Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:40:11.0478462Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:40:11.0478818Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:40:11.0478925Z AWS_REGION: us-east-1 2025-09-07T06:40:11.0479055Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:40:11.0479199Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:40:11.0481087Z AWS_SESSION_TOKEN: *** 2025-09-07T06:40:11.0483489Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:11.0483931Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:11.0484078Z ##[endgroup] 2025-09-07T06:40:11.0509277Z + set +e 2025-09-07T06:40:11.0509434Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:11.0509941Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:11.0513047Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:11.0513710Z /home/runner/_work/_temp/687064f9-75dd-4868-a9c2-99cedfe9b121.sh: line 5: aws: command not found 2025-09-07T06:40:11.0520619Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:11.0610978Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:11.0620178Z + sleep 1 2025-09-07T06:40:12.0633372Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:12.0647147Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:12.0647758Z /home/runner/_work/_temp/687064f9-75dd-4868-a9c2-99cedfe9b121.sh: line 5: aws: command not found 2025-09-07T06:40:12.0648426Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:12.0750099Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:12.0762036Z + sleep 2 2025-09-07T06:40:14.0782226Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:14.0786918Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:40:14.0787433Z /home/runner/_work/_temp/687064f9-75dd-4868-a9c2-99cedfe9b121.sh: line 5: aws: command not found 2025-09-07T06:40:14.0788009Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:40:14.0898939Z Error: Cannot perform an interactive login from a non TTY device 2025-09-07T06:40:14.0918015Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:14.0920075Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-07T06:40:14.5721537Z + IMAGE_SIZE=18063.334635734558 2025-09-07T06:40:14.5721985Z + echo 'Compressed size of image in MB: 18063.334635734558' 2025-09-07T06:40:14.5722431Z Compressed size of image in MB: 18063.334635734558 2025-09-07T06:40:14.5722794Z + set -e 2025-09-07T06:40:14.5723578Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:14.5848537Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:14.5849425Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:40:15.0721849Z pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77: Pulling from pytorch/ci-image 2025-09-07T06:40:15.0722638Z de66fc90c55d: Pulling fs layer 2025-09-07T06:40:15.0722990Z efc45b9044a6: Pulling fs layer 2025-09-07T06:40:15.0723285Z 06ce422a41d1: Pulling fs layer 2025-09-07T06:40:15.0723566Z 673cf5ffa968: Pulling fs layer 2025-09-07T06:40:15.0723864Z 3042b077c06a: Pulling fs layer 2025-09-07T06:40:15.0724146Z ed25a020f194: Pulling fs layer 2025-09-07T06:40:15.0724576Z a5876169851f: Pulling fs layer 2025-09-07T06:40:15.0724865Z 4971bfcf31c1: Pulling fs layer 2025-09-07T06:40:15.0725115Z 4d141034e9db: Pulling fs layer 2025-09-07T06:40:15.0725304Z 11edb6ea0bca: Pulling fs layer 2025-09-07T06:40:15.0725496Z dc4852f72739: Pulling fs layer 2025-09-07T06:40:15.0725676Z 829c85269cfc: Pulling fs layer 2025-09-07T06:40:15.0725858Z 77ab4e659dd8: Pulling fs layer 2025-09-07T06:40:15.0726045Z c0da146487b6: Pulling fs layer 2025-09-07T06:40:15.0726227Z a61c8111f466: Pulling fs layer 2025-09-07T06:40:15.0726412Z 720fb67e397f: Pulling fs layer 2025-09-07T06:40:15.0726595Z fa7524284edd: Pulling fs layer 2025-09-07T06:40:15.0726841Z 57cfc9fee363: Pulling fs layer 2025-09-07T06:40:15.0727315Z c9e2c9bad36d: Pulling fs layer 2025-09-07T06:40:15.0727507Z 8257abce8e9b: Pulling fs layer 2025-09-07T06:40:15.0727694Z 8b81352a9241: Pulling fs layer 2025-09-07T06:40:15.0727875Z 5acab4245292: Pulling fs layer 2025-09-07T06:40:15.0728056Z 93616f9ff93b: Pulling fs layer 2025-09-07T06:40:15.0728232Z ed25a020f194: Waiting 2025-09-07T06:40:15.0728410Z 89ff93a7db63: Pulling fs layer 2025-09-07T06:40:15.0728597Z adfe896bf742: Pulling fs layer 2025-09-07T06:40:15.0728776Z a5876169851f: Waiting 2025-09-07T06:40:15.0728940Z 4fa7b9266ac5: Pulling fs layer 2025-09-07T06:40:15.0729117Z c0da146487b6: Waiting 2025-09-07T06:40:15.0729274Z 4971bfcf31c1: Waiting 2025-09-07T06:40:15.0729443Z 96da3351f842: Pulling fs layer 2025-09-07T06:40:15.0729665Z a61c8111f466: Waiting 2025-09-07T06:40:15.0729829Z 6004d474c546: Pulling fs layer 2025-09-07T06:40:15.0730004Z 4d141034e9db: Waiting 2025-09-07T06:40:15.0730165Z d52b1118c4f3: Pulling fs layer 2025-09-07T06:40:15.0730338Z 829c85269cfc: Waiting 2025-09-07T06:40:15.0730629Z 77ab4e659dd8: Waiting 2025-09-07T06:40:15.0730788Z 11edb6ea0bca: Waiting 2025-09-07T06:40:15.0730956Z 90710e19b430: Pulling fs layer 2025-09-07T06:40:15.0731133Z 720fb67e397f: Waiting 2025-09-07T06:40:15.0731295Z e8f510972d1a: Pulling fs layer 2025-09-07T06:40:15.0731482Z 705289c9d65e: Pulling fs layer 2025-09-07T06:40:15.0731654Z fa7524284edd: Waiting 2025-09-07T06:40:15.0731810Z 5acab4245292: Waiting 2025-09-07T06:40:15.0731963Z 57cfc9fee363: Waiting 2025-09-07T06:40:15.0732124Z aa3590d8b0d4: Pulling fs layer 2025-09-07T06:40:15.0732301Z 8257abce8e9b: Waiting 2025-09-07T06:40:15.0732455Z dc4852f72739: Waiting 2025-09-07T06:40:15.0732619Z b790e385f849: Pulling fs layer 2025-09-07T06:40:15.0732792Z 8b81352a9241: Waiting 2025-09-07T06:40:15.0732948Z c9e2c9bad36d: Waiting 2025-09-07T06:40:15.0733115Z 7f422bd7611b: Pulling fs layer 2025-09-07T06:40:15.0733297Z 8e1167399aca: Pulling fs layer 2025-09-07T06:40:15.0733473Z 93616f9ff93b: Waiting 2025-09-07T06:40:15.0733630Z 673cf5ffa968: Waiting 2025-09-07T06:40:15.0733798Z d157c63b23dc: Pulling fs layer 2025-09-07T06:40:15.0733977Z 705289c9d65e: Waiting 2025-09-07T06:40:15.0734142Z ec0c7fb1e287: Pulling fs layer 2025-09-07T06:40:15.0734316Z adfe896bf742: Waiting 2025-09-07T06:40:15.0741294Z aa3590d8b0d4: Waiting 2025-09-07T06:40:15.0741445Z 89ff93a7db63: Waiting 2025-09-07T06:40:15.0741581Z 4f4fb700ef54: Pulling fs layer 2025-09-07T06:40:15.0741717Z d52b1118c4f3: Waiting 2025-09-07T06:40:15.0741844Z 1e33591be1b2: Pulling fs layer 2025-09-07T06:40:15.0741979Z 90710e19b430: Waiting 2025-09-07T06:40:15.0742096Z 96da3351f842: Waiting 2025-09-07T06:40:15.0742214Z 7988968c85d3: Pulling fs layer 2025-09-07T06:40:15.0742351Z 3042b077c06a: Waiting 2025-09-07T06:40:15.0742463Z 4fa7b9266ac5: Waiting 2025-09-07T06:40:15.0742592Z 91a6ca8580ee: Pulling fs layer 2025-09-07T06:40:15.0742725Z 6004d474c546: Waiting 2025-09-07T06:40:15.0744824Z b14b67d48e1d: Pulling fs layer 2025-09-07T06:40:15.0744962Z e8f510972d1a: Waiting 2025-09-07T06:40:15.0745080Z b192db73bfe1: Pulling fs layer 2025-09-07T06:40:15.0745226Z 7f422bd7611b: Waiting 2025-09-07T06:40:15.0745331Z d157c63b23dc: Waiting 2025-09-07T06:40:15.0745421Z 1e33591be1b2: Waiting 2025-09-07T06:40:15.0745507Z 8e1167399aca: Waiting 2025-09-07T06:40:15.0745601Z 2150bf331086: Pulling fs layer 2025-09-07T06:40:15.0745700Z 7988968c85d3: Waiting 2025-09-07T06:40:15.0745788Z 91a6ca8580ee: Waiting 2025-09-07T06:40:15.0745878Z ec0c7fb1e287: Waiting 2025-09-07T06:40:15.0747196Z 733c73768857: Pulling fs layer 2025-09-07T06:40:15.0747300Z 4f4fb700ef54: Waiting 2025-09-07T06:40:15.0747393Z b61655c9140a: Pulling fs layer 2025-09-07T06:40:15.0747497Z b14b67d48e1d: Waiting 2025-09-07T06:40:15.0747586Z b192db73bfe1: Waiting 2025-09-07T06:40:15.0747681Z e86fed99150a: Pulling fs layer 2025-09-07T06:40:15.0747787Z 81d56f8c0e9e: Pulling fs layer 2025-09-07T06:40:15.0747888Z b61655c9140a: Waiting 2025-09-07T06:40:15.0747976Z 2150bf331086: Waiting 2025-09-07T06:40:15.0748061Z e86fed99150a: Waiting 2025-09-07T06:40:15.0748150Z 81d56f8c0e9e: Waiting 2025-09-07T06:40:15.0749448Z c3379e965cca: Pulling fs layer 2025-09-07T06:40:15.0749552Z b790e385f849: Waiting 2025-09-07T06:40:15.0749648Z ca8d286566c5: Pulling fs layer 2025-09-07T06:40:15.0749747Z 733c73768857: Waiting 2025-09-07T06:40:15.0749839Z ac22ab965410: Pulling fs layer 2025-09-07T06:40:15.0749944Z 6fd45892b6e7: Pulling fs layer 2025-09-07T06:40:15.0750043Z ca8d286566c5: Waiting 2025-09-07T06:40:15.0750132Z ac22ab965410: Waiting 2025-09-07T06:40:15.2912966Z efc45b9044a6: Download complete 2025-09-07T06:40:15.4969894Z 673cf5ffa968: Download complete 2025-09-07T06:40:15.6578705Z de66fc90c55d: Verifying Checksum 2025-09-07T06:40:15.6579076Z de66fc90c55d: Download complete 2025-09-07T06:40:15.6793148Z 3042b077c06a: Verifying Checksum 2025-09-07T06:40:15.6793281Z 3042b077c06a: Download complete 2025-09-07T06:40:15.8610898Z ed25a020f194: Verifying Checksum 2025-09-07T06:40:15.8611432Z ed25a020f194: Download complete 2025-09-07T06:40:16.1310014Z 4971bfcf31c1: Verifying Checksum 2025-09-07T06:40:16.1310737Z 4971bfcf31c1: Download complete 2025-09-07T06:40:16.2441940Z de66fc90c55d: Pull complete 2025-09-07T06:40:16.2543666Z efc45b9044a6: Pull complete 2025-09-07T06:40:16.3317073Z 4d141034e9db: Verifying Checksum 2025-09-07T06:40:16.3318235Z 4d141034e9db: Download complete 2025-09-07T06:40:16.5328587Z 11edb6ea0bca: Verifying Checksum 2025-09-07T06:40:16.5328815Z 11edb6ea0bca: Download complete 2025-09-07T06:40:17.1683133Z a5876169851f: Verifying Checksum 2025-09-07T06:40:17.1683331Z a5876169851f: Download complete 2025-09-07T06:40:17.3833129Z 829c85269cfc: Download complete 2025-09-07T06:40:18.4443332Z 77ab4e659dd8: Verifying Checksum 2025-09-07T06:40:18.4443790Z 77ab4e659dd8: Download complete 2025-09-07T06:40:18.6521352Z 06ce422a41d1: Verifying Checksum 2025-09-07T06:40:18.6521561Z 06ce422a41d1: Download complete 2025-09-07T06:40:18.6647371Z c0da146487b6: Download complete 2025-09-07T06:40:18.8626710Z a61c8111f466: Download complete 2025-09-07T06:40:18.8746008Z 720fb67e397f: Verifying Checksum 2025-09-07T06:40:18.8746835Z 720fb67e397f: Download complete 2025-09-07T06:40:19.0926985Z 57cfc9fee363: Verifying Checksum 2025-09-07T06:40:19.0927346Z 57cfc9fee363: Download complete 2025-09-07T06:40:19.2828258Z c9e2c9bad36d: Download complete 2025-09-07T06:40:23.5894277Z 06ce422a41d1: Pull complete 2025-09-07T06:40:23.5985177Z 673cf5ffa968: Pull complete 2025-09-07T06:40:23.6100845Z 3042b077c06a: Pull complete 2025-09-07T06:40:23.6216635Z ed25a020f194: Pull complete 2025-09-07T06:40:24.6425715Z fa7524284edd: Download complete 2025-09-07T06:40:24.8566028Z a5876169851f: Pull complete 2025-09-07T06:40:24.8645701Z 4971bfcf31c1: Pull complete 2025-09-07T06:40:24.8723736Z 8b81352a9241: Verifying Checksum 2025-09-07T06:40:24.8723904Z 8b81352a9241: Download complete 2025-09-07T06:40:24.8756539Z 4d141034e9db: Pull complete 2025-09-07T06:40:24.8848839Z 11edb6ea0bca: Pull complete 2025-09-07T06:40:25.0824611Z 5acab4245292: Verifying Checksum 2025-09-07T06:40:25.0824988Z 5acab4245292: Download complete 2025-09-07T06:40:32.2036352Z 93616f9ff93b: Verifying Checksum 2025-09-07T06:40:32.2036780Z 93616f9ff93b: Download complete 2025-09-07T06:40:32.4896046Z 89ff93a7db63: Verifying Checksum 2025-09-07T06:40:32.4896417Z 89ff93a7db63: Download complete 2025-09-07T06:40:32.7070935Z adfe896bf742: Verifying Checksum 2025-09-07T06:40:32.7071299Z adfe896bf742: Download complete 2025-09-07T06:40:32.9351853Z 4fa7b9266ac5: Verifying Checksum 2025-09-07T06:40:32.9360425Z 4fa7b9266ac5: Download complete 2025-09-07T06:40:33.1666433Z 96da3351f842: Download complete 2025-09-07T06:40:33.5620818Z 6004d474c546: Verifying Checksum 2025-09-07T06:40:33.5621197Z 6004d474c546: Download complete 2025-09-07T06:40:33.7985680Z d52b1118c4f3: Download complete 2025-09-07T06:40:34.0619381Z 90710e19b430: Download complete 2025-09-07T06:40:34.5680910Z e8f510972d1a: Verifying Checksum 2025-09-07T06:40:34.5681292Z e8f510972d1a: Download complete 2025-09-07T06:40:34.7682321Z 705289c9d65e: Verifying Checksum 2025-09-07T06:40:34.7682719Z 705289c9d65e: Download complete 2025-09-07T06:40:35.0325644Z aa3590d8b0d4: Verifying Checksum 2025-09-07T06:40:35.0325913Z aa3590d8b0d4: Download complete 2025-09-07T06:40:35.6130058Z b790e385f849: Verifying Checksum 2025-09-07T06:40:35.6130473Z b790e385f849: Download complete 2025-09-07T06:40:35.7949959Z 7f422bd7611b: Verifying Checksum 2025-09-07T06:40:35.7950315Z 7f422bd7611b: Download complete 2025-09-07T06:40:36.0128856Z 8e1167399aca: Download complete 2025-09-07T06:40:36.2343304Z d157c63b23dc: Download complete 2025-09-07T06:40:36.4584985Z ec0c7fb1e287: Verifying Checksum 2025-09-07T06:40:36.4585359Z ec0c7fb1e287: Download complete 2025-09-07T06:40:36.5375410Z 4f4fb700ef54: Verifying Checksum 2025-09-07T06:40:36.5375774Z 4f4fb700ef54: Download complete 2025-09-07T06:40:36.7611406Z 1e33591be1b2: Verifying Checksum 2025-09-07T06:40:36.7611765Z 1e33591be1b2: Download complete 2025-09-07T06:40:36.9789343Z 7988968c85d3: Download complete 2025-09-07T06:40:37.2095522Z 91a6ca8580ee: Verifying Checksum 2025-09-07T06:40:37.2095890Z 91a6ca8580ee: Download complete 2025-09-07T06:40:37.4358589Z b14b67d48e1d: Verifying Checksum 2025-09-07T06:40:37.4358962Z b14b67d48e1d: Download complete 2025-09-07T06:40:37.6732225Z b192db73bfe1: Download complete 2025-09-07T06:40:37.8996060Z 2150bf331086: Verifying Checksum 2025-09-07T06:40:37.8996415Z 2150bf331086: Download complete 2025-09-07T06:40:38.1568678Z 733c73768857: Verifying Checksum 2025-09-07T06:40:38.1569039Z 733c73768857: Download complete 2025-09-07T06:40:50.8875551Z dc4852f72739: Verifying Checksum 2025-09-07T06:40:50.8875743Z dc4852f72739: Download complete 2025-09-07T06:40:51.1237038Z e86fed99150a: Verifying Checksum 2025-09-07T06:40:51.1237486Z e86fed99150a: Download complete 2025-09-07T06:40:51.3562166Z 81d56f8c0e9e: Download complete 2025-09-07T06:40:53.1707863Z c3379e965cca: Verifying Checksum 2025-09-07T06:40:53.1708232Z c3379e965cca: Download complete 2025-09-07T06:40:53.4039015Z ca8d286566c5: Verifying Checksum 2025-09-07T06:40:53.4039183Z ca8d286566c5: Download complete 2025-09-07T06:40:53.6026761Z ac22ab965410: Verifying Checksum 2025-09-07T06:40:53.6030604Z ac22ab965410: Download complete 2025-09-07T06:40:54.4636645Z 6fd45892b6e7: Verifying Checksum 2025-09-07T06:40:54.4637031Z 6fd45892b6e7: Download complete 2025-09-07T06:41:16.2372636Z dc4852f72739: Pull complete 2025-09-07T06:41:16.2484608Z 829c85269cfc: Pull complete 2025-09-07T06:41:16.8513950Z 77ab4e659dd8: Pull complete 2025-09-07T06:41:16.8626369Z c0da146487b6: Pull complete 2025-09-07T06:41:16.8717053Z a61c8111f466: Pull complete 2025-09-07T06:41:16.8856614Z 720fb67e397f: Pull complete 2025-09-07T06:41:20.5965489Z fa7524284edd: Pull complete 2025-09-07T06:41:20.6065060Z 57cfc9fee363: Pull complete 2025-09-07T06:41:20.6157988Z c9e2c9bad36d: Pull complete 2025-09-07T06:41:33.0226970Z b61655c9140a: Verifying Checksum 2025-09-07T06:41:33.0227400Z b61655c9140a: Download complete 2025-09-07T06:41:41.3582066Z 8257abce8e9b: Verifying Checksum 2025-09-07T06:42:38.5105944Z 8257abce8e9b: Pull complete 2025-09-07T06:42:38.5215368Z 8b81352a9241: Pull complete 2025-09-07T06:42:38.5340955Z 5acab4245292: Pull complete 2025-09-07T06:42:42.8951509Z 93616f9ff93b: Pull complete 2025-09-07T06:42:42.9062348Z 89ff93a7db63: Pull complete 2025-09-07T06:42:42.9151387Z adfe896bf742: Pull complete 2025-09-07T06:42:42.9258137Z 4fa7b9266ac5: Pull complete 2025-09-07T06:42:42.9346225Z 96da3351f842: Pull complete 2025-09-07T06:42:42.9651589Z 6004d474c546: Pull complete 2025-09-07T06:42:42.9768092Z d52b1118c4f3: Pull complete 2025-09-07T06:42:42.9853379Z 90710e19b430: Pull complete 2025-09-07T06:42:43.1945872Z e8f510972d1a: Pull complete 2025-09-07T06:42:43.2025812Z 705289c9d65e: Pull complete 2025-09-07T06:42:43.2146101Z aa3590d8b0d4: Pull complete 2025-09-07T06:42:43.3202830Z b790e385f849: Pull complete 2025-09-07T06:42:43.3287775Z 7f422bd7611b: Pull complete 2025-09-07T06:42:43.3473692Z 8e1167399aca: Pull complete 2025-09-07T06:42:43.3566273Z d157c63b23dc: Pull complete 2025-09-07T06:42:43.3675825Z ec0c7fb1e287: Pull complete 2025-09-07T06:42:43.3762286Z 4f4fb700ef54: Pull complete 2025-09-07T06:42:43.3852255Z 1e33591be1b2: Pull complete 2025-09-07T06:42:43.3939513Z 7988968c85d3: Pull complete 2025-09-07T06:42:43.4028679Z 91a6ca8580ee: Pull complete 2025-09-07T06:42:43.4115987Z b14b67d48e1d: Pull complete 2025-09-07T06:42:43.4218904Z b192db73bfe1: Pull complete 2025-09-07T06:42:43.4392908Z 2150bf331086: Pull complete 2025-09-07T06:42:43.4476137Z 733c73768857: Pull complete 2025-09-07T06:43:19.7221404Z b61655c9140a: Pull complete 2025-09-07T06:43:19.7337744Z e86fed99150a: Pull complete 2025-09-07T06:43:19.7419656Z 81d56f8c0e9e: Pull complete 2025-09-07T06:43:22.6048070Z c3379e965cca: Pull complete 2025-09-07T06:43:22.6140802Z ca8d286566c5: Pull complete 2025-09-07T06:43:22.6321116Z ac22ab965410: Pull complete 2025-09-07T06:43:23.3520703Z 6fd45892b6e7: Pull complete 2025-09-07T06:43:23.3573839Z Digest: sha256:9e860fd68cd38c78fd118edc6e8b6bbf754ab60affb0c456f2b2696bd7ea79a5 2025-09-07T06:43:23.3590062Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:43:23.3608126Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:43:23.3670537Z Prepare all required actions 2025-09-07T06:43:23.3687658Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-09-07T06:43:23.3687791Z with: 2025-09-07T06:43:23.3688021Z github-token: *** 2025-09-07T06:43:23.3688114Z env: 2025-09-07T06:43:23.3688204Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:23.3688338Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:23.3690912Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:23.3691074Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:23.3691444Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:23.3691812Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:23.3691919Z AWS_REGION: us-east-1 2025-09-07T06:43:23.3692082Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:23.3692239Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:23.3694116Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:23.3694212Z ##[endgroup] 2025-09-07T06:43:23.3706717Z ##[group]Run set -eux 2025-09-07T06:43:23.3706826Z set -eux 2025-09-07T06:43:23.3706990Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T06:43:23.3712483Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:23.3712623Z env: 2025-09-07T06:43:23.3712711Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:23.3712836Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:23.3713001Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:23.3713156Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:23.3713528Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:23.3713881Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:23.3713990Z AWS_REGION: us-east-1 2025-09-07T06:43:23.3714119Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:23.3714299Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:23.3716289Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:23.3716433Z GITHUB_TOKEN: *** 2025-09-07T06:43:23.3716523Z ##[endgroup] 2025-09-07T06:43:23.3737221Z + python3 .github/scripts/get_workflow_job_id.py 17524754565 linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b 2025-09-07T06:43:24.0025975Z Setting output job-id=49774353520 2025-09-07T06:43:24.0026266Z Setting output job-name=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:24.0116550Z Prepare all required actions 2025-09-07T06:43:24.0116755Z Getting action download info 2025-09-07T06:43:24.1843096Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-09-07T06:43:24.6147187Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-07T06:43:24.9567421Z ##[group]Run ./.github/actions/download-build-artifacts 2025-09-07T06:43:24.9567566Z with: 2025-09-07T06:43:24.9567665Z name: linux-noble-rocm-py3.12-mi300 2025-09-07T06:43:24.9567789Z s3-bucket: gha-artifacts 2025-09-07T06:43:24.9567885Z env: 2025-09-07T06:43:24.9567973Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:24.9568099Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:24.9568268Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:24.9568424Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:24.9568815Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:24.9569277Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:24.9571478Z AWS_REGION: us-east-1 2025-09-07T06:43:24.9571626Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:24.9571776Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:24.9573656Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:24.9573753Z ##[endgroup] 2025-09-07T06:43:24.9596219Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:43:24.9596349Z with: 2025-09-07T06:43:24.9596448Z name: linux-noble-rocm-py3.12-mi300 2025-09-07T06:43:24.9596571Z s3-bucket: gha-artifacts 2025-09-07T06:43:24.9596674Z region: us-east-1 2025-09-07T06:43:24.9596760Z env: 2025-09-07T06:43:24.9596846Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:24.9596973Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:24.9599165Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:24.9599330Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:24.9599701Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:24.9600057Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:24.9600166Z AWS_REGION: us-east-1 2025-09-07T06:43:24.9600299Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:24.9600441Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:24.9602321Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:24.9602418Z ##[endgroup] 2025-09-07T06:43:25.1864097Z (node:4919) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:43:25.1864692Z 2025-09-07T06:43:25.1864915Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:43:25.1865499Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:43:25.1866082Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:43:25.3065756Z Found 1 objects with prefix pytorch/pytorch/17524754565/linux-noble-rocm-py3.12-mi300/ 2025-09-07T06:43:25.3066426Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:43:35.1471819Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:43:35.1475457Z Artifact download has finished successfully 2025-09-07T06:43:35.1671216Z ##[group]Run unzip -o artifacts.zip 2025-09-07T06:43:35.1671382Z unzip -o artifacts.zip 2025-09-07T06:43:35.1678660Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:35.1678803Z env: 2025-09-07T06:43:35.1678898Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:35.1679028Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:35.1679320Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:35.1679494Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:35.1679874Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:35.1683109Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:35.1683235Z AWS_REGION: us-east-1 2025-09-07T06:43:35.1683415Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:35.1683560Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:35.1685568Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:35.1685669Z ##[endgroup] 2025-09-07T06:43:35.1720639Z Archive: artifacts.zip 2025-09-07T06:43:35.1721500Z creating: dist/ 2025-09-07T06:43:36.6834315Z inflating: dist/torch-2.9.0a0+git93fb23d-cp312-cp312-linux_x86_64.whl 2025-09-07T06:43:36.6917616Z inflating: dist/.ninja_log 2025-09-07T06:43:36.6918665Z creating: build/custom_test_artifacts/ 2025-09-07T06:43:36.6919479Z creating: build/custom_test_artifacts/custom-op-build/ 2025-09-07T06:43:36.6919898Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2025-09-07T06:43:36.6920395Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/pkgRedirects/ 2025-09-07T06:43:36.6928853Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeConfigureLog.yaml 2025-09-07T06:43:36.6929306Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/ 2025-09-07T06:43:36.6929716Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/CMakeSystem.cmake 2025-09-07T06:43:36.6930161Z creating: 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build/bin/c10_bit_cast_test 2025-09-07T06:43:40.4629465Z inflating: build/bin/c10_error_test 2025-09-07T06:43:40.4665008Z inflating: build/bin/c10_exception_test 2025-09-07T06:43:40.4699509Z inflating: build/bin/c10_flags_test 2025-09-07T06:43:40.4734036Z inflating: build/bin/c10_generic_math_test 2025-09-07T06:43:40.4769223Z inflating: build/bin/c10_irange_test 2025-09-07T06:43:40.4875909Z inflating: build/bin/c10_intrusive_ptr_test 2025-09-07T06:43:40.4912245Z inflating: build/bin/c10_lazy_test 2025-09-07T06:43:40.4950978Z inflating: build/bin/c10_logging_test 2025-09-07T06:43:40.5001474Z inflating: build/bin/c10_optional_test 2025-09-07T06:43:40.5042897Z inflating: build/bin/c10_ordered_preserving_dict_test 2025-09-07T06:43:40.5079203Z inflating: build/bin/c10_registry_test 2025-09-07T06:43:40.5178839Z inflating: build/bin/c10_small_vector_test 2025-09-07T06:43:40.5217088Z inflating: build/bin/c10_string_util_test 2025-09-07T06:43:40.5251997Z inflating: build/bin/c10_ssize_test 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build/bin/c10_hip_HIPAssertionsTest_multiple_writes_from_same_block 2025-09-07T06:43:40.5657013Z inflating: build/bin/c10_hip_HIPTest 2025-09-07T06:43:40.6029925Z inflating: build/bin/vec_test_all_types_DEFAULT 2025-09-07T06:43:40.6407206Z inflating: build/bin/vec_test_all_types_AVX512 2025-09-07T06:43:40.6790376Z inflating: build/bin/vec_test_all_types_AVX2 2025-09-07T06:43:40.6825691Z inflating: build/bin/BackoffTest 2025-09-07T06:43:40.6861719Z inflating: build/bin/FileStoreTest 2025-09-07T06:43:40.6900467Z inflating: build/bin/TCPStoreTest 2025-09-07T06:43:40.6937737Z inflating: build/bin/HashStoreTest 2025-09-07T06:43:40.6982128Z inflating: build/bin/ProcessGroupGlooTest 2025-09-07T06:43:40.6983783Z inflating: build/bin/example_allreduce 2025-09-07T06:43:40.6985977Z inflating: build/bin/torch_shm_manager 2025-09-07T06:43:40.7022272Z inflating: build/bin/static_runtime_bench 2025-09-07T06:43:40.7182875Z inflating: build/bin/static_runtime_test 2025-09-07T06:43:40.7231616Z 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build/bin/op_registration_test 2025-09-07T06:43:40.9863668Z inflating: build/bin/hip_complex_test 2025-09-07T06:43:40.9899590Z inflating: build/bin/hip_apply_test 2025-09-07T06:43:40.9933268Z inflating: build/bin/hip_distributions_test 2025-09-07T06:43:40.9966702Z inflating: build/bin/hip_generator_test 2025-09-07T06:43:41.0000418Z inflating: build/bin/hip_half_test 2025-09-07T06:43:41.0033911Z inflating: build/bin/hip_integer_divider_test 2025-09-07T06:43:41.0067931Z inflating: build/bin/hip_optional_test 2025-09-07T06:43:41.0101361Z inflating: build/bin/hip_packedtensoraccessor_test 2025-09-07T06:43:41.0136496Z inflating: build/bin/hip_dlconvertor_test 2025-09-07T06:43:41.0169879Z inflating: build/bin/hip_vectorized_test 2025-09-07T06:43:41.0863170Z inflating: build/bin/test_jit 2025-09-07T06:43:41.1099999Z inflating: build/bin/test_nativert 2025-09-07T06:43:41.1137163Z inflating: build/bin/test_dist_autograd 2025-09-07T06:43:41.1182472Z inflating: build/bin/test_cpp_rpc 2025-09-07T06:43:41.1186820Z inflating: build/bin/parallel_benchmark 2025-09-07T06:43:41.1906120Z inflating: build/bin/test_api 2025-09-07T06:43:41.2123139Z inflating: build/bin/test_lazy 2025-09-07T06:43:41.2123509Z creating: .additional_ci_files/ 2025-09-07T06:43:41.2178025Z inflating: .additional_ci_files/test-times.json 2025-09-07T06:43:41.2389447Z inflating: .additional_ci_files/test-class-times.json 2025-09-07T06:43:41.2420700Z ##[group]Run rm artifacts.zip 2025-09-07T06:43:41.2420850Z rm artifacts.zip 2025-09-07T06:43:41.2426859Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:41.2427038Z env: 2025-09-07T06:43:41.2427141Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.2427273Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.2427447Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.2427615Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.2427998Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.2428371Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.2428484Z AWS_REGION: us-east-1 2025-09-07T06:43:41.2428655Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.2428859Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.2430764Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.2430869Z ##[endgroup] 2025-09-07T06:43:41.3386678Z ##[group]Run df -H 2025-09-07T06:43:41.3386830Z df -H 2025-09-07T06:43:41.3392742Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:41.3392895Z env: 2025-09-07T06:43:41.3392990Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.3393126Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.3393304Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.3393471Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.3393869Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.3394254Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.3394465Z AWS_REGION: us-east-1 2025-09-07T06:43:41.3394626Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.3394778Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.3396820Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.3396936Z ##[endgroup] 2025-09-07T06:43:41.3434063Z Filesystem Size Used Avail Use% Mounted on 2025-09-07T06:43:41.3434748Z overlay 2.2T 304G 1.9T 15% / 2025-09-07T06:43:41.3435154Z tmpfs 68M 0 68M 0% /dev 2025-09-07T06:43:41.3435490Z /dev/vda1 2.2T 304G 1.9T 15% /run 2025-09-07T06:43:41.3438582Z shm 68M 4.1k 68M 1% /dev/shm 2025-09-07T06:43:41.3439081Z tmpfs 1.4T 13k 1.4T 1% /run/secrets/kubernetes.io/serviceaccount 2025-09-07T06:43:41.3439520Z tmpfs 675G 0 675G 0% /proc/acpi 2025-09-07T06:43:41.3439874Z tmpfs 675G 0 675G 0% /proc/scsi 2025-09-07T06:43:41.3440213Z tmpfs 675G 0 675G 0% /sys/firmware 2025-09-07T06:43:41.3462628Z Prepare all required actions 2025-09-07T06:43:41.3462843Z Getting action download info 2025-09-07T06:43:41.4866736Z ##[group]Run ./.github/actions/download-td-artifacts 2025-09-07T06:43:41.4866876Z with: 2025-09-07T06:43:41.4869350Z env: 2025-09-07T06:43:41.4869444Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.4869582Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.4869747Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.4869901Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.4870268Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.4870618Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.4870725Z AWS_REGION: us-east-1 2025-09-07T06:43:41.4870852Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.4871004Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.4875141Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.4875250Z ##[endgroup] 2025-09-07T06:43:41.4893309Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:43:41.4893436Z with: 2025-09-07T06:43:41.4893518Z name: td_results 2025-09-07T06:43:41.4893614Z s3-bucket: gha-artifacts 2025-09-07T06:43:41.4893712Z region: us-east-1 2025-09-07T06:43:41.4893795Z env: 2025-09-07T06:43:41.4896264Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.4896401Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.4896568Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.4896724Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.4897089Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.4897444Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.4897551Z AWS_REGION: us-east-1 2025-09-07T06:43:41.4897674Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.4897815Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.4899683Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.4899778Z ##[endgroup] 2025-09-07T06:43:41.7172697Z (node:4938) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:43:41.7172886Z 2025-09-07T06:43:41.7172965Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:43:41.7173173Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:43:41.7173385Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:43:41.8347157Z Found 1 objects with prefix pytorch/pytorch/17524754565/td_results/ 2025-09-07T06:43:41.8347384Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:43:41.9609928Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:43:41.9612617Z Artifact download has finished successfully 2025-09-07T06:43:41.9762731Z ##[group]Run mkdir -p .additional_ci_files 2025-09-07T06:43:41.9762914Z mkdir -p .additional_ci_files 2025-09-07T06:43:41.9763117Z mv td_results.json .additional_ci_files/td_results.json || true 2025-09-07T06:43:41.9770372Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:41.9770511Z env: 2025-09-07T06:43:41.9770601Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.9770733Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.9770904Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.9771067Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.9771576Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.9771940Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.9772053Z AWS_REGION: us-east-1 2025-09-07T06:43:41.9772186Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.9774233Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.9776449Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.9776555Z ##[endgroup] 2025-09-07T06:43:41.9845335Z ##[group]Run .github/scripts/parse_ref.py 2025-09-07T06:43:41.9845478Z .github/scripts/parse_ref.py 2025-09-07T06:43:41.9850833Z shell: /usr/bin/bash -e {0} 2025-09-07T06:43:41.9850937Z env: 2025-09-07T06:43:41.9851027Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:41.9851160Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:41.9851334Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:41.9851491Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:41.9855014Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:41.9855371Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:41.9855478Z AWS_REGION: us-east-1 2025-09-07T06:43:41.9855605Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:41.9855768Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:41.9857636Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:41.9857731Z ##[endgroup] 2025-09-07T06:43:41.9974329Z Setting output branch=main 2025-09-07T06:43:42.0039792Z Prepare all required actions 2025-09-07T06:43:42.0039966Z Getting action download info 2025-09-07T06:43:42.1268858Z ##[group]Run ./.github/actions/filter-test-configs 2025-09-07T06:43:42.1268988Z with: 2025-09-07T06:43:42.1269183Z github-token: *** 2025-09-07T06:43:42.1269759Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}]} 2025-09-07T06:43:42.1270535Z job-name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:42.1270723Z env: 2025-09-07T06:43:42.1270810Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:42.1270938Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:42.1271103Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:42.1271258Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:42.1271627Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:42.1272061Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:42.1272168Z AWS_REGION: us-east-1 2025-09-07T06:43:42.1272280Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:42.1272444Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:42.1274327Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:42.1274652Z ##[endgroup] 2025-09-07T06:43:42.1289029Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T06:43:42.1289149Z with: 2025-09-07T06:43:42.1289233Z shell: bash 2025-09-07T06:43:42.1289323Z timeout_minutes: 10 2025-09-07T06:43:42.1289417Z max_attempts: 5 2025-09-07T06:43:42.1289512Z retry_wait_seconds: 30 2025-09-07T06:43:42.1289795Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-09-07T06:43:42.1290086Z polling_interval_seconds: 1 2025-09-07T06:43:42.1290193Z warning_on_retry: true 2025-09-07T06:43:42.1290296Z continue_on_error: false 2025-09-07T06:43:42.1290397Z env: 2025-09-07T06:43:42.1290484Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:42.1290626Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:42.1290795Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:42.1290956Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:42.1291322Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:42.1291672Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:42.1291781Z AWS_REGION: us-east-1 2025-09-07T06:43:42.1291906Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:42.1292047Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:42.1293927Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:42.1294071Z GITHUB_TOKEN: *** 2025-09-07T06:43:42.1294164Z ##[endgroup] 2025-09-07T06:43:42.1698948Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-09-07T06:43:42.3118001Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T06:43:42.4783353Z Collecting requests==2.27.1 2025-09-07T06:43:42.6162519Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-09-07T06:43:42.6697114Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.1/63.1 KB 1.0 MB/s eta 0:00:00 2025-09-07T06:43:42.7486228Z Collecting pyyaml==6.0.2 2025-09-07T06:43:42.7732600Z Downloading PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (751 kB) 2025-09-07T06:43:42.9090142Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 751.2/751.2 KB 5.5 MB/s eta 0:00:00 2025-09-07T06:43:42.9961857Z Collecting urllib3<1.27,>=1.21.1 2025-09-07T06:43:43.0194860Z Downloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB) 2025-09-07T06:43:43.0651730Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 144.2/144.2 KB 3.0 MB/s eta 0:00:00 2025-09-07T06:43:43.1310741Z Collecting idna<4,>=2.5 2025-09-07T06:43:43.1531361Z Downloading idna-3.10-py3-none-any.whl (70 kB) 2025-09-07T06:43:43.1670113Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 70.4/70.4 KB 4.7 MB/s eta 0:00:00 2025-09-07T06:43:43.2958076Z Collecting charset-normalizer~=2.0.0 2025-09-07T06:43:43.3177307Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-09-07T06:43:43.3579969Z Collecting certifi>=2017.4.17 2025-09-07T06:43:43.3813537Z Downloading certifi-2025.8.3-py3-none-any.whl (161 kB) 2025-09-07T06:43:43.3836620Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 161.2/161.2 KB 85.9 MB/s eta 0:00:00 2025-09-07T06:43:43.4361279Z Installing collected packages: urllib3, pyyaml, idna, charset-normalizer, certifi, requests 2025-09-07T06:43:43.5330260Z WARNING: The script normalizer is installed in '/home/runner/.local/bin' which is not on PATH. 2025-09-07T06:43:43.5331917Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-09-07T06:43:43.5536041Z Successfully installed certifi-2025.8.3 charset-normalizer-2.0.12 idna-3.10 pyyaml-6.0.2 requests-2.27.1 urllib3-1.26.20 2025-09-07T06:43:44.1696045Z Command completed after 1 attempt(s). 2025-09-07T06:43:44.1751210Z ##[group]Run set -x 2025-09-07T06:43:44.1751413Z set -x 2025-09-07T06:43:44.1751556Z  2025-09-07T06:43:44.1751930Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:43:44.1752225Z # in runner workspace 2025-09-07T06:43:44.1752456Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-09-07T06:43:44.1759488Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:44.1759656Z env: 2025-09-07T06:43:44.1759762Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:44.1759917Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:44.1767103Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:44.1767274Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:44.1767652Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:44.1768027Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:44.1768143Z AWS_REGION: us-east-1 2025-09-07T06:43:44.1768297Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:44.1768447Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:44.1770333Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:44.1770438Z ##[endgroup] 2025-09-07T06:43:44.1793919Z + python3 /home/runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-09-07T06:43:44.1879222Z Setting output branch=main 2025-09-07T06:43:44.1929128Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:43:44.1929338Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:43:44.1932320Z echo "Job name: ${JOB_NAME}" 2025-09-07T06:43:44.1932448Z  2025-09-07T06:43:44.1932592Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:43:44.1932771Z # in runner workspace 2025-09-07T06:43:44.1932929Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-09-07T06:43:44.1933127Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-09-07T06:43:44.1933253Z  --job-name "${JOB_NAME}" \ 2025-09-07T06:43:44.1934040Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}]}" \ 2025-09-07T06:43:44.1934745Z  --selected-test-configs "" \ 2025-09-07T06:43:44.1934872Z  --pr-number "${PR_NUMBER}" \ 2025-09-07T06:43:44.1934988Z  --tag "${TAG}" \ 2025-09-07T06:43:44.1935230Z  --event-name "${EVENT_NAME}" \ 2025-09-07T06:43:44.1935350Z  --schedule "${SCHEDULE}" \ 2025-09-07T06:43:44.1935463Z  --branch "${HEAD_BRANCH}" 2025-09-07T06:43:44.1941167Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:44.1941308Z env: 2025-09-07T06:43:44.1941394Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:44.1941522Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:44.1941687Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:44.1941846Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:44.1942213Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:44.1942567Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:44.1942681Z AWS_REGION: us-east-1 2025-09-07T06:43:44.1942861Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:44.1943020Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:44.1944991Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:44.1945237Z GITHUB_TOKEN: *** 2025-09-07T06:43:44.1945418Z JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:44.1945603Z PR_NUMBER: 2025-09-07T06:43:44.1945692Z TAG: 2025-09-07T06:43:44.1945775Z EVENT_NAME: push 2025-09-07T06:43:44.1945864Z SCHEDULE: 2025-09-07T06:43:44.1945947Z HEAD_BRANCH: main 2025-09-07T06:43:44.1948234Z ##[endgroup] 2025-09-07T06:43:44.1972111Z Workflow: rocm-mi300 2025-09-07T06:43:44.1972530Z Job name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:44.4084923Z Setting output keep-going=True 2025-09-07T06:43:44.4085123Z Setting output ci-verbose-test-logs=False 2025-09-07T06:43:44.4085890Z Setting output ci-test-showlocals=False 2025-09-07T06:43:44.4086325Z Setting output ci-no-test-timeout=False 2025-09-07T06:43:44.4086695Z Setting output ci-no-td=False 2025-09-07T06:43:44.4087029Z Setting output ci-td-distributed=False 2025-09-07T06:43:44.4087376Z Setting output is-unstable=False 2025-09-07T06:43:44.4087711Z Setting output reenabled-issues= 2025-09-07T06:43:44.4089527Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}]} 2025-09-07T06:43:44.4091326Z Setting output is-test-matrix-empty=False 2025-09-07T06:43:44.4213103Z ##[group]Run echo "Filtered matrix:" 2025-09-07T06:43:44.4213267Z echo "Filtered matrix:" 2025-09-07T06:43:44.4214085Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.gfx942.1"}]}" 2025-09-07T06:43:44.4214908Z  2025-09-07T06:43:44.4218276Z echo 2025-09-07T06:43:44.4218395Z echo "Is the current job unstable? False" 2025-09-07T06:43:44.4218558Z  2025-09-07T06:43:44.4218636Z echo 2025-09-07T06:43:44.4218733Z echo "Is keep-going label set? True" 2025-09-07T06:43:44.4218852Z  2025-09-07T06:43:44.4219076Z echo 2025-09-07T06:43:44.4219167Z echo "Reenabled issues? " 2025-09-07T06:43:44.4224323Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:44.4226638Z env: 2025-09-07T06:43:44.4226741Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:44.4226870Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:44.4227035Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:44.4227192Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:44.4227558Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:44.4227906Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:44.4228013Z AWS_REGION: us-east-1 2025-09-07T06:43:44.4228164Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:44.4228308Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:44.4230222Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:44.4232197Z ##[endgroup] 2025-09-07T06:43:44.4255333Z Filtered matrix: 2025-09-07T06:43:44.4256079Z {include: [{config: default, shard: 1, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}, {config: default, shard: 2, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}, {config: default, shard: 3, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}, {config: default, shard: 4, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}, {config: default, shard: 5, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}, {config: default, shard: 6, num_shards: 6, runner: linux.rocm.gpu.gfx942.1}]} 2025-09-07T06:43:44.4256897Z 2025-09-07T06:43:44.4256983Z Is the current job unstable? False 2025-09-07T06:43:44.4257088Z 2025-09-07T06:43:44.4257146Z Is keep-going label set? True 2025-09-07T06:43:44.4257241Z 2025-09-07T06:43:44.4257288Z Reenabled issues? 2025-09-07T06:43:44.4294131Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:43:44.4296867Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:43:44.4301175Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:43:44.4301312Z env: 2025-09-07T06:43:44.4301398Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:44.4301523Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:44.4301686Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:44.4301844Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:44.4302211Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:44.4302562Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:44.4302669Z AWS_REGION: us-east-1 2025-09-07T06:43:44.4302815Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:44.4302959Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:44.4304934Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:44.4305035Z JOB_TIMEOUT: 300 2025-09-07T06:43:44.4305131Z ##[endgroup] 2025-09-07T06:43:44.4360802Z ##[group]Run set -x 2025-09-07T06:43:44.4360925Z set -x 2025-09-07T06:43:44.4361010Z  2025-09-07T06:43:44.4361111Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-09-07T06:43:44.4361259Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-09-07T06:43:44.4361408Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-09-07T06:43:44.4361542Z  TEST_COMMAND=.ci/caffe2/test.sh 2025-09-07T06:43:44.4363605Z else 2025-09-07T06:43:44.4363712Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:43:44.4363826Z fi 2025-09-07T06:43:44.4363903Z  2025-09-07T06:43:44.4364030Z # detached container should get cleaned up by teardown_ec2_linux 2025-09-07T06:43:44.4364220Z # TODO: Stop building test binaries as part of the build phase 2025-09-07T06:43:44.4364522Z # Used for GPU_FLAG since that doesn't play nice 2025-09-07T06:43:44.4364814Z # shellcheck disable=SC2086,SC2090 2025-09-07T06:43:44.4364941Z container_name=$(docker run \ 2025-09-07T06:43:44.4365063Z  ${GPU_FLAG:-} \ 2025-09-07T06:43:44.4365173Z  -e BUILD_ENVIRONMENT \ 2025-09-07T06:43:44.4365288Z  -e PR_NUMBER \ 2025-09-07T06:43:44.4365395Z  -e GITHUB_ACTIONS \ 2025-09-07T06:43:44.4365503Z  -e GITHUB_REPOSITORY \ 2025-09-07T06:43:44.4365615Z  -e GITHUB_WORKFLOW \ 2025-09-07T06:43:44.4365724Z  -e GITHUB_JOB \ 2025-09-07T06:43:44.4365828Z  -e GITHUB_RUN_ID \ 2025-09-07T06:43:44.4365932Z  -e GITHUB_RUN_NUMBER \ 2025-09-07T06:43:44.4366042Z  -e GITHUB_RUN_ATTEMPT \ 2025-09-07T06:43:44.4366151Z  -e JOB_ID \ 2025-09-07T06:43:44.4366250Z  -e JOB_NAME \ 2025-09-07T06:43:44.4366349Z  -e BRANCH \ 2025-09-07T06:43:44.4366444Z  -e SHA1 \ 2025-09-07T06:43:44.4366544Z  -e AWS_DEFAULT_REGION \ 2025-09-07T06:43:44.4366661Z  -e IN_WHEEL_TEST \ 2025-09-07T06:43:44.4368466Z  -e SHARD_NUMBER \ 2025-09-07T06:43:44.4368573Z  -e TEST_CONFIG \ 2025-09-07T06:43:44.4368677Z  -e NUM_TEST_SHARDS \ 2025-09-07T06:43:44.4368786Z  -e REENABLED_ISSUES \ 2025-09-07T06:43:44.4368899Z  -e CONTINUE_THROUGH_ERROR \ 2025-09-07T06:43:44.4369014Z  -e VERBOSE_TEST_LOGS \ 2025-09-07T06:43:44.4369123Z  -e TEST_SHOWLOCALS \ 2025-09-07T06:43:44.4369228Z  -e NO_TEST_TIMEOUT \ 2025-09-07T06:43:44.4369329Z  -e NO_TD \ 2025-09-07T06:43:44.4369438Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-09-07T06:43:44.4369571Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-09-07T06:43:44.4371201Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-09-07T06:43:44.4371329Z  -e TESTS_TO_INCLUDE \ 2025-09-07T06:43:44.4371445Z  -e DASHBOARD_TAG \ 2025-09-07T06:43:44.4371584Z  --env-file="${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" \ 2025-09-07T06:43:44.4371733Z  --ulimit stack=10485760:83886080 \ 2025-09-07T06:43:44.4371849Z  --ulimit core=0 \ 2025-09-07T06:43:44.4371964Z  --security-opt seccomp=unconfined \ 2025-09-07T06:43:44.4372089Z  --cap-add=SYS_PTRACE \ 2025-09-07T06:43:44.4372200Z  --shm-size="8g" \ 2025-09-07T06:43:44.4372298Z  --tty \ 2025-09-07T06:43:44.4372388Z  --detach \ 2025-09-07T06:43:44.4373947Z  --name="${container_name}" \ 2025-09-07T06:43:44.4374064Z  --user jenkins \ 2025-09-07T06:43:44.4374197Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-09-07T06:43:44.4374344Z  -w /var/lib/jenkins/workspace \ 2025-09-07T06:43:44.4374565Z  "${DOCKER_IMAGE}" 2025-09-07T06:43:44.4374662Z ) 2025-09-07T06:43:44.4374757Z # save container name for later step 2025-09-07T06:43:44.4375006Z echo "CONTAINER_NAME=${container_name}" >> "$GITHUB_ENV" 2025-09-07T06:43:44.4375265Z # jenkins user does not have write permission to mounted workspace; work-around by copying within container to jenkins home 2025-09-07T06:43:44.4375594Z docker exec -t "${container_name}" sh -c "cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && ${TEST_COMMAND}" 2025-09-07T06:43:44.4381040Z shell: /usr/bin/bash -e {0} 2025-09-07T06:43:44.4381146Z env: 2025-09-07T06:43:44.4381231Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:43:44.4381359Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T06:43:44.4381526Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T06:43:44.4381679Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T06:43:44.4382049Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T06:43:44.4382478Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:43:44.4382584Z AWS_REGION: us-east-1 2025-09-07T06:43:44.4382710Z AWS_ACCESS_KEY_ID: *** 2025-09-07T06:43:44.4384792Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T06:43:44.4386716Z AWS_SESSION_TOKEN: *** 2025-09-07T06:43:44.4386839Z BUILD_ENVIRONMENT: linux-noble-rocm-py3.12-mi300 2025-09-07T06:43:44.4386966Z PR_NUMBER: 2025-09-07T06:43:44.4387060Z GITHUB_REPOSITORY: pytorch/pytorch 2025-09-07T06:43:44.4387177Z GITHUB_WORKFLOW: rocm-mi300 2025-09-07T06:43:44.4387281Z GITHUB_JOB: test 2025-09-07T06:43:44.4387370Z GITHUB_RUN_ID: 17524754565 2025-09-07T06:43:44.4387472Z GITHUB_RUN_NUMBER: 9398 2025-09-07T06:43:44.4387570Z GITHUB_RUN_ATTEMPT: 1 2025-09-07T06:43:44.4387662Z JOB_ID: 49774353520 2025-09-07T06:43:44.4389435Z JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:44.4389617Z BRANCH: main 2025-09-07T06:43:44.4389719Z SHA1: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:44.4389844Z CONTINUE_THROUGH_ERROR: True 2025-09-07T06:43:44.4389950Z VERBOSE_TEST_LOGS: False 2025-09-07T06:43:44.4390049Z TEST_SHOWLOCALS: False 2025-09-07T06:43:44.4390145Z NO_TEST_TIMEOUT: False 2025-09-07T06:43:44.4390236Z NO_TD: False 2025-09-07T06:43:44.4390317Z TEST_CONFIG: default 2025-09-07T06:43:44.4390407Z SHARD_NUMBER: 1 2025-09-07T06:43:44.4390493Z NUM_TEST_SHARDS: 6 2025-09-07T06:43:44.4392157Z REENABLED_ISSUES: 2025-09-07T06:43:44.4392421Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:43:44.4392699Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-09-07T06:43:44.4392821Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-09-07T06:43:44.4392932Z TESTS_TO_INCLUDE: 2025-09-07T06:43:44.4393021Z DASHBOARD_TAG: 2025-09-07T06:43:44.4393111Z ##[endgroup] 2025-09-07T06:43:44.4412870Z + [[ default == \m\u\l\t\i\g\p\u ]] 2025-09-07T06:43:44.4413088Z + [[ linux-noble-rocm-py3.12-mi300 == *onnx* ]] 2025-09-07T06:43:44.4413251Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:43:44.4421333Z +++ nproc --ignore=2 2025-09-07T06:43:44.4432068Z ++ docker run --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --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=158 -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_17524754565 --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-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:43:46.5157770Z + container_name=d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T06:43:46.5158090Z + echo CONTAINER_NAME=d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T06:43:46.5159244Z + docker exec -t d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 sh -c 'cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && .ci/pytorch/test.sh' 2025-09-07T06:43:50.6599987Z Processing ./dist/torch-2.9.0a0+git93fb23d-cp312-cp312-linux_x86_64.whl 2025-09-07T06:43:50.9319536Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (3.19.1) 2025-09-07T06:43:50.9320468Z 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+git93fb23d) (4.15.0) 2025-09-07T06:43:50.9321273Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (80.9.0) 2025-09-07T06:43:50.9322011Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (1.13.3) 2025-09-07T06:43:50.9322746Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (2.8.8) 2025-09-07T06:43:50.9323466Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (3.1.6) 2025-09-07T06:43:50.9324203Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git93fb23d) (2025.7.0) 2025-09-07T06:43:50.9367849Z 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+git93fb23d) (1.3.0) 2025-09-07T06:43:50.9381558Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.9.0a0+git93fb23d) (3.0.2) 2025-09-07T06:43:51.0413351Z Installing collected packages: torch 2025-09-07T06:43:56.1065335Z 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. 2025-09-07T06:43:56.1065741Z helion 0.1.3 requires filecheck, which is not installed. 2025-09-07T06:43:56.1065950Z Successfully installed torch-2.9.0a0+git93fb23d 2025-09-07T06:43:56.1483354Z + export TERM=vt100 2025-09-07T06:43:56.1483541Z + TERM=vt100 2025-09-07T06:43:56.1486813Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:43:56.1497787Z + source .ci/pytorch/common.sh 2025-09-07T06:43:56.1502583Z +++ dirname .ci/pytorch/common.sh 2025-09-07T06:43:56.1513672Z ++ source .ci/pytorch/common_utils.sh 2025-09-07T06:43:56.1520953Z +++ declare -f -t trap_add 2025-09-07T06:43:56.1521789Z ++ set -ex -o pipefail 2025-09-07T06:43:56.1522023Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-09-07T06:43:56.1522208Z ++ unset HIP_PLATFORM 2025-09-07T06:43:56.1522348Z ++ export PYTORCH_TEST_WITH_ROCM=1 2025-09-07T06:43:56.1522501Z ++ PYTORCH_TEST_WITH_ROCM=1 2025-09-07T06:43:56.1522633Z ++ BUILD_TEST_LIBTORCH=0 2025-09-07T06:43:56.1522775Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:43:56.1525526Z + source .ci/pytorch/common-build.sh 2025-09-07T06:43:56.1526777Z ++ [[ linux-noble-rocm-py3.12-mi300 != *win-* ]] 2025-09-07T06:43:56.1532689Z ++++ dirname .ci/pytorch/common-build.sh 2025-09-07T06:43:56.1540461Z +++ cd .ci/pytorch 2025-09-07T06:43:56.1541092Z +++ pwd -P 2025-09-07T06:43:56.1543042Z ++ script_dir=/var/lib/jenkins/pytorch/.ci/pytorch 2025-09-07T06:43:56.1543257Z ++ [[ linux-noble-rocm-py3.12-mi300 == *-pch* ]] 2025-09-07T06:43:56.1543390Z ++ which sccache 2025-09-07T06:43:56.1551320Z ++ [[ -z '' ]] 2025-09-07T06:43:56.1551496Z ++ unset SCCACHE_BUCKET 2025-09-07T06:43:56.1551671Z ++ unset SCCACHE_REGION 2025-09-07T06:43:56.1551796Z ++ sccache --stop-server 2025-09-07T06:43:56.1569664Z ++ true 2025-09-07T06:43:56.1569874Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-09-07T06:43:56.1577621Z ++ trap_add sccache_epilogue EXIT 2025-09-07T06:43:56.1577844Z ++ trap_add_cmd=sccache_epilogue 2025-09-07T06:43:56.1577954Z ++ shift 2025-09-07T06:43:56.1578057Z ++ for trap_add_name in "$@" 2025-09-07T06:43:56.1584076Z ++++ trap -p EXIT 2025-09-07T06:43:56.1586488Z +++ eval 'extract_trap_cmd ' 2025-09-07T06:43:56.1586667Z ++++ extract_trap_cmd 2025-09-07T06:43:56.1586766Z ++++ printf '%s\n' '' 2025-09-07T06:43:56.1587025Z +++ printf '%s\n' sccache_epilogue 2025-09-07T06:43:56.1588703Z ++ trap -- ' 2025-09-07T06:43:56.1588882Z sccache_epilogue' EXIT 2025-09-07T06:43:56.1588996Z ++ [[ -n '' ]] 2025-09-07T06:43:56.1589107Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-09-07T06:43:56.1589255Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:43:56.1594072Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:43:56.1594185Z ++ sccache --start-server 2025-09-07T06:43:56.1607699Z sccache: Starting the server... 2025-09-07T06:43:56.1841813Z sccache: Listening on address 127.0.0.1:4226 2025-09-07T06:43:56.1851403Z ++ sccache --zero-stats 2025-09-07T06:43:56.1869446Z Statistics zeroed. 2025-09-07T06:43:56.1869608Z ++ which ccache 2025-09-07T06:43:56.1877583Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]] 2025-09-07T06:43:56.1877877Z + echo 'Environment variables:' 2025-09-07T06:43:56.1878010Z Environment variables: 2025-09-07T06:43:56.1882331Z + env 2025-09-07T06:43:56.1885107Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch 2025-09-07T06:43:56.1885274Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:43:56.1885500Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300 2025-09-07T06:43:56.1885662Z HOSTNAME=linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b 2025-09-07T06:43:56.1885911Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1886107Z GITHUB_ACTION=__self 2025-09-07T06:43:56.1886215Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:43:56.1886333Z GITHUB_RUN_NUMBER=9398 2025-09-07T06:43:56.1886443Z TEST_CONFIG=default 2025-09-07T06:43:56.1889376Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:43:56.1889525Z AWS_DEFAULT_REGION=us-east-1 2025-09-07T06:43:56.1889647Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:43:56.1889772Z GITHUB_REF_TYPE=branch 2025-09-07T06:43:56.1890046Z *** 2025-09-07T06:43:56.1890134Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:43:56.1890238Z GITHUB_ACTIONS=true 2025-09-07T06:43:56.1890341Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1890489Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1890685Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main 2025-09-07T06:43:56.1890859Z UCC_HOME=/usr 2025-09-07T06:43:56.1892523Z VERBOSE_TEST_LOGS=False 2025-09-07T06:43:56.1892627Z GITHUB_REF=refs/heads/main 2025-09-07T06:43:56.1892723Z SHARD_NUMBER=1 2025-09-07T06:43:56.1892809Z GITHUB_REF_PROTECTED=true 2025-09-07T06:43:56.1892938Z HOME=/var/lib/jenkins 2025-09-07T06:43:56.1893045Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:43:56.1893168Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:43:56.1893278Z LANG=C.UTF-8 2025-09-07T06:43:56.1893382Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6 2025-09-07T06:43:56.1893511Z PYTORCH_TEST_WITH_ROCM=1 2025-09-07T06:43:56.1893608Z NUM_TEST_SHARDS=6 2025-09-07T06:43:56.1893695Z UCX_HOME=/usr 2025-09-07T06:43:56.1893878Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1894580Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:56.1894766Z MAGMA_HOME=/opt/rocm/magma 2025-09-07T06:43:56.1896270Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1896501Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:43:56.1896650Z GITHUB_EVENT_NAME=push 2025-09-07T06:43:56.1896792Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0 2025-09-07T06:43:56.1896941Z DASHBOARD_TAG= 2025-09-07T06:43:56.1897027Z GITHUB_RUN_ID=17524754565 2025-09-07T06:43:56.1897223Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1897433Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:43:56.1897534Z PR_NUMBER= 2025-09-07T06:43:56.1897615Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:43:56.1897827Z ANACONDA_PYTHON_VERSION=3.12 2025-09-07T06:43:56.1897955Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:43:56.1898080Z TERM=vt100 2025-09-07T06:43:56.1899423Z INSTALLED_VISION=yes 2025-09-07T06:43:56.1899515Z BRANCH=main 2025-09-07T06:43:56.1899601Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:43:56.1899736Z TESTS_TO_INCLUDE= 2025-09-07T06:43:56.1899888Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm 2025-09-07T06:43:56.1900067Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:43:56.1900187Z PYTORCH_ROCM_ARCH=gfx90a;gfx942 2025-09-07T06:43:56.1900308Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d 2025-09-07T06:43:56.1900428Z REENABLED_ISSUES= 2025-09-07T06:43:56.1900510Z SHLVL=1 2025-09-07T06:43:56.1900584Z MAX_JOBS=158 2025-09-07T06:43:56.1900668Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:43:56.1900795Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1900930Z GITHUB_REF_NAME=main 2025-09-07T06:43:56.1901020Z ROCM_PATH=/opt/rocm 2025-09-07T06:43:56.1901115Z GITHUB_JOB=test 2025-09-07T06:43:56.1902381Z NO_TEST_TIMEOUT=False 2025-09-07T06:43:56.1902485Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:43:56.1902592Z LC_ALL=C.UTF-8 2025-09-07T06:43:56.1902678Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:43:56.1902775Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:43:56.1902873Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:43:56.1903213Z 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 2025-09-07T06:43:56.1903550Z GITHUB_BASE_REF= 2025-09-07T06:43:56.1903631Z CI=true 2025-09-07T06:43:56.1903716Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:43:56.1903817Z JOB_ID=49774353520 2025-09-07T06:43:56.1903900Z GITHUB_HEAD_REF= 2025-09-07T06:43:56.1903982Z GITHUB_ACTION_REF= 2025-09-07T06:43:56.1904069Z TEST_SHOWLOCALS=False 2025-09-07T06:43:56.1905390Z GITHUB_WORKFLOW=rocm-mi300 2025-09-07T06:43:56.1905498Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:43:56.1905696Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1905889Z NO_TD=False 2025-09-07T06:43:56.1905971Z OLDPWD=/var/lib/jenkins 2025-09-07T06:43:56.1906061Z _=/usr/bin/env 2025-09-07T06:43:56.1906180Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-09-07T06:43:56.1953979Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2025-09-07T06:43:56.1954210Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T06:43:56.1954492Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2025-09-07T06:43:56.1954699Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2025-09-07T06:43:56.1954853Z + BUILD_DIR=build 2025-09-07T06:43:56.1954946Z + BUILD_RENAMED_DIR=build_renamed 2025-09-07T06:43:56.1955056Z + BUILD_BIN_DIR=build/bin 2025-09-07T06:43:56.1955152Z + SHARD_NUMBER=1 2025-09-07T06:43:56.1955241Z + NUM_TEST_SHARDS=6 2025-09-07T06:43:56.1957051Z + export TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:43:56.1957181Z + TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:43:56.1957284Z + export VALGRIND=ON 2025-09-07T06:43:56.1957375Z + VALGRIND=ON 2025-09-07T06:43:56.1957481Z + [[ linux-noble-rocm-py3.12-mi300 == *clang9* ]] 2025-09-07T06:43:56.1957621Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]] 2025-09-07T06:43:56.1957736Z + detect_cuda_arch 2025-09-07T06:43:56.1957840Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]] 2025-09-07T06:43:56.1957974Z + [[ linux-noble-rocm-py3.12-mi300 == *s390x* ]] 2025-09-07T06:43:56.1958087Z + [[ 0 == \1 ]] 2025-09-07T06:43:56.1958167Z + [[ True == \1 ]] 2025-09-07T06:43:56.1958265Z + [[ linux-noble-rocm-py3.12-mi300 != *bazel* ]] 2025-09-07T06:43:56.1959970Z ++ realpath build/custom_test_artifacts 2025-09-07T06:43:56.1969907Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/pytorch/build/custom_test_artifacts 2025-09-07T06:43:56.1970150Z + [[ -n '' ]] 2025-09-07T06:43:56.1970239Z + echo 'Environment variables' 2025-09-07T06:43:56.1970347Z Environment variables 2025-09-07T06:43:56.1970439Z + env 2025-09-07T06:43:56.1977811Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch 2025-09-07T06:43:56.1978073Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:43:56.1978225Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300 2025-09-07T06:43:56.1978391Z HOSTNAME=linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b 2025-09-07T06:43:56.1978629Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1978834Z GITHUB_ACTION=__self 2025-09-07T06:43:56.1978948Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:43:56.1979067Z GITHUB_RUN_NUMBER=9398 2025-09-07T06:43:56.1979167Z TEST_CONFIG=default 2025-09-07T06:43:56.1979271Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:43:56.1979397Z AWS_DEFAULT_REGION=us-east-1 2025-09-07T06:43:56.1979519Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:43:56.1979664Z GITHUB_REF_TYPE=branch 2025-09-07T06:43:56.1979882Z *** 2025-09-07T06:43:56.1979978Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:43:56.1980087Z GITHUB_ACTIONS=true 2025-09-07T06:43:56.1980197Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1980359Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1983146Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main 2025-09-07T06:43:56.1983324Z UCC_HOME=/usr 2025-09-07T06:43:56.1983416Z TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:43:56.1983520Z VERBOSE_TEST_LOGS=False 2025-09-07T06:43:56.1983617Z GITHUB_REF=refs/heads/main 2025-09-07T06:43:56.1983713Z SHARD_NUMBER=1 2025-09-07T06:43:56.1983801Z GITHUB_REF_PROTECTED=true 2025-09-07T06:43:56.1983900Z HOME=/var/lib/jenkins 2025-09-07T06:43:56.1984008Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:43:56.1984131Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:43:56.1984240Z LANG=C.UTF-8 2025-09-07T06:43:56.1984345Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6 2025-09-07T06:43:56.1984691Z PYTORCH_TEST_WITH_ROCM=1 2025-09-07T06:43:56.1984840Z NUM_TEST_SHARDS=6 2025-09-07T06:43:56.1984962Z UCX_HOME=/usr 2025-09-07T06:43:56.1985220Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1985617Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T06:43:56.1985883Z MAGMA_HOME=/opt/rocm/magma 2025-09-07T06:43:56.1986155Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1986509Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:43:56.1986735Z GITHUB_EVENT_NAME=push 2025-09-07T06:43:56.1986955Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0 2025-09-07T06:43:56.1987113Z DASHBOARD_TAG= 2025-09-07T06:43:56.1987207Z GITHUB_RUN_ID=17524754565 2025-09-07T06:43:56.1987409Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1987774Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:43:56.1987884Z PR_NUMBER= 2025-09-07T06:43:56.1988000Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:43:56.1988092Z VALGRIND=ON 2025-09-07T06:43:56.1988178Z ANACONDA_PYTHON_VERSION=3.12 2025-09-07T06:43:56.1988308Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:43:56.1990366Z TERM=vt100 2025-09-07T06:43:56.1990464Z INSTALLED_VISION=yes 2025-09-07T06:43:56.1990561Z BRANCH=main 2025-09-07T06:43:56.1990650Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:43:56.1990754Z TESTS_TO_INCLUDE= 2025-09-07T06:43:56.1990907Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm 2025-09-07T06:43:56.1991088Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:43:56.1991208Z PYTORCH_ROCM_ARCH=gfx90a;gfx942 2025-09-07T06:43:56.1991331Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d 2025-09-07T06:43:56.1991451Z REENABLED_ISSUES= 2025-09-07T06:43:56.1991697Z SHLVL=1 2025-09-07T06:43:56.1991774Z MAX_JOBS=158 2025-09-07T06:43:56.1991864Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:43:56.1991991Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:43:56.1992127Z GITHUB_REF_NAME=main 2025-09-07T06:43:56.1992217Z ROCM_PATH=/opt/rocm 2025-09-07T06:43:56.1992304Z GITHUB_JOB=test 2025-09-07T06:43:56.1992389Z NO_TEST_TIMEOUT=False 2025-09-07T06:43:56.1992488Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:43:56.1992595Z LC_ALL=C.UTF-8 2025-09-07T06:43:56.1992683Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:43:56.1992780Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:43:56.1992878Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:43:56.1993218Z 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 2025-09-07T06:43:56.1993556Z GITHUB_BASE_REF= 2025-09-07T06:43:56.1993637Z CI=true 2025-09-07T06:43:56.1995379Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:43:56.1995496Z JOB_ID=49774353520 2025-09-07T06:43:56.1995582Z GITHUB_HEAD_REF= 2025-09-07T06:43:56.1995665Z GITHUB_ACTION_REF= 2025-09-07T06:43:56.1995752Z TEST_SHOWLOCALS=False 2025-09-07T06:43:56.1995847Z GITHUB_WORKFLOW=rocm-mi300 2025-09-07T06:43:56.1995951Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:43:56.1996148Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_edab8407-e6cd-4535-89d3-7f91dda61fcb 2025-09-07T06:43:56.1996342Z NO_TD=False 2025-09-07T06:43:56.1996423Z OLDPWD=/var/lib/jenkins 2025-09-07T06:43:56.1996514Z _=/usr/bin/env 2025-09-07T06:43:56.1997855Z + echo 'Testing pytorch' 2025-09-07T06:43:56.1997953Z Testing pytorch 2025-09-07T06:43:56.1998040Z + export LANG=C.UTF-8 2025-09-07T06:43:56.1998129Z + LANG=C.UTF-8 2025-09-07T06:43:56.1998210Z + PR_NUMBER= 2025-09-07T06:43:56.1998298Z + [[ default == \d\e\f\a\u\l\t ]] 2025-09-07T06:43:56.1998408Z + export CUDA_VISIBLE_DEVICES=0 2025-09-07T06:43:56.1998515Z + CUDA_VISIBLE_DEVICES=0 2025-09-07T06:43:56.1998615Z + export HIP_VISIBLE_DEVICES=0 2025-09-07T06:43:56.1998721Z + HIP_VISIBLE_DEVICES=0 2025-09-07T06:43:56.1998822Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-09-07T06:43:56.2000099Z + [[ default == \s\l\o\w ]] 2025-09-07T06:43:56.2000234Z + [[ linux-noble-rocm-py3.12-mi300 == *slow-gradcheck* ]] 2025-09-07T06:43:56.2000385Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]] 2025-09-07T06:43:56.2000516Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-09-07T06:43:56.2000646Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-09-07T06:43:56.2000770Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-09-07T06:43:56.2000885Z + [[ default == *crossref* ]] 2025-09-07T06:43:56.2000999Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-09-07T06:43:56.2001115Z + export VALGRIND=OFF 2025-09-07T06:43:56.2001205Z + VALGRIND=OFF 2025-09-07T06:43:56.2001286Z + rocminfo 2025-09-07T06:43:56.2106096Z ROCk module version 6.12.12 is loaded 2025-09-07T06:43:56.2543211Z ===================== 2025-09-07T06:43:56.2544151Z HSA System Attributes 2025-09-07T06:43:56.2544854Z ===================== 2025-09-07T06:43:56.2545041Z Runtime Version: 1.15 2025-09-07T06:43:56.2545302Z Runtime Ext Version: 1.7 2025-09-07T06:43:56.2545610Z System Timestamp Freq.: 1000.000000MHz 2025-09-07T06:43:56.2545909Z Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count) 2025-09-07T06:43:56.2546229Z Machine Model: LARGE 2025-09-07T06:43:56.2553169Z System Endianness: LITTLE 2025-09-07T06:43:56.2553416Z Mwaitx: DISABLED 2025-09-07T06:43:56.2553609Z XNACK enabled: NO 2025-09-07T06:43:56.2553781Z DMAbuf Support: YES 2025-09-07T06:43:56.2553953Z VMM Support: YES 2025-09-07T06:43:56.2554060Z 2025-09-07T06:43:56.2554118Z ========== 2025-09-07T06:43:56.2554277Z HSA Agents 2025-09-07T06:43:56.2554768Z ========== 2025-09-07T06:43:56.2554913Z ******* 2025-09-07T06:43:56.2555068Z Agent 1 2025-09-07T06:43:56.2555218Z ******* 2025-09-07T06:43:56.2555411Z Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.2555645Z Uuid: CPU-XX 2025-09-07T06:43:56.2555880Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.2556125Z Vendor Name: CPU 2025-09-07T06:43:56.2556360Z Feature: None specified 2025-09-07T06:43:56.2556596Z Profile: FULL_PROFILE 2025-09-07T06:43:56.2556836Z Float Round Mode: NEAR 2025-09-07T06:43:56.2557130Z Max Queue Number: 0(0x0) 2025-09-07T06:43:56.2557315Z Queue Min Size: 0(0x0) 2025-09-07T06:43:56.2557506Z Queue Max Size: 0(0x0) 2025-09-07T06:43:56.2557684Z Queue Type: MULTI 2025-09-07T06:43:56.2557862Z Node: 0 2025-09-07T06:43:56.2558030Z Device Type: CPU 2025-09-07T06:43:56.2558197Z Cache Info: 2025-09-07T06:43:56.2558341Z L1: 65536(0x10000) KB 2025-09-07T06:43:56.2558512Z Chip ID: 0(0x0) 2025-09-07T06:43:56.2558687Z ASIC Revision: 0(0x0) 2025-09-07T06:43:56.2558880Z Cacheline Size: 64(0x40) 2025-09-07T06:43:56.2559064Z Max Clock Freq. (MHz): 0 2025-09-07T06:43:56.2561950Z BDFID: 0 2025-09-07T06:43:56.2562135Z Internal Node ID: 0 2025-09-07T06:43:56.2562384Z Compute Unit: 80 2025-09-07T06:43:56.2562559Z SIMDs per CU: 0 2025-09-07T06:43:56.2562737Z Shader Engines: 0 2025-09-07T06:43:56.2562921Z Shader Arrs. per Eng.: 0 2025-09-07T06:43:56.2563107Z WatchPts on Addr. Ranges:1 2025-09-07T06:43:56.2563277Z Memory Properties: 2025-09-07T06:43:56.2563406Z Features: None 2025-09-07T06:43:56.2563534Z Pool Info: 2025-09-07T06:43:56.2563657Z Pool 1 2025-09-07T06:43:56.2565520Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:43:56.2565668Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:43:56.2565811Z Allocatable: TRUE 2025-09-07T06:43:56.2565961Z Alloc Granule: 4KB 2025-09-07T06:43:56.2566174Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2566330Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2566480Z Accessible by all: TRUE 2025-09-07T06:43:56.2566613Z Pool 2 2025-09-07T06:43:56.2566738Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:43:56.2566882Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:43:56.2567019Z Allocatable: TRUE 2025-09-07T06:43:56.2568264Z Alloc Granule: 4KB 2025-09-07T06:43:56.2568420Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2568572Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2568769Z Accessible by all: TRUE 2025-09-07T06:43:56.2568904Z Pool 3 2025-09-07T06:43:56.2569026Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-09-07T06:43:56.2569165Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:43:56.2569303Z Allocatable: TRUE 2025-09-07T06:43:56.2569449Z Alloc Granule: 4KB 2025-09-07T06:43:56.2569601Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2570771Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2570925Z Accessible by all: TRUE 2025-09-07T06:43:56.2571054Z Pool 4 2025-09-07T06:43:56.2571174Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:43:56.2571311Z Size: 660245920(0x275a8da0) KB 2025-09-07T06:43:56.2571455Z Allocatable: TRUE 2025-09-07T06:43:56.2571602Z Alloc Granule: 4KB 2025-09-07T06:43:56.2571754Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2571908Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2572059Z Accessible by all: TRUE 2025-09-07T06:43:56.2572193Z ISA Info: 2025-09-07T06:43:56.2573271Z ******* 2025-09-07T06:43:56.2573371Z Agent 2 2025-09-07T06:43:56.2573463Z ******* 2025-09-07T06:43:56.2573577Z Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.2573716Z Uuid: CPU-XX 2025-09-07T06:43:56.2573861Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.2574017Z Vendor Name: CPU 2025-09-07T06:43:56.2574162Z Feature: None specified 2025-09-07T06:43:56.2574309Z Profile: FULL_PROFILE 2025-09-07T06:43:56.2574521Z Float Round Mode: NEAR 2025-09-07T06:43:56.2574667Z Max Queue Number: 0(0x0) 2025-09-07T06:43:56.2575812Z Queue Min Size: 0(0x0) 2025-09-07T06:43:56.2575957Z Queue Max Size: 0(0x0) 2025-09-07T06:43:56.2576095Z Queue Type: MULTI 2025-09-07T06:43:56.2576227Z Node: 1 2025-09-07T06:43:56.2576360Z Device Type: CPU 2025-09-07T06:43:56.2576485Z Cache Info: 2025-09-07T06:43:56.2576599Z L1: 65536(0x10000) KB 2025-09-07T06:43:56.2576776Z Chip ID: 0(0x0) 2025-09-07T06:43:56.2576914Z ASIC Revision: 0(0x0) 2025-09-07T06:43:56.2577057Z Cacheline Size: 64(0x40) 2025-09-07T06:43:56.2577202Z Max Clock Freq. (MHz): 0 2025-09-07T06:43:56.2578323Z BDFID: 0 2025-09-07T06:43:56.2578462Z Internal Node ID: 1 2025-09-07T06:43:56.2578604Z Compute Unit: 80 2025-09-07T06:43:56.2578742Z SIMDs per CU: 0 2025-09-07T06:43:56.2578882Z Shader Engines: 0 2025-09-07T06:43:56.2579028Z Shader Arrs. per Eng.: 0 2025-09-07T06:43:56.2579178Z WatchPts on Addr. Ranges:1 2025-09-07T06:43:56.2579357Z Memory Properties: 2025-09-07T06:43:56.2579460Z Features: None 2025-09-07T06:43:56.2579562Z Pool Info: 2025-09-07T06:43:56.2579659Z Pool 1 2025-09-07T06:43:56.2580761Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:43:56.2580908Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:43:56.2581050Z Allocatable: TRUE 2025-09-07T06:43:56.2581196Z Alloc Granule: 4KB 2025-09-07T06:43:56.2581350Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2581503Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2581651Z Accessible by all: TRUE 2025-09-07T06:43:56.2581781Z Pool 2 2025-09-07T06:43:56.2581908Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:43:56.2582053Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:43:56.2582192Z Allocatable: TRUE 2025-09-07T06:43:56.2583312Z Alloc Granule: 4KB 2025-09-07T06:43:56.2583466Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2583618Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2583766Z Accessible by all: TRUE 2025-09-07T06:43:56.2583895Z Pool 3 2025-09-07T06:43:56.2584017Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-09-07T06:43:56.2584156Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:43:56.2584293Z Allocatable: TRUE 2025-09-07T06:43:56.2584502Z Alloc Granule: 4KB 2025-09-07T06:43:56.2584656Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2584807Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2585967Z Accessible by all: TRUE 2025-09-07T06:43:56.2586100Z Pool 4 2025-09-07T06:43:56.2586220Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:43:56.2586357Z Size: 656328620(0x271ec7ac) KB 2025-09-07T06:43:56.2586495Z Allocatable: TRUE 2025-09-07T06:43:56.2586640Z Alloc Granule: 4KB 2025-09-07T06:43:56.2586791Z Alloc Recommended Granule:4KB 2025-09-07T06:43:56.2586944Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2587096Z Accessible by all: TRUE 2025-09-07T06:43:56.2587271Z ISA Info: 2025-09-07T06:43:56.2587368Z ******* 2025-09-07T06:43:56.2588480Z Agent 3 2025-09-07T06:43:56.2588576Z ******* 2025-09-07T06:43:56.2588683Z Name: gfx942 2025-09-07T06:43:56.2588819Z Uuid: GPU-d6ad5c386168b0b9 2025-09-07T06:43:56.2588966Z Marketing Name: AMD Instinct Mi325X VF 2025-09-07T06:43:56.2589115Z Vendor Name: AMD 2025-09-07T06:43:56.2589257Z Feature: KERNEL_DISPATCH 2025-09-07T06:43:56.2589399Z Profile: BASE_PROFILE 2025-09-07T06:43:56.2589542Z Float Round Mode: NEAR 2025-09-07T06:43:56.2589687Z Max Queue Number: 128(0x80) 2025-09-07T06:43:56.2589875Z Queue Min Size: 64(0x40) 2025-09-07T06:43:56.2591046Z Queue Max Size: 131072(0x20000) 2025-09-07T06:43:56.2591188Z Queue Type: MULTI 2025-09-07T06:43:56.2591320Z Node: 2 2025-09-07T06:43:56.2591453Z Device Type: GPU 2025-09-07T06:43:56.2591578Z Cache Info: 2025-09-07T06:43:56.2591763Z L1: 32(0x20) KB 2025-09-07T06:43:56.2591888Z L2: 4096(0x1000) KB 2025-09-07T06:43:56.2592009Z L3: 262144(0x40000) KB 2025-09-07T06:43:56.2592137Z Chip ID: 29881(0x74b9) 2025-09-07T06:43:56.2592274Z ASIC Revision: 1(0x1) 2025-09-07T06:43:56.2592423Z Cacheline Size: 128(0x80) 2025-09-07T06:43:56.2593602Z Max Clock Freq. (MHz): 2100 2025-09-07T06:43:56.2593748Z BDFID: 35584 2025-09-07T06:43:56.2593883Z Internal Node ID: 2 2025-09-07T06:43:56.2594038Z Compute Unit: 304 2025-09-07T06:43:56.2594175Z SIMDs per CU: 4 2025-09-07T06:43:56.2594316Z Shader Engines: 32 2025-09-07T06:43:56.2594534Z Shader Arrs. per Eng.: 1 2025-09-07T06:43:56.2594686Z WatchPts on Addr. Ranges:4 2025-09-07T06:43:56.2594836Z Coherent Host Access: FALSE 2025-09-07T06:43:56.2594968Z Memory Properties: 2025-09-07T06:43:56.2596762Z Features: KERNEL_DISPATCH 2025-09-07T06:43:56.2596915Z Fast F16 Operation: TRUE 2025-09-07T06:43:56.2597067Z Wavefront Size: 64(0x40) 2025-09-07T06:43:56.2597217Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:43:56.2597359Z Workgroup Max Size per Dimension: 2025-09-07T06:43:56.2597478Z x 1024(0x400) 2025-09-07T06:43:56.2597601Z y 1024(0x400) 2025-09-07T06:43:56.2597722Z z 1024(0x400) 2025-09-07T06:43:56.2597858Z Max Waves Per CU: 32(0x20) 2025-09-07T06:43:56.2598007Z Max Work-item Per CU: 2048(0x800) 2025-09-07T06:43:56.2598154Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:43:56.2599563Z Grid Max Size per Dimension: 2025-09-07T06:43:56.2599677Z x 4294967295(0xffffffff) 2025-09-07T06:43:56.2599873Z y 4294967295(0xffffffff) 2025-09-07T06:43:56.2599995Z z 4294967295(0xffffffff) 2025-09-07T06:43:56.2600135Z Max fbarriers/Workgrp: 32 2025-09-07T06:43:56.2600327Z Packet Processor uCode:: 177 2025-09-07T06:43:56.2600481Z SDMA engine uCode:: 24 2025-09-07T06:43:56.2600631Z IOMMU Support:: None 2025-09-07T06:43:56.2600760Z Pool Info: 2025-09-07T06:43:56.2600860Z Pool 1 2025-09-07T06:43:56.2600985Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-09-07T06:43:56.2602203Z Size: 268107776(0xffb0000) KB 2025-09-07T06:43:56.2602349Z Allocatable: TRUE 2025-09-07T06:43:56.2602544Z Alloc Granule: 4KB 2025-09-07T06:43:56.2602702Z Alloc Recommended Granule:2048KB 2025-09-07T06:43:56.2602858Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2603008Z Accessible by all: FALSE 2025-09-07T06:43:56.2603139Z Pool 2 2025-09-07T06:43:56.2603262Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-09-07T06:43:56.2603403Z Size: 268107776(0xffb0000) KB 2025-09-07T06:43:56.2603541Z Allocatable: TRUE 2025-09-07T06:43:56.2604830Z Alloc Granule: 4KB 2025-09-07T06:43:56.2604987Z Alloc Recommended Granule:2048KB 2025-09-07T06:43:56.2605139Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2605292Z Accessible by all: FALSE 2025-09-07T06:43:56.2605426Z Pool 3 2025-09-07T06:43:56.2605545Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-09-07T06:43:56.2605681Z Size: 268107776(0xffb0000) KB 2025-09-07T06:43:56.2605819Z Allocatable: TRUE 2025-09-07T06:43:56.2605965Z Alloc Granule: 4KB 2025-09-07T06:43:56.2606118Z Alloc Recommended Granule:2048KB 2025-09-07T06:43:56.2606270Z Alloc Alignment: 4KB 2025-09-07T06:43:56.2607475Z Accessible by all: FALSE 2025-09-07T06:43:56.2607610Z Pool 4 2025-09-07T06:43:56.2607726Z Segment: GROUP 2025-09-07T06:43:56.2607861Z Size: 64(0x40) KB 2025-09-07T06:43:56.2608005Z Allocatable: FALSE 2025-09-07T06:43:56.2608152Z Alloc Granule: 0KB 2025-09-07T06:43:56.2608302Z Alloc Recommended Granule:0KB 2025-09-07T06:43:56.2608453Z Alloc Alignment: 0KB 2025-09-07T06:43:56.2608602Z Accessible by all: FALSE 2025-09-07T06:43:56.2608734Z ISA Info: 2025-09-07T06:43:56.2608832Z ISA 1 2025-09-07T06:43:56.2609988Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-09-07T06:43:56.2610149Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-09-07T06:43:56.2610303Z Profiles: HSA_PROFILE_BASE 2025-09-07T06:43:56.2610454Z Default Rounding Mode: NEAR 2025-09-07T06:43:56.2610612Z Default Rounding Mode: NEAR 2025-09-07T06:43:56.2610807Z Fast f16: TRUE 2025-09-07T06:43:56.2610951Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:43:56.2611088Z Workgroup Max Size per Dimension: 2025-09-07T06:43:56.2611214Z x 1024(0x400) 2025-09-07T06:43:56.2611339Z y 1024(0x400) 2025-09-07T06:43:56.2611460Z z 1024(0x400) 2025-09-07T06:43:56.2612620Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:43:56.2612757Z Grid Max Size per Dimension: 2025-09-07T06:43:56.2612872Z x 4294967295(0xffffffff) 2025-09-07T06:43:56.2612995Z y 4294967295(0xffffffff) 2025-09-07T06:43:56.2613158Z z 4294967295(0xffffffff) 2025-09-07T06:43:56.2613297Z FBarrier Max Size: 32 2025-09-07T06:43:56.2613425Z ISA 2 2025-09-07T06:43:56.2613560Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-09-07T06:43:56.2613726Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-09-07T06:43:56.2613879Z Profiles: HSA_PROFILE_BASE 2025-09-07T06:43:56.2614030Z Default Rounding Mode: NEAR 2025-09-07T06:43:56.2615320Z Default Rounding Mode: NEAR 2025-09-07T06:43:56.2615469Z Fast f16: TRUE 2025-09-07T06:43:56.2615612Z Workgroup Max Size: 1024(0x400) 2025-09-07T06:43:56.2615749Z Workgroup Max Size per Dimension: 2025-09-07T06:43:56.2615869Z x 1024(0x400) 2025-09-07T06:43:56.2615997Z y 1024(0x400) 2025-09-07T06:43:56.2616118Z z 1024(0x400) 2025-09-07T06:43:56.2616250Z Grid Max Size: 4294967295(0xffffffff) 2025-09-07T06:43:56.2616381Z Grid Max Size per Dimension: 2025-09-07T06:43:56.2616494Z x 4294967295(0xffffffff) 2025-09-07T06:43:56.2616618Z y 4294967295(0xffffffff) 2025-09-07T06:43:56.2617790Z z 4294967295(0xffffffff) 2025-09-07T06:43:56.2617927Z FBarrier Max Size: 32 2025-09-07T06:43:56.2618055Z *** Done *** 2025-09-07T06:43:56.2618152Z + rocminfo 2025-09-07T06:43:56.2618244Z + grep -E 'Name:.*\sgfx|Marketing' 2025-09-07T06:43:56.3233396Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.3233623Z Marketing Name: AMD EPYC 9575F 64-Core Processor 2025-09-07T06:43:56.3233776Z Name: gfx942 2025-09-07T06:43:56.3233929Z Marketing Name: AMD Instinct Mi325X VF 2025-09-07T06:43:56.3282345Z + MAYBE_ROCM=rocm/ 2025-09-07T06:43:56.3282485Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]] 2025-09-07T06:43:56.3288006Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]] 2025-09-07T06:43:56.3288314Z + pip_install ninja==1.10.2 2025-09-07T06:43:56.3288488Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-09-07T06:43:56.3288694Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-09-07T06:43:56.5798350Z Collecting ninja==1.10.2 2025-09-07T06:43:56.6565272Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-09-07T06:43:56.6863339Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-09-07T06:43:56.8282715Z Installing collected packages: ninja 2025-09-07T06:43:56.8283441Z Attempting uninstall: ninja 2025-09-07T06:43:56.8294608Z Found existing installation: ninja 1.11.1.3 2025-09-07T06:43:56.8303988Z Uninstalling ninja-1.11.1.3: 2025-09-07T06:43:56.8363648Z Successfully uninstalled ninja-1.11.1.3 2025-09-07T06:43:56.8462228Z Successfully installed ninja-1.10.2 2025-09-07T06:43:56.8844070Z + 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 2025-09-07T06:43:56.8846128Z + 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 2025-09-07T06:43:56.8847255Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]] 2025-09-07T06:43:56.8847832Z + [[ linux-noble-rocm-py3.12-mi300 == *asan* ]] 2025-09-07T06:43:56.8848214Z + [[ linux-noble-rocm-py3.12-mi300 == *-debug* ]] 2025-09-07T06:43:56.8848597Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]] 2025-09-07T06:43:56.8849143Z + echo 'We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass' 2025-09-07T06:43:56.8849806Z We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass 2025-09-07T06:43:56.8850265Z + cd test 2025-09-07T06:43:56.8851606Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-09-07T06:43:57.7638611Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-09-07T06:43:57.7638814Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-09-07T06:43:57.7638960Z + [[ default == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-09-07T06:43:57.7640639Z + DYNAMO_BENCHMARK_FLAGS=() 2025-09-07T06:43:57.7641402Z + [[ default == *pr_time_benchmarks* ]] 2025-09-07T06:43:57.7641645Z + [[ default == *dynamo_eager* ]] 2025-09-07T06:43:57.7641776Z + [[ default == *aot_eager* ]] 2025-09-07T06:43:57.7646783Z + [[ default == *aot_inductor* ]] 2025-09-07T06:43:57.7646927Z + [[ default == *max_autotune_inductor* ]] 2025-09-07T06:43:57.7647050Z + [[ default == *inductor* ]] 2025-09-07T06:43:57.7647166Z + [[ default == *dynamic* ]] 2025-09-07T06:43:57.7647272Z + [[ default == *cpu* ]] 2025-09-07T06:43:57.7647391Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-09-07T06:43:57.7649105Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]] 2025-09-07T06:43:57.7649264Z + [[ linux-noble-rocm-py3.12-mi300 == *-bazel-* ]] 2025-09-07T06:43:57.7653812Z + cd test 2025-09-07T06:43:57.7653949Z + python -c 'import torch; print(torch.__config__.show())' 2025-09-07T06:43:58.4927980Z PyTorch built with: 2025-09-07T06:43:58.4928348Z - GCC 11.5 2025-09-07T06:43:58.4928604Z - C++ Version: 201703 2025-09-07T06:43:58.4929216Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:43:58.4929988Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:43:58.4930440Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-09-07T06:43:58.4930800Z - LAPACK is enabled (usually provided by MKL) 2025-09-07T06:43:58.4931137Z - NNPACK is enabled 2025-09-07T06:43:58.4931425Z - CPU capability usage: AVX512 2025-09-07T06:43:58.4931726Z - HIP Runtime 6.4.43484 2025-09-07T06:43:58.4937658Z - MIOpen 3.4.0 2025-09-07T06:43:58.4937766Z - Magma 2.7.2 2025-09-07T06:43:58.4939622Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=93fb23d6fae7c4e82c4239a1033e522088742634, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_FBGEMM_GENAI -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, 2025-09-07T06:43:58.4941315Z 2025-09-07T06:43:58.7246123Z + cd test 2025-09-07T06:43:58.7246603Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-09-07T06:43:59.3689739Z ATen/Parallel: 2025-09-07T06:43:59.3690941Z at::get_num_threads() : 160 2025-09-07T06:43:59.3691374Z at::get_num_interop_threads() : 160 2025-09-07T06:43:59.3698988Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-09-07T06:43:59.3699213Z omp_get_max_threads() : 160 2025-09-07T06:43:59.3699567Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:43:59.3699905Z mkl_get_max_threads() : 160 2025-09-07T06:43:59.3700141Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:43:59.3700411Z std::thread::hardware_concurrency() : 160 2025-09-07T06:43:59.3700600Z Environment variables: 2025-09-07T06:43:59.3700773Z OMP_NUM_THREADS : [not set] 2025-09-07T06:43:59.3700938Z MKL_NUM_THREADS : [not set] 2025-09-07T06:43:59.3701105Z ATen parallel backend: OpenMP 2025-09-07T06:43:59.3701215Z 2025-09-07T06:43:59.5409849Z + [[ default == *numpy_2* ]] 2025-09-07T06:43:59.5410625Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]] 2025-09-07T06:43:59.5411072Z + [[ default == *backward* ]] 2025-09-07T06:43:59.5411377Z + [[ default == *xla* ]] 2025-09-07T06:43:59.5411683Z + [[ default == *vllm* ]] 2025-09-07T06:43:59.5411978Z + [[ default == *executorch* ]] 2025-09-07T06:43:59.5412288Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2025-09-07T06:43:59.5412659Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]] 2025-09-07T06:43:59.5413028Z + [[ default == distributed ]] 2025-09-07T06:43:59.5413341Z + [[ default == *operator_benchmark* ]] 2025-09-07T06:43:59.5413679Z + [[ default == *inductor_distributed* ]] 2025-09-07T06:43:59.5420614Z + [[ default == *inductor-halide* ]] 2025-09-07T06:43:59.5420857Z + [[ default == *inductor-triton-cpu* ]] 2025-09-07T06:43:59.5421106Z + [[ default == *inductor-micro-benchmark* ]] 2025-09-07T06:43:59.5421347Z + [[ default == *huggingface* ]] 2025-09-07T06:43:59.5421554Z + [[ default == *timm* ]] 2025-09-07T06:43:59.5421746Z + [[ default == cachebench ]] 2025-09-07T06:43:59.5421951Z + [[ default == verify_cachebench ]] 2025-09-07T06:43:59.5422167Z + [[ default == *torchbench* ]] 2025-09-07T06:43:59.5422384Z + [[ default == *inductor_cpp_wrapper* ]] 2025-09-07T06:43:59.5422612Z + [[ default == *inductor* ]] 2025-09-07T06:43:59.5422816Z + [[ default == *einops* ]] 2025-09-07T06:43:59.5425503Z + [[ default == *dynamo_wrapped* ]] 2025-09-07T06:43:59.5425676Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-09-07T06:43:59.5425861Z + [[ -n '' ]] 2025-09-07T06:43:59.5425972Z + [[ 1 == 1 ]] 2025-09-07T06:43:59.5426083Z + [[ 6 -gt 1 ]] 2025-09-07T06:43:59.5426224Z + test_lazy_tensor_meta_reference_disabled 2025-09-07T06:43:59.5426428Z + export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-09-07T06:43:59.5426640Z + TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-09-07T06:43:59.5426858Z + echo 'Testing lazy tensor operations without meta reference' 2025-09-07T06:43:59.5427093Z Testing lazy tensor operations without meta reference 2025-09-07T06:43:59.5428997Z + python test/run_test.py --include lazy/test_ts_opinfo.py --verbose 2025-09-07T06:44:00.8021469Z /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. 2025-09-07T06:44:00.8022394Z import pkg_resources 2025-09-07T06:44:01.1189156Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-09-07T06:44:01.2581518Z Ignoring disabled issues: [''] 2025-09-07T06:44:01.2641975Z Found test times from artifacts 2025-09-07T06:44:01.2898975Z Found test times from artifacts 2025-09-07T06:44:01.2907791Z Running all tests 2025-09-07T06:44:01.2908800Z Running parallel tests on 1 processes 2025-09-07T06:44:01.2908960Z Name: tests to run (est. time: 0.0min) 2025-09-07T06:44:01.2909091Z Serial tests (1): 2025-09-07T06:44:01.2909220Z lazy/test_ts_opinfo 1/1 2025-09-07T06:44:01.2909344Z Parallel tests (0): 2025-09-07T06:44:01.2909769Z Name: excluded (est. time: 0.0min) 2025-09-07T06:44:01.2909894Z Serial tests (0): 2025-09-07T06:44:01.2910008Z Parallel tests (0): 2025-09-07T06:44:01.2910153Z Running lazy/test_ts_opinfo 1/1 ... [2025-09-07 06:44:01.290629] 2025-09-07T06:44:01.2912860Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:44:01.2913290Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:44:01.290810] 2025-09-07T06:44:04.0604619Z 2025-09-07T06:44:04.0606084Z 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_a06ac1deef1cbb67_.log 2025-09-07T06:44:04.0608216Z 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 2025-09-07T06:44:04.0609715Z 2025-09-07T06:44:04.0610003Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T06:44:04.0610490Z Uploading artifacts took 0.00 seconds 2025-09-07T06:44:05.3629228Z /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. 2025-09-07T06:44:05.3630683Z import pkg_resources 2025-09-07T06:44:06.1426305Z Running test batch 'tests to run' cost 4.85 seconds 2025-09-07T06:44:06.6514243Z 2025-09-07T06:44:06.6514553Z real 0m7.111s 2025-09-07T06:44:06.6514709Z user 0m17.495s 2025-09-07T06:44:06.6514963Z sys 0m13.526s 2025-09-07T06:44:06.6515354Z + export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE 2025-09-07T06:44:06.6515761Z + test_without_numpy 2025-09-07T06:44:06.6521469Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:44:06.6532514Z + pushd .ci/pytorch 2025-09-07T06:44:06.6532750Z ~/pytorch/.ci/pytorch ~/pytorch 2025-09-07T06:44:06.6539597Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');from unittest import TestCase;import torch;x=torch.randn(3,3);TestCase().assertRaises(RuntimeError, lambda: x.numpy())' 2025-09-07T06:44:07.0824527Z /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:84.) 2025-09-07T06:44:07.0825905Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:44:07.4700539Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2025-09-07T06:44:07.9047808Z /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:84.) 2025-09-07T06:44:07.9049152Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:44:08.0970186Z tensor([0., 1.]) 2025-09-07T06:44:08.2904219Z + [[ default == *dynamo_wrapped* ]] 2025-09-07T06:44:08.2910112Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch; import torch.onnx' 2025-09-07T06:44:08.7212566Z /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:84.) 2025-09-07T06:44:08.7213293Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:44:09.1379128Z + popd 2025-09-07T06:44:09.1379428Z ~/pytorch 2025-09-07T06:44:09.1379683Z + install_torchvision 2025-09-07T06:44:09.1379963Z + local orig_preload 2025-09-07T06:44:09.1380220Z + local commit 2025-09-07T06:44:09.1391164Z ++ get_pinned_commit vision 2025-09-07T06:44:09.1391423Z ++ cat .github/ci_commit_pins/vision.txt 2025-09-07T06:44:09.1397816Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:09.1398012Z + orig_preload= 2025-09-07T06:44:09.1398138Z + '[' -n '' ']' 2025-09-07T06:44:09.1398295Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]] 2025-09-07T06:44:09.1398651Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision 2025-09-07T06:44:09.1399086Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:09.1399373Z + local wheel_dir=dist/vision 2025-09-07T06:44:09.1399521Z + local found_whl=0 2025-09-07T06:44:09.1399668Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:44:09.1402138Z + [[ -f dist/vision/*.whl ]] 2025-09-07T06:44:09.1402284Z + '[' 0 == 0 ']' 2025-09-07T06:44:09.1402652Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:09.2831842Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:09.2832425Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-mgnj0670 2025-09-07T06:44:09.2842971Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-mgnj0670 2025-09-07T06:44:10.7623760Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-09-07T06:44:10.7642133Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:10.9845627Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:11.3277135Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:44:12.7297877Z Preparing metadata (setup.py) ... [?25l- \ | done 2025-09-07T06:44:12.7327951Z [?25hBuilding wheels for collected packages: torchvision 2025-09-07T06:44:12.7399768Z  DEPRECATION: Building 'torchvision' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torchvision'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T06:44:41.9866253Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2025-09-07T06:44:41.9880220Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl size=1581354 sha256=0ed9cdf459432e80b182f3b9ca2162885863b8741465c303b2fd89ce0867fde4 2025-09-07T06:44:41.9882632Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/10/ba/61/eb5228b3631dc6bb4f478b3aa59575551a5473e4596e4c001a 2025-09-07T06:44:41.9902691Z Successfully built torchvision 2025-09-07T06:44:42.0482837Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:44:42.0483441Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl 2025-09-07T06:44:42.0484124Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl') 2025-09-07T06:44:42.0484725Z + local args 2025-09-07T06:44:42.0485146Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl == *\ * ]] 2025-09-07T06:44:42.0485970Z + for path in "${args[@]}" 2025-09-07T06:44:42.0486453Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl' 2025-09-07T06:44:42.0487132Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl 2025-09-07T06:44:42.0487906Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl 2025-09-07T06:44:42.1945162Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl 2025-09-07T06:44:42.1990096Z Installing collected packages: torchvision 2025-09-07T06:44:42.4316162Z Successfully installed torchvision-0.22.0a0+966da7e 2025-09-07T06:44:42.4654823Z + '[' -n '' ']' 2025-09-07T06:44:42.4655119Z + test_python_shard 1 2025-09-07T06:44:42.4655398Z + [[ -z 6 ]] 2025-09-07T06:44:42.4656056Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 6 --verbose --upload-artifacts-while-running 2025-09-07T06:44:43.7655987Z /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. 2025-09-07T06:44:43.7657565Z import pkg_resources 2025-09-07T06:44:44.0735138Z Excluding test_cuda_nvml_based_avail on ROCm 2025-09-07T06:44:44.0873132Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-09-07T06:44:44.0933871Z Found test times from artifacts 2025-09-07T06:44:44.1189443Z Found test times from artifacts 2025-09-07T06:44:44.1196015Z Running all tests 2025-09-07T06:44:44.1370464Z Running parallel tests on 1 processes 2025-09-07T06:44:44.1371237Z Name: tests to run (est. time: 153.32min) 2025-09-07T06:44:44.1371647Z Serial tests (102): 2025-09-07T06:44:44.1372001Z test_reductions 1/1 2025-09-07T06:44:44.1372296Z export/test_export 1/1 2025-09-07T06:44:44.1372599Z inductor/test_memory_planning 1/1 2025-09-07T06:44:44.1372924Z inductor/test_remote_cache 1/1 2025-09-07T06:44:44.1373240Z inductor/test_triton_syntax 1/1 2025-09-07T06:44:44.1381277Z test_torch 1/1 2025-09-07T06:44:44.1381588Z doctests 1/1 2025-09-07T06:44:44.1381882Z dynamo/test_activation_checkpointing 1/1 2025-09-07T06:44:44.1382240Z dynamo/test_aot_autograd 1/1 2025-09-07T06:44:44.1382547Z dynamo/test_aot_compile 1/1 2025-09-07T06:44:44.1382862Z dynamo/test_autograd_function 1/1 2025-09-07T06:44:44.1383222Z dynamo/test_backends 1/1 2025-09-07T06:44:44.1383541Z dynamo/test_backward_higher_order_ops 1/1 2025-09-07T06:44:44.1383797Z dynamo/test_base_hop 1/1 2025-09-07T06:44:44.1383991Z dynamo/test_base_output 1/1 2025-09-07T06:44:44.1384146Z dynamo/test_buffers_override 1/1 2025-09-07T06:44:44.1384295Z dynamo/test_bytecode_utils 1/1 2025-09-07T06:44:44.1384535Z dynamo/test_callback 1/1 2025-09-07T06:44:44.1384886Z dynamo/test_compile 1/1 2025-09-07T06:44:44.1385019Z dynamo/test_compiler_bisector 1/1 2025-09-07T06:44:44.1385159Z dynamo/test_comptime 1/1 2025-09-07T06:44:44.1385289Z dynamo/test_config 1/1 2025-09-07T06:44:44.1385413Z dynamo/test_ctx_manager 1/1 2025-09-07T06:44:44.1385551Z dynamo/test_cudagraphs 1/1 2025-09-07T06:44:44.1385705Z dynamo/test_cudagraphs_expandable_segments 1/1 2025-09-07T06:44:44.1385866Z dynamo/test_debug_utils 1/1 2025-09-07T06:44:44.1385998Z dynamo/test_decorators 1/1 2025-09-07T06:44:44.1386134Z dynamo/test_deque_reconstruct 1/1 2025-09-07T06:44:44.1386276Z dynamo/test_deviceguard 1/1 2025-09-07T06:44:44.1386407Z dynamo/test_dicts 1/1 2025-09-07T06:44:44.1386538Z dynamo/test_dynamic_shapes 1/3 2025-09-07T06:44:44.1386694Z export/test_functionalized_assertions 1/1 2025-09-07T06:44:44.1386844Z export/test_hop 1/1 2025-09-07T06:44:44.1387065Z export/test_package 1/1 2025-09-07T06:44:44.1387202Z export/test_pass_infra 1/1 2025-09-07T06:44:44.1387332Z export/test_passes 1/1 2025-09-07T06:44:44.1389880Z export/test_retraceability 1/1 2025-09-07T06:44:44.1390028Z functorch/test_vmap 1/1 2025-09-07T06:44:44.1390162Z functorch/test_vmap_registrations 1/1 2025-09-07T06:44:44.1390315Z higher_order_ops/test_invoke_quant 1/1 2025-09-07T06:44:44.1390470Z higher_order_ops/test_invoke_subgraph 1/1 2025-09-07T06:44:44.1390622Z higher_order_ops/test_with_effects 1/1 2025-09-07T06:44:44.1390770Z inductor/test_alignment 1/1 2025-09-07T06:44:44.1390908Z inductor/test_analysis 1/1 2025-09-07T06:44:44.1391048Z inductor/test_aot_inductor_arrayref 1/1 2025-09-07T06:44:44.1391191Z inductor/test_b2b_gemm 1/1 2025-09-07T06:44:44.1391324Z inductor/test_compiled_optimizers 2/2 2025-09-07T06:44:44.1391471Z inductor/test_cuda_repro 1/1 2025-09-07T06:44:44.1391697Z inductor/test_cudagraph_trees 1/1 2025-09-07T06:44:44.1391875Z inductor/test_cudagraph_trees_expandable_segments 1/1 2025-09-07T06:44:44.1392046Z inductor/test_dependencies 1/1 2025-09-07T06:44:44.1392184Z inductor/test_device_assert 1/1 2025-09-07T06:44:44.1392328Z inductor/test_distributed_patterns 1/1 2025-09-07T06:44:44.1392480Z inductor/test_efficient_conv_bn_eval 1/1 2025-09-07T06:44:44.1392633Z inductor/test_external_callables 1/1 2025-09-07T06:44:44.1392778Z inductor/test_flex_attention 2/4 2025-09-07T06:44:44.1392914Z inductor/test_fp8 1/1 2025-09-07T06:44:44.1393043Z inductor/test_minifier_isolate 1/1 2025-09-07T06:44:44.1394678Z inductor/test_op_completeness 1/1 2025-09-07T06:44:44.1394808Z inductor/test_op_dtype_prop 1/1 2025-09-07T06:44:44.1394945Z inductor/test_torchinductor_codegen_dynamic_shapes 4/4 2025-09-07T06:44:44.1395088Z inductor/test_torchinductor_opinfo 2/9 2025-09-07T06:44:44.1395208Z inductor/test_torchinductor_opinfo 8/9 2025-09-07T06:44:44.1395329Z optim/test_lrscheduler 1/1 2025-09-07T06:44:44.1395439Z test_appending_byte_serializer 1/1 2025-09-07T06:44:44.1395550Z test_autograd 1/1 2025-09-07T06:44:44.1395650Z test_cpp_extensions_aot_ninja 1/1 2025-09-07T06:44:44.1395767Z test_cpp_extensions_aot_no_ninja 1/1 2025-09-07T06:44:44.1395882Z test_cpp_extensions_jit 1/1 2025-09-07T06:44:44.1395990Z test_cuda_expandable_segments 1/1 2025-09-07T06:44:44.1396097Z test_decomp 3/12 2025-09-07T06:44:44.1396186Z test_decomp 9/12 2025-09-07T06:44:44.1396274Z test_fx 2/3 2025-09-07T06:44:44.1396362Z test_mkl_verbose 1/1 2025-09-07T06:44:44.1396460Z test_mkldnn_fusion 1/1 2025-09-07T06:44:44.1396557Z test_monitor 1/1 2025-09-07T06:44:44.1396651Z test_multiprocessing 1/1 2025-09-07T06:44:44.1396759Z test_multiprocessing_spawn 1/1 2025-09-07T06:44:44.1396869Z test_nestedtensor 2/2 2025-09-07T06:44:44.1396964Z test_ops 4/4 2025-09-07T06:44:44.1397051Z test_quantization 5/5 2025-09-07T06:44:44.1397148Z test_subclass 1/1 2025-09-07T06:44:44.1397243Z test_tensor_creation_ops 1/1 2025-09-07T06:44:44.1397403Z test_utils 1/1 2025-09-07T06:44:44.1397504Z torch_np/numpy_tests/core/test_dlpack 1/1 2025-09-07T06:44:44.1397630Z torch_np/numpy_tests/core/test_dtype 1/1 2025-09-07T06:44:44.1397756Z torch_np/numpy_tests/core/test_einsum 1/1 2025-09-07T06:44:44.1397881Z torch_np/numpy_tests/core/test_getlimits 1/1 2025-09-07T06:44:44.1398014Z torch_np/numpy_tests/core/test_indexing 1/1 2025-09-07T06:44:44.1398141Z torch_np/numpy_tests/core/test_numeric 1/1 2025-09-07T06:44:44.1399535Z torch_np/numpy_tests/core/test_numerictypes 1/1 2025-09-07T06:44:44.1399674Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 2025-09-07T06:44:44.1399815Z torch_np/numpy_tests/core/test_scalar_methods 1/1 2025-09-07T06:44:44.1399956Z torch_np/numpy_tests/core/test_scalarinherit 1/1 2025-09-07T06:44:44.1400093Z torch_np/numpy_tests/core/test_scalarmath 1/1 2025-09-07T06:44:44.1400277Z torch_np/numpy_tests/core/test_shape_base 1/1 2025-09-07T06:44:44.1400409Z torch_np/numpy_tests/fft/test_helper 1/1 2025-09-07T06:44:44.1400536Z torch_np/numpy_tests/fft/test_pocketfft 1/1 2025-09-07T06:44:44.1400659Z torch_np/test_ndarray_methods 1/1 2025-09-07T06:44:44.1400770Z torch_np/test_random 1/1 2025-09-07T06:44:44.1400874Z torch_np/test_reductions 1/1 2025-09-07T06:44:44.1400983Z torch_np/test_ufuncs_basic 1/1 2025-09-07T06:44:44.1401090Z Parallel tests (0): 2025-09-07T06:44:44.1401188Z Name: excluded (est. time: 0.0min) 2025-09-07T06:44:44.1401295Z Serial tests (0): 2025-09-07T06:44:44.1401387Z Parallel tests (0): 2025-09-07T06:44:44.1401510Z Running test_reductions 1/1 ... [2025-09-07 06:44:44.137195] 2025-09-07T06:44:44.1401652Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:44:44.1402020Z 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'] ... [2025-09-07 06:44:44.137374] 2025-09-07T06:45:57.1460877Z 2025-09-07T06:45:57.1461490Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_00dc650fdc3c8a4c_.log 2025-09-07T06:45:57.2043041Z Running 4755 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_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 2025-09-07T06:45:57.2606070Z 2025-09-07T06:45:57.2606196Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T06:45:57.2606389Z Uploading artifacts took 0.00 seconds 2025-09-07T06:45:57.2606564Z Running export/test_export 1/1 ... [2025-09-07 06:45:57.149988] 2025-09-07T06:45:57.2606735Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:45:57.2610867Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_export.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:45:57.150184] 2025-09-07T06:46:54.9034532Z 2025-09-07T06:46:54.9041558Z export/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_export_1.1_4d0aad52e32a4e29_.log 2025-09-07T06:46:54.9086245Z Running 445 items in this shard: test/export/test_export.py::TestDynamismExpression::test_export_assume_static_by_default, test/export/test_export.py::TestDynamismExpression::test_export_constraints_error, test/export/test_export.py::TestDynamismExpression::test_export_constraints_error_not_in_range, test/export/test_export.py::TestDynamismExpression::test_export_inline_constraints, test/export/test_export.py::TestDynamismExpression::test_export_slice_maxsize, test/export/test_export.py::TestDynamismExpression::test_export_slice_unbacked_dim1, test/export/test_export.py::TestDynamismExpression::test_export_strict_narrow_unbacked_expr, test/export/test_export.py::TestDynamismExpression::test_no_grad_param_inplace, test/export/test_export.py::TestDynamismExpression::test_reshape_view_backed_size_oblivious, test/export/test_export.py::TestExport::test__scaled_dot_product_flash_attention, test/export/test_export.py::TestExport::test_additional_inputs_constants, test/export/test_export.py::TestExport::test_allow_explicit_guards_as_runtime_asserts, test/export/test_export.py::TestExport::test_args_type_checked, test/export/test_export.py::TestExport::test_aten_lift_fresh_copy, test/export/test_export.py::TestExport::test_attention, test/export/test_export.py::TestExport::test_attr_assignment_extra, test/export/test_export.py::TestExport::test_automatic_constrain_size, test/export/test_export.py::TestExport::test_automatic_dynamic_shapes_constant_relation, test/export/test_export.py::TestExport::test_automatic_dynamic_shapes_linear_relation, test/export/test_export.py::TestExport::test_automatic_dynamic_shapes_simple_equality, test/export/test_export.py::TestExport::test_baddbmm, test/export/test_export.py::TestExport::test_basic, test/export/test_export.py::TestExport::test_basic_non_strict_fake_tensor, test/export/test_export.py::TestExport::test_basic_non_strict_real_tensor, test/export/test_export.py::TestExport::test_bincount, test/export/test_export.py::TestExport::test_buffer_util, test/export/test_export.py::TestExport::test_capture_subclass_constructor, test/export/test_export.py::TestExport::test_capture_subclass_constructor_torch_ir, test/export/test_export.py::TestExport::test_capture_subclass_wrong, test/export/test_export.py::TestExport::test_ccode_python_mod, test/export/test_export.py::TestExport::test_check_specialized_int, test/export/test_export.py::TestExport::test_checks_to_constrain_range, test/export/test_export.py::TestExport::test_cleanup_dynamic_markers, test/export/test_export.py::TestExport::test_colin_unbacked_backed_vr_sub, test/export/test_export.py::TestExport::test_colon_parameter, test/export/test_export.py::TestExport::test_compiling_state, test/export/test_export.py::TestExport::test_cond_access_identical_symint_closure, test/export/test_export.py::TestExport::test_cond_branches_return_constant_int, test/export/test_export.py::TestExport::test_cond_branches_return_same_int, test/export/test_export.py::TestExport::test_cond_buffers, test/export/test_export.py::TestExport::test_cond_contains_unbacked_no_escape, test/export/test_export.py::TestExport::test_cond_int_closure, test/export/test_export.py::TestExport::test_cond_unflatten, test/export/test_export.py::TestExport::test_cond_with_module_stack_export_with, test/export/test_export.py::TestExport::test_cond_with_module_stack_export_with_unflatten, test/export/test_export.py::TestExport::test_constant_aliasing, test/export/test_export.py::TestExport::test_constant_input_naming, test/export/test_export.py::TestExport::test_constant_no_user_inp, test/export/test_export.py::TestExport::test_constant_output, test/export/test_export.py::TestExport::test_constant_output_dup, test/export/test_export.py::TestExport::test_constant_requires_grad_const, test/export/test_export.py::TestExport::test_constant_return, test/export/test_export.py::TestExport::test_constant_tensor_mutation, test/export/test_export.py::TestExport::test_constant_tensor_with_non_functional, test/export/test_export.py::TestExport::test_constant_tensor_with_non_functional_nested, test/export/test_export.py::TestExport::test_constrain_decomp, test/export/test_export.py::TestExport::test_constrain_size_in_eager, test/export/test_export.py::TestExport::test_constrain_size_with_constrain_value, test/export/test_export.py::TestExport::test_constrain_size_with_various_cases, test/export/test_export.py::TestExport::test_conv_dynamic, test/export/test_export.py::TestExport::test_crop_like, test/export/test_export.py::TestExport::test_cse_for_symint, test/export/test_export.py::TestExport::test_custom_op_auto_functionalize, test/export/test_export.py::TestExport::test_custom_op_auto_functionalize_pre_dispatch, test/export/test_export.py::TestExport::test_custom_op_auto_warn_pre_dispatch, test/export/test_export.py::TestExport::test_custom_op_preserve, test/export/test_export.py::TestExport::test_custom_pytree, test/export/test_export.py::TestExport::test_custom_tag_metadata_re_export, test/export/test_export.py::TestExport::test_decomp_batch_norm_functional_predispatch, test/export/test_export.py::TestExport::test_decomp_item_in_prim_after_decomposition, test/export/test_export.py::TestExport::test_decomp_item_in_prim_before_decomposition, test/export/test_export.py::TestExport::test_default_decomposition_core_cia_ops, test/export/test_export.py::TestExport::test_derived_dim_1_2, test/export/test_export.py::TestExport::test_derived_dim_basic, test/export/test_export.py::TestExport::test_derived_dim_integer, test/export/test_export.py::TestExport::test_derived_dim_nested, test/export/test_export.py::TestExport::test_derived_dim_out_of_order, test/export/test_export.py::TestExport::test_derived_dim_out_of_order_repeat_derived, test/export/test_export.py::TestExport::test_derived_dim_out_of_order_simplified, test/export/test_export.py::TestExport::test_derived_dim_out_of_order_simplified_repeat_non_derived, test/export/test_export.py::TestExport::test_derived_dim_repeat_derived, test/export/test_export.py::TestExport::test_detect_leak_nonstrict, test/export/test_export.py::TestExport::test_detect_leak_nonstrict_with_stacktrace, test/export/test_export.py::TestExport::test_detect_leak_strict, test/export/test_export.py::TestExport::test_device_to_dynamic, test/export/test_export.py::TestExport::test_device_to_gpu, test/export/test_export.py::TestExport::test_device_to_mutation, test/export/test_export.py::TestExport::test_device_to_mutation_float, test/export/test_export.py::TestExport::test_device_to_static, test/export/test_export.py::TestExport::test_dim_1_2, test/export/test_export.py::TestExport::test_dim_auto_and_dim, test/export/test_export.py::TestExport::test_dim_dynamic, test/export/test_export.py::TestExport::test_dim_dynamic_divisibility, test/export/test_export.py::TestExport::test_dim_dynamic_specialization, test/export/test_export.py::TestExport::test_dim_hint_range_violations, test/export/test_export.py::TestExport::test_dim_hint_ranges, test/export/test_export.py::TestExport::test_disable_forced_specializations_errors, test/export/test_export.py::TestExport::test_disable_forced_specializations_ok, test/export/test_export.py::TestExport::test_distributed_all_gather, test/export/test_export.py::TestExport::test_distributed_all_gather_into_tensor, test/export/test_export.py::TestExport::test_distributed_all_reduce, test/export/test_export.py::TestExport::test_distributed_all_to_all_single, test/export/test_export.py::TestExport::test_distributed_reduce_scatter_tensor, test/export/test_export.py::TestExport::test_dont_duck_size_for_auto_dynamic, test/export/test_export.py::TestExport::test_double_lifted_constants, test/export/test_export.py::TestExport::test_draft_export_checks_aliasing, test/export/test_export.py::TestExport::test_draft_export_checks_mutation, test/export/test_export.py::TestExport::test_draft_export_checks_mutation_list, test/export/test_export.py::TestExport::test_draft_export_checks_mutation_with_nan, test/export/test_export.py::TestExport::test_draft_export_fake_kernel_inference_errors, test/export/test_export.py::TestExport::test_draft_export_infers_fake_kernel, test/export/test_export.py::TestExport::test_duplicate_modules_with_non_persistent_buffers, test/export/test_export.py::TestExport::test_dynamic_lr_shift, test/export/test_export.py::TestExport::test_dynamic_shapes_bounds, test/export/test_export.py::TestExport::test_dynamic_shapes_builder_basic, test/export/test_export.py::TestExport::test_dynamic_shapes_builder_kwargs, test/export/test_export.py::TestExport::test_dynamic_shapes_builder_pytree, test/export/test_export.py::TestExport::test_dynamic_shapes_dataclass, test/export/test_export.py::TestExport::test_dynamic_shapes_inferred_basic, test/export/test_export.py::TestExport::test_dynamic_shapes_serdes_generic, test/export/test_export.py::TestExport::test_dynamic_shapes_serdes_user_errors, test/export/test_export.py::TestExport::test_dynamic_shapes_serdes_various, test/export/test_export.py::TestExport::test_dynamic_shapes_spec_with_pytree, test/export/test_export.py::TestExport::test_dynamic_sym_round, test/export/test_export.py::TestExport::test_ends_of_bounds_oblivious, test/export/test_export.py::TestExport::test_error_does_not_reference_eager_fallback, test/export/test_export.py::TestExport::test_error_when_passing_mutating_primitive_op, test/export/test_export.py::TestExport::test_exception, test/export/test_export.py::TestExport::test_expand_copy_export_handles_implicit_true, test/export/test_export.py::TestExport::test_export_api_with_dynamic_shapes, test/export/test_export.py::TestExport::test_export_as_backend, test/export/test_export.py::TestExport::test_export_associative_scan_lifted_buffers, test/export/test_export.py::TestExport::test_export_associative_scan_symbol_dim, test/export/test_export.py::TestExport::test_export_associative_scan_symbol_scandim, test/export/test_export.py::TestExport::test_export_aten_to_unflatten, test/export/test_export.py::TestExport::test_export_aten_to_unflatten_subclass, test/export/test_export.py::TestExport::test_export_aten_to_unflatten_subclass_pre_dispatch, test/export/test_export.py::TestExport::test_export_cond_preserve_torch_fn_for_subgraphs, test/export/test_export.py::TestExport::test_export_cond_symbool_pred, test/export/test_export.py::TestExport::test_export_cond_warns_constant_pred, test/export/test_export.py::TestExport::test_export_custom_decomp_table_basic_pop, test/export/test_export.py::TestExport::test_export_custom_decomp_table_container_methods, test/export/test_export.py::TestExport::test_export_custom_op_lib, test/export/test_export.py::TestExport::test_export_custom_triton_kernel, test/export/test_export.py::TestExport::test_export_custom_triton_kernel_mutable, test/export/test_export.py::TestExport::test_export_cyclic_reference_leak, test/export/test_export.py::TestExport::test_export_decomp_torture_case_1, test/export/test_export.py::TestExport::test_export_decomp_torture_case_2, test/export/test_export.py::TestExport::test_export_decomps_dynamic, test/export/test_export.py::TestExport::test_export_decomps_simple, test/export/test_export.py::TestExport::test_export_dynamo_config, test/export/test_export.py::TestExport::test_export_for_training_run_decomp, test/export/test_export.py::TestExport::test_export_for_training_with_container_type, test/export/test_export.py::TestExport::test_export_for_training_with_dynamic_shapes, test/export/test_export.py::TestExport::test_export_for_training_with_mutation, test/export/test_export.py::TestExport::test_export_for_training_with_state_dict_hooks, test/export/test_export.py::TestExport::test_export_func_with_default_kwargs, test/export/test_export.py::TestExport::test_export_func_with_keyword_only_args, test/export/test_export.py::TestExport::test_export_func_with_kwargs, test/export/test_export.py::TestExport::test_export_func_with_pytree_kwargs, test/export/test_export.py::TestExport::test_export_func_with_var_keyword_args, test/export/test_export.py::TestExport::test_export_func_with_var_keyword_pytree_args, test/export/test_export.py::TestExport::test_export_func_with_var_postional_args, test/export/test_export.py::TestExport::test_export_function_schema, test/export/test_export.py::TestExport::test_export_graph_with_no_inputs, test/export/test_export.py::TestExport::test_export_input_mutation_bug, test/export/test_export.py::TestExport::test_export_input_mutation_dynamic_shape, test/export/test_export.py::TestExport::test_export_input_mutation_static_shape, test/export/test_export.py::TestExport::test_export_linear_preserve_dynamic_shape, test/export/test_export.py::TestExport::test_export_max_nonstrict, test/export/test_export.py::TestExport::test_export_max_onnx_reported, test/export/test_export.py::TestExport::test_export_method, test/export/test_export.py::TestExport::test_export_mod_constraints, test/export/test_export.py::TestExport::test_export_module, test/export/test_export.py::TestExport::test_export_preserve_linear_at_aot_level, test/export/test_export.py::TestExport::test_export_preserve_linear_but_not_custom_op, test/export/test_export.py::TestExport::test_export_scan_pytree_output, test/export/test_export.py::TestExport::test_export_script_module, test/export/test_export.py::TestExport::test_export_statically_known_true, test/export/test_export.py::TestExport::test_export_then_compile_tensor_ctor, test/export/test_export.py::TestExport::test_export_with_autocast, test/export/test_export.py::TestExport::test_export_with_fake_tensor_inputs, test/export/test_export.py::TestExport::test_export_with_fake_tensor_inputs_on_cuda_devices, test/export/test_export.py::TestExport::test_export_with_inline_constraints, test/export/test_export.py::TestExport::test_export_with_inline_constraints_complex, test/export/test_export.py::TestExport::test_export_with_set_grad_enabled, test/export/test_export.py::TestExport::test_export_with_wrong_inputs, test/export/test_export.py::TestExport::test_external_call_non_strict_real_tensor, test/export/test_export.py::TestExport::test_fake_inputs, test/export/test_export.py::TestExport::test_fake_weights, test/export/test_export.py::TestExport::test_filter_traceback_frames, test/export/test_export.py::TestExport::test_float_conversion, test/export/test_export.py::TestExport::test_float_conversion_from_int, test/export/test_export.py::TestExport::test_fqn, test/export/test_export.py::TestExport::test_from_node_metadata_export, test/export/test_export.py::TestExport::test_full_on_scalar_tensor, test/export/test_export.py::TestExport::test_function_holding_tensor, test/export/test_export.py::TestExport::test_hints_wrapper, test/export/test_export.py::TestExport::test_hoo_inline_users_issue, test/export/test_export.py::TestExport::test_if_functional, test/export/test_export.py::TestExport::test_if_post_autograd_op_preserved, test/export/test_export.py::TestExport::test_inline_script_class_method, test/export/test_export.py::TestExport::test_inline_script_class_method_recursive, test/export/test_export.py::TestExport::test_inline_script_function, test/export/test_export.py::TestExport::test_inline_script_method, test/export/test_export.py::TestExport::test_int_shape_specialization, test/export/test_export.py::TestExport::test_intermediate_shape_comp, test/export/test_export.py::TestExport::test_is_exporting, test/export/test_export.py::TestExport::test_is_non_negative_check_function, test/export/test_export.py::TestExport::test_is_nonzero, test/export/test_export.py::TestExport::test_isnonzero, test/export/test_export.py::TestExport::test_issue_113041, test/export/test_export.py::TestExport::test_issue_157289, test/export/test_export.py::TestExport::test_istft_op, test/export/test_export.py::TestExport::test_keep_composite_ops_invalid, test/export/test_export.py::TestExport::test_keep_composite_ops_linear_convd, test/export/test_export.py::TestExport::test_keep_composite_ops_linear_convd_for_training_ir, test/export/test_export.py::TestExport::test_kwarg_dynamic_shapes_diff_order, test/export/test_export.py::TestExport::test_kwargs_reorder, test/export/test_export.py::TestExport::test_layer_norm_unbacked_normalized_shape, test/export/test_export.py::TestExport::test_layer_sharing, test/export/test_export.py::TestExport::test_lazy_module_kwargs, test/export/test_export.py::TestExport::test_lifted_constants, test/export/test_export.py::TestExport::test_linear_conv, test/export/test_export.py::TestExport::test_malformed_fqn_from_source_name, test/export/test_export.py::TestExport::test_map, test/export/test_export.py::TestExport::test_map_buffers, test/export/test_export.py::TestExport::test_mask_nonzero_static, test/export/test_export.py::TestExport::test_masked_select_dynamic, test/export/test_export.py::TestExport::test_math_pow, test/export/test_export.py::TestExport::test_mismatched_dynamic_shapes, test/export/test_export.py::TestExport::test_mixed_input, test/export/test_export.py::TestExport::test_module, test/export/test_export.py::TestExport::test_module_dict_key, test/export/test_export.py::TestExport::test_module_input, test/export/test_export.py::TestExport::test_module_input_subclasses_parameterization_nested, test/export/test_export.py::TestExport::test_module_list_slice, test/export/test_export.py::TestExport::test_module_with_dict_container_inp_out, test/export/test_export.py::TestExport::test_modules_access_for_deleted_submodule, test/export/test_export.py::TestExport::test_more_multidimensional_slicing, test/export/test_export.py::TestExport::test_multidimensional_slicing, test/export/test_export.py::TestExport::test_multinomial_dynamic, test/export/test_export.py::TestExport::test_multiple_definitions_same_name_dim, test/export/test_export.py::TestExport::test_nested_dynamic_shapes_spec, test/export/test_export.py::TestExport::test_nested_module, test/export/test_export.py::TestExport::test_nested_module_with_constant_buffer, test/export/test_export.py::TestExport::test_nested_module_with_init_buffer, test/export/test_export.py::TestExport::test_nested_module_with_parameter, test/export/test_export.py::TestExport::test_nn_module_stack, test/export/test_export.py::TestExport::test_nn_module_stack_shared_submodule, test/export/test_export.py::TestExport::test_no_check_is_size_error, test/export/test_export.py::TestExport::test_no_suggested_fixes_for_data_dependent_errors, test/export/test_export.py::TestExport::test_no_tensor_computation, test/export/test_export.py::TestExport::test_no_tensor_computation_2, test/export/test_export.py::TestExport::test_no_tensor_computation_3, test/export/test_export.py::TestExport::test_no_tensor_computation_4, test/export/test_export.py::TestExport::test_non_arg_name_dynamic_shapes_api, test/export/test_export.py::TestExport::test_non_arg_name_dynamic_shapes_api_with_container_type, test/export/test_export.py::TestExport::test_non_arg_name_dynamic_shapes_api_with_kwarg, test/export/test_export.py::TestExport::test_non_persistent_buffer, test/export/test_export.py::TestExport::test_non_strict_dynamic_shapes, test/export/test_export.py::TestExport::test_non_strict_dynamic_shapes_suggested_fixes, test/export/test_export.py::TestExport::test_none_buffers, test/export/test_export.py::TestExport::test_nonstrict_retrace_preserves_metadata, test/export/test_export.py::TestExport::test_nonzero_2, test/export/test_export.py::TestExport::test_nonzero_dynamic, test/export/test_export.py::TestExport::test_not_registered_parameter, test/export/test_export.py::TestExport::test_operator_aten_tensor_mode_variant, test/export/test_export.py::TestExport::test_output_node_name, test/export/test_export.py::TestExport::test_pad_sequence, test/export/test_export.py::TestExport::test_param_util, test/export/test_export.py::TestExport::test_partial_patched_forward, test/export/test_export.py::TestExport::test_placeholder_naming_collisions, test/export/test_export.py::TestExport::test_placeholder_naming_collisions_hoo_subgraphs, test/export/test_export.py::TestExport::test_placeholder_naming_order, test/export/test_export.py::TestExport::test_placeholder_naming_order_variadic, test/export/test_export.py::TestExport::test_placeholder_update_preserving, test/export/test_export.py::TestExport::test_predispatch_cond, test/export/test_export.py::TestExport::test_predispatch_grad_wrappers, test/export/test_export.py::TestExport::test_preserve_module_call_signature_unflatten_specialization, test/export/test_export.py::TestExport::test_preserve_requires_grad_placeholders, test/export/test_export.py::TestExport::test_preserve_shape_dynamism_for_unused_inputs, test/export/test_export.py::TestExport::test_profiling_code, test/export/test_export.py::TestExport::test_python_asserts_with_sym_int, test/export/test_export.py::TestExport::test_pytree_register_data_class, test/export/test_export.py::TestExport::test_pytree_register_nested_data_class, test/export/test_export.py::TestExport::test_raise_user_error_when_guard_on_data_dependent_operation, test/export/test_export.py::TestExport::test_range_constraints_with_replacement, test/export/test_export.py::TestExport::test_real_tensor_alias_dtype_mismatch, test/export/test_export.py::TestExport::test_real_tensor_bool_cast, test/export/test_export.py::TestExport::test_real_tensor_errors_on_aliasing_custom_op, test/export/test_export.py::TestExport::test_real_tensor_for_max_op, test/export/test_export.py::TestExport::test_real_tensor_size_mismatch, test/export/test_export.py::TestExport::test_redundant_assert_max_upper_bound, test/export/test_export.py::TestExport::test_redundant_asserts, test/export/test_export.py::TestExport::test_refine_dynamic_shapes_from_suggested_fixes, test/export/test_export.py::TestExport::test_register_constant, test/export/test_export.py::TestExport::test_repeat_interleave, test/export/test_export.py::TestExport::test_replace_unbacked_with_very_large_upperbound, test/export/test_export.py::TestExport::test_replaced_unbacked_bindings, test/export/test_export.py::TestExport::test_reshape_view_helper, test/export/test_export.py::TestExport::test_retracable_ep, test/export/test_export.py::TestExport::test_retrace_pre_autograd, test/export/test_export.py::TestExport::test_run_decomposition_supports_user_input_mutation, test/export/test_export.py::TestExport::test_run_decompositions_keep_metadata, test/export/test_export.py::TestExport::test_run_decompositions_keep_tensor_constant_metadata, test/export/test_export.py::TestExport::test_runtime_assert_for_prim, test/export/test_export.py::TestExport::test_runtime_assert_for_prm_str, test/export/test_export.py::TestExport::test_runtime_assert_with_size, test/export/test_export.py::TestExport::test_sdpa_gqa, test/export/test_export.py::TestExport::test_sequential_slicing, test/export/test_export.py::TestExport::test_set_example_inputs, test/export/test_export.py::TestExport::test_set_grad_as_side_effect, test/export/test_export.py::TestExport::test_set_grad_empty, test/export/test_export.py::TestExport::test_set_grad_unflatten, test/export/test_export.py::TestExport::test_setgrad_lifted_tensor, test/export/test_export.py::TestExport::test_shared_submodule_nn_module_stack, test/export/test_export.py::TestExport::test_simple_export_for_training, test/export/test_export.py::TestExport::test_simple_unbacked_view, test/export/test_export.py::TestExport::test_size_input, test/export/test_export.py::TestExport::test_slice_nn_module_stack, test/export/test_export.py::TestExport::test_solver_unsupported_sympy_function, test/export/test_export.py::TestExport::test_specialize_derived_dim_roots, test/export/test_export.py::TestExport::test_split_const_gm_with_lifted_constants, test/export/test_export.py::TestExport::test_stack_trace, test/export/test_export.py::TestExport::test_stack_trace_make_fx, test/export/test_export.py::TestExport::test_state_primitives, test/export/test_export.py::TestExport::test_state_shape_attribute_assignment, test/export/test_export.py::TestExport::test_state_tensors, test/export/test_export.py::TestExport::test_static_dim_constraints, test/export/test_export.py::TestExport::test_subclass_nested_attr_access, test/export/test_export.py::TestExport::test_subclass_nested_attr_access_complicated_metadata, test/export/test_export.py::TestExport::test_subclass_nested_attr_access_const_metadata, test/export/test_export.py::TestExport::test_subclass_nested_attr_access_const_metadata_not_top_level, test/export/test_export.py::TestExport::test_subclass_nested_attr_access_submodule, test/export/test_export.py::TestExport::test_subclasses_parameterization, test/export/test_export.py::TestExport::test_subclasses_parameterization_nested, test/export/test_export.py::TestExport::test_suggest_torch_checks_with_non_negative_check, test/export/test_export.py::TestExport::test_suggest_torch_checks_with_regular_check, test/export/test_export.py::TestExport::test_suggested_fixes_for_data_dependent_errors_basic, test/export/test_export.py::TestExport::test_suggested_fixes_for_data_dependent_errors_puzzlers, test/export/test_export.py::TestExport::test_suggested_fixes_new_roots, test/export/test_export.py::TestExport::test_sym_float_operators, test/export/test_export.py::TestExport::test_sym_or_sym_and, test/export/test_export.py::TestExport::test_sym_sqrt, test/export/test_export.py::TestExport::test_symbool_item, test/export/test_export.py::TestExport::test_symfloat_item, test/export/test_export.py::TestExport::test_symint_input_additional_inputs, test/export/test_export.py::TestExport::test_symint_input_basic, test/export/test_export.py::TestExport::test_symint_input_ranges, test/export/test_export.py::TestExport::test_symint_input_shapes_collection, test/export/test_export.py::TestExport::test_symint_input_specialization, test/export/test_export.py::TestExport::test_symint_item, test/export/test_export.py::TestExport::test_symint_output, test/export/test_export.py::TestExport::test_symint_tensor_return, test/export/test_export.py::TestExport::test_tensor_attribute_zero_args, test/export/test_export.py::TestExport::test_tensor_constant_aten_to, test/export/test_export.py::TestExport::test_tensor_constant_with_wrapped_method, test/export/test_export.py::TestExport::test_to_module_with_mutated_buffer, test/export/test_export.py::TestExport::test_to_module_with_mutated_buffer_multiple, test/export/test_export.py::TestExport::test_to_module_with_mutated_buffer_multiple_update_sub_later, test/export/test_export.py::TestExport::test_tolist, test/export/test_export.py::TestExport::test_torch_check_eq_commutativity, test/export/test_export.py::TestExport::test_torch_fn, test/export/test_export.py::TestExport::test_trace_under_fake, test/export/test_export.py::TestExport::test_train_eval_on_exported_preautograd_module, test/export/test_export.py::TestExport::test_unbacked_3d_matmul, test/export/test_export.py::TestExport::test_unbacked_bincount, test/export/test_export.py::TestExport::test_unbacked_bindings_for_divisible_u_symint, test/export/test_export.py::TestExport::test_unbacked_deferred_runtime_retrace, test/export/test_export.py::TestExport::test_unbacked_expand, test/export/test_export.py::TestExport::test_unbacked_infer_size, test/export/test_export.py::TestExport::test_unbacked_kth_value, test/export/test_export.py::TestExport::test_unbacked_linear_layer_norm_input, test/export/test_export.py::TestExport::test_unbacked_noncontig_lin, test/export/test_export.py::TestExport::test_unbacked_pad, test/export/test_export.py::TestExport::test_unbacked_scalar_constructor, test/export/test_export.py::TestExport::test_unbacked_slice, test/export/test_export.py::TestExport::test_unbacked_to_cond, test/export/test_export.py::TestExport::test_unbacked_to_cond_passthrough, test/export/test_export.py::TestExport::test_unbacked_unsqueeze, test/export/test_export.py::TestExport::test_unflatten_asserts, test/export/test_export.py::TestExport::test_unflatten_buffer_update_child2parent_swap, test/export/test_export.py::TestExport::test_unflatten_closure, test/export/test_export.py::TestExport::test_unflatten_isinstance, test/export/test_export.py::TestExport::test_unflatten_multiple_graphs_dispatch, test/export/test_export.py::TestExport::test_unflatten_multiple_graphs_preserve_signature_no_error, test/export/test_export.py::TestExport::test_unflatten_multiple_graphs_shared_submodule, test/export/test_export.py::TestExport::test_unflatten_multiple_graphs_state, test/export/test_export.py::TestExport::test_unflatten_no_unroll, test/export/test_export.py::TestExport::test_unflatten_placeholder_update_child2parent_swap, test/export/test_export.py::TestExport::test_unflatten_placeholder_update_grandchild2cousin_swap, test/export/test_export.py::TestExport::test_unflatten_random_dag_5, test/export/test_export.py::TestExport::test_unflatten_random_dag_6, test/export/test_export.py::TestExport::test_unflatten_random_dag_buf_8, test/export/test_export.py::TestExport::test_unflatten_random_dag_const_preserving_3, test/export/test_export.py::TestExport::test_unflatten_random_dag_const_preserving_3_1, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_4, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_6, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_9, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_preserving_10, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_preserving_4, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_preserving_4_1, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_preserving_5, test/export/test_export.py::TestExport::test_unflatten_random_dag_mutating_buf_preserving_7, test/export/test_export.py::TestExport::test_unflatten_random_dag_preserving_4, test/export/test_export.py::TestExport::test_unused_aliases, test/export/test_export.py::TestExport::test_unused_constant, test/export/test_export.py::TestExport::test_use_embedding_twice, test/export/test_export.py::TestExport::test_user_input_and_buffer_mutation, test/export/test_export.py::TestExport::test_vmap, test/export/test_export.py::TestExport::test_while_loop_assert_separation, test/export/test_export.py::TestExport::test_while_loop_index_assertions, test/export/test_export.py::TestExport::test_while_loop_simple, test/export/test_export.py::TestExport::test_while_loop_tensor_constant_idx, test/export/test_export.py::TestExport::test_wrapper_module, test/export/test_export.py::TestOneOffModelExportResult::test_assert_tensor_metadata_device_index, test/export/test_export.py::TestOneOffModelExportResult::test_constant_fqn, test/export/test_export.py::TestOneOffModelExportResult::test_constant_name, test/export/test_export.py::TestOneOffModelExportResult::test_duplicated_getitem, test/export/test_export.py::TestOneOffModelExportResult::test_hf_logging_logger, test/export/test_export.py::TestOneOffModelExportResult::test_input_output_no_stacktrace, test/export/test_export.py::TestOneOffModelExportResult::test_int_list_output, test/export/test_export.py::TestOneOffModelExportResult::test_logging_logger, test/export/test_export.py::TestOneOffModelExportResult::test_nested_retrace, test/export/test_export.py::TestOneOffModelExportResult::test_none_input_output, test/export/test_export.py::TestOneOffModelExportResult::test_primitive_constant_output, test/export/test_export.py::TestOneOffModelExportResult::test_print, test/export/test_export.py::TestOneOffModelExportResult::test_print_graph_signature, test/export/test_export.py::TestOneOffModelExportResult::test_scaled_dot_product_attention_cpu, test/export/test_export.py::TestOneOffModelExportResult::test_scaled_dot_product_attention_cuda, test/export/test_export.py::TestOneOffModelExportResult::test_strict_export_with_shared_parameters, test/export/test_export.py::TestOneOffModelExportResult::test_torchrec_jagged_tensor, test/export/test_export.py::TestOneOffModelExportResult::test_unbacked_sdpa, test/export/test_export.py::TestOneOffModelExportResult::test_warning, test/export/test_export.py::TestExportCustomClass::test_export_script_module, test/export/test_export.py::TestExportCustomClass::test_export_unbacked_lt, test/export/test_export.py::TestExportCustomClass::test_int_lift_constant, test/export/test_export.py::TestExportCustomClass::test_is_fx_tracing, test/export/test_export.py::TestExportCustomClass::test_item, test/export/test_export.py::TestExportCustomClass::test_lift_custom_obj, test/export/test_export.py::TestExportCustomClass::test_preserve_cia_op, test/export/test_export.py::TestExportCustomClass::test_preserve_non_cia_op, test/export/test_export.py::TestExportCustomClass::test_unbacked_contiguous, test/export/test_export.py::TestExportCustomClass::test_unbacked_select_index 2025-09-07T06:46:54.9129163Z 2025-09-07T06:46:54.9129260Z Running inductor/test_memory_planning 1/1 ... [2025-09-07 06:46:54.903685] 2025-09-07T06:46:54.9129443Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:46:54.9129842Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_memory_planning.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:46:54.903899] 2025-09-07T06:47:16.2574831Z 2025-09-07T06:47:16.2580118Z inductor/test_memory_planning 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_memory_planning_1.1_f1d3cca29f24334a_.log 2025-09-07T06:47:16.2589162Z Running 4 items in this shard: test/inductor/test_memory_planning.py::TestMemoryPlanning::test_aoti, test/inductor/test_memory_planning.py::TestMemoryPlanning::test_cpp_wrapper, test/inductor/test_memory_planning.py::TestMemoryPlanning::test_python_wrapper, test/inductor/test_memory_planning.py::TestMemoryPlanning::test_unbacked_symint 2025-09-07T06:47:16.2589929Z 2025-09-07T06:47:16.2590053Z Running inductor/test_remote_cache 1/1 ... [2025-09-07 06:47:16.257526] 2025-09-07T06:47:16.2590638Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:47:16.2591260Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_remote_cache.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:47:16.257783] 2025-09-07T06:47:18.2260871Z 2025-09-07T06:47:18.2262839Z inductor/test_remote_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_remote_cache_1.1_108a791332ce411f_.log 2025-09-07T06:47:18.2264673Z Running 3 items in this shard: test/inductor/test_remote_cache.py::TestRemoteCache::test_failure_logging, test/inductor/test_remote_cache.py::TestRemoteCache::test_failure_no_sample, test/inductor/test_remote_cache.py::TestRemoteCache::test_normal_logging 2025-09-07T06:47:18.2265899Z 2025-09-07T06:47:18.2266736Z Running inductor/test_triton_syntax 1/1 ... [2025-09-07 06:47:18.226008] 2025-09-07T06:47:18.2267211Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:47:18.2268287Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_triton_syntax.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:47:18.226210] 2025-09-07T06:47:34.0183633Z 2025-09-07T06:47:34.0184846Z inductor/test_triton_syntax 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_syntax_1.1_77844ff904711e32_.log 2025-09-07T06:47:34.0185499Z Running 1 items in this shard: test/inductor/test_triton_syntax.py::TestTritonSyntacticallyValid::test_triton_sqrt 2025-09-07T06:47:34.0185736Z 2025-09-07T06:47:34.0187457Z Running test_torch 1/1 ... [2025-09-07 06:47:34.018329] 2025-09-07T06:47:34.0187668Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:47:34.0195265Z 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'] ... [2025-09-07 06:47:34.018552] 2025-09-07T06:49:06.5508947Z 2025-09-07T06:49:06.5513393Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_a7ca335bb1b02668_.log 2025-09-07T06:49:06.5615528Z Running 976 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_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_int32, 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, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_bool_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_bool_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_complex128_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_complex64_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float16_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float32_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float32_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float64_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_float64_uint8, 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, 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test/test_torch.py::TestTorchDeviceTypeCUDA::test_memory_format_to_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_memory_format_type_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_memory_format_type_shortcuts_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_module_share_memory_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cpu_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cpu_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cpu_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_deterministic_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_multinomial_deterministic_cuda_float32, 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test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_AdaptiveAvgPool3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_AdaptiveMaxPool2d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_AvgPool3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_CTCLoss_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_EmbeddingBag_max_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_FractionalMaxPool2d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_FractionalMaxPool3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxPool3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool1d_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool1d_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool1d_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool2d_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool2d_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool2d_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool3d_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool3d_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_MaxUnpool3d_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_NLLLoss_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_ReflectionPad1d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_ReflectionPad3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_ReplicationPad1d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_ReplicationPad2d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_ReplicationPad3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_bincount_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_grid_sample_2d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_grid_sample_3d_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_histc_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_histc_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_interpolate_bicubic_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_interpolate_bilinear_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_interpolate_linear_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_interpolate_trilinear_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_kthvalue_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_median_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_put_accumulate_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_alert_put_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_resize_quantized_cuda_qint32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_resize_quantized_cuda_qint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_resize_quantized_cuda_quint2x4, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_resize_quantized_cuda_quint4x2, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nondeterministic_resize_quantized_cuda_quint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_normal_kstest_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_normal_kstest_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_normal_kstest_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_normal_kstest_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_nullary_op_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_pairwise_distance_empty_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_parallel_cow_materialize_error_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_and_graph_partition_AdamW_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_and_graph_partition_Adam_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_and_graph_partition_SGD_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_AdamW_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_Adam_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_SGD_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_pdist_empty_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_pdist_norm_large_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_pickle_gradscaler_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_pin_memory_from_constructor_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_accumulate_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_empty_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_put_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_reduced_type_float_copy_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_reduced_type_float_copy_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_repeat_interleave_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scalar_check_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_add_bool_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_add_non_unique_index_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_add_one_dim_deterministic_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_add_to_large_input_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_bool_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_multiply_unsupported_dtypes_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_multiply_unsupported_dtypes_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_non_unique_index_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_operations_to_large_input_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_reduce_scalar_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_to_large_input_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_scatter_zero_size_index_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_serialization_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_default_tensor_type_warnings_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_set_storage_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_shift_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_skip_xla_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_all_devices_non_blocking_False_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_all_devices_non_blocking_True_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_uint16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_uint32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_uint64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_errors_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_from_tensor_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_meta_ok_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_qint32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_qint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_quint4x2, 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 2025-09-07T06:49:06.5710556Z 2025-09-07T06:49:06.5710627Z Running doctests 1/1 ... [2025-09-07 06:49:06.551963] 2025-09-07T06:49:06.5710833Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2025-09-07T06:49:06.5716755Z Listing tests 2025-09-07T06:49:06.6190437Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=565. 2025-09-07T06:49:06.6190767Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6191007Z 2025-09-07T06:49:06.6191081Z Load a model from a github repo or a local directory. 2025-09-07T06:49:06.6191181Z 2025-09-07T06:49:06.6191264Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T06:49:06.6191461Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T06:49:06.6191576Z 2025-09-07T06:49:06.6191643Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T06:49:06.6191807Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T06:49:06.6191957Z ref (a tag or a branch). 2025-09-07T06:49:06.6192023Z 2025-09-07T06:49:06.6192096Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T06:49:06.6192247Z path to a local directory. 2025-09-07T06:49:06.6192410Z 2025-09-07T06:49:06.6192451Z Args: 2025-09-07T06:49:06.6192562Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T06:49:06.6195651Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T06:49:06.6195934Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T06:49:06.6196180Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T06:49:06.6196409Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T06:49:06.6196606Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T06:49:06.6196767Z repo/dir's ``hubconf.py``. 2025-09-07T06:49:06.6196925Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T06:49:06.6197196Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T06:49:06.6197384Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T06:49:06.6197566Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T06:49:06.6197776Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T06:49:06.6197958Z only run code from repos that they trust. 2025-09-07T06:49:06.6198052Z 2025-09-07T06:49:06.6198138Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T06:49:06.6198291Z be trusted. 2025-09-07T06:49:06.6198432Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T06:49:06.6198598Z without requiring explicit confirmation. 2025-09-07T06:49:06.6198759Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T06:49:06.6198942Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T06:49:06.6199145Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T06:49:06.6199330Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T06:49:06.6199511Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T06:49:06.6199700Z is only present for backward compatibility and will be removed in 2025-09-07T06:49:06.6199855Z v2.0. 2025-09-07T06:49:06.6199913Z 2025-09-07T06:49:06.6199996Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T06:49:06.6200191Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T06:49:06.6200383Z the github repo unconditionally. Does not have any effect if 2025-09-07T06:49:06.6200546Z ``source = 'local'``. Default is ``False``. 2025-09-07T06:49:06.6200716Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T06:49:06.6203123Z local caches. Note that the message about first download cannot be 2025-09-07T06:49:06.6203313Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T06:49:06.6203451Z Default is ``True``. 2025-09-07T06:49:06.6203622Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T06:49:06.6203860Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T06:49:06.6204090Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T06:49:06.6204284Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T06:49:06.6204568Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T06:49:06.6204680Z 2025-09-07T06:49:06.6204714Z Returns: 2025-09-07T06:49:06.6204832Z The output of the ``model`` callable when called with the given 2025-09-07T06:49:06.6204975Z ``*args`` and ``**kwargs``. 2025-09-07T06:49:06.6205048Z 2025-09-07T06:49:06.6205083Z Example: 2025-09-07T06:49:06.6205229Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:06.6205354Z >>> # from a github repo 2025-09-07T06:49:06.6205458Z >>> repo = "pytorch/vision" 2025-09-07T06:49:06.6205561Z >>> model = torch.hub.load( 2025-09-07T06:49:06.6205695Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T06:49:06.6205825Z ... ) 2025-09-07T06:49:06.6205909Z >>> # from a local directory 2025-09-07T06:49:06.6206024Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T06:49:06.6206144Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.6206294Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T06:49:06.6206418Z 2025-09-07T06:49:06.6206507Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6206628Z 2025-09-07T06:49:06.6206784Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=657. 2025-09-07T06:49:06.6207117Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6207245Z 2025-09-07T06:49:06.6207309Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T06:49:06.6207409Z 2025-09-07T06:49:06.6207440Z Args: 2025-09-07T06:49:06.6207553Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T06:49:06.6207693Z ``hubconf.py``. 2025-09-07T06:49:06.6207820Z model (str): name of an entrypoint defined in the directory's 2025-09-07T06:49:06.6207965Z ``hubconf.py``. 2025-09-07T06:49:06.6208108Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T06:49:06.6208302Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T06:49:06.6208418Z 2025-09-07T06:49:06.6208459Z Returns: 2025-09-07T06:49:06.6208572Z a single model with corresponding pretrained weights. 2025-09-07T06:49:06.6208683Z 2025-09-07T06:49:06.6208720Z Example: 2025-09-07T06:49:06.6208827Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T06:49:06.6210628Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T06:49:06.6210758Z >>> model = _load_local( 2025-09-07T06:49:06.6210856Z ... path, 2025-09-07T06:49:06.6210942Z ... "resnet50", 2025-09-07T06:49:06.6211053Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T06:49:06.6211170Z ... ) 2025-09-07T06:49:06.6211216Z 2025-09-07T06:49:06.6211304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6211425Z 2025-09-07T06:49:06.6211587Z 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=696. 2025-09-07T06:49:06.6211868Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6212047Z Download object at the given URL to a local path. 2025-09-07T06:49:06.6212141Z 2025-09-07T06:49:06.6212173Z Args: 2025-09-07T06:49:06.6213383Z url (str): URL of the object to download 2025-09-07T06:49:06.6213547Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T06:49:06.6213780Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T06:49:06.6213967Z Default: None 2025-09-07T06:49:06.6214115Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T06:49:06.6214273Z Default: True 2025-09-07T06:49:06.6214332Z 2025-09-07T06:49:06.6214448Z Example: 2025-09-07T06:49:06.6214547Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:06.6214674Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T06:49:06.6214795Z >>> torch.hub.download_url_to_file( 2025-09-07T06:49:06.6214970Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T06:49:06.6216213Z ... "/tmp/temporary_file", 2025-09-07T06:49:06.6216371Z ... ) 2025-09-07T06:49:06.6216421Z 2025-09-07T06:49:06.6216452Z 2025-09-07T06:49:06.6216581Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6216703Z 2025-09-07T06:49:06.6216868Z 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=821. 2025-09-07T06:49:06.6217155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6217334Z Loads the Torch serialized object at the given URL. 2025-09-07T06:49:06.6217424Z 2025-09-07T06:49:06.6217493Z If downloaded file is a zip file, it will be automatically 2025-09-07T06:49:06.6217626Z decompressed. 2025-09-07T06:49:06.6217679Z 2025-09-07T06:49:06.6217756Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T06:49:06.6217897Z returned. 2025-09-07T06:49:06.6219092Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T06:49:06.6219280Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T06:49:06.6219386Z 2025-09-07T06:49:06.6219418Z Args: 2025-09-07T06:49:06.6219509Z url (str): URL of the object to download 2025-09-07T06:49:06.6219658Z model_dir (str, optional): directory in which to save the object 2025-09-07T06:49:06.6219884Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T06:49:06.6220126Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T06:49:06.6220287Z Default: True 2025-09-07T06:49:06.6220458Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T06:49:06.6220685Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T06:49:06.6220886Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T06:49:06.6222091Z ensure unique names and to verify the contents of the file. 2025-09-07T06:49:06.6222231Z Default: False 2025-09-07T06:49:06.6222408Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T06:49:06.6222665Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T06:49:06.6222899Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T06:49:06.6223024Z 2025-09-07T06:49:06.6223058Z Example: 2025-09-07T06:49:06.6223159Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:06.6223299Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T06:49:06.6223466Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T06:49:06.6223613Z ... ) 2025-09-07T06:49:06.6223661Z 2025-09-07T06:49:06.6224796Z 2025-09-07T06:49:06.6224936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6225058Z 2025-09-07T06:49:06.6674220Z 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. 2025-09-07T06:49:06.6675249Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6675612Z 2025-09-07T06:49:06.6675761Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T06:49:06.6675995Z 2025-09-07T06:49:06.6676228Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T06:49:06.6676679Z physical layout of :attr:`self`. 2025-09-07T06:49:06.6676884Z 2025-09-07T06:49:06.6677139Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T06:49:06.6677668Z starting from the outermost to the innermost dimension. 2025-09-07T06:49:06.6677954Z 2025-09-07T06:49:06.6678333Z Note that the dim order may not always be uniquely determined. 2025-09-07T06:49:06.6684313Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T06:49:06.6684946Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T06:49:06.6685211Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T06:49:06.6685448Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T06:49:06.6685639Z Otherwise, it will raise TypeError. 2025-09-07T06:49:06.6685719Z 2025-09-07T06:49:06.6685750Z Args: 2025-09-07T06:49:06.6685910Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T06:49:06.6686056Z 2025-09-07T06:49:06.6686100Z Examples:: 2025-09-07T06:49:06.6686216Z 2025-09-07T06:49:06.6686262Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T06:49:06.6686378Z (0, 1, 2, 3) 2025-09-07T06:49:06.6686494Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T06:49:06.6686619Z (0, 2, 1, 3) 2025-09-07T06:49:06.6686743Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T06:49:06.6686882Z (0, 2, 3, 1) 2025-09-07T06:49:06.6688386Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T06:49:06.6688497Z (0, 1, 2, 3) 2025-09-07T06:49:06.6688576Z >>> try: 2025-09-07T06:49:06.6688688Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T06:49:06.6688828Z ... except RuntimeError as e: 2025-09-07T06:49:06.6688934Z ... print(e) 2025-09-07T06:49:06.6689099Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T06:49:06.6689281Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T06:49:06.6689439Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T06:49:06.6689598Z ... ) # It can be mapped to contiguous format 2025-09-07T06:49:06.6689711Z (0, 1, 2, 3) 2025-09-07T06:49:06.6690873Z >>> try: 2025-09-07T06:49:06.6690987Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T06:49:06.6691129Z ... except TypeError as e: 2025-09-07T06:49:06.6691231Z ... print(e) 2025-09-07T06:49:06.6691367Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T06:49:06.6691487Z 2025-09-07T06:49:06.6691521Z .. warning:: 2025-09-07T06:49:06.6691640Z The dim_order tensor API is experimental and subject to change. 2025-09-07T06:49:06.6691745Z 2025-09-07T06:49:06.6691833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6691955Z 2025-09-07T06:49:06.6861393Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=397. 2025-09-07T06:49:06.6862296Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6868588Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T06:49:06.6868944Z 2025-09-07T06:49:06.6869152Z This is helpful when you want to visualize data over some 2025-09-07T06:49:06.6869635Z range of inputs. See below for a plotting example. 2025-09-07T06:49:06.6869914Z 2025-09-07T06:49:06.6870107Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T06:49:06.6870584Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T06:49:06.6871196Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T06:49:06.6871669Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T06:49:06.6872138Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T06:49:06.6872555Z to the result shape. 2025-09-07T06:49:06.6872761Z 2025-09-07T06:49:06.6872870Z .. note:: 2025-09-07T06:49:06.6873368Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T06:49:06.6873738Z single element. 2025-09-07T06:49:06.6873923Z 2025-09-07T06:49:06.6874033Z .. warning:: 2025-09-07T06:49:06.6876941Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T06:49:06.6877112Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T06:49:06.6877207Z 2025-09-07T06:49:06.6877266Z In the future `torch.meshgrid` will transition to 2025-09-07T06:49:06.6877408Z `indexing='xy'` as the default. 2025-09-07T06:49:06.6877486Z 2025-09-07T06:49:06.6877561Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T06:49:06.6877725Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T06:49:06.6877826Z 2025-09-07T06:49:06.6877862Z .. seealso:: 2025-09-07T06:49:06.6877981Z 2025-09-07T06:49:06.6878049Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T06:49:06.6878196Z collects the data in a tensor of vectors. 2025-09-07T06:49:06.6878286Z 2025-09-07T06:49:06.6878319Z Args: 2025-09-07T06:49:06.6878462Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T06:49:06.6878655Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T06:49:06.6878750Z 2025-09-07T06:49:06.6878815Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T06:49:06.6878975Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T06:49:06.6879071Z 2025-09-07T06:49:06.6879128Z If "xy" is selected, the first dimension corresponds 2025-09-07T06:49:06.6879275Z to the cardinality of the second input and the second 2025-09-07T06:49:06.6879425Z dimension corresponds to the cardinality of the first 2025-09-07T06:49:06.6879561Z input. 2025-09-07T06:49:06.6879624Z 2025-09-07T06:49:06.6879679Z If "ij" is selected, the dimensions are in the same 2025-09-07T06:49:06.6879818Z order as the cardinality of the inputs. 2025-09-07T06:49:06.6881313Z 2025-09-07T06:49:06.6881352Z Returns: 2025-09-07T06:49:06.6881470Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T06:49:06.6881624Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T06:49:06.6881782Z output will also have :math:`N` tensors, where each tensor 2025-09-07T06:49:06.6881930Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T06:49:06.6882016Z 2025-09-07T06:49:06.6882051Z Example:: 2025-09-07T06:49:06.6882105Z 2025-09-07T06:49:06.6882150Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T06:49:06.6882266Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T06:49:06.6882346Z 2025-09-07T06:49:06.6882418Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T06:49:06.6882578Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T06:49:06.6882704Z cartesian product. 2025-09-07T06:49:06.6883940Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T06:49:06.6884068Z >>> grid_x 2025-09-07T06:49:06.6884163Z tensor([[1, 1, 1], 2025-09-07T06:49:06.6884262Z [2, 2, 2], 2025-09-07T06:49:06.6884456Z [3, 3, 3]]) 2025-09-07T06:49:06.6884552Z >>> grid_y 2025-09-07T06:49:06.6884644Z tensor([[4, 5, 6], 2025-09-07T06:49:06.6884739Z [4, 5, 6], 2025-09-07T06:49:06.6884834Z [4, 5, 6]]) 2025-09-07T06:49:06.6884899Z 2025-09-07T06:49:06.6884962Z This correspondence can be seen when these grids are 2025-09-07T06:49:06.6885098Z stacked properly. 2025-09-07T06:49:06.6886344Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T06:49:06.6886553Z ... torch.cartesian_prod(x, y)) 2025-09-07T06:49:06.6886671Z True 2025-09-07T06:49:06.6886724Z 2025-09-07T06:49:06.6886812Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T06:49:06.6886944Z plotting. 2025-09-07T06:49:06.6887053Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T06:49:06.6887184Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T06:49:06.6887317Z >>> import matplotlib.pyplot as plt 2025-09-07T06:49:06.6887446Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T06:49:06.6887573Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T06:49:06.6888786Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T06:49:06.6888928Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T06:49:06.6889056Z >>> ax = plt.axes(projection='3d') 2025-09-07T06:49:06.6889243Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T06:49:06.6889373Z >>> plt.show() 2025-09-07T06:49:06.6889438Z 2025-09-07T06:49:06.6889491Z .. image:: ../_static/img/meshgrid.png 2025-09-07T06:49:06.6889605Z :width: 512 2025-09-07T06:49:06.6889662Z 2025-09-07T06:49:06.6889694Z 2025-09-07T06:49:06.6889827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6889953Z 2025-09-07T06:49:06.6890122Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=793. 2025-09-07T06:49:06.6890408Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6891724Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T06:49:06.6891892Z 2025-09-07T06:49:06.6891957Z Returns the unique elements of the input tensor. 2025-09-07T06:49:06.6892052Z 2025-09-07T06:49:06.6892158Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T06:49:06.6892368Z this function also eliminates non-consecutive duplicate values. 2025-09-07T06:49:06.6892476Z 2025-09-07T06:49:06.6892556Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T06:49:06.6892768Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T06:49:06.6892998Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T06:49:06.6893195Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T06:49:06.6893294Z 2025-09-07T06:49:06.6893327Z Args: 2025-09-07T06:49:06.6893414Z input (Tensor): the input tensor 2025-09-07T06:49:06.6893569Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T06:49:06.6894848Z before returning as output. 2025-09-07T06:49:06.6895003Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T06:49:06.6895190Z elements in the original input ended up in the returned unique list. 2025-09-07T06:49:06.6895380Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T06:49:06.6895525Z element. 2025-09-07T06:49:06.6895655Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T06:49:06.6895833Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T06:49:06.6896016Z tensors indexed by the given dimension is treated as one of the 2025-09-07T06:49:06.6896195Z elements to apply the unique operation upon. See examples for more 2025-09-07T06:49:06.6896345Z details. Default: ``None`` 2025-09-07T06:49:06.6896420Z 2025-09-07T06:49:06.6896453Z Returns: 2025-09-07T06:49:06.6897697Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T06:49:06.6897834Z 2025-09-07T06:49:06.6897905Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T06:49:06.6898064Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T06:49:06.6898217Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T06:49:06.6898389Z returned tensor (same shape as input) representing the indices 2025-09-07T06:49:06.6898564Z for where elements in the original input map to in the output; 2025-09-07T06:49:06.6898736Z otherwise, this function will only return a single tensor. 2025-09-07T06:49:06.6898884Z - **counts** (*Tensor*): (optional) if 2025-09-07T06:49:06.6899030Z :attr:`return_counts` is True, there will be an additional 2025-09-07T06:49:06.6899191Z returned tensor (same shape as output or output.size(dim), 2025-09-07T06:49:06.6899399Z if dim was specified) representing the number of occurrences 2025-09-07T06:49:06.6900588Z for each unique value or tensor. 2025-09-07T06:49:06.6900669Z 2025-09-07T06:49:06.6900706Z Example:: 2025-09-07T06:49:06.6900756Z 2025-09-07T06:49:06.6900834Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T06:49:06.6900979Z >>> output 2025-09-07T06:49:06.6901068Z tensor([1, 2, 3]) 2025-09-07T06:49:06.6901131Z 2025-09-07T06:49:06.6901181Z >>> output, inverse_indices = torch.unique( 2025-09-07T06:49:06.6901351Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T06:49:06.6901505Z >>> output 2025-09-07T06:49:06.6901591Z tensor([1, 2, 3]) 2025-09-07T06:49:06.6901689Z >>> inverse_indices 2025-09-07T06:49:06.6901789Z tensor([0, 2, 1, 2]) 2025-09-07T06:49:06.6902885Z 2025-09-07T06:49:06.6902943Z >>> output, inverse_indices = torch.unique( 2025-09-07T06:49:06.6903116Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T06:49:06.6903268Z >>> output 2025-09-07T06:49:06.6903355Z tensor([1, 2, 3]) 2025-09-07T06:49:06.6903451Z >>> inverse_indices 2025-09-07T06:49:06.6903549Z tensor([[0, 2], 2025-09-07T06:49:06.6903640Z [1, 2]]) 2025-09-07T06:49:06.6903698Z 2025-09-07T06:49:06.6903737Z >>> a = torch.tensor([ 2025-09-07T06:49:06.6903835Z ... [ 2025-09-07T06:49:06.6903922Z ... [1, 1, 0, 0], 2025-09-07T06:49:06.6904026Z ... [1, 1, 0, 0], 2025-09-07T06:49:06.6905263Z ... [0, 0, 1, 1], 2025-09-07T06:49:06.6905361Z ... ], 2025-09-07T06:49:06.6905442Z ... [ 2025-09-07T06:49:06.6905526Z ... [0, 0, 1, 1], 2025-09-07T06:49:06.6905625Z ... [0, 0, 1, 1], 2025-09-07T06:49:06.6905727Z ... [1, 1, 1, 1], 2025-09-07T06:49:06.6905821Z ... ], 2025-09-07T06:49:06.6905903Z ... [ 2025-09-07T06:49:06.6905985Z ... [1, 1, 0, 0], 2025-09-07T06:49:06.6906082Z ... [1, 1, 0, 0], 2025-09-07T06:49:06.6906178Z ... [0, 0, 1, 1], 2025-09-07T06:49:06.6907313Z ... ], 2025-09-07T06:49:06.6907395Z ... ]) 2025-09-07T06:49:06.6907445Z 2025-09-07T06:49:06.6907525Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T06:49:06.6907712Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T06:49:06.6907870Z >>> # each other, so one of them will be removed. 2025-09-07T06:49:06.6907997Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T06:49:06.6908105Z tensor(True) 2025-09-07T06:49:06.6908206Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T06:49:06.6908323Z >>> a_unique_dim0 2025-09-07T06:49:06.6908420Z tensor([[[0, 0, 1, 1], 2025-09-07T06:49:06.6909605Z [0, 0, 1, 1], 2025-09-07T06:49:06.6909711Z [1, 1, 1, 1]], 2025-09-07T06:49:06.6909809Z [[1, 1, 0, 0], 2025-09-07T06:49:06.6909904Z [1, 1, 0, 0], 2025-09-07T06:49:06.6910001Z [0, 0, 1, 1]]]) 2025-09-07T06:49:06.6910068Z 2025-09-07T06:49:06.6910146Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T06:49:06.6910294Z >>> # `a_unique_dim0`: 2025-09-07T06:49:06.6910406Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T06:49:06.6910520Z tensor(True) 2025-09-07T06:49:06.6910621Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T06:49:06.6910734Z tensor(True) 2025-09-07T06:49:06.6910790Z 2025-09-07T06:49:06.6911987Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T06:49:06.6912171Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T06:49:06.6912363Z >>> # them will be removed. 2025-09-07T06:49:06.6912477Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T06:49:06.6912582Z tensor(True) 2025-09-07T06:49:06.6912677Z >>> torch.unique(a, dim=1) 2025-09-07T06:49:06.6912784Z tensor([[[0, 0, 1, 1], 2025-09-07T06:49:06.6912883Z [1, 1, 0, 0]], 2025-09-07T06:49:06.6912980Z [[1, 1, 1, 1], 2025-09-07T06:49:06.6913075Z [0, 0, 1, 1]], 2025-09-07T06:49:06.6913170Z [[0, 0, 1, 1], 2025-09-07T06:49:06.6914316Z [1, 1, 0, 0]]]) 2025-09-07T06:49:06.6914461Z 2025-09-07T06:49:06.6914538Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T06:49:06.6914711Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T06:49:06.6914867Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T06:49:06.6915018Z >>> # sub-tensors will be removed. 2025-09-07T06:49:06.6915135Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T06:49:06.6915240Z tensor(True) 2025-09-07T06:49:06.6915333Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T06:49:06.6915437Z tensor(True) 2025-09-07T06:49:06.6915531Z >>> torch.unique(a, dim=2) 2025-09-07T06:49:06.6915636Z tensor([[[0, 1], 2025-09-07T06:49:06.6916801Z [0, 1], 2025-09-07T06:49:06.6916897Z [1, 0]], 2025-09-07T06:49:06.6916989Z [[1, 0], 2025-09-07T06:49:06.6917078Z [1, 0], 2025-09-07T06:49:06.6917166Z [1, 1]], 2025-09-07T06:49:06.6917256Z [[0, 1], 2025-09-07T06:49:06.6917344Z [0, 1], 2025-09-07T06:49:06.6917433Z [1, 0]]]) 2025-09-07T06:49:06.6917521Z 2025-09-07T06:49:06.6917653Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.6917780Z 2025-09-07T06:49:06.6986672Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1285. 2025-09-07T06:49:06.6986965Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.6988922Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T06:49:06.6989072Z 2025-09-07T06:49:06.6989140Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T06:49:06.6989242Z 2025-09-07T06:49:06.6989325Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T06:49:06.6989530Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T06:49:06.6989730Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T06:49:06.6989924Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T06:49:06.6990211Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T06:49:06.6990391Z set of devices using the :attr:`map_location` argument. 2025-09-07T06:49:06.6990490Z 2025-09-07T06:49:06.6990579Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T06:49:06.6990779Z storage with two arguments: storage and location. The storage argument 2025-09-07T06:49:06.6993390Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T06:49:06.6993591Z Each serialized storage has a location tag associated with it which 2025-09-07T06:49:06.6993776Z identifies the device it was saved from, and this tag is the second 2025-09-07T06:49:06.6993976Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T06:49:06.6994188Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T06:49:06.6994544Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T06:49:06.6994745Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T06:49:06.6994983Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T06:49:06.6995191Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T06:49:06.6995312Z 2025-09-07T06:49:06.6995396Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T06:49:06.6995592Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T06:49:06.6995707Z 2025-09-07T06:49:06.6995797Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T06:49:06.6995996Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T06:49:06.6996139Z storages (values). 2025-09-07T06:49:06.6996200Z 2025-09-07T06:49:06.6996283Z User extensions can register their own location tags and tagging and 2025-09-07T06:49:06.6996490Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T06:49:06.6996618Z 2025-09-07T06:49:06.6996704Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T06:49:06.6996827Z 2025-09-07T06:49:06.6996860Z Args: 2025-09-07T06:49:06.6998298Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T06:49:06.6998517Z or a string or os.PathLike object containing a file name 2025-09-07T06:49:06.6998730Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T06:49:06.6998913Z locations 2025-09-07T06:49:06.6999053Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T06:49:06.6999235Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T06:49:06.6999421Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T06:49:06.6999594Z loading only tensors, primitive types, dictionaries 2025-09-07T06:49:06.6999768Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T06:49:06.6999934Z See :ref:`weights-only` for more details. 2025-09-07T06:49:06.7000132Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T06:49:06.7001433Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T06:49:06.7001702Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T06:49:06.7001969Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T06:49:06.7002230Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T06:49:06.7002481Z will be lazily loaded when their data is accessed. 2025-09-07T06:49:06.7002660Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T06:49:06.7002860Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T06:49:06.7003012Z :attr:`errors=...`. 2025-09-07T06:49:06.7003081Z 2025-09-07T06:49:06.7003118Z .. warning:: 2025-09-07T06:49:06.7003246Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T06:49:06.7003428Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T06:49:06.7004748Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T06:49:06.7004961Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T06:49:06.7005177Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T06:49:06.7005363Z 2025-09-07T06:49:06.7005398Z .. note:: 2025-09-07T06:49:06.7005539Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T06:49:06.7005755Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T06:49:06.7005974Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T06:49:06.7006105Z 2025-09-07T06:49:06.7006138Z .. note:: 2025-09-07T06:49:06.7006275Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T06:49:06.7006473Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T06:49:06.7006657Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T06:49:06.7007856Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T06:49:06.7008076Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T06:49:06.7008282Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T06:49:06.7008481Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T06:49:06.7008596Z 2025-09-07T06:49:06.7008631Z Example: 2025-09-07T06:49:06.7008732Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T06:49:06.7008871Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T06:49:06.7009004Z # Load all tensors onto the CPU 2025-09-07T06:49:06.7009117Z >>> torch.load( 2025-09-07T06:49:06.7009215Z ... "tensors.pt", 2025-09-07T06:49:06.7009328Z ... map_location=torch.device("cpu"), 2025-09-07T06:49:06.7010424Z ... weights_only=True, 2025-09-07T06:49:06.7010530Z ... ) 2025-09-07T06:49:06.7010634Z # Load all tensors onto the CPU, using a function 2025-09-07T06:49:06.7010759Z >>> torch.load( 2025-09-07T06:49:06.7010857Z ... "tensors.pt", 2025-09-07T06:49:06.7010974Z ... map_location=lambda storage, loc: storage, 2025-09-07T06:49:06.7011100Z ... weights_only=True, 2025-09-07T06:49:06.7011200Z ... ) 2025-09-07T06:49:06.7011289Z # Load all tensors onto GPU 1 2025-09-07T06:49:06.7011397Z >>> torch.load( 2025-09-07T06:49:06.7011490Z ... "tensors.pt", 2025-09-07T06:49:06.7012585Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T06:49:06.7012720Z ... weights_only=True, 2025-09-07T06:49:06.7012833Z ... ) # type: ignore[attr-defined] 2025-09-07T06:49:06.7012953Z # Map tensors from GPU 1 to GPU 0 2025-09-07T06:49:06.7013063Z >>> torch.load( 2025-09-07T06:49:06.7013156Z ... "tensors.pt", 2025-09-07T06:49:06.7013266Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T06:49:06.7013388Z ... weights_only=True, 2025-09-07T06:49:06.7013485Z ... ) 2025-09-07T06:49:06.7013618Z # Load tensor from io.BytesIO object 2025-09-07T06:49:06.7013784Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T06:49:06.7015153Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T06:49:06.7015274Z ... buffer = io.BytesIO(f.read()) 2025-09-07T06:49:06.7015396Z >>> torch.load(buffer, weights_only=False) 2025-09-07T06:49:06.7015536Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T06:49:06.7015711Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T06:49:06.7015903Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T06:49:06.7016038Z 2025-09-07T06:49:06.7016169Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7016291Z 2025-09-07T06:49:06.7026293Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=375. 2025-09-07T06:49:06.7026591Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.7028442Z Registers the function implementation as the fallback for the given key. 2025-09-07T06:49:06.7028565Z 2025-09-07T06:49:06.7028645Z This function only works for a library with global namespace ("_"). 2025-09-07T06:49:06.7028757Z 2025-09-07T06:49:06.7028793Z Args: 2025-09-07T06:49:06.7028941Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T06:49:06.7029120Z to register a fallthrough. 2025-09-07T06:49:06.7029307Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T06:49:06.7029516Z the dispatch key that the library was created with. 2025-09-07T06:49:06.7029745Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T06:49:06.7030033Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T06:49:06.7030180Z 2025-09-07T06:49:06.7030221Z Example:: 2025-09-07T06:49:06.7030276Z 2025-09-07T06:49:06.7031593Z >>> my_lib = Library("_", "IMPL") 2025-09-07T06:49:06.7031733Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T06:49:06.7031868Z >>> # Handle all autocast ops generically 2025-09-07T06:49:06.7031986Z >>> # ... 2025-09-07T06:49:06.7032098Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T06:49:06.7032217Z 2025-09-07T06:49:06.7032471Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T06:49:06.7032716Z 2025-09-07T06:49:06.7032767Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T06:49:06.7032879Z ^ 2025-09-07T06:49:06.7062767Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=948. 2025-09-07T06:49:06.7063056Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.7065145Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T06:49:06.7065262Z 2025-09-07T06:49:06.7065330Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T06:49:06.7065431Z 2025-09-07T06:49:06.7065515Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T06:49:06.7065714Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T06:49:06.7065909Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T06:49:06.7066079Z what the properties of the output Tensors are. 2025-09-07T06:49:06.7066172Z 2025-09-07T06:49:06.7066349Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T06:49:06.7066535Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T06:49:06.7066721Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T06:49:06.7066914Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T06:49:06.7067103Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T06:49:06.7067289Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T06:49:06.7067471Z (and may not directly access the storage or data of any input or 2025-09-07T06:49:06.7067613Z intermediate Tensors). 2025-09-07T06:49:06.7067677Z 2025-09-07T06:49:06.7067736Z This API may be used as a decorator (see examples). 2025-09-07T06:49:06.7067829Z 2025-09-07T06:49:06.7067881Z For a detailed guide on custom ops, please see 2025-09-07T06:49:06.7068140Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T06:49:06.7070248Z 2025-09-07T06:49:06.7070283Z Args: 2025-09-07T06:49:06.7070408Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T06:49:06.7070566Z func: Fake tensor implementation. 2025-09-07T06:49:06.7070715Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T06:49:06.7070955Z allow_override: Flag controlling if we want to override an 2025-09-07T06:49:06.7071120Z existing registered fake impl. This is by default off, 2025-09-07T06:49:06.7071282Z and will error you're trying to register a fake impl to 2025-09-07T06:49:06.7071442Z an operator that already has a fake impl. This also only 2025-09-07T06:49:06.7071597Z applies if the custom operator was not created via 2025-09-07T06:49:06.7071763Z torch.library.custom_op, as overriding and existing fake 2025-09-07T06:49:06.7071918Z impl is already allowed. 2025-09-07T06:49:06.7071996Z 2025-09-07T06:49:06.7072031Z Examples: 2025-09-07T06:49:06.7072116Z >>> import torch 2025-09-07T06:49:06.7072218Z >>> import numpy as np 2025-09-07T06:49:06.7072327Z >>> from torch import Tensor 2025-09-07T06:49:06.7073697Z >>> 2025-09-07T06:49:06.7073813Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T06:49:06.7073994Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T06:49:06.7074184Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T06:49:06.7074471Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T06:49:06.7074603Z >>> 2025-09-07T06:49:06.7074712Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T06:49:06.7074854Z >>> def _(x, weight, bias): 2025-09-07T06:49:06.7074967Z >>> assert x.dim() == 2 2025-09-07T06:49:06.7075083Z >>> assert weight.dim() == 2 2025-09-07T06:49:06.7075197Z >>> assert bias.dim() == 1 2025-09-07T06:49:06.7076390Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T06:49:06.7076526Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T06:49:06.7076656Z >>> assert x.device == weight.device 2025-09-07T06:49:06.7076766Z >>> 2025-09-07T06:49:06.7076856Z >>> return (x @ weight.t()) + bias 2025-09-07T06:49:06.7076963Z >>> 2025-09-07T06:49:06.7077070Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T06:49:06.7077209Z >>> x = torch.randn(2, 3) 2025-09-07T06:49:06.7077319Z >>> w = torch.randn(3, 3) 2025-09-07T06:49:06.7077428Z >>> b = torch.randn(3) 2025-09-07T06:49:06.7077550Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T06:49:06.7078685Z >>> 2025-09-07T06:49:06.7078830Z >>> assert y.shape == (2, 3) 2025-09-07T06:49:06.7078935Z >>> 2025-09-07T06:49:06.7079044Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T06:49:06.7081572Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T06:49:06.7081739Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T06:49:06.7081868Z >>> x_np = x.numpy(force=True) 2025-09-07T06:49:06.7081995Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T06:49:06.7082132Z >>> return torch.tensor(res, device=x.device) 2025-09-07T06:49:06.7082247Z >>> 2025-09-07T06:49:06.7082359Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T06:49:06.7083866Z >>> def _(x): 2025-09-07T06:49:06.7083987Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T06:49:06.7084136Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T06:49:06.7084444Z >>> # we use the ctx object to construct a new symint that 2025-09-07T06:49:06.7084585Z >>> # represents the data-dependent size. 2025-09-07T06:49:06.7084714Z >>> ctx = torch.library.get_ctx() 2025-09-07T06:49:06.7084836Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T06:49:06.7084953Z >>> shape = [nnz, x.dim()] 2025-09-07T06:49:06.7085083Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T06:49:06.7085212Z >>> return result 2025-09-07T06:49:06.7085306Z >>> 2025-09-07T06:49:06.7086593Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T06:49:06.7086724Z >>> 2025-09-07T06:49:06.7086811Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T06:49:06.7086976Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T06:49:06.7087145Z >>> trace.print_readable() 2025-09-07T06:49:06.7087252Z >>> 2025-09-07T06:49:06.7087376Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T06:49:06.7087495Z 2025-09-07T06:49:06.7087526Z 2025-09-07T06:49:06.7087744Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T06:49:06.7087952Z 2025-09-07T06:49:06.7087984Z _._ = None 2025-09-07T06:49:06.7088058Z ^ 2025-09-07T06:49:06.7089375Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1083. 2025-09-07T06:49:06.7089673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7089854Z Register a backward formula for this custom op. 2025-09-07T06:49:06.7089942Z 2025-09-07T06:49:06.7090016Z In order for an operator to work with autograd, you need to register 2025-09-07T06:49:06.7090161Z a backward formula: 2025-09-07T06:49:06.7090300Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T06:49:06.7090453Z by providing us a "backward" function. 2025-09-07T06:49:06.7090606Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T06:49:06.7090770Z use `setup_context` to save values for backward. 2025-09-07T06:49:06.7090858Z 2025-09-07T06:49:06.7090941Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T06:49:06.7092306Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T06:49:06.7092479Z the number of outputs of the operator. 2025-09-07T06:49:06.7092649Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T06:49:06.7092852Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T06:49:06.7093032Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T06:49:06.7093128Z 2025-09-07T06:49:06.7093209Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T06:49:06.7093465Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T06:49:06.7093663Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T06:49:06.7093852Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T06:49:06.7094031Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T06:49:06.7095756Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T06:49:06.7095877Z 2025-09-07T06:49:06.7095956Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T06:49:06.7096148Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T06:49:06.7096354Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T06:49:06.7096547Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T06:49:06.7096716Z 2025-09-07T06:49:06.7096798Z If you need different autograd behavior on different devices, then we 2025-09-07T06:49:06.7096990Z recommend creating two different custom operators, one for each device 2025-09-07T06:49:06.7097184Z that needs different behavior, and switching between them at runtime. 2025-09-07T06:49:06.7097309Z 2025-09-07T06:49:06.7097350Z Examples: 2025-09-07T06:49:06.7097436Z >>> import torch 2025-09-07T06:49:06.7097549Z >>> import numpy as np 2025-09-07T06:49:06.7097657Z >>> from torch import Tensor 2025-09-07T06:49:06.7097759Z >>> 2025-09-07T06:49:06.7097891Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T06:49:06.7098043Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T06:49:06.7098167Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:06.7099702Z >>> y_np = np.sin(x_np) 2025-09-07T06:49:06.7099836Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T06:49:06.7099967Z >>> 2025-09-07T06:49:06.7100074Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T06:49:06.7100203Z >>> x, = inputs 2025-09-07T06:49:06.7100306Z >>> ctx.save_for_backward(x) 2025-09-07T06:49:06.7100413Z >>> 2025-09-07T06:49:06.7100503Z >>> def backward(ctx, grad): 2025-09-07T06:49:06.7100628Z >>> x, = ctx.saved_tensors 2025-09-07T06:49:06.7100739Z >>> return grad * x.cos() 2025-09-07T06:49:06.7100842Z >>> 2025-09-07T06:49:06.7100934Z >>> torch.library.register_autograd( 2025-09-07T06:49:06.7102756Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T06:49:06.7102889Z ... ) 2025-09-07T06:49:06.7102968Z >>> 2025-09-07T06:49:06.7103062Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T06:49:06.7103184Z >>> y = numpy_sin(x) 2025-09-07T06:49:06.7103318Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T06:49:06.7103466Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T06:49:06.7103577Z >>> 2025-09-07T06:49:06.7103667Z >>> # Example with a keyword-only arg 2025-09-07T06:49:06.7103817Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T06:49:06.7105155Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T06:49:06.7105294Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:06.7105408Z >>> y_np = x_np * val 2025-09-07T06:49:06.7105537Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T06:49:06.7105660Z >>> 2025-09-07T06:49:06.7105785Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T06:49:06.7105950Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T06:49:06.7106063Z >>> 2025-09-07T06:49:06.7106146Z >>> def backward(ctx, grad): 2025-09-07T06:49:06.7106260Z >>> return grad * ctx.val 2025-09-07T06:49:06.7106418Z >>> 2025-09-07T06:49:06.7107514Z >>> torch.library.register_autograd( 2025-09-07T06:49:06.7107663Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T06:49:06.7107793Z ... ) 2025-09-07T06:49:06.7107869Z >>> 2025-09-07T06:49:06.7107959Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T06:49:06.7108079Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T06:49:06.7108213Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T06:49:06.7108375Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T06:49:06.7108474Z 2025-09-07T06:49:06.7108504Z 2025-09-07T06:49:06.7108635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7108757Z 2025-09-07T06:49:06.7108944Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1482. 2025-09-07T06:49:06.7110319Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.7110523Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T06:49:06.7110634Z 2025-09-07T06:49:06.7110715Z This function retrieves the kernel that would be executed for a given 2025-09-07T06:49:06.7110967Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T06:49:06.7111153Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T06:49:06.7111329Z case for this function is to retrieve the original kernel for a given 2025-09-07T06:49:06.7111512Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T06:49:06.7111685Z that calls into the original kernel for certain cases. 2025-09-07T06:49:06.7111780Z 2025-09-07T06:49:06.7111812Z Args: 2025-09-07T06:49:06.7111928Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T06:49:06.7112127Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T06:49:06.7113344Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T06:49:06.7113542Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T06:49:06.7113647Z 2025-09-07T06:49:06.7113680Z Returns: 2025-09-07T06:49:06.7113812Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T06:49:06.7113969Z call the kernel. 2025-09-07T06:49:06.7114035Z 2025-09-07T06:49:06.7114067Z Raises: 2025-09-07T06:49:06.7114169Z RuntimeError: If the operator does not exist. 2025-09-07T06:49:06.7114256Z 2025-09-07T06:49:06.7114289Z Example: 2025-09-07T06:49:06.7114484Z >>> # Get the CPU kernel for torch.add 2025-09-07T06:49:06.7114634Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T06:49:06.7114771Z >>> 2025-09-07T06:49:06.7115866Z >>> # You can also use DispatchKey enum 2025-09-07T06:49:06.7116041Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T06:49:06.7116198Z >>> 2025-09-07T06:49:06.7116284Z >>> # Or use an OpOverload directly 2025-09-07T06:49:06.7116442Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T06:49:06.7116585Z >>> 2025-09-07T06:49:06.7116703Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T06:49:06.7116860Z >>> # Get the original kernel for torch.sin 2025-09-07T06:49:06.7117018Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T06:49:06.7117159Z >>> 2025-09-07T06:49:06.7117280Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T06:49:06.7118422Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T06:49:06.7118551Z >>> if (x < 0).any(): 2025-09-07T06:49:06.7118735Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T06:49:06.7118870Z >>> else: 2025-09-07T06:49:06.7118970Z >>> return torch.zeros_like(x) 2025-09-07T06:49:06.7119076Z >>> 2025-09-07T06:49:06.7119172Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T06:49:06.7119350Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T06:49:06.7119543Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T06:49:06.7119714Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T06:49:06.7119847Z >>> 2025-09-07T06:49:06.7120913Z >>> # Test the conditional behavior 2025-09-07T06:49:06.7121038Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T06:49:06.7121165Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T06:49:06.7121326Z >>> torch.sin(x_positive) 2025-09-07T06:49:06.7121438Z tensor([0., 0.]) 2025-09-07T06:49:06.7121536Z >>> torch.sin(x_mixed) 2025-09-07T06:49:06.7121639Z tensor([-0.8415, 0.9093]) 2025-09-07T06:49:06.7121736Z 2025-09-07T06:49:06.7121943Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T06:49:06.7122139Z 2025-09-07T06:49:06.7122206Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T06:49:06.7122339Z ^ 2025-09-07T06:49:06.7123539Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1571. 2025-09-07T06:49:06.7123826Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7124029Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T06:49:06.7124179Z registered correctly. 2025-09-07T06:49:06.7124248Z 2025-09-07T06:49:06.7124327Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T06:49:06.7124615Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T06:49:06.7124811Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T06:49:06.7125003Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T06:49:06.7125173Z ``opcheck`` tests these metadata and properties. 2025-09-07T06:49:06.7125262Z 2025-09-07T06:49:06.7125307Z Concretely, we test the following: 2025-09-07T06:49:06.7125384Z 2025-09-07T06:49:06.7126458Z - test_schema: If the schema matches the implementation of 2025-09-07T06:49:06.7126640Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T06:49:06.7126829Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T06:49:06.7127007Z specifies that we return a new Tensor, then we check that the 2025-09-07T06:49:06.7127196Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T06:49:06.7127348Z a view of an existing one). 2025-09-07T06:49:06.7127491Z - test_autograd_registration: If the operator supports training 2025-09-07T06:49:06.7127668Z (autograd): we check that its autograd formula is registered via 2025-09-07T06:49:06.7127847Z torch.library.register_autograd or a manual registration to one 2025-09-07T06:49:06.7128029Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T06:49:06.7128187Z registrations may lead to undefined behavior. 2025-09-07T06:49:06.7129318Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T06:49:06.7129486Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T06:49:06.7129660Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T06:49:06.7129852Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T06:49:06.7130100Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T06:49:06.7130272Z operator and that it is correct. This test takes the result of 2025-09-07T06:49:06.7130443Z running the operator on real tensors and the result of running 2025-09-07T06:49:06.7130611Z the operator on FakeTensors and checks that they have the same 2025-09-07T06:49:06.7130771Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T06:49:06.7130930Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T06:49:06.7131099Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T06:49:06.7132264Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T06:49:06.7132435Z same under eager-mode PyTorch and torch.compile. 2025-09-07T06:49:06.7132595Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T06:49:06.7132806Z other things it tests are that the operator supports 2025-09-07T06:49:06.7132983Z functionalization and that the backward pass (if it exists) also 2025-09-07T06:49:06.7133144Z supports FakeTensor and functionalization. 2025-09-07T06:49:06.7133230Z 2025-09-07T06:49:06.7133303Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T06:49:06.7133472Z representative set of inputs. If your operator supports 2025-09-07T06:49:06.7133652Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T06:49:06.7133847Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T06:49:06.7134019Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T06:49:06.7134112Z 2025-09-07T06:49:06.7135244Z Args: 2025-09-07T06:49:06.7135359Z op: The operator. Must either be a function decorated with 2025-09-07T06:49:06.7135538Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T06:49:06.7135737Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T06:49:06.7135892Z args: The args to the operator 2025-09-07T06:49:06.7136010Z kwargs: The kwargs to the operator 2025-09-07T06:49:06.7136154Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T06:49:06.7136307Z Example: ("test_schema", "test_faketensor") 2025-09-07T06:49:06.7136463Z raise_exception: If we should raise an exception on the first 2025-09-07T06:49:06.7136626Z error. If False, we will return a dict with information 2025-09-07T06:49:06.7136763Z on if each test passed or not. 2025-09-07T06:49:06.7137906Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T06:49:06.7138077Z If specified ``atol`` must also be specified. 2025-09-07T06:49:06.7138230Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T06:49:06.7138401Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T06:49:06.7138577Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T06:49:06.7138745Z If specified ``rtol`` must also be specified. 2025-09-07T06:49:06.7138895Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T06:49:06.7139055Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T06:49:06.7139149Z 2025-09-07T06:49:06.7139186Z .. warning:: 2025-09-07T06:49:06.7139239Z 2025-09-07T06:49:06.7139319Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T06:49:06.7139503Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T06:49:06.7140662Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T06:49:06.7140850Z mathematically correct. Use both to test custom ops that support 2025-09-07T06:49:06.7141004Z gradient computation. 2025-09-07T06:49:06.7141074Z 2025-09-07T06:49:06.7141153Z Example: 2025-09-07T06:49:06.7141204Z 2025-09-07T06:49:06.7141257Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:06.7141418Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T06:49:06.7141576Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T06:49:06.7141704Z >>> x_np = x.numpy(force=True) 2025-09-07T06:49:06.7141817Z >>> z_np = x_np * y 2025-09-07T06:49:06.7141937Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T06:49:06.7142051Z >>> 2025-09-07T06:49:06.7143124Z >>> @numpy_mul.register_fake 2025-09-07T06:49:06.7143237Z >>> def _(x, y): 2025-09-07T06:49:06.7143340Z >>> return torch.empty_like(x) 2025-09-07T06:49:06.7143445Z >>> 2025-09-07T06:49:06.7143537Z >>> def setup_context(ctx, inputs, output): 2025-09-07T06:49:06.7143700Z >>> y, = inputs 2025-09-07T06:49:06.7143796Z >>> ctx.y = y 2025-09-07T06:49:06.7143891Z >>> 2025-09-07T06:49:06.7143974Z >>> def backward(ctx, grad): 2025-09-07T06:49:06.7144087Z >>> return grad * ctx.y, None 2025-09-07T06:49:06.7144191Z >>> 2025-09-07T06:49:06.7145399Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T06:49:06.7145545Z >>> 2025-09-07T06:49:06.7145627Z >>> sample_inputs = [ 2025-09-07T06:49:06.7145733Z >>> (torch.randn(3), 3.14), 2025-09-07T06:49:06.7145857Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T06:49:06.7145995Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T06:49:06.7146152Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T06:49:06.7146285Z >>> ] 2025-09-07T06:49:06.7146365Z >>> 2025-09-07T06:49:06.7146450Z >>> for args in sample_inputs: 2025-09-07T06:49:06.7146581Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T06:49:06.7147660Z 2025-09-07T06:49:06.7147690Z 2025-09-07T06:49:06.7147825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7147948Z 2025-09-07T06:49:06.7433262Z 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=1877. 2025-09-07T06:49:06.7434254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7434984Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T06:49:06.7435297Z 2025-09-07T06:49:06.7435393Z Example 2025-09-07T06:49:06.7435604Z ======= 2025-09-07T06:49:06.7435735Z 2025-09-07T06:49:06.7435963Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T06:49:06.7436287Z 2025-09-07T06:49:06.7436404Z >>> t = torch.empty((10, 20)) 2025-09-07T06:49:06.7436853Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T06:49:06.7440864Z 200 2025-09-07T06:49:06.7440997Z 2025-09-07T06:49:06.7441118Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:06.7441474Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T06:49:06.7441852Z >>> size = compute_required_storage_length( 2025-09-07T06:49:06.7442228Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T06:49:06.7442555Z ... ) 2025-09-07T06:49:06.7442787Z >>> size == t.storage().size() 2025-09-07T06:49:06.7443074Z True 2025-09-07T06:49:06.7443198Z 2025-09-07T06:49:06.7443396Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T06:49:06.7443693Z 2025-09-07T06:49:06.7443810Z >>> slice = torch.empty(100)[20:40] 2025-09-07T06:49:06.7444132Z >>> slice.storage().size() 2025-09-07T06:49:06.7445749Z 100 2025-09-07T06:49:06.7445793Z 2025-09-07T06:49:06.7445840Z >>> compute_required_storage_length( 2025-09-07T06:49:06.7446074Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T06:49:06.7446202Z ... ) 2025-09-07T06:49:06.7446275Z 40 2025-09-07T06:49:06.7446317Z 2025-09-07T06:49:06.7446349Z 2025-09-07T06:49:06.7446478Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7446599Z 2025-09-07T06:49:06.7451377Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202. 2025-09-07T06:49:06.7451692Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7451878Z Return the sum of each row of the given sparse tensor. 2025-09-07T06:49:06.7451972Z 2025-09-07T06:49:06.7453524Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T06:49:06.7453723Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T06:49:06.7453966Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T06:49:06.7454137Z returns a dense tensor instead of a sparse tensor. 2025-09-07T06:49:06.7454226Z 2025-09-07T06:49:06.7454319Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T06:49:06.7454608Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T06:49:06.7454715Z 2025-09-07T06:49:06.7454795Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T06:49:06.7454994Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T06:49:06.7455117Z 2025-09-07T06:49:06.7455160Z Args: 2025-09-07T06:49:06.7455256Z input (Tensor): the input sparse tensor 2025-09-07T06:49:06.7455433Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T06:49:06.7455604Z over all dims. 2025-09-07T06:49:06.7455764Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T06:49:06.7455940Z Default: dtype of :attr:`input`. 2025-09-07T06:49:06.7456017Z 2025-09-07T06:49:06.7457430Z Example:: 2025-09-07T06:49:06.7457485Z 2025-09-07T06:49:06.7457526Z >>> nnz = 3 2025-09-07T06:49:06.7457617Z >>> dims = [5, 5, 2, 3] 2025-09-07T06:49:06.7457745Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T06:49:06.7457905Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T06:49:06.7458056Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T06:49:06.7458177Z >>> size = torch.Size(dims) 2025-09-07T06:49:06.7458306Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:06.7458446Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T06:49:06.7458558Z >>> S 2025-09-07T06:49:06.7458648Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T06:49:06.7459821Z [2, 4, 1]]), 2025-09-07T06:49:06.7459962Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T06:49:06.7460097Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T06:49:06.7460177Z 2025-09-07T06:49:06.7460222Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T06:49:06.7460343Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T06:49:06.7460421Z 2025-09-07T06:49:06.7460464Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T06:49:06.7460585Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T06:49:06.7460718Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T06:49:06.7460808Z 2025-09-07T06:49:06.7460879Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T06:49:06.7461025Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T06:49:06.7461144Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T06:49:06.7462273Z values=tensor([[-1.4512, 0.4073], 2025-09-07T06:49:06.7462445Z [-0.8901, 0.2017], 2025-09-07T06:49:06.7462561Z [-0.3183, -1.7539]]), 2025-09-07T06:49:06.7462689Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T06:49:06.7462777Z 2025-09-07T06:49:06.7462836Z # when sum over all sparse dim, return a dense tensor 2025-09-07T06:49:06.7462968Z # with summed dims squeezed 2025-09-07T06:49:06.7463083Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T06:49:06.7463198Z tensor([-2.6596, -1.1450]) 2025-09-07T06:49:06.7463297Z 2025-09-07T06:49:06.7463426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7463548Z 2025-09-07T06:49:06.7463740Z msg = Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T06:49:06.7465220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7465420Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T06:49:06.7465522Z 2025-09-07T06:49:06.7465603Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T06:49:06.7465793Z variants that extends the gradcheck function with support to input 2025-09-07T06:49:06.7465968Z functions that operate on or/and return sparse tensors. 2025-09-07T06:49:06.7466065Z 2025-09-07T06:49:06.7466142Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T06:49:06.7466290Z on strided tensors only. 2025-09-07T06:49:06.7466358Z 2025-09-07T06:49:06.7466392Z For example: 2025-09-07T06:49:06.7466446Z 2025-09-07T06:49:06.7466532Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T06:49:06.7466686Z >>> x = ( 2025-09-07T06:49:06.7466793Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T06:49:06.7467956Z ... .to_sparse_coo() 2025-09-07T06:49:06.7468067Z ... .requires_grad_(True) 2025-09-07T06:49:06.7468168Z ... ) 2025-09-07T06:49:06.7468261Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T06:49:06.7468375Z True 2025-09-07T06:49:06.7468448Z 2025-09-07T06:49:06.7468577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.7468699Z 2025-09-07T06:49:06.7575270Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=434. 2025-09-07T06:49:06.7575580Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.7575772Z Retrieves the CUDA runtime API module. 2025-09-07T06:49:06.7575852Z 2025-09-07T06:49:06.7575854Z 2025-09-07T06:49:06.7578022Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T06:49:06.7578248Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T06:49:06.7578456Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T06:49:06.7578573Z 2025-09-07T06:49:06.7578608Z Args: 2025-09-07T06:49:06.7578686Z ``None`` 2025-09-07T06:49:06.7578742Z 2025-09-07T06:49:06.7578774Z Returns: 2025-09-07T06:49:06.7578880Z module: The CUDA runtime API module (_cudart). 2025-09-07T06:49:06.7578971Z 2025-09-07T06:49:06.7579007Z Raises: 2025-09-07T06:49:06.7579141Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T06:49:06.7579381Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T06:49:06.7579543Z 2025-09-07T06:49:06.7579597Z Example of CUDA operations with profiling: 2025-09-07T06:49:06.7579720Z >>> import torch 2025-09-07T06:49:06.7581183Z >>> from torch.cuda import cudart, check_error 2025-09-07T06:49:06.7581309Z >>> import os 2025-09-07T06:49:06.7581403Z >>> 2025-09-07T06:49:06.7581583Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T06:49:06.7581695Z >>> 2025-09-07T06:49:06.7581793Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T06:49:06.7581925Z >>> stream = torch.cuda.Stream() 2025-09-07T06:49:06.7582050Z >>> with torch.cuda.stream(stream): 2025-09-07T06:49:06.7582182Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T06:49:06.7582314Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T06:49:06.7582436Z >>> z = torch.mul(x, y) 2025-09-07T06:49:06.7583644Z >>> return z 2025-09-07T06:49:06.7583737Z >>> 2025-09-07T06:49:06.7583824Z >>> torch.cuda.synchronize() 2025-09-07T06:49:06.7583951Z >>> print("====== Start nsys profiling ======") 2025-09-07T06:49:06.7584089Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T06:49:06.7584228Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T06:49:06.7584550Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T06:49:06.7584690Z >>> print("CUDA operations completed.") 2025-09-07T06:49:06.7584832Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T06:49:06.7584973Z >>> print("====== End nsys profiling ======") 2025-09-07T06:49:06.7585054Z 2025-09-07T06:49:06.7585128Z To run this example and save the profiling information, execute: 2025-09-07T06:49:06.7586441Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T06:49:06.7586602Z 2025-09-07T06:49:06.7586689Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T06:49:06.7586882Z the profiling information to a file named `trace_name.prof`. 2025-09-07T06:49:06.7587068Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T06:49:06.7587240Z after the `cudaProfilerStart` call in the script. 2025-09-07T06:49:06.7587416Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T06:49:06.7587579Z CSV file and print a summary, respectively. 2025-09-07T06:49:06.7587748Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T06:49:06.7587924Z overwrite of the output file if it already exists. 2025-09-07T06:49:06.7588045Z 2025-09-07T06:49:06.7588310Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-09-07T06:49:06.7589628Z 2025-09-07T06:49:06.7589748Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T06:49:06.7589934Z ^ 2025-09-07T06:49:06.7995749Z 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. 2025-09-07T06:49:06.7996697Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.7997061Z 2025-09-07T06:49:06.7997301Z Append the given callback function to this ``Future``, which will be run 2025-09-07T06:49:06.7997834Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T06:49:06.7998335Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T06:49:06.7998831Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T06:49:06.7999309Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T06:49:06.8004713Z is the reference to this ``Future``. The callback function can use the 2025-09-07T06:49:06.8004920Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T06:49:06.8005109Z already completed, the given callback will be run immediately inline. 2025-09-07T06:49:06.8005230Z 2025-09-07T06:49:06.8005309Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T06:49:06.8005603Z callback might be invoked while the async kernels that are populating 2025-09-07T06:49:06.8005801Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T06:49:06.8005989Z callback will be invoked with some dedicated streams set as current 2025-09-07T06:49:06.8006170Z (fetched from a global pool) which will be synchronized with those 2025-09-07T06:49:06.8006358Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T06:49:06.8006547Z will be scheduled on the device after the kernels complete. In other 2025-09-07T06:49:06.8006730Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T06:49:06.8006915Z manipulate the result without any additional synchronization. This is 2025-09-07T06:49:06.8007088Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T06:49:06.8007181Z 2025-09-07T06:49:06.8007259Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T06:49:06.8007478Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T06:49:06.8007657Z these tensors are still running on the device, as long as the callback 2025-09-07T06:49:06.8007840Z didn't change streams during its execution. If one wants to change 2025-09-07T06:49:06.8008026Z streams, one must be careful to re-synchronize them with the original 2025-09-07T06:49:06.8008216Z streams, that is, those that were current when the callback was invoked. 2025-09-07T06:49:06.8008328Z 2025-09-07T06:49:06.8009858Z Args: 2025-09-07T06:49:06.8009984Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T06:49:06.8010136Z the only argument. 2025-09-07T06:49:06.8010214Z 2025-09-07T06:49:06.8010248Z Returns: 2025-09-07T06:49:06.8010355Z A new ``Future`` object that holds the return value of the 2025-09-07T06:49:06.8010518Z ``callback`` and will be marked as completed when the given 2025-09-07T06:49:06.8010663Z ``callback`` finishes. 2025-09-07T06:49:06.8010728Z 2025-09-07T06:49:06.8010805Z .. note:: Note that if the callback function throws, either 2025-09-07T06:49:06.8010979Z through the original future being completed with an exception and 2025-09-07T06:49:06.8011156Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T06:49:06.8011330Z future returned by ``then`` will be marked appropriately with the 2025-09-07T06:49:06.8013100Z encountered error. However, if this callback later completes 2025-09-07T06:49:06.8013285Z additional futures, those futures are not marked as completed with 2025-09-07T06:49:06.8013466Z an error and the user is responsible for handling completion/waiting 2025-09-07T06:49:06.8013617Z on those futures independently. 2025-09-07T06:49:06.8013693Z 2025-09-07T06:49:06.8013732Z Example:: 2025-09-07T06:49:06.8013779Z 2025-09-07T06:49:06.8013841Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T06:49:06.8013973Z >>> def callback(fut): 2025-09-07T06:49:06.8014091Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T06:49:06.8014220Z >>> fut = torch.futures.Future() 2025-09-07T06:49:06.8014453Z >>> # The inserted callback will print the return value when 2025-09-07T06:49:06.8014599Z >>> # receiving the response from "worker1" 2025-09-07T06:49:06.8015800Z >>> cb_fut = fut.then(callback) 2025-09-07T06:49:06.8015914Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T06:49:06.8016044Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T06:49:06.8016166Z ... ) 2025-09-07T06:49:06.8016249Z >>> fut.set_result(5) 2025-09-07T06:49:06.8016349Z RPC return value is 5. 2025-09-07T06:49:06.8016450Z Chained cb done. None 2025-09-07T06:49:06.8016511Z 2025-09-07T06:49:06.8016602Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.8016726Z 2025-09-07T06:49:06.8016976Z 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. 2025-09-07T06:49:06.8017288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.8017416Z 2025-09-07T06:49:06.8017491Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T06:49:06.8018711Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T06:49:06.8018865Z cannot be marked completed twice. 2025-09-07T06:49:06.8018868Z 2025-09-07T06:49:06.8018945Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T06:49:06.8019022Z called even if the asynchronous kernels that are populating those 2025-09-07T06:49:06.8019101Z tensors haven't yet completed running on the device, provided that the 2025-09-07T06:49:06.8019182Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T06:49:06.8019257Z when this method is called. Put simply, it's safe to call this method 2025-09-07T06:49:06.8019382Z immediately after launching those kernels, without any additional 2025-09-07T06:49:06.8019461Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T06:49:06.8019537Z method will record events on all the relevant current streams and will 2025-09-07T06:49:06.8019610Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T06:49:06.8019644Z ``Future``. 2025-09-07T06:49:06.8019646Z 2025-09-07T06:49:06.8019677Z Args: 2025-09-07T06:49:06.8019739Z result (object): the result object of this ``Future``. 2025-09-07T06:49:06.8019741Z 2025-09-07T06:49:06.8019775Z Example:: 2025-09-07T06:49:06.8019777Z 2025-09-07T06:49:06.8019834Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T06:49:06.8019872Z >>> import threading 2025-09-07T06:49:06.8019908Z >>> import time 2025-09-07T06:49:06.8019952Z >>> def slow_set_future(fut, value): 2025-09-07T06:49:06.8020987Z ... time.sleep(0.5) 2025-09-07T06:49:06.8021037Z ... fut.set_result(value) 2025-09-07T06:49:06.8021085Z >>> fut = torch.futures.Future() 2025-09-07T06:49:06.8021126Z >>> t = threading.Thread( 2025-09-07T06:49:06.8021167Z ... target=slow_set_future, 2025-09-07T06:49:06.8021210Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T06:49:06.8021241Z ... ) 2025-09-07T06:49:06.8021276Z >>> t.start() 2025-09-07T06:49:06.8021313Z >>> print(fut.wait()) 2025-09-07T06:49:06.8021348Z tensor([3., 3.]) 2025-09-07T06:49:06.8021381Z >>> t.join() 2025-09-07T06:49:06.8021383Z 2025-09-07T06:49:06.8021473Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.8021476Z 2025-09-07T06:49:06.8056313Z 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. 2025-09-07T06:49:06.8056433Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.8057554Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T06:49:06.8057751Z required drivers are available and at least one device is visible. 2025-09-07T06:49:06.8057840Z See :ref:`accelerator` for details. 2025-09-07T06:49:06.8057846Z 2025-09-07T06:49:06.8057908Z Returns: 2025-09-07T06:49:06.8058050Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T06:49:06.8058055Z 2025-09-07T06:49:06.8058222Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T06:49:06.8058355Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T06:49:06.8063294Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T06:49:06.8063392Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T06:49:06.8063395Z 2025-09-07T06:49:06.8063450Z Example:: 2025-09-07T06:49:06.8063456Z 2025-09-07T06:49:06.8063977Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:06.8064020Z 2025-09-07T06:49:06.8064268Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T06:49:06.8064271Z 2025-09-07T06:49:06.8064506Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:06.8064556Z ^ 2025-09-07T06:49:06.8064817Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=212. 2025-09-07T06:49:06.8064932Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:06.8065038Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T06:49:06.8065041Z 2025-09-07T06:49:06.8065086Z Args: 2025-09-07T06:49:06.8065229Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T06:49:06.8065491Z the current :ref:`accelerator` device type. If not given, 2025-09-07T06:49:06.8065584Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T06:49:06.8065587Z 2025-09-07T06:49:06.8065730Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T06:49:06.8065732Z 2025-09-07T06:49:06.8065778Z Example:: 2025-09-07T06:49:06.8065780Z 2025-09-07T06:49:06.8065846Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:06.8065960Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:06.8066036Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T06:49:06.8068004Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T06:49:06.8068055Z >>> start_event.record() 2025-09-07T06:49:06.8068164Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T06:49:06.8068209Z >>> sum = torch.sum(tensor) 2025-09-07T06:49:06.8068254Z >>> end_event.record() 2025-09-07T06:49:06.8068307Z >>> torch.accelerator.synchronize() 2025-09-07T06:49:06.8068375Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T06:49:06.8068408Z 2025-09-07T06:49:06.8068608Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T06:49:06.8068611Z 2025-09-07T06:49:06.8068704Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:06.8068746Z ^ 2025-09-07T06:49:06.8075658Z 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. 2025-09-07T06:49:06.8076717Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.8076986Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T06:49:06.8077110Z defined there from the comfort of Python runtime 2025-09-07T06:49:06.8077238Z Example:: 2025-09-07T06:49:06.8077259Z 2025-09-07T06:49:06.8077371Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T06:49:06.8077469Z >>> lib = torch.mps.compile_shader( 2025-09-07T06:49:06.8077791Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T06:49:06.8077862Z ... ) 2025-09-07T06:49:06.8077965Z >>> x = torch.zeros(16, device="mps") 2025-09-07T06:49:06.8084340Z >>> lib.full(x, 3.14) 2025-09-07T06:49:06.8084532Z 2025-09-07T06:49:06.8084691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.8084694Z 2025-09-07T06:49:06.8290013Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=39. 2025-09-07T06:49:06.8290390Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.8290400Z 2025-09-07T06:49:06.8290638Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T06:49:06.8290849Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T06:49:06.8291080Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T06:49:06.8291214Z vectorizing those operations. 2025-09-07T06:49:06.8291221Z 2025-09-07T06:49:06.8291436Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T06:49:06.8291638Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T06:49:06.8291844Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T06:49:06.8292034Z compute batched gradients when composed with autograd. 2025-09-07T06:49:06.8292211Z 2025-09-07T06:49:06.8292327Z .. note:: 2025-09-07T06:49:06.8292533Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T06:49:06.8292626Z convenience. Use whichever one you'd like. 2025-09-07T06:49:06.8292628Z 2025-09-07T06:49:06.8292679Z Args: 2025-09-07T06:49:06.8296283Z func (function): A Python function that takes one or more arguments. 2025-09-07T06:49:06.8296366Z Must return one or more Tensors. 2025-09-07T06:49:06.8296479Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T06:49:06.8296581Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T06:49:06.8296685Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T06:49:06.8296784Z input is None, then that indicates there is no map dimension. 2025-09-07T06:49:06.8296831Z Default: 0. 2025-09-07T06:49:06.8296939Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T06:49:06.8297039Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T06:49:06.8297123Z it should have one element per output. Default: 0. 2025-09-07T06:49:06.8297219Z randomness (str): Specifies whether the randomness in this 2025-09-07T06:49:06.8297322Z vmap should be the same or different across batches. If 'different', 2025-09-07T06:49:06.8297422Z the randomness for each batch will be different. If 'same', the 2025-09-07T06:49:06.8297524Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T06:49:06.8297627Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T06:49:06.8297723Z only applies to random PyTorch operations and does not apply to 2025-09-07T06:49:06.8297791Z Python's random module or numpy randomness. 2025-09-07T06:49:06.8297899Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T06:49:06.8298029Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T06:49:06.8298154Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T06:49:06.8298295Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T06:49:06.8298297Z 2025-09-07T06:49:06.8298348Z Returns: 2025-09-07T06:49:06.8298465Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T06:49:06.8298564Z ``func``, except each input has an extra dimension at the index 2025-09-07T06:49:06.8298676Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T06:49:06.8298773Z ``func``, except each output has an extra dimension at the index 2025-09-07T06:49:06.8298849Z specified by ``out_dims``. 2025-09-07T06:49:06.8298854Z 2025-09-07T06:49:06.8298906Z .. warning: 2025-09-07T06:49:06.8299020Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T06:49:06.8299124Z perform any side-effects in ``func``, with the exception of 2025-09-07T06:49:06.8299331Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T06:49:06.8299452Z Python data structures and assigning values to variables not captured 2025-09-07T06:49:06.8299514Z in ``func``. 2025-09-07T06:49:06.8299519Z 2025-09-07T06:49:06.8299631Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T06:49:06.8299747Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T06:49:06.8299865Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T06:49:06.8299869Z 2025-09-07T06:49:06.8299959Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:06.8300074Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T06:49:06.8300154Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T06:49:06.8300225Z >>> batched_dot(x, y) 2025-09-07T06:49:06.8300278Z 2025-09-07T06:49:06.8300409Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T06:49:06.8300475Z model authoring experience. 2025-09-07T06:49:06.8300480Z 2025-09-07T06:49:06.8300560Z >>> batch_size, feature_size = 3, 5 2025-09-07T06:49:06.8300655Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T06:49:06.8300712Z >>> 2025-09-07T06:49:06.8300772Z >>> def model(feature_vec): 2025-09-07T06:49:06.8300858Z >>> # Very simple linear model with activation 2025-09-07T06:49:06.8300945Z >>> return feature_vec.dot(weights).relu() 2025-09-07T06:49:06.8300997Z >>> 2025-09-07T06:49:06.8301093Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T06:49:06.8301165Z >>> result = torch.vmap(model)(examples) 2025-09-07T06:49:06.8301168Z 2025-09-07T06:49:06.8301297Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T06:49:06.8301420Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T06:49:06.8303507Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T06:49:06.8303611Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T06:49:06.8303704Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T06:49:06.8303794Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T06:49:06.8303838Z call to ``autograd.grad``. 2025-09-07T06:49:06.8303840Z 2025-09-07T06:49:06.8303878Z >>> # Setup 2025-09-07T06:49:06.8303910Z >>> N = 5 2025-09-07T06:49:06.8303951Z >>> f = lambda x: x**2 2025-09-07T06:49:06.8304002Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T06:49:06.8304038Z >>> y = f(x) 2025-09-07T06:49:06.8304077Z >>> I_N = torch.eye(N) 2025-09-07T06:49:06.8304109Z >>> 2025-09-07T06:49:06.8304152Z >>> # Sequential approach 2025-09-07T06:49:06.8304239Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T06:49:06.8304293Z >>> for v in I_N.unbind()] 2025-09-07T06:49:06.8304343Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T06:49:06.8304469Z >>> 2025-09-07T06:49:06.8304519Z >>> # vectorized gradient computation 2025-09-07T06:49:06.8304559Z >>> def get_vjp(v): 2025-09-07T06:49:06.8304611Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T06:49:06.8304660Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T06:49:06.8304662Z 2025-09-07T06:49:06.8304760Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T06:49:06.8315287Z 2025-09-07T06:49:06.8315516Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:06.8315622Z >>> batched_dot = torch.vmap( 2025-09-07T06:49:06.8315720Z ... torch.vmap(torch.dot) 2025-09-07T06:49:06.8315837Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T06:49:06.8315968Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T06:49:06.8316369Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T06:49:06.8316377Z 2025-09-07T06:49:06.8316591Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T06:49:06.8316718Z the dimension that each inputs are batched along as 2025-09-07T06:49:06.8316723Z 2025-09-07T06:49:06.8316818Z >>> torch.dot # [N], [N] -> [] 2025-09-07T06:49:06.8317004Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T06:49:06.8317110Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T06:49:06.8317279Z >>> batched_dot( 2025-09-07T06:49:06.8317351Z ... x, y 2025-09-07T06:49:06.8317510Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T06:49:06.8317516Z 2025-09-07T06:49:06.8317715Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T06:49:06.8317882Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T06:49:06.8317998Z 2025-09-07T06:49:06.8318091Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:06.8318273Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T06:49:06.8318374Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T06:49:06.8318453Z >>> batched_dot( 2025-09-07T06:49:06.8318527Z ... x, y 2025-09-07T06:49:06.8321992Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T06:49:06.8321998Z 2025-09-07T06:49:06.8322192Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T06:49:06.8322291Z matching the shape of the input: 2025-09-07T06:49:06.8322295Z 2025-09-07T06:49:06.8322412Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T06:49:06.8322515Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T06:49:06.8322597Z >>> input = {"x": x, "y": y} 2025-09-07T06:49:06.8322735Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T06:49:06.8322804Z >>> batched_dot(input) 2025-09-07T06:49:06.8322809Z 2025-09-07T06:49:06.8322964Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T06:49:06.8323043Z along any dimension by using ``out_dims`` 2025-09-07T06:49:06.8323046Z 2025-09-07T06:49:06.8323103Z >>> f = lambda x: x**2 2025-09-07T06:49:06.8323160Z >>> x = torch.randn(2, 5) 2025-09-07T06:49:06.8323248Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T06:49:06.8323305Z >>> batched_pow(x) # [5, 2] 2025-09-07T06:49:06.8323307Z 2025-09-07T06:49:06.8323467Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T06:49:06.8323520Z accept kwargs 2025-09-07T06:49:06.8323525Z 2025-09-07T06:49:06.8323589Z >>> x = torch.randn([2, 5]) 2025-09-07T06:49:06.8323650Z >>> def fn(x, scale=4.): 2025-09-07T06:49:06.8323709Z >>> return x * scale 2025-09-07T06:49:06.8323762Z >>> 2025-09-07T06:49:06.8323830Z >>> batched_pow = torch.vmap(fn) 2025-09-07T06:49:06.8325942Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T06:49:06.8326086Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T06:49:06.8326091Z 2025-09-07T06:49:06.8326157Z .. note:: 2025-09-07T06:49:06.8326284Z vmap does not provide general autobatching or handle variable-length 2025-09-07T06:49:06.8326349Z sequences out of the box. 2025-09-07T06:49:06.8326354Z 2025-09-07T06:49:06.8326490Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.8326494Z 2025-09-07T06:49:06.8326751Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=306. 2025-09-07T06:49:06.8326897Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:06.8327030Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T06:49:06.8327222Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T06:49:06.8327296Z compute higher-order gradients. 2025-09-07T06:49:06.8327298Z 2025-09-07T06:49:06.8327349Z Args: 2025-09-07T06:49:06.8327460Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T06:49:06.8327609Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T06:49:06.8327758Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T06:49:06.8327819Z ``(output, aux)``. 2025-09-07T06:49:06.8327971Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T06:49:06.8328084Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T06:49:06.8328211Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T06:49:06.8328370Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T06:49:06.8328378Z 2025-09-07T06:49:06.8328432Z Returns: 2025-09-07T06:49:06.8330377Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T06:49:06.8330519Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T06:49:06.8330668Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T06:49:06.8330797Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T06:49:06.8330888Z respect to each ``argnums`` value is returned. 2025-09-07T06:49:06.8330890Z 2025-09-07T06:49:06.8330951Z Example of using ``grad``: 2025-09-07T06:49:06.8330956Z 2025-09-07T06:49:06.8331021Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.8331088Z >>> from torch.func import grad 2025-09-07T06:49:06.8331147Z >>> x = torch.randn([]) 2025-09-07T06:49:06.8331234Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T06:49:06.8331308Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T06:49:06.8331359Z >>> 2025-09-07T06:49:06.8331427Z >>> # Second-order gradients 2025-09-07T06:49:06.8331513Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T06:49:06.8331599Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T06:49:06.8331601Z 2025-09-07T06:49:06.8331747Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T06:49:06.8331751Z 2025-09-07T06:49:06.8331807Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.8331877Z >>> from torch.func import grad, vmap 2025-09-07T06:49:06.8331951Z >>> batch_size, feature_size = 3, 5 2025-09-07T06:49:06.8332001Z >>> 2025-09-07T06:49:06.8332069Z >>> def model(weights, feature_vec): 2025-09-07T06:49:06.8332151Z >>> # Very simple linear model with activation 2025-09-07T06:49:06.8333698Z >>> assert feature_vec.dim() == 1 2025-09-07T06:49:06.8333767Z >>> return feature_vec.dot(weights).relu() 2025-09-07T06:49:06.8354504Z >>> 2025-09-07T06:49:06.8354573Z >>> def compute_loss(weights, example, target): 2025-09-07T06:49:06.8354622Z >>> y = model(weights, example) 2025-09-07T06:49:06.8354680Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T06:49:06.8354716Z >>> 2025-09-07T06:49:06.8354789Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T06:49:06.8354848Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T06:49:06.8354897Z >>> targets = torch.randn(batch_size) 2025-09-07T06:49:06.8354947Z >>> inputs = (weights, examples, targets) 2025-09-07T06:49:06.8355034Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T06:49:06.8355074Z ... *inputs 2025-09-07T06:49:06.8355110Z ... ) 2025-09-07T06:49:06.8355112Z 2025-09-07T06:49:06.8355240Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T06:49:06.8355242Z 2025-09-07T06:49:06.8355285Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.8355330Z >>> from torch.func import grad 2025-09-07T06:49:06.8355375Z >>> def my_loss_func(y, y_pred): 2025-09-07T06:49:06.8355430Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T06:49:06.8355478Z >>> loss = loss_per_sample.mean() 2025-09-07T06:49:06.8355531Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T06:49:06.8356967Z >>> 2025-09-07T06:49:06.8357035Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T06:49:06.8357080Z >>> y_true = torch.rand(4) 2025-09-07T06:49:06.8357140Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T06:49:06.8357182Z >>> out = fn(y_true, y_preds) 2025-09-07T06:49:06.8357278Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T06:49:06.8357340Z 2025-09-07T06:49:06.8357383Z .. note:: 2025-09-07T06:49:06.8357453Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T06:49:06.8357455Z 2025-09-07T06:49:06.8357513Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T06:49:06.8357516Z 2025-09-07T06:49:06.8357558Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.8357595Z >>> def f(x): 2025-09-07T06:49:06.8357640Z >>> with torch.no_grad(): 2025-09-07T06:49:06.8357679Z >>> c = x ** 2 2025-09-07T06:49:06.8357721Z >>> return x - c 2025-09-07T06:49:06.8357723Z 2025-09-07T06:49:06.8357798Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T06:49:06.8357800Z 2025-09-07T06:49:06.8357873Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T06:49:06.8357875Z 2025-09-07T06:49:06.8357916Z >>> # xdoctest: +SKIP 2025-09-07T06:49:06.8357961Z >>> with torch.no_grad(): 2025-09-07T06:49:06.8357999Z >>> grad(f)(x) 2025-09-07T06:49:06.8358001Z 2025-09-07T06:49:06.8358086Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T06:49:06.8358169Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T06:49:06.8359461Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T06:49:06.8359464Z 2025-09-07T06:49:06.8359499Z 2025-09-07T06:49:06.8359594Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:06.8359596Z 2025-09-07T06:49:07.1038999Z 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. 2025-09-07T06:49:07.1039327Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1039359Z 2025-09-07T06:49:07.1039625Z Config object that specifies the supported data types passed as arguments to 2025-09-07T06:49:07.1039866Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T06:49:07.1039974Z weights, and biases. 2025-09-07T06:49:07.1039981Z 2025-09-07T06:49:07.1040150Z For example, consider the following reference model: 2025-09-07T06:49:07.1040156Z 2025-09-07T06:49:07.1040339Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T06:49:07.1040344Z 2025-09-07T06:49:07.1040562Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T06:49:07.1040805Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T06:49:07.1041031Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T06:49:07.1041259Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T06:49:07.1041477Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T06:49:07.1041888Z the second quantize op (quant2). 2025-09-07T06:49:07.1041896Z 2025-09-07T06:49:07.1042115Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T06:49:07.1042330Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T06:49:07.1042542Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T06:49:07.1042746Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T06:49:07.1047163Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T06:49:07.1047321Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T06:49:07.1047451Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T06:49:07.1047509Z the observers. 2025-09-07T06:49:07.1047512Z 2025-09-07T06:49:07.1047638Z These dtypes are matched against the ones specified in the user's 2025-09-07T06:49:07.1047877Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T06:49:07.1048016Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T06:49:07.1048139Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T06:49:07.1048220Z the pattern will not be quantized. 2025-09-07T06:49:07.1048223Z 2025-09-07T06:49:07.1048291Z Example usage:: 2025-09-07T06:49:07.1048295Z 2025-09-07T06:49:07.1048370Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:07.1048436Z >>> dtype_config1 = DTypeConfig( 2025-09-07T06:49:07.1048508Z ... input_dtype=torch.quint8, 2025-09-07T06:49:07.1048580Z ... output_dtype=torch.quint8, 2025-09-07T06:49:07.1048641Z ... weight_dtype=torch.qint8, 2025-09-07T06:49:07.1048709Z ... bias_dtype=torch.float) 2025-09-07T06:49:07.1048715Z 2025-09-07T06:49:07.1048782Z >>> dtype_config2 = DTypeConfig( 2025-09-07T06:49:07.1048864Z ... input_dtype=DTypeWithConstraints( 2025-09-07T06:49:07.1048936Z ... dtype=torch.quint8, 2025-09-07T06:49:07.1049004Z ... quant_min_lower_bound=0, 2025-09-07T06:49:07.1049079Z ... quant_max_upper_bound=255, 2025-09-07T06:49:07.1051326Z ... ), 2025-09-07T06:49:07.1051419Z ... output_dtype=DTypeWithConstraints( 2025-09-07T06:49:07.1051491Z ... dtype=torch.quint8, 2025-09-07T06:49:07.1051554Z ... quant_min_lower_bound=0, 2025-09-07T06:49:07.1051619Z ... quant_max_upper_bound=255, 2025-09-07T06:49:07.1051669Z ... ), 2025-09-07T06:49:07.1051756Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T06:49:07.1051820Z ... dtype=torch.qint8, 2025-09-07T06:49:07.1051897Z ... quant_min_lower_bound=-128, 2025-09-07T06:49:07.1051964Z ... quant_max_upper_bound=127, 2025-09-07T06:49:07.1052015Z ... ), 2025-09-07T06:49:07.1052077Z ... bias_dtype=torch.float) 2025-09-07T06:49:07.1052079Z 2025-09-07T06:49:07.1052153Z >>> dtype_config1.input_dtype 2025-09-07T06:49:07.1052215Z torch.quint8 2025-09-07T06:49:07.1052218Z 2025-09-07T06:49:07.1052282Z >>> dtype_config2.input_dtype 2025-09-07T06:49:07.1052337Z torch.quint8 2025-09-07T06:49:07.1052340Z 2025-09-07T06:49:07.1052428Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T06:49:07.1052732Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T06:49:07.1052736Z 2025-09-07T06:49:07.1052885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1052888Z 2025-09-07T06:49:07.1364572Z 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. 2025-09-07T06:49:07.1365106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1365278Z 2025-09-07T06:49:07.1365490Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T06:49:07.1365652Z 2025-09-07T06:49:07.1365747Z The generated tables are presented in both a list-of-lists format 2025-09-07T06:49:07.1365888Z 2025-09-07T06:49:07.1365981Z The reason for the two tables are that they handle different things: 2025-09-07T06:49:07.1366193Z 1.) the first table handles all tensor level information 2025-09-07T06:49:07.1366405Z 2.) the second table handles and displays all channel based information 2025-09-07T06:49:07.1366536Z 2025-09-07T06:49:07.1366672Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T06:49:07.1366988Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T06:49:07.1367313Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T06:49:07.1367538Z 2025-09-07T06:49:07.1367597Z Tensor table columns: 2025-09-07T06:49:07.1367774Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:07.1367983Z ---- --------- --------- --------- --------- --------- 2025-09-07T06:49:07.1368100Z 2025-09-07T06:49:07.1368162Z Per-Channel table columns: 2025-09-07T06:49:07.1368344Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:07.1368562Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T06:49:07.1368677Z 2025-09-07T06:49:07.1368717Z Args: 2025-09-07T06:49:07.1368882Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:07.1369073Z contain this filter substring 2025-09-07T06:49:07.1372076Z Default = "", results in all the features being printed 2025-09-07T06:49:07.1372314Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:07.1372568Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:07.1372708Z 2025-09-07T06:49:07.1372760Z Returns a dictionary with two keys: 2025-09-07T06:49:07.1372923Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T06:49:07.1373089Z "tensor_level_info", "channel_level_info" 2025-09-07T06:49:07.1373228Z Each key maps to a tuple with: 2025-09-07T06:49:07.1373365Z A list of the headers of each table 2025-09-07T06:49:07.1373528Z A list of lists containing the table information row by row 2025-09-07T06:49:07.1373707Z The 0th index row will contain the headers of the columns 2025-09-07T06:49:07.1373853Z The rest of the rows will contain data 2025-09-07T06:49:07.1373944Z 2025-09-07T06:49:07.1373983Z Example Use: 2025-09-07T06:49:07.1374085Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:07.1374226Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T06:49:07.1374499Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:07.1374702Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T06:49:07.1374836Z 2025-09-07T06:49:07.1374926Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1375052Z 2025-09-07T06:49:07.1375363Z 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. 2025-09-07T06:49:07.1375794Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1377380Z 2025-09-07T06:49:07.1377486Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T06:49:07.1377619Z 2025-09-07T06:49:07.1377737Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T06:49:07.1378014Z 1.) the first table handles all tensor level information 2025-09-07T06:49:07.1378186Z 2.) the second table handles and displays all channel based information 2025-09-07T06:49:07.1378298Z 2025-09-07T06:49:07.1378408Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T06:49:07.1378666Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T06:49:07.1378938Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T06:49:07.1379093Z 2025-09-07T06:49:07.1379131Z Tensor table columns: 2025-09-07T06:49:07.1379262Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:07.1379426Z ---- --------- --------- --------- --------- --------- 2025-09-07T06:49:07.1379520Z 2025-09-07T06:49:07.1379562Z Per-Channel table columns: 2025-09-07T06:49:07.1380814Z 2025-09-07T06:49:07.1380903Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:07.1381081Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T06:49:07.1381178Z 2025-09-07T06:49:07.1381211Z Args: 2025-09-07T06:49:07.1381349Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:07.1381520Z contain this filter substring 2025-09-07T06:49:07.1381658Z Default = "", results in all the features being printed 2025-09-07T06:49:07.1381844Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:07.1382058Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:07.1382182Z 2025-09-07T06:49:07.1382216Z Example Use: 2025-09-07T06:49:07.1382314Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:07.1382459Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T06:49:07.1383700Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:07.1383839Z ... ) 2025-09-07T06:49:07.1383953Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T06:49:07.1384102Z >>> # for all modules in block 1 of the model 2025-09-07T06:49:07.1384184Z 2025-09-07T06:49:07.1384273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1384475Z 2025-09-07T06:49:07.1384791Z 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. 2025-09-07T06:49:07.1385211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1385341Z 2025-09-07T06:49:07.1385424Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T06:49:07.1385549Z 2025-09-07T06:49:07.1385645Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T06:49:07.1385871Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T06:49:07.1387165Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T06:49:07.1387396Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T06:49:07.1387561Z further deep dives. 2025-09-07T06:49:07.1387617Z 2025-09-07T06:49:07.1387649Z Note: 2025-09-07T06:49:07.1387785Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T06:49:07.1387976Z When the tensor information is plotted, it will plot: 2025-09-07T06:49:07.1388122Z idx as the x val, feature value as the y_val 2025-09-07T06:49:07.1388267Z When the channel information is plotted, it will plot: 2025-09-07T06:49:07.1388452Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T06:49:07.1388711Z The reason for this is that we want to be able to compare values across the 2025-09-07T06:49:07.1388912Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T06:49:07.1390129Z This means each module is represented by only 1 x value 2025-09-07T06:49:07.1390263Z Args: 2025-09-07T06:49:07.1390388Z feature_filter (str): Filters the features presented to only those that 2025-09-07T06:49:07.1390546Z contain this filter substring 2025-09-07T06:49:07.1390714Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:07.1390982Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:07.1391107Z 2025-09-07T06:49:07.1391141Z Example Use: 2025-09-07T06:49:07.1391237Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:07.1391378Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T06:49:07.1391590Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:07.1391729Z ... ) 2025-09-07T06:49:07.1392871Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T06:49:07.1393045Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T06:49:07.1393211Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T06:49:07.1393310Z 2025-09-07T06:49:07.1393400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1393523Z 2025-09-07T06:49:07.1393834Z 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. 2025-09-07T06:49:07.1394262Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1394473Z 2025-09-07T06:49:07.1394573Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T06:49:07.1394708Z 2025-09-07T06:49:07.1394739Z Note: 2025-09-07T06:49:07.1394874Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T06:49:07.1395096Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T06:49:07.1395310Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T06:49:07.1396544Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T06:49:07.1396669Z 2025-09-07T06:49:07.1396700Z Args: 2025-09-07T06:49:07.1396835Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:07.1397003Z contain this filter substring 2025-09-07T06:49:07.1397139Z Default = "", results in all the features being printed 2025-09-07T06:49:07.1397329Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:07.1397545Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:07.1397745Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T06:49:07.1397928Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T06:49:07.1398031Z 2025-09-07T06:49:07.1398066Z Example Use: 2025-09-07T06:49:07.1398154Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.1399323Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T06:49:07.1399532Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:07.1399669Z ... ) 2025-09-07T06:49:07.1399806Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T06:49:07.1400026Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T06:49:07.1400281Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T06:49:07.1400396Z 2025-09-07T06:49:07.1400484Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1400609Z 2025-09-07T06:49:07.1440840Z 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. 2025-09-07T06:49:07.1441230Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1441379Z 2025-09-07T06:49:07.1441489Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T06:49:07.1446472Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T06:49:07.1446733Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T06:49:07.1447076Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T06:49:07.1447344Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T06:49:07.1447542Z issues a use might have. 2025-09-07T06:49:07.1447626Z 2025-09-07T06:49:07.1447682Z Currently supports generating reports on: 2025-09-07T06:49:07.1447896Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T06:49:07.1448135Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T06:49:07.1448386Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T06:49:07.1448624Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T06:49:07.1448752Z 2025-09-07T06:49:07.1448899Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T06:49:07.1449204Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T06:49:07.1449502Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T06:49:07.1449748Z has the capability to remove all the observers it inserted as well. 2025-09-07T06:49:07.1449863Z 2025-09-07T06:49:07.1449965Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T06:49:07.1450101Z 2025-09-07T06:49:07.1450226Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T06:49:07.1450492Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T06:49:07.1450639Z 2025-09-07T06:49:07.1452810Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T06:49:07.1453036Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T06:49:07.1453161Z 2025-09-07T06:49:07.1453280Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T06:49:07.1453538Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T06:49:07.1453724Z can be removed at the end if desired 2025-09-07T06:49:07.1453808Z 2025-09-07T06:49:07.1453917Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T06:49:07.1454142Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T06:49:07.1454261Z 2025-09-07T06:49:07.1454351Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T06:49:07.1454683Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T06:49:07.1454933Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T06:49:07.1455188Z as long as we haven't removed the observers yet. 2025-09-07T06:49:07.1456533Z 2025-09-07T06:49:07.1456569Z Note: 2025-09-07T06:49:07.1456723Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T06:49:07.1456976Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T06:49:07.1457224Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T06:49:07.1457406Z the QuantizationTracer class. 2025-09-07T06:49:07.1457482Z 2025-09-07T06:49:07.1457526Z General Flow for Fx workflow: 2025-09-07T06:49:07.1457729Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T06:49:07.1457943Z 2.) Prepare your model with prepare_fx 2025-09-07T06:49:07.1458107Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T06:49:07.1458331Z 4.) Calibrate your model with data 2025-09-07T06:49:07.1458530Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T06:49:07.1459798Z Optional 2025-09-07T06:49:07.1459938Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T06:49:07.1460157Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T06:49:07.1460317Z - Table 2025-09-07T06:49:07.1460406Z - Histogram 2025-09-07T06:49:07.1460494Z - Line plot 2025-09-07T06:49:07.1460657Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T06:49:07.1460805Z 2025-09-07T06:49:07.1460850Z Example (with QuantizationTracer): 2025-09-07T06:49:07.1460965Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.1461070Z >>> # get the necessary qconfig 2025-09-07T06:49:07.1461187Z >>> config = PrepareCustomConfig() 2025-09-07T06:49:07.1462368Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T06:49:07.1462521Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T06:49:07.1462646Z ... ) 2025-09-07T06:49:07.1462692Z 2025-09-07T06:49:07.1462744Z >>> # initialize our model and get GraphModule 2025-09-07T06:49:07.1462868Z >>> model = SomeModel() 2025-09-07T06:49:07.1463020Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T06:49:07.1463205Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T06:49:07.1463301Z 2025-09-07T06:49:07.1463358Z >>> # get our set of detectors and ModelReport instance 2025-09-07T06:49:07.1463488Z >>> detector_set = set( 2025-09-07T06:49:07.1463581Z ... [ 2025-09-07T06:49:07.1463682Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T06:49:07.1464954Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T06:49:07.1465092Z ... ] 2025-09-07T06:49:07.1465177Z ... ) 2025-09-07T06:49:07.1465300Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T06:49:07.1465408Z 2025-09-07T06:49:07.1465467Z >>> # now we insert the observers and calibrate the model 2025-09-07T06:49:07.1465646Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T06:49:07.1465820Z >>> for i in range(num_callibration_batches): 2025-09-07T06:49:07.1465953Z >>> example_input = get_callibration_input() 2025-09-07T06:49:07.1466088Z >>> tracer_model_with_observers(example_input) 2025-09-07T06:49:07.1466177Z 2025-09-07T06:49:07.1466266Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T06:49:07.1466449Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T06:49:07.1467614Z ... remove_inserted_observers=True 2025-09-07T06:49:07.1467723Z ... ) 2025-09-07T06:49:07.1467770Z 2025-09-07T06:49:07.1467852Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T06:49:07.1468067Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T06:49:07.1468161Z 2025-09-07T06:49:07.1468246Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T06:49:07.1468429Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T06:49:07.1468531Z 2025-09-07T06:49:07.1468628Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T06:49:07.1468833Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T06:49:07.1468942Z 2025-09-07T06:49:07.1468945Z 2025-09-07T06:49:07.1469035Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1469158Z 2025-09-07T06:49:07.1752756Z 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. 2025-09-07T06:49:07.1759115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1759490Z 2025-09-07T06:49:07.1759754Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T06:49:07.1760338Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T06:49:07.1760908Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T06:49:07.1761364Z input by the user. 2025-09-07T06:49:07.1761744Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T06:49:07.1762227Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T06:49:07.1762490Z 2025-09-07T06:49:07.1762598Z Note:: 2025-09-07T06:49:07.1763035Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T06:49:07.1763447Z 2025-09-07T06:49:07.1763535Z Args: 2025-09-07T06:49:07.1763776Z model (nn.Module): 2025-09-07T06:49:07.1764169Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T06:49:07.1764764Z sparsified should be added separately using the register_layer() function 2025-09-07T06:49:07.1765035Z aggregate_fn (Optional, Callable): 2025-09-07T06:49:07.1765201Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T06:49:07.1765389Z specifies how inputs should be aggregated over time. 2025-09-07T06:49:07.1765586Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T06:49:07.1767304Z Example 2025-09-07T06:49:07.1767438Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T06:49:07.1767584Z reduce_fn (Optional, Callable): 2025-09-07T06:49:07.1767752Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T06:49:07.1767975Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T06:49:07.1768151Z calling agg_fn() on all inputs. 2025-09-07T06:49:07.1768266Z Example 2025-09-07T06:49:07.1768396Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T06:49:07.1768546Z mask_fn (Optional, Callable): 2025-09-07T06:49:07.1768731Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T06:49:07.1768966Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T06:49:07.1770268Z register_layer(). 2025-09-07T06:49:07.1770466Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T06:49:07.1770658Z arguments. 2025-09-07T06:49:07.1770764Z features (Optional, list): 2025-09-07T06:49:07.1770897Z default selected features to sparsify. 2025-09-07T06:49:07.1771141Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T06:49:07.1771309Z For example, 2025-09-07T06:49:07.1771464Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T06:49:07.1771634Z feature_dim (Optional, int): 2025-09-07T06:49:07.1771809Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T06:49:07.1771982Z for sparsification. 2025-09-07T06:49:07.1773146Z sparse_config (Dict): 2025-09-07T06:49:07.1773302Z Default configuration for the mask_fn. This config will be passed 2025-09-07T06:49:07.1773453Z with the mask_fn() 2025-09-07T06:49:07.1773526Z 2025-09-07T06:49:07.1773560Z Example: 2025-09-07T06:49:07.1773642Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.1773795Z >>> model = SomeModel() 2025-09-07T06:49:07.1773952Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T06:49:07.1774109Z >>> # Initialize aggregate_fn 2025-09-07T06:49:07.1774215Z >>> def agg_fn(x, y): 2025-09-07T06:49:07.1774312Z >>> return x + y 2025-09-07T06:49:07.1774476Z >>> 2025-09-07T06:49:07.1775630Z >>> # Initialize reduce_fn 2025-09-07T06:49:07.1775736Z >>> def reduce_fn(x): 2025-09-07T06:49:07.1775837Z >>> return torch.mean(x, dim=0) 2025-09-07T06:49:07.1775944Z >>> 2025-09-07T06:49:07.1776024Z >>> # Initialize mask_fn 2025-09-07T06:49:07.1776126Z >>> def mask_fn(data): 2025-09-07T06:49:07.1776242Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T06:49:07.1776360Z >>> 2025-09-07T06:49:07.1776431Z >>> 2025-09-07T06:49:07.1776514Z >>> act_sparsifier.register_layer( 2025-09-07T06:49:07.1776629Z ... model.some_layer, 2025-09-07T06:49:07.1777758Z ... aggregate_fn=agg_fn, 2025-09-07T06:49:07.1777871Z ... reduce_fn=reduce_fn, 2025-09-07T06:49:07.1777978Z ... mask_fn=mask_fn, 2025-09-07T06:49:07.1778073Z ... ) 2025-09-07T06:49:07.1778148Z >>> 2025-09-07T06:49:07.1778226Z >>> # start training process 2025-09-07T06:49:07.1778329Z >>> for _ in [...]: 2025-09-07T06:49:07.1778422Z >>> # epoch starts 2025-09-07T06:49:07.1778544Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T06:49:07.1778677Z >>> # epoch ends 2025-09-07T06:49:07.1778773Z >>> act_sparsifier.step() 2025-09-07T06:49:07.1779894Z >>> # end training process 2025-09-07T06:49:07.1780003Z >>> sparsifier.squash_mask() 2025-09-07T06:49:07.1780072Z 2025-09-07T06:49:07.1780165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1780290Z 2025-09-07T06:49:07.1780574Z 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. 2025-09-07T06:49:07.1780981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1781110Z 2025-09-07T06:49:07.1781183Z Abstract method that needs to be implemented by the child class. 2025-09-07T06:49:07.1781381Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T06:49:07.1781600Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T06:49:07.1781765Z is called. 2025-09-07T06:49:07.1781812Z 2025-09-07T06:49:07.1781845Z Example: 2025-09-07T06:49:07.1781930Z >>> def get_schedule_param(self): 2025-09-07T06:49:07.1783093Z ... new_param = {} 2025-09-07T06:49:07.1783217Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T06:49:07.1783349Z ... new_param[name] = ( 2025-09-07T06:49:07.1783496Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T06:49:07.1783640Z ... ) 2025-09-07T06:49:07.1783774Z ... return new_param 2025-09-07T06:49:07.1783842Z 2025-09-07T06:49:07.1783957Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T06:49:07.1784146Z would be halved 2025-09-07T06:49:07.1784200Z 2025-09-07T06:49:07.1784288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1784488Z 2025-09-07T06:49:07.1851639Z 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. 2025-09-07T06:49:07.1854210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.1854488Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T06:49:07.1854593Z 2025-09-07T06:49:07.1854672Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T06:49:07.1854933Z the module will have a `sparse_params` dict attached to it. 2025-09-07T06:49:07.1855044Z 2025-09-07T06:49:07.1855079Z Args: 2025-09-07T06:49:07.1855199Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T06:49:07.1855361Z representing the modules and keys that will have 2025-09-07T06:49:07.1855507Z sparsity parameters saved 2025-09-07T06:49:07.1855666Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T06:49:07.1855835Z saved for specific layers. The keys in the dict 2025-09-07T06:49:07.1855994Z should be the module fqn, while the values should 2025-09-07T06:49:07.1856146Z be a list of strings with the names of the variables 2025-09-07T06:49:07.1856289Z to save in the `sparse_params` 2025-09-07T06:49:07.1856372Z 2025-09-07T06:49:07.1856415Z Examples: 2025-09-07T06:49:07.1856525Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T06:49:07.1856657Z >>> # Don't save any sparse params 2025-09-07T06:49:07.1858190Z >>> sparsifier.squash_mask() 2025-09-07T06:49:07.1858324Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T06:49:07.1858446Z False 2025-09-07T06:49:07.1858499Z 2025-09-07T06:49:07.1858548Z >>> # Keep sparse params per layer 2025-09-07T06:49:07.1858672Z >>> sparsifier.squash_mask( 2025-09-07T06:49:07.1858794Z ... params_to_keep_per_layer={ 2025-09-07T06:49:07.1858923Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T06:49:07.1859060Z ... "submodule2.linear42": ("baz",), 2025-09-07T06:49:07.1859179Z ... } 2025-09-07T06:49:07.1859265Z ... ) 2025-09-07T06:49:07.1859376Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:07.1860600Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:07.1860736Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:07.1860867Z {'baz': 0.1} 2025-09-07T06:49:07.1860929Z 2025-09-07T06:49:07.1860977Z >>> # Keep sparse params for all layers 2025-09-07T06:49:07.1861126Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T06:49:07.1861281Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:07.1861411Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:07.1861539Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:07.1861668Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:07.1861736Z 2025-09-07T06:49:07.1861808Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T06:49:07.1861955Z >>> # some other layers 2025-09-07T06:49:07.1863095Z >>> sparsifier.squash_mask( 2025-09-07T06:49:07.1863222Z ... params_to_keep=("foo", "bar"), 2025-09-07T06:49:07.1863418Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T06:49:07.1863555Z ... ) 2025-09-07T06:49:07.1863664Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:07.1863793Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:07.1863918Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:07.1864053Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T06:49:07.1864160Z 2025-09-07T06:49:07.1864294Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.1864507Z 2025-09-07T06:49:07.2393135Z 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. 2025-09-07T06:49:07.2393479Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2393761Z A quantized long short-term memory (LSTM). 2025-09-07T06:49:07.2393855Z 2025-09-07T06:49:07.2393963Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T06:49:07.2394102Z 2025-09-07T06:49:07.2394138Z Attributes: 2025-09-07T06:49:07.2394241Z layers : instances of the `_LSTMLayer` 2025-09-07T06:49:07.2394322Z 2025-09-07T06:49:07.2394361Z .. note:: 2025-09-07T06:49:07.2394643Z To access the weights and biases, you need to access them per layer. 2025-09-07T06:49:07.2394821Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T06:49:07.2394920Z 2025-09-07T06:49:07.2394958Z Examples:: 2025-09-07T06:49:07.2395049Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2396669Z >>> custom_module_config = { 2025-09-07T06:49:07.2396800Z ... 'float_to_observed_custom_module_class': { 2025-09-07T06:49:07.2396937Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T06:49:07.2397054Z ... }, 2025-09-07T06:49:07.2397170Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T06:49:07.2397318Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T06:49:07.2397437Z ... } 2025-09-07T06:49:07.2397519Z ... } 2025-09-07T06:49:07.2397647Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T06:49:07.2397833Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T06:49:07.2397972Z 2025-09-07T06:49:07.2399198Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2399321Z 2025-09-07T06:49:07.2438418Z 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. 2025-09-07T06:49:07.2438765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2438977Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T06:49:07.2439141Z several quantized input planes. 2025-09-07T06:49:07.2439221Z 2025-09-07T06:49:07.2439300Z For details on input arguments, parameters, and implementation see 2025-09-07T06:49:07.2439456Z :class:`~torch.nn.Conv1d`. 2025-09-07T06:49:07.2439526Z 2025-09-07T06:49:07.2439564Z .. note:: 2025-09-07T06:49:07.2439691Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T06:49:07.2439797Z 2025-09-07T06:49:07.2439831Z .. note:: 2025-09-07T06:49:07.2441810Z Only `torch.quint8` is supported for the input data type. 2025-09-07T06:49:07.2441928Z 2025-09-07T06:49:07.2441930Z 2025-09-07T06:49:07.2441972Z Attributes: 2025-09-07T06:49:07.2442102Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T06:49:07.2442256Z parameter. 2025-09-07T06:49:07.2442388Z scale (Tensor): scalar for the output scale 2025-09-07T06:49:07.2442539Z zero_point (Tensor): scalar for the output zero point 2025-09-07T06:49:07.2442638Z 2025-09-07T06:49:07.2442786Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T06:49:07.2442881Z 2025-09-07T06:49:07.2442999Z Examples:: 2025-09-07T06:49:07.2443052Z 2025-09-07T06:49:07.2443111Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T06:49:07.2443254Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T06:49:07.2443383Z >>> input = torch.randn(20, 16, 100) 2025-09-07T06:49:07.2444904Z >>> # quantize input to quint8 2025-09-07T06:49:07.2445022Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2445168Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T06:49:07.2445330Z ... dtype=torch.quint8) 2025-09-07T06:49:07.2445456Z >>> output = m(q_input) 2025-09-07T06:49:07.2445524Z 2025-09-07T06:49:07.2445555Z 2025-09-07T06:49:07.2445687Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2445859Z 2025-09-07T06:49:07.2637839Z 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. 2025-09-07T06:49:07.2638159Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2638562Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T06:49:07.2638743Z 2025-09-07T06:49:07.2640970Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T06:49:07.2641207Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T06:49:07.2641408Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T06:49:07.2641555Z (UAI 2018). 2025-09-07T06:49:07.2641609Z 2025-09-07T06:49:07.2641693Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T06:49:07.2641906Z but using exponential weights instead of equal weights across iterations. 2025-09-07T06:49:07.2642033Z 2025-09-07T06:49:07.2642118Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T06:49:07.2642320Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T06:49:07.2642483Z parameters of the :attr:`model`. 2025-09-07T06:49:07.2642560Z 2025-09-07T06:49:07.2642593Z Args: 2025-09-07T06:49:07.2644496Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T06:49:07.2644689Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T06:49:07.2644851Z stored on the :attr:`device` 2025-09-07T06:49:07.2645008Z avg_fn (function, optional): the averaging function used to update 2025-09-07T06:49:07.2645187Z parameters; the function must take in the current value of the 2025-09-07T06:49:07.2645378Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T06:49:07.2645574Z parameter, and the number of models already averaged; if None, 2025-09-07T06:49:07.2645739Z an equally weighted average is used (default: None) 2025-09-07T06:49:07.2645913Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T06:49:07.2646122Z parameters inplace; the function must take in the current values of the 2025-09-07T06:49:07.2646335Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T06:49:07.2646544Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T06:49:07.2646718Z an equally weighted average is used (default: None) 2025-09-07T06:49:07.2646885Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T06:49:07.2647072Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T06:49:07.2647190Z 2025-09-07T06:49:07.2647227Z Example: 2025-09-07T06:49:07.2647433Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:07.2647566Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T06:49:07.2647720Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T06:49:07.2647903Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T06:49:07.2648068Z >>> T_max=300) 2025-09-07T06:49:07.2649534Z >>> swa_start = 160 2025-09-07T06:49:07.2649656Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T06:49:07.2649785Z >>> for i in range(300): 2025-09-07T06:49:07.2649899Z >>> for input, target in loader: 2025-09-07T06:49:07.2650020Z >>> optimizer.zero_grad() 2025-09-07T06:49:07.2650150Z >>> loss_fn(model(input), target).backward() 2025-09-07T06:49:07.2650278Z >>> optimizer.step() 2025-09-07T06:49:07.2650447Z >>> if i > swa_start: 2025-09-07T06:49:07.2650572Z >>> swa_model.update_parameters(model) 2025-09-07T06:49:07.2650698Z >>> swa_scheduler.step() 2025-09-07T06:49:07.2650807Z >>> else: 2025-09-07T06:49:07.2651970Z >>> scheduler.step() 2025-09-07T06:49:07.2652075Z >>> 2025-09-07T06:49:07.2652181Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T06:49:07.2652335Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T06:49:07.2652431Z 2025-09-07T06:49:07.2652539Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T06:49:07.2652752Z If no averaging function is provided, the default is to compute 2025-09-07T06:49:07.2652911Z equally-weighted average of the weights (SWA). 2025-09-07T06:49:07.2653001Z 2025-09-07T06:49:07.2653035Z Example: 2025-09-07T06:49:07.2653134Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:07.2653302Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T06:49:07.2654566Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T06:49:07.2654750Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T06:49:07.2654864Z 2025-09-07T06:49:07.2654905Z .. note:: 2025-09-07T06:49:07.2655031Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T06:49:07.2655218Z need to update the activation statistics for Batch Normalization. 2025-09-07T06:49:07.2655407Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T06:49:07.2655604Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T06:49:07.2655804Z statistics in a post-training step by passing data through the model. The 2025-09-07T06:49:07.2656004Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T06:49:07.2656212Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T06:49:07.2656409Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T06:49:07.2657587Z approach yields the best results in your problem. 2025-09-07T06:49:07.2657684Z 2025-09-07T06:49:07.2657718Z .. note:: 2025-09-07T06:49:07.2657858Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T06:49:07.2657984Z 2025-09-07T06:49:07.2658017Z .. note:: 2025-09-07T06:49:07.2658136Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T06:49:07.2658310Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T06:49:07.2658483Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T06:49:07.2658655Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T06:49:07.2658800Z to update the parameters. 2025-09-07T06:49:07.2658874Z 2025-09-07T06:49:07.2659001Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T06:49:07.2659172Z https://arxiv.org/abs/1803.05407 2025-09-07T06:49:07.2660348Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T06:49:07.2660505Z Average: 2025-09-07T06:49:07.2660601Z https://arxiv.org/abs/1806.05594 2025-09-07T06:49:07.2660753Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T06:49:07.2660903Z https://arxiv.org/abs/1904.11943 2025-09-07T06:49:07.2661059Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T06:49:07.2661211Z Generalizes Well: 2025-09-07T06:49:07.2661315Z https://arxiv.org/abs/2001.02312 2025-09-07T06:49:07.2661428Z .. _Polyak averaging: 2025-09-07T06:49:07.2661556Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T06:49:07.2661729Z 2025-09-07T06:49:07.2662870Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2662995Z 2025-09-07T06:49:07.2663164Z 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. 2025-09-07T06:49:07.2663449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2663652Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T06:49:07.2663763Z 2025-09-07T06:49:07.2663846Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T06:49:07.2664038Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T06:49:07.2664151Z 2025-09-07T06:49:07.2664183Z Args: 2025-09-07T06:49:07.2664294Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T06:49:07.2664551Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T06:49:07.2664719Z together or separately for each group. 2025-09-07T06:49:07.2664876Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T06:49:07.2666036Z (default: 10) 2025-09-07T06:49:07.2666175Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T06:49:07.2666361Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T06:49:07.2666511Z (default: "cos") 2025-09-07T06:49:07.2666644Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T06:49:07.2666744Z 2025-09-07T06:49:07.2666812Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T06:49:07.2666890Z schedulers to switch to a constant learning rate late in the training 2025-09-07T06:49:07.2666932Z as in the example below. 2025-09-07T06:49:07.2666934Z 2025-09-07T06:49:07.2666967Z Example: 2025-09-07T06:49:07.2667024Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T06:49:07.2667075Z >>> loader, optimizer, model = ... 2025-09-07T06:49:07.2667120Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T06:49:07.2667201Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T06:49:07.2667243Z >>> lr_lambda=lr_lambda) 2025-09-07T06:49:07.2667308Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T06:49:07.2667375Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T06:49:07.2668436Z >>> swa_start = 160 2025-09-07T06:49:07.2668481Z >>> for i in range(300): 2025-09-07T06:49:07.2668526Z >>> for input, target in loader: 2025-09-07T06:49:07.2668570Z >>> optimizer.zero_grad() 2025-09-07T06:49:07.2668623Z >>> loss_fn(model(input), target).backward() 2025-09-07T06:49:07.2668667Z >>> optimizer.step() 2025-09-07T06:49:07.2668710Z >>> if i > swa_start: 2025-09-07T06:49:07.2668803Z >>> swa_scheduler.step() 2025-09-07T06:49:07.2668838Z >>> else: 2025-09-07T06:49:07.2668878Z >>> scheduler.step() 2025-09-07T06:49:07.2668880Z 2025-09-07T06:49:07.2668961Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T06:49:07.2669005Z https://arxiv.org/abs/1803.05407 2025-09-07T06:49:07.2669037Z 2025-09-07T06:49:07.2669125Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2669127Z 2025-09-07T06:49:07.2739707Z 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=868. 2025-09-07T06:49:07.2740516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2740655Z Load the optimizer state. 2025-09-07T06:49:07.2740666Z 2025-09-07T06:49:07.2740740Z Args: 2025-09-07T06:49:07.2741209Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T06:49:07.2741320Z from a call to :meth:`state_dict`. 2025-09-07T06:49:07.2741328Z 2025-09-07T06:49:07.2746431Z .. warning:: 2025-09-07T06:49:07.2746685Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T06:49:07.2746841Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T06:49:07.2746844Z 2025-09-07T06:49:07.2746929Z .. note:: 2025-09-07T06:49:07.2747117Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T06:49:07.2747244Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T06:49:07.2747449Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T06:49:07.2747558Z differ from those initialized in the optimizer), 2025-09-07T06:49:07.2747766Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T06:49:07.2747839Z accordingly. 2025-09-07T06:49:07.2748023Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T06:49:07.2748223Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T06:49:07.2748342Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T06:49:07.2748348Z 2025-09-07T06:49:07.2748419Z Example: 2025-09-07T06:49:07.2748496Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2748587Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T06:49:07.2748719Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T06:49:07.2748834Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T06:49:07.2748904Z ... optim, 2025-09-07T06:49:07.2748988Z ... start_factor=0.1, 2025-09-07T06:49:07.2749062Z ... end_factor=1, 2025-09-07T06:49:07.2749142Z ... total_iters=20, 2025-09-07T06:49:07.2749200Z ... ) 2025-09-07T06:49:07.2751909Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T06:49:07.2751984Z ... optim, 2025-09-07T06:49:07.2752059Z ... T_max=80, 2025-09-07T06:49:07.2752123Z ... eta_min=3e-5, 2025-09-07T06:49:07.2752182Z ... ) 2025-09-07T06:49:07.2752291Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T06:49:07.2752361Z ... optim, 2025-09-07T06:49:07.2752460Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T06:49:07.2752528Z ... milestones=[20], 2025-09-07T06:49:07.2752585Z ... ) 2025-09-07T06:49:07.2752692Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T06:49:07.2752982Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T06:49:07.2753095Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T06:49:07.2753097Z 2025-09-07T06:49:07.2753161Z 2025-09-07T06:49:07.2753326Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2753331Z 2025-09-07T06:49:07.2841853Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=811. 2025-09-07T06:49:07.2842779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2843040Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T06:49:07.2843049Z 2025-09-07T06:49:07.2843297Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T06:49:07.2843851Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T06:49:07.2843858Z 2025-09-07T06:49:07.2849354Z Args: 2025-09-07T06:49:07.2849466Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:07.2849568Z schedulers (list): List of chained schedulers. 2025-09-07T06:49:07.2849711Z milestones (list): List of integers that reflects milestone points. 2025-09-07T06:49:07.2849846Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T06:49:07.2849852Z 2025-09-07T06:49:07.2849916Z Example: 2025-09-07T06:49:07.2849984Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2850083Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T06:49:07.2850159Z >>> # lr = 0.005 if epoch == 0 2025-09-07T06:49:07.2850224Z >>> # lr = 0.005 if epoch == 1 2025-09-07T06:49:07.2850282Z >>> # lr = 0.005 if epoch == 2 2025-09-07T06:49:07.2850340Z >>> # ... 2025-09-07T06:49:07.2850414Z >>> # lr = 0.05 if epoch == 20 2025-09-07T06:49:07.2850486Z >>> # lr = 0.045 if epoch == 21 2025-09-07T06:49:07.2850557Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T06:49:07.2850685Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T06:49:07.2850789Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T06:49:07.2850861Z >>> scheduler = SequentialLR( 2025-09-07T06:49:07.2850929Z ... optimizer, 2025-09-07T06:49:07.2851015Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T06:49:07.2851087Z ... milestones=[20], 2025-09-07T06:49:07.2851139Z ... ) 2025-09-07T06:49:07.2853371Z >>> for epoch in range(100): 2025-09-07T06:49:07.2853440Z >>> train(...) 2025-09-07T06:49:07.2853505Z >>> validate(...) 2025-09-07T06:49:07.2853569Z >>> scheduler.step() 2025-09-07T06:49:07.2853573Z 2025-09-07T06:49:07.2853712Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T06:49:07.2853767Z 2025-09-07T06:49:07.2853926Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2853932Z 2025-09-07T06:49:07.2856906Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1236. 2025-09-07T06:49:07.2857061Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:07.2857376Z Reduce learning rate when a metric has stopped improving. 2025-09-07T06:49:07.2857381Z 2025-09-07T06:49:07.2857497Z Models often benefit from reducing the learning rate by a factor 2025-09-07T06:49:07.2857582Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T06:49:07.2857700Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T06:49:07.2857762Z of epochs, the learning rate is reduced. 2025-09-07T06:49:07.2857766Z 2025-09-07T06:49:07.2857812Z Args: 2025-09-07T06:49:07.2857873Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:07.2858123Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T06:49:07.2858197Z be reduced when the quantity monitored has stopped 2025-09-07T06:49:07.2858276Z decreasing; in `max` mode it will be reduced when the 2025-09-07T06:49:07.2858365Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T06:49:07.2860707Z factor (float): Factor by which the learning rate will be 2025-09-07T06:49:07.2860780Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T06:49:07.2860880Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T06:49:07.2860941Z which the learning rate will be reduced. 2025-09-07T06:49:07.2861050Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T06:49:07.2861206Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T06:49:07.2861385Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T06:49:07.2861452Z we have what is considered an intolerable epoch. 2025-09-07T06:49:07.2861576Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T06:49:07.2861655Z the learning rate is reduced at the end of this epoch. 2025-09-07T06:49:07.2861795Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T06:49:07.2861925Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T06:49:07.2861992Z the learning rate is not adjusted. 2025-09-07T06:49:07.2862066Z Default: 10. 2025-09-07T06:49:07.2862159Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T06:49:07.2862233Z to only focus on significant changes. Default: 1e-4. 2025-09-07T06:49:07.2862320Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T06:49:07.2862390Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T06:49:07.2862464Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T06:49:07.2862539Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T06:49:07.2862628Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T06:49:07.2862720Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T06:49:07.2862803Z normal operation after lr has been reduced. Default: 0. 2025-09-07T06:49:07.2862891Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T06:49:07.2862975Z lower bound on the learning rate of all param groups 2025-09-07T06:49:07.2863046Z or each group respectively. Default: 0. 2025-09-07T06:49:07.2865311Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T06:49:07.2865413Z between new and old lr is smaller than eps, the update is 2025-09-07T06:49:07.2865473Z ignored. Default: 1e-8. 2025-09-07T06:49:07.2865480Z 2025-09-07T06:49:07.2865520Z Example: 2025-09-07T06:49:07.2865581Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2865690Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T06:49:07.2865759Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T06:49:07.2865815Z >>> for epoch in range(10): 2025-09-07T06:49:07.2865862Z >>> train(...) 2025-09-07T06:49:07.2865916Z >>> val_loss = validate(...) 2025-09-07T06:49:07.2865994Z >>> # Note that step should be called after validate() 2025-09-07T06:49:07.2866047Z >>> scheduler.step(val_loss) 2025-09-07T06:49:07.2866049Z 2025-09-07T06:49:07.2866139Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T06:49:07.2866177Z 2025-09-07T06:49:07.2866425Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T06:49:07.2866428Z 2025-09-07T06:49:07.2866481Z scheduler.step(val_loss) 2025-09-07T06:49:07.2866521Z ^ 2025-09-07T06:49:07.2866747Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1433. 2025-09-07T06:49:07.2866866Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2866999Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T06:49:07.2867003Z 2025-09-07T06:49:07.2867129Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T06:49:07.2867302Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T06:49:07.2867454Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T06:49:07.2867497Z or per-cycle basis. 2025-09-07T06:49:07.2867499Z 2025-09-07T06:49:07.2867586Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T06:49:07.2867660Z `step` should be called after a batch has been used for training. 2025-09-07T06:49:07.2867662Z 2025-09-07T06:49:07.2869120Z This class has three built-in policies, as put forth in the paper: 2025-09-07T06:49:07.2869122Z 2025-09-07T06:49:07.2869208Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T06:49:07.2869322Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T06:49:07.2869438Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T06:49:07.2869482Z at each cycle iteration. 2025-09-07T06:49:07.2869484Z 2025-09-07T06:49:07.2869569Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T06:49:07.2869578Z 2025-09-07T06:49:07.2869615Z Args: 2025-09-07T06:49:07.2869668Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:07.2869738Z base_lr (float or list): Initial learning rate which is the 2025-09-07T06:49:07.2869803Z lower boundary in the cycle for each parameter group. 2025-09-07T06:49:07.2869876Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T06:49:07.2869927Z for each parameter group. Functionally, 2025-09-07T06:49:07.2869989Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T06:49:07.2870042Z The lr at any cycle is the sum of base_lr 2025-09-07T06:49:07.2870098Z and some scaling of the amplitude; therefore 2025-09-07T06:49:07.2870155Z max_lr may not actually be reached depending on 2025-09-07T06:49:07.2870196Z scaling function. 2025-09-07T06:49:07.2870264Z step_size_up (int): Number of training iterations in the 2025-09-07T06:49:07.2870318Z increasing half of a cycle. Default: 2000 2025-09-07T06:49:07.2870386Z step_size_down (int): Number of training iterations in the 2025-09-07T06:49:07.2870451Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T06:49:07.2870503Z it is set to step_size_up. Default: None 2025-09-07T06:49:07.2871748Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T06:49:07.2871813Z Values correspond to policies detailed above. 2025-09-07T06:49:07.2871871Z If scale_fn is not None, this argument is ignored. 2025-09-07T06:49:07.2871916Z Default: 'triangular' 2025-09-07T06:49:07.2871979Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T06:49:07.2872023Z gamma**(cycle iterations) 2025-09-07T06:49:07.2872063Z Default: 1.0 2025-09-07T06:49:07.2872137Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T06:49:07.2872223Z argument lambda function, where 2025-09-07T06:49:07.2872271Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T06:49:07.2872318Z If specified, then 'mode' is ignored. 2025-09-07T06:49:07.2872358Z Default: None 2025-09-07T06:49:07.2872409Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T06:49:07.2872469Z Defines whether scale_fn is evaluated on 2025-09-07T06:49:07.2872521Z cycle number or cycle iterations (training 2025-09-07T06:49:07.2872573Z iterations since start of cycle). 2025-09-07T06:49:07.2872613Z Default: 'cycle' 2025-09-07T06:49:07.2872688Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T06:49:07.2872760Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T06:49:07.2872796Z Default: True 2025-09-07T06:49:07.2875890Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T06:49:07.2876092Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T06:49:07.2876160Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T06:49:07.2876224Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T06:49:07.2876267Z Default: 0.8 2025-09-07T06:49:07.2876349Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T06:49:07.2876410Z for each parameter group. Functionally, 2025-09-07T06:49:07.2876488Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T06:49:07.2876566Z The momentum at any cycle is the difference of max_momentum 2025-09-07T06:49:07.2876624Z and some scaling of the amplitude; therefore 2025-09-07T06:49:07.2876698Z base_momentum may not actually be reached depending on 2025-09-07T06:49:07.2876769Z scaling function. Note that momentum is cycled inversely 2025-09-07T06:49:07.2876866Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T06:49:07.2876915Z and learning rate is 'base_lr' 2025-09-07T06:49:07.2876961Z Default: 0.9 2025-09-07T06:49:07.2877047Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T06:49:07.2877135Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T06:49:07.2877218Z batch instead of after each epoch, this number represents the total 2025-09-07T06:49:07.2877309Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T06:49:07.2877382Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T06:49:07.2877428Z Default: -1 2025-09-07T06:49:07.2877430Z 2025-09-07T06:49:07.2877468Z Example: 2025-09-07T06:49:07.2877512Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2877603Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T06:49:07.2877662Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T06:49:07.2877700Z ... optimizer, 2025-09-07T06:49:07.2877740Z ... base_lr=0.01, 2025-09-07T06:49:07.2877777Z ... max_lr=0.1, 2025-09-07T06:49:07.2877820Z ... step_size_up=10, 2025-09-07T06:49:07.2877865Z ... ) 2025-09-07T06:49:07.2877928Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T06:49:07.2879462Z >>> for epoch in range(10): 2025-09-07T06:49:07.2879511Z >>> for batch in data_loader: 2025-09-07T06:49:07.2879552Z >>> train_batch(...) 2025-09-07T06:49:07.2879593Z >>> scheduler.step() 2025-09-07T06:49:07.2879595Z 2025-09-07T06:49:07.2879656Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T06:49:07.2879658Z 2025-09-07T06:49:07.2879780Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T06:49:07.2879904Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T06:49:07.2879935Z 2025-09-07T06:49:07.2880026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2880028Z 2025-09-07T06:49:07.2880261Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1725. 2025-09-07T06:49:07.2880354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2880442Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T06:49:07.2880444Z 2025-09-07T06:49:07.2880512Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T06:49:07.2880595Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T06:49:07.2880646Z of epochs between two warm restarts in SGDR: 2025-09-07T06:49:07.2880677Z 2025-09-07T06:49:07.2880712Z .. math:: 2025-09-07T06:49:07.2880780Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T06:49:07.2880837Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T06:49:07.2880840Z 2025-09-07T06:49:07.2880902Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T06:49:07.2880973Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T06:49:07.2880975Z 2025-09-07T06:49:07.2881015Z It has been proposed in 2025-09-07T06:49:07.2882148Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T06:49:07.2882151Z 2025-09-07T06:49:07.2882187Z Args: 2025-09-07T06:49:07.2882238Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:07.2882298Z T_0 (int): Number of iterations until the first restart. 2025-09-07T06:49:07.2882400Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T06:49:07.2882481Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T06:49:07.2882558Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T06:49:07.2882560Z 2025-09-07T06:49:07.2882624Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T06:49:07.2882671Z https://arxiv.org/abs/1608.03983 2025-09-07T06:49:07.2882673Z 2025-09-07T06:49:07.2882706Z Example: 2025-09-07T06:49:07.2882746Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2882814Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T06:49:07.2882897Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T06:49:07.2882937Z ... optimizer, T_0=20 2025-09-07T06:49:07.2882969Z ... ) 2025-09-07T06:49:07.2883010Z >>> for epoch in range(100): 2025-09-07T06:49:07.2883047Z >>> train(...) 2025-09-07T06:49:07.2883086Z >>> validate(...) 2025-09-07T06:49:07.2883129Z >>> scheduler.step() 2025-09-07T06:49:07.2883131Z 2025-09-07T06:49:07.2883215Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T06:49:07.2883247Z 2025-09-07T06:49:07.2883334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2883336Z 2025-09-07T06:49:07.2884617Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1875. 2025-09-07T06:49:07.2884710Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.2884815Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T06:49:07.2884818Z 2025-09-07T06:49:07.2884921Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T06:49:07.2885022Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T06:49:07.2885113Z lower than the initial learning rate. 2025-09-07T06:49:07.2885196Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T06:49:07.2885271Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T06:49:07.2885273Z 2025-09-07T06:49:07.2885359Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T06:49:07.2885430Z `step` should be called after a batch has been used for training. 2025-09-07T06:49:07.2885433Z 2025-09-07T06:49:07.2885475Z This scheduler is not chainable. 2025-09-07T06:49:07.2885477Z 2025-09-07T06:49:07.2885560Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T06:49:07.2885612Z of two ways (listed in order of precedence): 2025-09-07T06:49:07.2885614Z 2025-09-07T06:49:07.2885670Z #. A value for total_steps is explicitly provided. 2025-09-07T06:49:07.2885774Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T06:49:07.2885821Z (steps_per_epoch) are provided. 2025-09-07T06:49:07.2885883Z In this case, the number of total steps is inferred by 2025-09-07T06:49:07.2885931Z total_steps = epochs * steps_per_epoch 2025-09-07T06:49:07.2885933Z 2025-09-07T06:49:07.2886016Z You must either provide a value for total_steps or provide a value for both 2025-09-07T06:49:07.2886058Z epochs and steps_per_epoch. 2025-09-07T06:49:07.2886060Z 2025-09-07T06:49:07.2886164Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T06:49:07.2887286Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T06:49:07.2887377Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T06:49:07.2887379Z 2025-09-07T06:49:07.2887412Z Args: 2025-09-07T06:49:07.2887464Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:07.2887544Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T06:49:07.2887590Z for each parameter group. 2025-09-07T06:49:07.2887664Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T06:49:07.2887740Z if a value is not provided here, then it must be inferred by providing 2025-09-07T06:49:07.2887788Z a value for epochs and steps_per_epoch. 2025-09-07T06:49:07.2887827Z Default: None 2025-09-07T06:49:07.2887897Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T06:49:07.2887985Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T06:49:07.2888037Z if a value for total_steps is not provided. 2025-09-07T06:49:07.2888075Z Default: None 2025-09-07T06:49:07.2888153Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T06:49:07.2888239Z used along with epochs in order to infer the total number of steps in the 2025-09-07T06:49:07.2888295Z cycle if a value for total_steps is not provided. 2025-09-07T06:49:07.2888332Z Default: None 2025-09-07T06:49:07.2888413Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T06:49:07.2888457Z increasing the learning rate. 2025-09-07T06:49:07.2888494Z Default: 0.3 2025-09-07T06:49:07.2889537Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T06:49:07.2889629Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T06:49:07.2889669Z linear annealing. 2025-09-07T06:49:07.2889706Z Default: 'cos' 2025-09-07T06:49:07.2889778Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T06:49:07.2889845Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T06:49:07.2889884Z Default: True 2025-09-07T06:49:07.2889993Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T06:49:07.2890067Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T06:49:07.2890128Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T06:49:07.2890182Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T06:49:07.2890218Z Default: 0.85 2025-09-07T06:49:07.2890292Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T06:49:07.2890343Z for each parameter group. Functionally, 2025-09-07T06:49:07.2890413Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T06:49:07.2890460Z Note that momentum is cycled inversely 2025-09-07T06:49:07.2890533Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T06:49:07.2890578Z and learning rate is 'base_lr' 2025-09-07T06:49:07.2890643Z Default: 0.95 2025-09-07T06:49:07.2890713Z div_factor (float): Determines the initial learning rate via 2025-09-07T06:49:07.2890757Z initial_lr = max_lr/div_factor 2025-09-07T06:49:07.2890793Z Default: 25 2025-09-07T06:49:07.2891864Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T06:49:07.2891916Z min_lr = initial_lr/final_div_factor 2025-09-07T06:49:07.2891952Z Default: 1e4 2025-09-07T06:49:07.2892040Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T06:49:07.2892129Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T06:49:07.2892215Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T06:49:07.2892252Z 'pct_start'). 2025-09-07T06:49:07.2892331Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T06:49:07.2892416Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T06:49:07.2892493Z batch instead of after each epoch, this number represents the total 2025-09-07T06:49:07.2892570Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T06:49:07.2892640Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T06:49:07.2892676Z Default: -1 2025-09-07T06:49:07.2892679Z 2025-09-07T06:49:07.2892712Z Example: 2025-09-07T06:49:07.2892751Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.2892808Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T06:49:07.2892891Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T06:49:07.2892953Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T06:49:07.2893029Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T06:49:07.2893066Z ... ) 2025-09-07T06:49:07.2893109Z >>> for epoch in range(10): 2025-09-07T06:49:07.2894141Z >>> for batch in data_loader: 2025-09-07T06:49:07.2894184Z >>> train_batch(...) 2025-09-07T06:49:07.2894225Z >>> optimizer.step() 2025-09-07T06:49:07.2894263Z >>> scheduler.step() 2025-09-07T06:49:07.2894265Z 2025-09-07T06:49:07.2894330Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T06:49:07.2894333Z 2025-09-07T06:49:07.2894513Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T06:49:07.2894560Z https://arxiv.org/abs/1708.07120 2025-09-07T06:49:07.2894590Z 2025-09-07T06:49:07.2894678Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.2894681Z 2025-09-07T06:49:07.5579837Z 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. 2025-09-07T06:49:07.5580550Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.5580564Z 2025-09-07T06:49:07.5580805Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T06:49:07.5581066Z kernel registration will override any existing fake kernel registrations. 2025-09-07T06:49:07.5581074Z 2025-09-07T06:49:07.5581289Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T06:49:07.5581527Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T06:49:07.5581728Z are a record of the input and output tensor metadata. Based on this 2025-09-07T06:49:07.5581958Z information we will match a given input to the recorded profile, and return 2025-09-07T06:49:07.5582185Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T06:49:07.5582343Z doesn't exist then an exception will be thrown. 2025-09-07T06:49:07.5582462Z 2025-09-07T06:49:07.5582700Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T06:49:07.5588896Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T06:49:07.5589054Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T06:49:07.5589194Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T06:49:07.5589327Z possible that data-dependent operations may produce outputs of different 2025-09-07T06:49:07.5589451Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T06:49:07.5589570Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T06:49:07.5589629Z potentially unsafe. 2025-09-07T06:49:07.5589634Z 2025-09-07T06:49:07.5589682Z Args: 2025-09-07T06:49:07.5589812Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T06:49:07.5589936Z name to a set of operator profiles from which we will generate fake 2025-09-07T06:49:07.5589997Z kernels. 2025-09-07T06:49:07.5590001Z 2025-09-07T06:49:07.5590053Z Examples: 2025-09-07T06:49:07.5590057Z 2025-09-07T06:49:07.5590160Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T06:49:07.5590216Z >>> import torch 2025-09-07T06:49:07.5590312Z >>> from torch.export._draft_export import draft_export 2025-09-07T06:49:07.5590361Z >>> 2025-09-07T06:49:07.5590480Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T06:49:07.5590560Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T06:49:07.5590619Z >>> return x + y 2025-09-07T06:49:07.5590669Z >>> 2025-09-07T06:49:07.5590736Z >>> class M(torch.nn.Module): 2025-09-07T06:49:07.5590804Z >>> def forward(self, a, b): 2025-09-07T06:49:07.5593077Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T06:49:07.5593144Z >>> return res 2025-09-07T06:49:07.5593198Z >>> 2025-09-07T06:49:07.5593309Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T06:49:07.5593357Z >>> 2025-09-07T06:49:07.5593526Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T06:49:07.5593599Z >>> decomp = ep.run_decompositions() 2025-09-07T06:49:07.5593603Z 2025-09-07T06:49:07.5593606Z 2025-09-07T06:49:07.5593745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.5593749Z 2025-09-07T06:49:07.5629498Z 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=397. 2025-09-07T06:49:07.5629671Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:07.5630194Z Register a FakeTensor implementation for this custom op. 2025-09-07T06:49:07.5630207Z 2025-09-07T06:49:07.5630439Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T06:49:07.5630471Z 2025-09-07T06:49:07.5630983Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T06:49:07.5631170Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T06:49:07.5631297Z Given some input Tensors with certain properties 2025-09-07T06:49:07.5631513Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T06:49:07.5631606Z the output Tensors are. 2025-09-07T06:49:07.5631613Z 2025-09-07T06:49:07.5631789Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T06:49:07.5631795Z 2025-09-07T06:49:07.5631864Z Args: 2025-09-07T06:49:07.5636460Z fn (Callable): The function to register as the FakeTensor 2025-09-07T06:49:07.5636562Z implementation. 2025-09-07T06:49:07.5636566Z 2025-09-07T06:49:07.5636650Z Examples: 2025-09-07T06:49:07.5636894Z >>> import torch 2025-09-07T06:49:07.5636989Z >>> import numpy as np 2025-09-07T06:49:07.5637080Z >>> from torch import Tensor 2025-09-07T06:49:07.5637152Z >>> 2025-09-07T06:49:07.5637312Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T06:49:07.5637478Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T06:49:07.5637631Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T06:49:07.5637735Z >>> return (x @ weight.t()) + bias 2025-09-07T06:49:07.5637798Z >>> 2025-09-07T06:49:07.5637894Z >>> @linear.register_fake 2025-09-07T06:49:07.5637985Z >>> def _(x, weight, bias): 2025-09-07T06:49:07.5638062Z >>> assert x.dim() == 2 2025-09-07T06:49:07.5638155Z >>> assert weight.dim() == 2 2025-09-07T06:49:07.5638248Z >>> assert bias.dim() == 1 2025-09-07T06:49:07.5638358Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T06:49:07.5638470Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T06:49:07.5638576Z >>> assert x.device == weight.device 2025-09-07T06:49:07.5638715Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T06:49:07.5638791Z >>> 2025-09-07T06:49:07.5641447Z >>> x = torch.randn(2, 2) 2025-09-07T06:49:07.5641550Z >>> weight = torch.randn(2, 2) 2025-09-07T06:49:07.5641634Z >>> bias = torch.randn(2) 2025-09-07T06:49:07.5641749Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:07.5641896Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T06:49:07.5642006Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:07.5642196Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T06:49:07.5642271Z >>> 2025-09-07T06:49:07.5642411Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T06:49:07.5642565Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T06:49:07.5642632Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T06:49:07.5642700Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:07.5642771Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T06:49:07.5642853Z >>> return torch.tensor(res, device=x.device) 2025-09-07T06:49:07.5642895Z >>> 2025-09-07T06:49:07.5642961Z >>> @nonzero.register_fake 2025-09-07T06:49:07.5643015Z >>> def _(x): 2025-09-07T06:49:07.5643101Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T06:49:07.5643192Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T06:49:07.5643283Z >>> # we use the ctx object to construct a new symint that 2025-09-07T06:49:07.5643366Z >>> # represents the data-dependent size. 2025-09-07T06:49:07.5645202Z >>> ctx = torch.library.get_ctx() 2025-09-07T06:49:07.5645281Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T06:49:07.5645344Z >>> shape = [nnz, x.dim()] 2025-09-07T06:49:07.5645435Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T06:49:07.5645495Z >>> return result 2025-09-07T06:49:07.5645542Z >>> 2025-09-07T06:49:07.5645615Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T06:49:07.5645690Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:07.5645775Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T06:49:07.5645845Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:07.5645922Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T06:49:07.5645927Z 2025-09-07T06:49:07.5646032Z 2025-09-07T06:49:07.5646299Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T06:49:07.5646304Z 2025-09-07T06:49:07.5646353Z _._ = None 2025-09-07T06:49:07.5646399Z ^ 2025-09-07T06:49:07.5665344Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=96. 2025-09-07T06:49:07.5665877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.5666112Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T06:49:07.5666120Z 2025-09-07T06:49:07.5666285Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T06:49:07.5666468Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T06:49:07.5670976Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T06:49:07.5671084Z that is simpler; 2025-09-07T06:49:07.5671280Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T06:49:07.5671440Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T06:49:07.5671591Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T06:49:07.5671755Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T06:49:07.5671835Z a TorchDispatchMode. 2025-09-07T06:49:07.5671841Z 2025-09-07T06:49:07.5672003Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T06:49:07.5672083Z when the implementation 2025-09-07T06:49:07.5672226Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T06:49:07.5672349Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T06:49:07.5672502Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T06:49:07.5672691Z makes the implementation visible to these subsystems, allowing them 2025-09-07T06:49:07.5672773Z to optimize the triton kernel(s). 2025-09-07T06:49:07.5672777Z 2025-09-07T06:49:07.5672906Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T06:49:07.5673031Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T06:49:07.5673150Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T06:49:07.5673155Z 2025-09-07T06:49:07.5673212Z Args: 2025-09-07T06:49:07.5673347Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T06:49:07.5673478Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T06:49:07.5675954Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T06:49:07.5676104Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T06:49:07.5676238Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T06:49:07.5676577Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T06:49:07.5676715Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T06:49:07.5676864Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T06:49:07.5676975Z schema (None | str): A schema string for the operator. If None 2025-09-07T06:49:07.5677091Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T06:49:07.5677208Z annotations. We recommend letting us infer a schema unless you 2025-09-07T06:49:07.5677279Z have a specific reason not to. 2025-09-07T06:49:07.5677381Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T06:49:07.5677384Z 2025-09-07T06:49:07.5677438Z Example:: 2025-09-07T06:49:07.5677543Z 2025-09-07T06:49:07.5677635Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:07.5677696Z >>> import torch 2025-09-07T06:49:07.5677793Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T06:49:07.5677845Z >>> 2025-09-07T06:49:07.5677904Z >>> import triton 2025-09-07T06:49:07.5677987Z >>> from triton import language as tl 2025-09-07T06:49:07.5678037Z >>> 2025-09-07T06:49:07.5678095Z >>> @triton.jit 2025-09-07T06:49:07.5678151Z >>> def add_kernel( 2025-09-07T06:49:07.5678206Z >>> in_ptr0, 2025-09-07T06:49:07.5678271Z >>> in_ptr1, 2025-09-07T06:49:07.5678337Z >>> out_ptr, 2025-09-07T06:49:07.5678398Z >>> n_elements, 2025-09-07T06:49:07.5678473Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T06:49:07.5678534Z >>> ): 2025-09-07T06:49:07.5678614Z >>> pid = tl.program_id(axis=0) 2025-09-07T06:49:07.5678695Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T06:49:07.5678801Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T06:49:07.5678869Z >>> mask = offsets < n_elements 2025-09-07T06:49:07.5678959Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T06:49:07.5679044Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T06:49:07.5679116Z >>> output = x + y 2025-09-07T06:49:07.5679212Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T06:49:07.5681188Z >>> 2025-09-07T06:49:07.5681281Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T06:49:07.5681398Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T06:49:07.5681474Z >>> output = torch.empty_like(x) 2025-09-07T06:49:07.5681538Z >>> n_elements = output.numel() 2025-09-07T06:49:07.5681588Z >>> 2025-09-07T06:49:07.5681653Z >>> def grid(meta): 2025-09-07T06:49:07.5681755Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T06:49:07.5681812Z >>> 2025-09-07T06:49:07.5681924Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T06:49:07.5682036Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T06:49:07.5682095Z >>> return output 2025-09-07T06:49:07.5682145Z >>> 2025-09-07T06:49:07.5682202Z >>> @torch.compile 2025-09-07T06:49:07.5682258Z >>> def f(x, y): 2025-09-07T06:49:07.5682325Z >>> return add(x, y) 2025-09-07T06:49:07.5682372Z >>> 2025-09-07T06:49:07.5682449Z >>> x = torch.randn(3, device="cuda") 2025-09-07T06:49:07.5682511Z >>> y = torch.randn(3, device="cuda") 2025-09-07T06:49:07.5682560Z >>> 2025-09-07T06:49:07.5682614Z >>> z = f(x, y) 2025-09-07T06:49:07.5682690Z >>> assert torch.allclose(z, x + y) 2025-09-07T06:49:07.5682695Z 2025-09-07T06:49:07.5684025Z 2025-09-07T06:49:07.5684144Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.5684194Z 2025-09-07T06:49:07.5684554Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=296. 2025-09-07T06:49:07.5684665Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.5684753Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T06:49:07.5684805Z non-strict ``torch.export``. 2025-09-07T06:49:07.5684808Z 2025-09-07T06:49:07.5684886Z These technologies perform Dispatcher-based tracing (via 2025-09-07T06:49:07.5684977Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T06:49:07.5685067Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T06:49:07.5685122Z can actually be traced into a graph. 2025-09-07T06:49:07.5685124Z 2025-09-07T06:49:07.5685218Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T06:49:07.5685270Z 2025-09-07T06:49:07.5685319Z Examples: 2025-09-07T06:49:07.5685321Z 2025-09-07T06:49:07.5685370Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.5685416Z >>> import torch 2025-09-07T06:49:07.5685459Z >>> import triton 2025-09-07T06:49:07.5685518Z >>> from triton import language as tl 2025-09-07T06:49:07.5685597Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T06:49:07.5685661Z >>> from torch.library import wrap_triton 2025-09-07T06:49:07.5685695Z >>> 2025-09-07T06:49:07.5685741Z >>> @triton.jit 2025-09-07T06:49:07.5685788Z >>> def add_kernel( 2025-09-07T06:49:07.5687055Z >>> in_ptr0, 2025-09-07T06:49:07.5687104Z >>> in_ptr1, 2025-09-07T06:49:07.5687141Z >>> out_ptr, 2025-09-07T06:49:07.5687184Z >>> n_elements, 2025-09-07T06:49:07.5687236Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T06:49:07.5687281Z >>> ): 2025-09-07T06:49:07.5687339Z >>> pid = tl.program_id(axis=0) 2025-09-07T06:49:07.5687396Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T06:49:07.5687469Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T06:49:07.5687520Z >>> mask = offsets < n_elements 2025-09-07T06:49:07.5687579Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T06:49:07.5687636Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T06:49:07.5687684Z >>> output = x + y 2025-09-07T06:49:07.5687749Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T06:49:07.5687789Z >>> 2025-09-07T06:49:07.5687835Z >>> def add(x, y): 2025-09-07T06:49:07.5687888Z >>> output = torch.empty_like(x) 2025-09-07T06:49:07.5687939Z >>> n_elements = output.numel() 2025-09-07T06:49:07.5687978Z >>> 2025-09-07T06:49:07.5688027Z >>> def grid_fn(meta): 2025-09-07T06:49:07.5688105Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T06:49:07.5688145Z >>> 2025-09-07T06:49:07.5689409Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T06:49:07.5689460Z >>> return output 2025-09-07T06:49:07.5689491Z >>> 2025-09-07T06:49:07.5689547Z >>> x = torch.randn(3, device="cuda") 2025-09-07T06:49:07.5689599Z >>> y = torch.randn(3, device="cuda") 2025-09-07T06:49:07.5689648Z >>> gm = make_fx(add)(x, y) 2025-09-07T06:49:07.5689695Z >>> print(gm.code) 2025-09-07T06:49:07.5689746Z >>> # def forward(self, x_1, y_1): 2025-09-07T06:49:07.5689855Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T06:49:07.5689957Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T06:49:07.5690025Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T06:49:07.5690082Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T06:49:07.5690202Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T06:49:07.5690261Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T06:49:07.5690305Z >>> # }) 2025-09-07T06:49:07.5690353Z >>> # return empty_like 2025-09-07T06:49:07.5690355Z 2025-09-07T06:49:07.5690395Z 2025-09-07T06:49:07.5690502Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.5690504Z 2025-09-07T06:49:07.6985444Z 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. 2025-09-07T06:49:07.6986458Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.6987031Z Return a new dict with new, potentially nested, key value pair 2025-09-07T06:49:07.6987548Z 2025-09-07T06:49:07.6987659Z >>> purchase = { 2025-09-07T06:49:07.6987945Z ... "name": "Alice", 2025-09-07T06:49:07.6988313Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:07.6988723Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:07.6989044Z ... } 2025-09-07T06:49:07.6989378Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T06:49:07.6989809Z {'credit card': '5555-1234-1234-1234', 2025-09-07T06:49:07.6990126Z 'name': 'Alice', 2025-09-07T06:49:07.6990467Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T06:49:07.6990822Z 2025-09-07T06:49:07.6991300Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.6991640Z 2025-09-07T06:49:07.6992259Z 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. 2025-09-07T06:49:07.6993228Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.6993727Z Update value in a (potentially) nested dictionary 2025-09-07T06:49:07.6993959Z 2025-09-07T06:49:07.6994026Z inputs: 2025-09-07T06:49:07.6994161Z d - dictionary on which to operate 2025-09-07T06:49:07.6994483Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T06:49:07.6994705Z func - function to operate on that value 2025-09-07T06:49:07.6994820Z 2025-09-07T06:49:07.6994917Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T06:49:07.6995176Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T06:49:07.6995397Z original dictionary. 2025-09-07T06:49:07.6995489Z 2025-09-07T06:49:07.6995594Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T06:49:07.6995855Z specified by the keys, with the innermost value set to func(default). 2025-09-07T06:49:07.6996014Z 2025-09-07T06:49:07.6996080Z >>> inc = lambda x: x + 1 2025-09-07T06:49:07.6996240Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T06:49:07.6996395Z {'a': 1} 2025-09-07T06:49:07.7000093Z 2025-09-07T06:49:07.7000161Z >>> transaction = { 2025-09-07T06:49:07.7000308Z ... "name": "Alice", 2025-09-07T06:49:07.7000488Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:07.7000693Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:07.7000844Z ... } 2025-09-07T06:49:07.7001008Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T06:49:07.7001213Z {'credit card': '5555-1234-1234-1234', 2025-09-07T06:49:07.7001363Z 'name': 'Alice', 2025-09-07T06:49:07.7001531Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T06:49:07.7001668Z 2025-09-07T06:49:07.7001731Z >>> # updating a value when k0 is not in d 2025-09-07T06:49:07.7001914Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T06:49:07.7002157Z {1: {2: {3: 'bar'}}} 2025-09-07T06:49:07.7002294Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T06:49:07.7002453Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T06:49:07.7004343Z 2025-09-07T06:49:07.7004574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.7004705Z 2025-09-07T06:49:07.7004935Z 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. 2025-09-07T06:49:07.7005296Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.7005496Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T06:49:07.7005599Z 2025-09-07T06:49:07.7005682Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T06:49:07.7005866Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T06:49:07.7006032Z 2025-09-07T06:49:07.7006119Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T06:49:07.7006285Z structures such as dictionaries and lists. 2025-09-07T06:49:07.7006372Z 2025-09-07T06:49:07.7006413Z >>> transaction = { 2025-09-07T06:49:07.7007755Z ... "name": "Alice", 2025-09-07T06:49:07.7007904Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:07.7008063Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:07.7008182Z ... } 2025-09-07T06:49:07.7008286Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T06:49:07.7008413Z 'Apple' 2025-09-07T06:49:07.7008507Z >>> get_in(["name"], transaction) 2025-09-07T06:49:07.7008618Z 'Alice' 2025-09-07T06:49:07.7008716Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T06:49:07.7008862Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T06:49:07.7009019Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T06:49:07.7010315Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T06:49:07.7010447Z 0 2025-09-07T06:49:07.7010538Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T06:49:07.7010663Z Traceback (most recent call last): 2025-09-07T06:49:07.7010777Z ... 2025-09-07T06:49:07.7010858Z KeyError: 'y' 2025-09-07T06:49:07.7010916Z 2025-09-07T06:49:07.7010950Z See Also: 2025-09-07T06:49:07.7011038Z itertoolz.get 2025-09-07T06:49:07.7011139Z operator.getitem 2025-09-07T06:49:07.7011235Z 2025-09-07T06:49:07.7011374Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.7011504Z 2025-09-07T06:49:07.7012841Z 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. 2025-09-07T06:49:07.7013206Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.7013393Z Group a collection by a key function 2025-09-07T06:49:07.7013475Z 2025-09-07T06:49:07.7013544Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T06:49:07.7013695Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T06:49:07.7013836Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T06:49:07.7013930Z 2025-09-07T06:49:07.7013973Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T06:49:07.7014109Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T06:49:07.7014249Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T06:49:07.7014325Z 2025-09-07T06:49:07.7014460Z Non-callable keys imply grouping on a member. 2025-09-07T06:49:07.7014547Z 2025-09-07T06:49:07.7014581Z >>> groupby( 2025-09-07T06:49:07.7015750Z ... "gender", 2025-09-07T06:49:07.7015840Z ... [ 2025-09-07T06:49:07.7015933Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T06:49:07.7016060Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T06:49:07.7016229Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T06:49:07.7016341Z ... ], 2025-09-07T06:49:07.7016427Z ... ) # doctest:+SKIP 2025-09-07T06:49:07.7016532Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T06:49:07.7016649Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T06:49:07.7016766Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T06:49:07.7016842Z 2025-09-07T06:49:07.7016895Z Not to be confused with ``itertools.groupby`` 2025-09-07T06:49:07.7016978Z 2025-09-07T06:49:07.7018053Z See Also: 2025-09-07T06:49:07.7018138Z countby 2025-09-07T06:49:07.7018215Z 2025-09-07T06:49:07.7018344Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.7018467Z 2025-09-07T06:49:07.7516341Z 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. 2025-09-07T06:49:07.7518000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.7518764Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T06:49:07.7519438Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T06:49:07.7519742Z 2025-09-07T06:49:07.7519896Z It is useful when tracing the code profile. 2025-09-07T06:49:07.7520136Z 2025-09-07T06:49:07.7520247Z Args: 2025-09-07T06:49:07.7528274Z name (str): Label assigned to the block of code. 2025-09-07T06:49:07.7528568Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T06:49:07.7528824Z non-distributed cases. 2025-09-07T06:49:07.7528948Z 2025-09-07T06:49:07.7529011Z Example: 2025-09-07T06:49:07.7529231Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T06:49:07.7529500Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T06:49:07.7529773Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T06:49:07.7530009Z ... y = x**2 2025-09-07T06:49:07.7530223Z ... with torch.autograd.profiler.record_function( 2025-09-07T06:49:07.7530454Z ... "label-z" 2025-09-07T06:49:07.7530635Z ... ): # label the block 2025-09-07T06:49:07.7533278Z ... z = y**3 2025-09-07T06:49:07.7533472Z ... y.backward() 2025-09-07T06:49:07.7533614Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T06:49:07.7533779Z >>> # NOTE: some columns were removed for brevity 2025-09-07T06:49:07.7533986Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T06:49:07.7534242Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:07.7534566Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T06:49:07.7534804Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:07.7535008Z pow 60.77% 47.470us 3 2025-09-07T06:49:07.7535180Z mul 21.73% 25.465us 2 2025-09-07T06:49:07.7535368Z PowBackward0 12.03% 121.891us 1 2025-09-07T06:49:07.7537296Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T06:49:07.7537524Z label-z 2.13% 12.421us 1 2025-09-07T06:49:07.7537736Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T06:49:07.7537967Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:07.7538168Z Self CPU time total: 234.344us 2025-09-07T06:49:07.7538316Z CUDA time total: 0.000us 2025-09-07T06:49:07.7538411Z 2025-09-07T06:49:07.7538453Z 2025-09-07T06:49:07.7538765Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.7538928Z 2025-09-07T06:49:07.8388605Z 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. 2025-09-07T06:49:07.8389536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8390052Z Register a container-like type as pytree node. 2025-09-07T06:49:07.8390298Z 2025-09-07T06:49:07.8390401Z Args: 2025-09-07T06:49:07.8395475Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T06:49:07.8395800Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T06:49:07.8396127Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T06:49:07.8396603Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T06:49:07.8396871Z passed to the ``unflatten_fn``. 2025-09-07T06:49:07.8397132Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T06:49:07.8397462Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T06:49:07.8397738Z The function should return an instance of ``cls``. 2025-09-07T06:49:07.8398017Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T06:49:07.8398292Z qualified name used when serializing the tree spec. 2025-09-07T06:49:07.8398588Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T06:49:07.8401059Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T06:49:07.8401400Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T06:49:07.8401747Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T06:49:07.8402085Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T06:49:07.8402406Z original context. This is used for json deserialization, which is being used in 2025-09-07T06:49:07.8402658Z :mod:`torch.export` right now. 2025-09-07T06:49:07.8402781Z 2025-09-07T06:49:07.8402841Z Example:: 2025-09-07T06:49:07.8402919Z 2025-09-07T06:49:07.8402983Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8403164Z >>> # Registry a Python type with lambda functions 2025-09-07T06:49:07.8403353Z >>> register_pytree_node( 2025-09-07T06:49:07.8403505Z ... set, 2025-09-07T06:49:07.8405086Z ... lambda s: (sorted(s), None, None), 2025-09-07T06:49:07.8405236Z ... lambda children, _: set(children), 2025-09-07T06:49:07.8405370Z ... ) 2025-09-07T06:49:07.8405459Z 2025-09-07T06:49:07.8405608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8405749Z 2025-09-07T06:49:07.8461176Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1226. 2025-09-07T06:49:07.8461529Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8461658Z 2025-09-07T06:49:07.8461737Z Context passed to policy function during selective checkpointing. 2025-09-07T06:49:07.8461849Z 2025-09-07T06:49:07.8461929Z This class is used to pass relevant metadata to the policy function during 2025-09-07T06:49:07.8462135Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T06:49:07.8463932Z of the policy function is during recomputation or not. 2025-09-07T06:49:07.8464065Z 2025-09-07T06:49:07.8464100Z Example: 2025-09-07T06:49:07.8464186Z >>> # xdoctest: +SKIP(stub) 2025-09-07T06:49:07.8464467Z >>> 2025-09-07T06:49:07.8464563Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T06:49:07.8464697Z >>> print(ctx.is_recompute) 2025-09-07T06:49:07.8464803Z >>> 2025-09-07T06:49:07.8464942Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T06:49:07.8465108Z >>> 2025-09-07T06:49:07.8465224Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T06:49:07.8466749Z >>> fn, x, y, 2025-09-07T06:49:07.8466863Z >>> use_reentrant=False, 2025-09-07T06:49:07.8466977Z >>> context_fn=context_fn, 2025-09-07T06:49:07.8467084Z >>> ) 2025-09-07T06:49:07.8467131Z 2025-09-07T06:49:07.8467219Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8467342Z 2025-09-07T06:49:07.8467552Z 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=1366. 2025-09-07T06:49:07.8467941Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8468069Z 2025-09-07T06:49:07.8468153Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T06:49:07.8468272Z 2025-09-07T06:49:07.8468351Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T06:49:07.8468524Z operations are recomputed during the backward pass. 2025-09-07T06:49:07.8468621Z 2025-09-07T06:49:07.8468652Z Args: 2025-09-07T06:49:07.8468741Z policy_fn_or_list (Callable or List): 2025-09-07T06:49:07.8468891Z - If a policy function is provided, it should accept a 2025-09-07T06:49:07.8469088Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T06:49:07.8469305Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T06:49:07.8469517Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T06:49:07.8469739Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T06:49:07.8469935Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T06:49:07.8470133Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T06:49:07.8472866Z operations. 2025-09-07T06:49:07.8473004Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T06:49:07.8473190Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T06:49:07.8473376Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T06:49:07.8473520Z is disabled. 2025-09-07T06:49:07.8473607Z Returns: 2025-09-07T06:49:07.8473694Z A tuple of two context managers. 2025-09-07T06:49:07.8473773Z 2025-09-07T06:49:07.8473805Z Example: 2025-09-07T06:49:07.8473889Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T06:49:07.8474006Z >>> import functools 2025-09-07T06:49:07.8474097Z >>> 2025-09-07T06:49:07.8475470Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T06:49:07.8475600Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T06:49:07.8475711Z >>> 2025-09-07T06:49:07.8475787Z >>> ops_to_save = [ 2025-09-07T06:49:07.8475891Z >>> torch.ops.aten.mm.default, 2025-09-07T06:49:07.8475998Z >>> ] 2025-09-07T06:49:07.8476070Z >>> 2025-09-07T06:49:07.8476157Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T06:49:07.8476277Z >>> if op in ops_to_save: 2025-09-07T06:49:07.8476392Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T06:49:07.8476503Z >>> else: 2025-09-07T06:49:07.8477600Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T06:49:07.8477720Z >>> 2025-09-07T06:49:07.8477855Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T06:49:07.8478016Z >>> 2025-09-07T06:49:07.8478095Z >>> # or equivalently 2025-09-07T06:49:07.8478306Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T06:49:07.8478465Z >>> 2025-09-07T06:49:07.8478538Z >>> def fn(x, y): 2025-09-07T06:49:07.8478667Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T06:49:07.8478802Z >>> 2025-09-07T06:49:07.8478895Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T06:49:07.8480000Z >>> fn, x, y, 2025-09-07T06:49:07.8480096Z >>> use_reentrant=False, 2025-09-07T06:49:07.8480204Z >>> context_fn=context_fn, 2025-09-07T06:49:07.8480306Z >>> ) 2025-09-07T06:49:07.8480350Z 2025-09-07T06:49:07.8480440Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8480562Z 2025-09-07T06:49:07.8668958Z 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. 2025-09-07T06:49:07.8669397Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8669557Z 2025-09-07T06:49:07.8669671Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T06:49:07.8669810Z 2025-09-07T06:49:07.8669914Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T06:49:07.8670138Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T06:49:07.8672433Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T06:49:07.8672642Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T06:49:07.8672847Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T06:49:07.8672973Z 2025-09-07T06:49:07.8673065Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T06:49:07.8673275Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T06:49:07.8673483Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T06:49:07.8673686Z model for inference deployment it is better to switch to using it in this 2025-09-07T06:49:07.8673840Z benchmark. 2025-09-07T06:49:07.8673892Z 2025-09-07T06:49:07.8673927Z Example:: 2025-09-07T06:49:07.8673973Z 2025-09-07T06:49:07.8674020Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:07.8674153Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T06:49:07.8675595Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T06:49:07.8675741Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T06:49:07.8675879Z >>> for input in inputs: 2025-09-07T06:49:07.8676025Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T06:49:07.8676190Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T06:49:07.8676344Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T06:49:07.8676499Z >>> stats = bench.benchmark( 2025-09-07T06:49:07.8676609Z ... num_calling_threads=4, 2025-09-07T06:49:07.8676721Z ... num_warmup_iters = 100, 2025-09-07T06:49:07.8676832Z ... num_iters = 1000, 2025-09-07T06:49:07.8676929Z ... ) 2025-09-07T06:49:07.8678078Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T06:49:07.8678246Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T06:49:07.8678346Z 2025-09-07T06:49:07.8678437Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8678560Z 2025-09-07T06:49:07.8751391Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1158. 2025-09-07T06:49:07.8751733Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8751886Z 2025-09-07T06:49:07.8751948Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T06:49:07.8752123Z 2025-09-07T06:49:07.8752212Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:07.8752424Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T06:49:07.8752540Z 2025-09-07T06:49:07.8752617Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:07.8752781Z constructor. Full list arguments can be found at 2025-09-07T06:49:07.8755118Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T06:49:07.8755280Z 2025-09-07T06:49:07.8755317Z .. warning:: 2025-09-07T06:49:07.8755473Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:07.8755679Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:07.8755872Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:07.8756135Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:07.8756335Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:07.8756528Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:07.8756687Z register the ops through the dispatcher. 2025-09-07T06:49:07.8758544Z 2025-09-07T06:49:07.8758640Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:07.8758861Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:07.8759067Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:07.8759258Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:07.8759457Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:07.8759653Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:07.8759823Z CPython version of choice. 2025-09-07T06:49:07.8759893Z 2025-09-07T06:49:07.8759930Z Example: 2025-09-07T06:49:07.8760014Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8760145Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:07.8760283Z >>> from setuptools import setup 2025-09-07T06:49:07.8760445Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T06:49:07.8760591Z >>> setup( 2025-09-07T06:49:07.8760680Z ... name='extension', 2025-09-07T06:49:07.8760783Z ... ext_modules=[ 2025-09-07T06:49:07.8760880Z ... CppExtension( 2025-09-07T06:49:07.8760986Z ... name='extension', 2025-09-07T06:49:07.8761126Z ... sources=['extension.cpp'], 2025-09-07T06:49:07.8761272Z ... extra_compile_args=['-g'], 2025-09-07T06:49:07.8761437Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T06:49:07.8763660Z ... ], 2025-09-07T06:49:07.8763768Z ... cmdclass={ 2025-09-07T06:49:07.8763873Z ... 'build_ext': BuildExtension 2025-09-07T06:49:07.8763991Z ... }) 2025-09-07T06:49:07.8764041Z 2025-09-07T06:49:07.8764129Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8764255Z 2025-09-07T06:49:07.8764559Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1228. 2025-09-07T06:49:07.8764878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8765008Z 2025-09-07T06:49:07.8765069Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T06:49:07.8765164Z 2025-09-07T06:49:07.8765248Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:07.8765449Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T06:49:07.8765641Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T06:49:07.8767255Z library. 2025-09-07T06:49:07.8767302Z 2025-09-07T06:49:07.8767447Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:07.8767609Z constructor. Full list arguments can be found at 2025-09-07T06:49:07.8767824Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T06:49:07.8767974Z 2025-09-07T06:49:07.8768009Z .. warning:: 2025-09-07T06:49:07.8768146Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:07.8768343Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:07.8768525Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:07.8768708Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:07.8768900Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:07.8769087Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:07.8769290Z register the ops through the dispatcher. 2025-09-07T06:49:07.8769372Z 2025-09-07T06:49:07.8769450Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:07.8769632Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:07.8770975Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:07.8771170Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:07.8771354Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:07.8771538Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:07.8771688Z CPython version of choice. 2025-09-07T06:49:07.8771756Z 2025-09-07T06:49:07.8771790Z Example: 2025-09-07T06:49:07.8771873Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8771991Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:07.8772129Z >>> from setuptools import setup 2025-09-07T06:49:07.8772287Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T06:49:07.8772429Z >>> setup( 2025-09-07T06:49:07.8773565Z ... name='cuda_extension', 2025-09-07T06:49:07.8773678Z ... ext_modules=[ 2025-09-07T06:49:07.8773776Z ... CUDAExtension( 2025-09-07T06:49:07.8773886Z ... name='cuda_extension', 2025-09-07T06:49:07.8774023Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:07.8774168Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T06:49:07.8774298Z ... 'nvcc': ['-O2']}, 2025-09-07T06:49:07.8774537Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T06:49:07.8774659Z ... ], 2025-09-07T06:49:07.8774741Z ... cmdclass={ 2025-09-07T06:49:07.8774840Z ... 'build_ext': BuildExtension 2025-09-07T06:49:07.8775981Z ... }) 2025-09-07T06:49:07.8776032Z 2025-09-07T06:49:07.8776075Z Compute capabilities: 2025-09-07T06:49:07.8776135Z 2025-09-07T06:49:07.8776240Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T06:49:07.8776484Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T06:49:07.8776720Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T06:49:07.8776959Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T06:49:07.8777195Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T06:49:07.8777372Z support (see below for details on PTX). 2025-09-07T06:49:07.8777451Z 2025-09-07T06:49:07.8777556Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T06:49:07.8777739Z CCs you want the extension to support: 2025-09-07T06:49:07.8777823Z 2025-09-07T06:49:07.8777946Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T06:49:07.8779182Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T06:49:07.8779303Z 2025-09-07T06:49:07.8779414Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T06:49:07.8779663Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T06:49:07.8779905Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T06:49:07.8780140Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T06:49:07.8780382Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T06:49:07.8780622Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T06:49:07.8780957Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T06:49:07.8781206Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T06:49:07.8781385Z "8.0 8.6" would be better. 2025-09-07T06:49:07.8781447Z 2025-09-07T06:49:07.8781549Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T06:49:07.8782824Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T06:49:07.8782958Z 2025-09-07T06:49:07.8783072Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T06:49:07.8783296Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T06:49:07.8783406Z 2025-09-07T06:49:07.8783441Z Example use: 2025-09-07T06:49:07.8783527Z #include 2025-09-07T06:49:07.8783648Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T06:49:07.8783741Z 2025-09-07T06:49:07.8783774Z Instead of: 2025-09-07T06:49:07.8783866Z #include 2025-09-07T06:49:07.8783992Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T06:49:07.8784085Z 2025-09-07T06:49:07.8784179Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T06:49:07.8784557Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T06:49:07.8785812Z 2025-09-07T06:49:07.8785857Z Relocatable device code linking: 2025-09-07T06:49:07.8785929Z 2025-09-07T06:49:07.8786026Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T06:49:07.8786247Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T06:49:07.8786496Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T06:49:07.8786768Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T06:49:07.8787024Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T06:49:07.8787233Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T06:49:07.8787390Z Note that it needs to be used at both steps to be useful. 2025-09-07T06:49:07.8787483Z 2025-09-07T06:49:07.8787608Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T06:49:07.8787830Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T06:49:07.8789036Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T06:49:07.8789244Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T06:49:07.8789354Z 2025-09-07T06:49:07.8789428Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T06:49:07.8789536Z 2025-09-07T06:49:07.8789570Z Example: 2025-09-07T06:49:07.8789702Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8789820Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:07.8789944Z >>> CUDAExtension( 2025-09-07T06:49:07.8790042Z ... name='cuda_extension', 2025-09-07T06:49:07.8790174Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:07.8790303Z ... dlink=True, 2025-09-07T06:49:07.8790410Z ... dlink_libraries=["dlink_lib"], 2025-09-07T06:49:07.8791640Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T06:49:07.8791778Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T06:49:07.8791862Z 2025-09-07T06:49:07.8791950Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8792073Z 2025-09-07T06:49:07.8792259Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1420. 2025-09-07T06:49:07.8792619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8792746Z 2025-09-07T06:49:07.8792806Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T06:49:07.8792901Z 2025-09-07T06:49:07.8792984Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:07.8793172Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T06:49:07.8793309Z extension. 2025-09-07T06:49:07.8793357Z 2025-09-07T06:49:07.8793432Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:07.8793571Z constructor. 2025-09-07T06:49:07.8793619Z 2025-09-07T06:49:07.8795339Z .. warning:: 2025-09-07T06:49:07.8795476Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:07.8795666Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:07.8795856Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:07.8796045Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:07.8796239Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:07.8796427Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:07.8796589Z register the ops through the dispatcher. 2025-09-07T06:49:07.8796672Z 2025-09-07T06:49:07.8796752Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:07.8796937Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:07.8797119Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:07.8798340Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:07.8798527Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:07.8798717Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:07.8798871Z CPython version of choice. 2025-09-07T06:49:07.8798939Z 2025-09-07T06:49:07.8798974Z Example: 2025-09-07T06:49:07.8799057Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8799176Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:07.8799344Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T06:49:07.8799489Z >>> setup( 2025-09-07T06:49:07.8799579Z ... name='xpu_extension', 2025-09-07T06:49:07.8799685Z ... ext_modules=[ 2025-09-07T06:49:07.8800765Z ... SyclExtension( 2025-09-07T06:49:07.8800874Z ... name='xpu_extension', 2025-09-07T06:49:07.8801014Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T06:49:07.8801176Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T06:49:07.8801310Z ... ], 2025-09-07T06:49:07.8801396Z ... cmdclass={ 2025-09-07T06:49:07.8801497Z ... 'build_ext': BuildExtension 2025-09-07T06:49:07.8801657Z ... }) 2025-09-07T06:49:07.8801706Z 2025-09-07T06:49:07.8801810Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T06:49:07.8802038Z building process of the extension. If down the road a new card is installed the 2025-09-07T06:49:07.8802248Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T06:49:07.8803464Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T06:49:07.8803641Z to support: 2025-09-07T06:49:07.8803689Z 2025-09-07T06:49:07.8803765Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T06:49:07.8803873Z 2025-09-07T06:49:07.8803979Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T06:49:07.8804216Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T06:49:07.8804484Z 2025-09-07T06:49:07.8804543Z Note: Ninja is required to build SyclExtension. 2025-09-07T06:49:07.8804629Z 2025-09-07T06:49:07.8804717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8804840Z 2025-09-07T06:49:07.8805032Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1597. 2025-09-07T06:49:07.8805326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8805454Z 2025-09-07T06:49:07.8805509Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T06:49:07.8805597Z 2025-09-07T06:49:07.8806708Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T06:49:07.8806896Z compile the given sources into a dynamic library. This library is 2025-09-07T06:49:07.8807084Z subsequently loaded into the current Python process as a module and 2025-09-07T06:49:07.8807250Z returned from this function, ready for use. 2025-09-07T06:49:07.8807335Z 2025-09-07T06:49:07.8807411Z By default, the directory to which the build file is emitted and the 2025-09-07T06:49:07.8807600Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T06:49:07.8807791Z ```` is the temporary folder on the current platform and ```` 2025-09-07T06:49:07.8807973Z the name of the extension. This location can be overridden in two ways. 2025-09-07T06:49:07.8808154Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T06:49:07.8808338Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T06:49:07.8808524Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T06:49:07.8809700Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T06:49:07.8809883Z the library will be compiled into that folder directly. 2025-09-07T06:49:07.8809983Z 2025-09-07T06:49:07.8810060Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T06:49:07.8810254Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T06:49:07.8810454Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T06:49:07.8810645Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T06:49:07.8810833Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T06:49:07.8811001Z ``extra_cflags`` to pass further include directories. 2025-09-07T06:49:07.8811093Z 2025-09-07T06:49:07.8811176Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T06:49:07.8811358Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T06:49:07.8811540Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T06:49:07.8812717Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T06:49:07.8812888Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T06:49:07.8813099Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T06:49:07.8813287Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T06:49:07.8813476Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T06:49:07.8813619Z safest option. 2025-09-07T06:49:07.8813670Z 2025-09-07T06:49:07.8813750Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T06:49:07.8813936Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T06:49:07.8814116Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T06:49:07.8814299Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T06:49:07.8814544Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T06:49:07.8815720Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T06:49:07.8815911Z variable. 2025-09-07T06:49:07.8815963Z 2025-09-07T06:49:07.8815994Z Args: 2025-09-07T06:49:07.8816114Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T06:49:07.8816263Z name of the pybind11 module! 2025-09-07T06:49:07.8816410Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T06:49:07.8816596Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T06:49:07.8816786Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T06:49:07.8816828Z when building CUDA sources. 2025-09-07T06:49:07.8816904Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T06:49:07.8816951Z compiler when building SYCL sources. 2025-09-07T06:49:07.8817026Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T06:49:07.8817102Z extra_include_paths: optional list of include directories to forward 2025-09-07T06:49:07.8817144Z to the build. 2025-09-07T06:49:07.8818195Z build_directory: optional path to use as build workspace. 2025-09-07T06:49:07.8818267Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T06:49:07.8818344Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T06:49:07.8818406Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:07.8818478Z automatically determined based on the existence of ``.cu`` or 2025-09-07T06:49:07.8818542Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T06:49:07.8818584Z and libraries to be included. 2025-09-07T06:49:07.8818660Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T06:49:07.8818720Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:07.8818794Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T06:49:07.8818865Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T06:49:07.8818906Z libraries to be included. 2025-09-07T06:49:07.8818979Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T06:49:07.8819047Z library as a Python module. If ``False``, behavior depends on 2025-09-07T06:49:07.8819087Z ``is_standalone``. 2025-09-07T06:49:07.8819157Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T06:49:07.8819226Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T06:49:07.8819267Z standalone executable. 2025-09-07T06:49:07.8819269Z 2025-09-07T06:49:07.8819303Z Returns: 2025-09-07T06:49:07.8819345Z If ``is_python_module`` is ``True``: 2025-09-07T06:49:07.8819411Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T06:49:07.8819413Z 2025-09-07T06:49:07.8819485Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T06:49:07.8820574Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T06:49:07.8820612Z a side effect.) 2025-09-07T06:49:07.8820614Z 2025-09-07T06:49:07.8820656Z If ``is_standalone`` is ``True``. 2025-09-07T06:49:07.8820726Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T06:49:07.8820792Z added to the PATH environment variable as a side effect.) 2025-09-07T06:49:07.8820794Z 2025-09-07T06:49:07.8820826Z Example: 2025-09-07T06:49:07.8820865Z >>> # xdoctest: +SKIP 2025-09-07T06:49:07.8820917Z >>> from torch.utils.cpp_extension import load 2025-09-07T06:49:07.8820953Z >>> module = load( 2025-09-07T06:49:07.8820992Z ... name='extension', 2025-09-07T06:49:07.8821053Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:07.8821093Z ... extra_cflags=['-O2'], 2025-09-07T06:49:07.8821131Z ... verbose=True) 2025-09-07T06:49:07.8821167Z 2025-09-07T06:49:07.8821257Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8821259Z 2025-09-07T06:49:07.8821446Z 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=1885. 2025-09-07T06:49:07.8821538Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:07.8821540Z 2025-09-07T06:49:07.8821617Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T06:49:07.8821619Z 2025-09-07T06:49:07.8821699Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T06:49:07.8821781Z strings rather than filenames. These strings are stored to files in the 2025-09-07T06:49:07.8821856Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T06:49:07.8821896Z identical to :func:`load`. 2025-09-07T06:49:07.8821898Z 2025-09-07T06:49:07.8822889Z See `the 2025-09-07T06:49:07.8823020Z tests `_ 2025-09-07T06:49:07.8823067Z for good examples of using this function. 2025-09-07T06:49:07.8823070Z 2025-09-07T06:49:07.8823153Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T06:49:07.8823237Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T06:49:07.8823322Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T06:49:07.8823392Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T06:49:07.8823429Z `` 2025-09-07T06:49:07.8823431Z 2025-09-07T06:49:07.8823509Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T06:49:07.8823592Z automatically generated for each function specified. ``functions`` can 2025-09-07T06:49:07.8823668Z either be a list of function names, or a dictionary mapping from function 2025-09-07T06:49:07.8823750Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T06:49:07.8823787Z as its docstring. 2025-09-07T06:49:07.8823789Z 2025-09-07T06:49:07.8823864Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T06:49:07.8823930Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T06:49:07.8824005Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T06:49:07.8824083Z separately, but ultimately linked into a single library. Note that no 2025-09-07T06:49:07.8824163Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T06:49:07.8824239Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T06:49:07.8824316Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T06:49:07.8824359Z include its name in ``functions``). 2025-09-07T06:49:07.8824361Z 2025-09-07T06:49:07.8825465Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T06:49:07.8825602Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T06:49:07.8825674Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T06:49:07.8825750Z linked into a single library. Note that no bindings are generated for 2025-09-07T06:49:07.8825824Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T06:49:07.8825898Z create a C++ function that calls it, and either declare or define this 2025-09-07T06:49:07.8825968Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T06:49:07.8826004Z in ``functions``). 2025-09-07T06:49:07.8826006Z 2025-09-07T06:49:07.8826008Z 2025-09-07T06:49:07.8826009Z 2025-09-07T06:49:07.8826077Z See :func:`load` for a description of arguments omitted below. 2025-09-07T06:49:07.8826079Z 2025-09-07T06:49:07.8826110Z Args: 2025-09-07T06:49:07.8826187Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T06:49:07.8826306Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T06:49:07.8826383Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T06:49:07.8826455Z functions: A list of function names for which to generate function 2025-09-07T06:49:07.8826531Z bindings. If a dictionary is given, it should map function names to 2025-09-07T06:49:07.8826598Z docstrings (which are otherwise just the function names). 2025-09-07T06:49:07.8826676Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T06:49:07.8826734Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:07.8826806Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T06:49:07.8826863Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T06:49:07.8826906Z and libraries to be included. 2025-09-07T06:49:07.8826982Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T06:49:07.8828014Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:07.8828087Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T06:49:07.8828146Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T06:49:07.8828187Z and libraries to be included. 2025-09-07T06:49:07.8828260Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T06:49:07.8828331Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T06:49:07.8828407Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T06:49:07.8828478Z function. This redirection might cause issues in obscure cases 2025-09-07T06:49:07.8828546Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T06:49:07.8828581Z causes issues. 2025-09-07T06:49:07.8828669Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T06:49:07.8828758Z ``#include `` and ``#include `` lines. 2025-09-07T06:49:07.8828818Z Use this option to improve cold start times when you 2025-09-07T06:49:07.8828907Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T06:49:07.8828909Z 2025-09-07T06:49:07.8828941Z Example: 2025-09-07T06:49:07.8829000Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:07.8829058Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T06:49:07.8829094Z >>> source = """ 2025-09-07T06:49:07.8829150Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T06:49:07.8829191Z return x.sin() + y.sin(); 2025-09-07T06:49:07.8829222Z } 2025-09-07T06:49:07.8830201Z """ 2025-09-07T06:49:07.8830259Z >>> module = load_inline(name='inline_extension', 2025-09-07T06:49:07.8830306Z ... cpp_sources=[source], 2025-09-07T06:49:07.8830353Z ... functions=['sin_add']) 2025-09-07T06:49:07.8830394Z 2025-09-07T06:49:07.8830429Z .. note:: 2025-09-07T06:49:07.8830514Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T06:49:07.8830595Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T06:49:07.8830670Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T06:49:07.8830756Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T06:49:07.8830841Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T06:49:07.8830962Z are not included when you install torch and must be additionally installed. 2025-09-07T06:49:07.8830965Z 2025-09-07T06:49:07.8831054Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T06:49:07.8831131Z the extension. This may use up too many resources on some systems. One 2025-09-07T06:49:07.8831249Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T06:49:07.8831294Z variable to a non-negative number. 2025-09-07T06:49:07.8831296Z 2025-09-07T06:49:07.8831383Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:07.8831385Z 2025-09-07T06:49:08.0841543Z msg = Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T06:49:08.0841774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.0843677Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T06:49:08.0843717Z 2025-09-07T06:49:08.0843832Z Args: 2025-09-07T06:49:08.0853589Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T06:49:08.0853723Z num_samples (int): number of samples to draw 2025-09-07T06:49:08.0853943Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T06:49:08.0854079Z If not, they are drawn without replacement, which means that when a 2025-09-07T06:49:08.0854222Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T06:49:08.0854328Z generator (Generator): Generator used in sampling. 2025-09-07T06:49:08.0854332Z 2025-09-07T06:49:08.0854512Z Example: 2025-09-07T06:49:08.0854610Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:08.0854668Z >>> list( 2025-09-07T06:49:08.0854749Z ... WeightedRandomSampler( 2025-09-07T06:49:08.0854847Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T06:49:08.0854901Z ... ) 2025-09-07T06:49:08.0854958Z ... ) 2025-09-07T06:49:08.0855024Z [4, 4, 1, 4, 5] 2025-09-07T06:49:08.0855082Z >>> list( 2025-09-07T06:49:08.0855160Z ... WeightedRandomSampler( 2025-09-07T06:49:08.0855270Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T06:49:08.0855322Z ... ) 2025-09-07T06:49:08.0855379Z ... ) 2025-09-07T06:49:08.0855435Z [0, 1, 4, 3, 2] 2025-09-07T06:49:08.0855489Z 2025-09-07T06:49:08.0855649Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.0855654Z 2025-09-07T06:49:08.0858522Z msg = Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T06:49:08.0858686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.0858820Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T06:49:08.0858826Z 2025-09-07T06:49:08.0858881Z Args: 2025-09-07T06:49:08.0859023Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T06:49:08.0859111Z batch_size (int): Size of mini-batch. 2025-09-07T06:49:08.0859785Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T06:49:08.0859872Z its size would be less than ``batch_size`` 2025-09-07T06:49:08.0859877Z 2025-09-07T06:49:08.0859935Z Example: 2025-09-07T06:49:08.0859990Z >>> list( 2025-09-07T06:49:08.0860064Z ... BatchSampler( 2025-09-07T06:49:08.0860187Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T06:49:08.0860242Z ... ) 2025-09-07T06:49:08.0860294Z ... ) 2025-09-07T06:49:08.0860369Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T06:49:08.0860421Z >>> list( 2025-09-07T06:49:08.0860577Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T06:49:08.0860629Z ... ) 2025-09-07T06:49:08.0860690Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T06:49:08.0860743Z 2025-09-07T06:49:08.0861033Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.0861041Z 2025-09-07T06:49:08.0889054Z 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. 2025-09-07T06:49:08.0889699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.0889940Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T06:49:08.0889949Z 2025-09-07T06:49:08.0890081Z It is especially useful in conjunction with 2025-09-07T06:49:08.0890305Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T06:49:08.0890531Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T06:49:08.0890731Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T06:49:08.0890851Z original dataset that is exclusive to it. 2025-09-07T06:49:08.0890873Z 2025-09-07T06:49:08.0890980Z .. note:: 2025-09-07T06:49:08.0891190Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T06:49:08.0891323Z returns the same elements in the same order. 2025-09-07T06:49:08.0891339Z 2025-09-07T06:49:08.0891419Z Args: 2025-09-07T06:49:08.0891528Z dataset: Dataset used for sampling. 2025-09-07T06:49:08.0891720Z num_replicas (int, optional): Number of processes participating in 2025-09-07T06:49:08.0891921Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T06:49:08.0892035Z current distributed group. 2025-09-07T06:49:08.0892228Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T06:49:08.0892401Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T06:49:08.0892479Z group. 2025-09-07T06:49:08.0897282Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T06:49:08.0897380Z indices. 2025-09-07T06:49:08.0897552Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T06:49:08.0897711Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T06:49:08.0897852Z processes in the distributed group. Default: ``0``. 2025-09-07T06:49:08.0898017Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T06:49:08.0898167Z tail of the data to make it evenly divisible across the number of 2025-09-07T06:49:08.0898317Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T06:49:08.0898475Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T06:49:08.0898483Z 2025-09-07T06:49:08.0898558Z .. warning:: 2025-09-07T06:49:08.0898713Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T06:49:08.0898917Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T06:49:08.0899320Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T06:49:08.0899425Z the same ordering will be always used. 2025-09-07T06:49:08.0899430Z 2025-09-07T06:49:08.0899506Z Example:: 2025-09-07T06:49:08.0899512Z 2025-09-07T06:49:08.0899598Z >>> # xdoctest: +SKIP 2025-09-07T06:49:08.0899770Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T06:49:08.0899905Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T06:49:08.0900007Z ... sampler=sampler) 2025-09-07T06:49:08.0900123Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T06:49:08.0900215Z ... if is_distributed: 2025-09-07T06:49:08.0900317Z ... sampler.set_epoch(epoch) 2025-09-07T06:49:08.0902897Z ... train(loader) 2025-09-07T06:49:08.0903052Z 2025-09-07T06:49:08.0903203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.0903206Z 2025-09-07T06:49:08.0981911Z msg = Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T06:49:08.0982084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.0982090Z 2025-09-07T06:49:08.0982150Z Iterable-style DataPipe. 2025-09-07T06:49:08.0982152Z 2025-09-07T06:49:08.0982274Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T06:49:08.0982404Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T06:49:08.0982617Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T06:49:08.0982767Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T06:49:08.0982775Z 2025-09-07T06:49:08.0982899Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T06:49:08.0983073Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T06:49:08.0983242Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T06:49:08.0983383Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T06:49:08.0983487Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T06:49:08.0983490Z 2025-09-07T06:49:08.0983537Z Note: 2025-09-07T06:49:08.0983644Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T06:49:08.0983805Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T06:49:08.0983988Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T06:49:08.0986816Z The code example below presents details on how this constraint looks in practice. 2025-09-07T06:49:08.0987017Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T06:49:08.0987020Z 2025-09-07T06:49:08.0987164Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T06:49:08.0987332Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T06:49:08.0987499Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T06:49:08.0987569Z operations in succession. 2025-09-07T06:49:08.0987573Z 2025-09-07T06:49:08.0987642Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T06:49:08.0987736Z https://github.com/pytorch/data/issues/45 2025-09-07T06:49:08.0987738Z 2025-09-07T06:49:08.0987787Z Note: 2025-09-07T06:49:08.0988007Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T06:49:08.0988147Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T06:49:08.0988255Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T06:49:08.0988369Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T06:49:08.0988490Z each copy independently to avoid having duplicate data returned from the 2025-09-07T06:49:08.0988606Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T06:49:08.0988721Z process, returns information about the worker. It can be used in either the 2025-09-07T06:49:08.0988853Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T06:49:08.0988946Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T06:49:08.0988951Z 2025-09-07T06:49:08.0989075Z Examples: 2025-09-07T06:49:08.0989125Z General Usage: 2025-09-07T06:49:08.0989186Z >>> # xdoctest: +SKIP 2025-09-07T06:49:08.0989297Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T06:49:08.0989380Z >>> dp = IterableWrapper(range(10)) 2025-09-07T06:49:08.0989481Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T06:49:08.0989548Z >>> map_dp_2 = dp.map( 2025-09-07T06:49:08.0989615Z ... lambda x: x + 1 2025-09-07T06:49:08.0989688Z ... ) # Using functional form (recommended) 2025-09-07T06:49:08.0989763Z >>> list(map_dp_1) 2025-09-07T06:49:08.0989830Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T06:49:08.0991799Z >>> list(map_dp_2) 2025-09-07T06:49:08.0991858Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T06:49:08.0991942Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T06:49:08.0991995Z >>> list(filter_dp) 2025-09-07T06:49:08.0992050Z [2, 4, 6, 8, 10] 2025-09-07T06:49:08.0992119Z Single Iterator Constraint Example: 2025-09-07T06:49:08.0992229Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T06:49:08.0992295Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T06:49:08.0992352Z >>> it1 = iter(source_dp) 2025-09-07T06:49:08.0992400Z >>> list(it1) 2025-09-07T06:49:08.0992465Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T06:49:08.0992541Z >>> it1 = iter(source_dp) 2025-09-07T06:49:08.0992610Z >>> it2 = iter( 2025-09-07T06:49:08.0992661Z ... source_dp 2025-09-07T06:49:08.0992727Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T06:49:08.0992768Z >>> next(it2) 2025-09-07T06:49:08.0992805Z 0 2025-09-07T06:49:08.0992881Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T06:49:08.0992885Z 2025-09-07T06:49:08.0992984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.0992992Z 2025-09-07T06:49:08.1116478Z msg = Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T06:49:08.1116963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.1117171Z 2025-09-07T06:49:08.1117345Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T06:49:08.1117560Z 2025-09-07T06:49:08.1117616Z (functional name: ``groupby``). 2025-09-07T06:49:08.1117721Z 2025-09-07T06:49:08.1117892Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T06:49:08.1118224Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T06:49:08.1118522Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T06:49:08.1118985Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T06:49:08.1119171Z 2025-09-07T06:49:08.1119332Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T06:49:08.1119717Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T06:49:08.1119900Z 2025-09-07T06:49:08.1119956Z Args: 2025-09-07T06:49:08.1120084Z datapipe: Iterable datapipe to be grouped 2025-09-07T06:49:08.1123376Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T06:49:08.1123632Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T06:49:08.1123842Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T06:49:08.1124161Z buffer_size: The size of buffer for ungrouped data 2025-09-07T06:49:08.1124657Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T06:49:08.1124926Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T06:49:08.1125223Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T06:49:08.1125439Z when the buffer is full 2025-09-07T06:49:08.1125517Z 2025-09-07T06:49:08.1125557Z Example: 2025-09-07T06:49:08.1125654Z >>> import os 2025-09-07T06:49:08.1125764Z >>> # xdoctest: +SKIP 2025-09-07T06:49:08.1125918Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:08.1126083Z >>> def group_fn(file): 2025-09-07T06:49:08.1126233Z ... return os.path.basename(file).split(".")[0] 2025-09-07T06:49:08.1126389Z >>> source_dp = IterableWrapper( 2025-09-07T06:49:08.1126548Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T06:49:08.1126705Z ... ) 2025-09-07T06:49:08.1126833Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T06:49:08.1126975Z >>> list(dp0) 2025-09-07T06:49:08.1127107Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T06:49:08.1127302Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T06:49:08.1127493Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T06:49:08.1127651Z >>> list(dp1) 2025-09-07T06:49:08.1127788Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T06:49:08.1128039Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T06:49:08.1128261Z >>> dp2 = source_dp.groupby( 2025-09-07T06:49:08.1128400Z ... group_key_fn=group_fn, 2025-09-07T06:49:08.1128532Z ... buffer_size=3, 2025-09-07T06:49:08.1128658Z ... group_size=3, 2025-09-07T06:49:08.1131845Z ... guaranteed_group_size=2, 2025-09-07T06:49:08.1131970Z ... ) 2025-09-07T06:49:08.1132068Z >>> list(dp2) 2025-09-07T06:49:08.1132191Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T06:49:08.1132300Z 2025-09-07T06:49:08.1132393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.1132529Z 2025-09-07T06:49:08.1132778Z msg = Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T06:49:08.1133138Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.1133271Z 2025-09-07T06:49:08.1133398Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T06:49:08.1133562Z 2025-09-07T06:49:08.1133593Z Args: 2025-09-07T06:49:08.1133698Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T06:49:08.1133863Z mode: An optional string that specifies the mode in which 2025-09-07T06:49:08.1135545Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T06:49:08.1135721Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T06:49:08.1135889Z encoding: An optional string that specifies the encoding of the 2025-09-07T06:49:08.1136088Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T06:49:08.1136264Z length: Nominal length of the datapipe 2025-09-07T06:49:08.1136345Z 2025-09-07T06:49:08.1136377Z Note: 2025-09-07T06:49:08.1136514Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T06:49:08.1136684Z to close them explicitly. 2025-09-07T06:49:08.1136755Z 2025-09-07T06:49:08.1136789Z Example: 2025-09-07T06:49:08.1136871Z >>> # xdoctest: +SKIP 2025-09-07T06:49:08.1138196Z >>> from torchdata.datapipes.iter import ( 2025-09-07T06:49:08.1138372Z ... FileLister, 2025-09-07T06:49:08.1138471Z ... FileOpener, 2025-09-07T06:49:08.1138564Z ... StreamReader, 2025-09-07T06:49:08.1138656Z ... ) 2025-09-07T06:49:08.1138778Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T06:49:08.1138929Z >>> dp = FileOpener(dp) 2025-09-07T06:49:08.1139031Z >>> dp = StreamReader(dp) 2025-09-07T06:49:08.1139130Z >>> list(dp) 2025-09-07T06:49:08.1139217Z [('./abc.txt', 'abc')] 2025-09-07T06:49:08.1139279Z 2025-09-07T06:49:08.1139369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.1139494Z 2025-09-07T06:49:08.1152685Z msg = Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T06:49:08.1153101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.1153236Z 2025-09-07T06:49:08.1153403Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T06:49:08.1153591Z 2025-09-07T06:49:08.1153662Z A list of the child DataPipes is returned from this operation. 2025-09-07T06:49:08.1153765Z 2025-09-07T06:49:08.1153800Z Args: 2025-09-07T06:49:08.1153894Z datapipe: Iterable DataPipe being filtered 2025-09-07T06:49:08.1154047Z num_instances: number of instances of the DataPipe to create 2025-09-07T06:49:08.1154282Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T06:49:08.1154656Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T06:49:08.1154909Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T06:49:08.1155129Z DataPipes while waiting for their values to be yielded. 2025-09-07T06:49:08.1155302Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T06:49:08.1155401Z 2025-09-07T06:49:08.1155434Z Examples: 2025-09-07T06:49:08.1155529Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:08.1155676Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:08.1155814Z >>> def odd_or_even(n): 2025-09-07T06:49:08.1157416Z ... return n % 2 2025-09-07T06:49:08.1157531Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T06:49:08.1157694Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T06:49:08.1157837Z >>> list(dp1) 2025-09-07T06:49:08.1157920Z [0, 2, 4] 2025-09-07T06:49:08.1158000Z >>> list(dp2) 2025-09-07T06:49:08.1158081Z [1, 3] 2025-09-07T06:49:08.1158210Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T06:49:08.1158371Z >>> def odd_or_even_no_zero(n): 2025-09-07T06:49:08.1158491Z ... return n % 2 if n != 0 else None 2025-09-07T06:49:08.1158682Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T06:49:08.1159996Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T06:49:08.1160138Z ... ) 2025-09-07T06:49:08.1160214Z >>> list(dp1) 2025-09-07T06:49:08.1160296Z [2, 4] 2025-09-07T06:49:08.1160372Z >>> list(dp2) 2025-09-07T06:49:08.1160452Z [1, 3] 2025-09-07T06:49:08.1160496Z 2025-09-07T06:49:08.1160584Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.1160706Z 2025-09-07T06:49:08.1160937Z msg = Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T06:49:08.1161287Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.1161411Z 2025-09-07T06:49:08.1161519Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T06:49:08.1161713Z 2025-09-07T06:49:08.1162954Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T06:49:08.1163185Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T06:49:08.1163288Z 2025-09-07T06:49:08.1163320Z Args: 2025-09-07T06:49:08.1163503Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T06:49:08.1163674Z 2025-09-07T06:49:08.1163708Z Example: 2025-09-07T06:49:08.1163802Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:08.1163949Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:08.1164085Z >>> dp1, dp2, dp3 = ( 2025-09-07T06:49:08.1164191Z ... IterableWrapper(range(3)), 2025-09-07T06:49:08.1164310Z ... IterableWrapper(range(10, 15)), 2025-09-07T06:49:08.1164511Z ... IterableWrapper(range(20, 25)), 2025-09-07T06:49:08.1165756Z ... ) 2025-09-07T06:49:08.1165850Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T06:49:08.1165956Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T06:49:08.1166028Z 2025-09-07T06:49:08.1166117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.1166239Z 2025-09-07T06:49:08.1166460Z msg = Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T06:49:08.1166802Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.1166927Z 2025-09-07T06:49:08.1167035Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T06:49:08.1167176Z 2025-09-07T06:49:08.1167259Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T06:49:08.1167374Z 2025-09-07T06:49:08.1167406Z Args: 2025-09-07T06:49:08.1167508Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T06:49:08.1167598Z 2025-09-07T06:49:08.1167635Z Example: 2025-09-07T06:49:08.1168813Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:08.1168965Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:08.1169099Z >>> dp1, dp2, dp3 = ( 2025-09-07T06:49:08.1169203Z ... IterableWrapper(range(5)), 2025-09-07T06:49:08.1169323Z ... IterableWrapper(range(10, 15)), 2025-09-07T06:49:08.1169442Z ... IterableWrapper(range(20, 25)), 2025-09-07T06:49:08.1169548Z ... ) 2025-09-07T06:49:08.1169630Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T06:49:08.1169749Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T06:49:08.1169834Z 2025-09-07T06:49:08.1169920Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.1170043Z 2025-09-07T06:49:08.4449037Z 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. 2025-09-07T06:49:08.4450832Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:08.4451386Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T06:49:08.4451658Z 2025-09-07T06:49:08.4452040Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T06:49:08.4452503Z 2025-09-07T06:49:08.4452658Z .. math:: 2025-09-07T06:49:08.4452812Z 2025-09-07T06:49:08.4453067Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T06:49:08.4453373Z 2025-09-07T06:49:08.4453600Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T06:49:08.4454499Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T06:49:08.4454708Z 2025-09-07T06:49:08.4454967Z In addition, they are only considered close if they have the same 2025-09-07T06:49:08.4455178Z 2025-09-07T06:49:08.4455310Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T06:49:08.4455598Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T06:49:08.4455864Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T06:49:08.4456116Z - stride (if ``check_stride`` is ``True``). 2025-09-07T06:49:08.4456273Z 2025-09-07T06:49:08.4456460Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T06:49:08.4456719Z 2025-09-07T06:49:08.4456941Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T06:49:08.4457453Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T06:49:08.4457900Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T06:49:08.4458362Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T06:49:08.4458662Z 2025-09-07T06:49:08.4458844Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T06:49:08.4465773Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T06:49:08.4466051Z definition above. 2025-09-07T06:49:08.4466129Z 2025-09-07T06:49:08.4466275Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T06:49:08.4466635Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T06:49:08.4467007Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T06:49:08.4467393Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T06:49:08.4467720Z their elements are considered close according to the above definition. 2025-09-07T06:49:08.4467876Z 2025-09-07T06:49:08.4467924Z .. note:: 2025-09-07T06:49:08.4467995Z 2025-09-07T06:49:08.4468144Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T06:49:08.4468481Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T06:49:08.4468794Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T06:49:08.4468973Z 2025-09-07T06:49:08.4469012Z Args: 2025-09-07T06:49:08.4469133Z actual (Any): Actual input. 2025-09-07T06:49:08.4469288Z expected (Any): Expected input. 2025-09-07T06:49:08.4469552Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T06:49:08.4469927Z are allowed. Otherwise type equality is required. 2025-09-07T06:49:08.4470221Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T06:49:08.4470546Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T06:49:08.4470944Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T06:49:08.4471269Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T06:49:08.4473446Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T06:49:08.4473689Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T06:49:08.4473918Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T06:49:08.4474206Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T06:49:08.4474613Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T06:49:08.4474894Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T06:49:08.4475121Z :func:`torch.promote_types`) before being compared. 2025-09-07T06:49:08.4475346Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T06:49:08.4475628Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T06:49:08.4475821Z compared. 2025-09-07T06:49:08.4476011Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T06:49:08.4477601Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T06:49:08.4477899Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T06:49:08.4478107Z should return the new message. 2025-09-07T06:49:08.4478191Z 2025-09-07T06:49:08.4478224Z Raises: 2025-09-07T06:49:08.4478362Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T06:49:08.4478549Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T06:49:08.4478763Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T06:49:08.4479041Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T06:49:08.4479249Z different types. 2025-09-07T06:49:08.4479449Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T06:49:08.4479760Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T06:49:08.4481217Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T06:49:08.4481480Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T06:49:08.4481670Z :attr:`~torch.Tensor.layout`. 2025-09-07T06:49:08.4481833Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T06:49:08.4482081Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T06:49:08.4482357Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T06:49:08.4482537Z :attr:`~torch.Tensor.device`. 2025-09-07T06:49:08.4482774Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T06:49:08.4483040Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T06:49:08.4483318Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T06:49:08.4483474Z 2025-09-07T06:49:08.4483600Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T06:49:08.4486330Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T06:49:08.4486426Z 2025-09-07T06:49:08.4486481Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4486620Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T06:49:08.4486745Z +===========================+============+==========+ 2025-09-07T06:49:08.4486877Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T06:49:08.4487070Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4487204Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T06:49:08.4487333Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4487462Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4487590Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4487718Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T06:49:08.4488956Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4489088Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T06:49:08.4489218Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4489350Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4489479Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4489615Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T06:49:08.4489748Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4489878Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4490006Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4490134Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4490260Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4491400Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4491530Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4491659Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4491787Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4491917Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:08.4492043Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4492171Z | other | ``0.0`` | ``0.0`` | 2025-09-07T06:49:08.4492297Z +---------------------------+------------+----------+ 2025-09-07T06:49:08.4492381Z 2025-09-07T06:49:08.4492418Z .. note:: 2025-09-07T06:49:08.4492469Z 2025-09-07T06:49:08.4492604Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T06:49:08.4492890Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T06:49:08.4494137Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T06:49:08.4494272Z 2025-09-07T06:49:08.4494313Z >>> import functools 2025-09-07T06:49:08.4494546Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T06:49:08.4494717Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T06:49:08.4494839Z Traceback (most recent call last): 2025-09-07T06:49:08.4494955Z ... 2025-09-07T06:49:08.4495094Z AssertionError: Scalars are not equal! 2025-09-07T06:49:08.4495211Z 2025-09-07T06:49:08.4495307Z Expected 1e-10 but got 1e-09. 2025-09-07T06:49:08.4495430Z Absolute difference: 9.000000000000001e-10 2025-09-07T06:49:08.4496577Z Relative difference: 9.0 2025-09-07T06:49:08.4496653Z 2025-09-07T06:49:08.4496689Z Examples: 2025-09-07T06:49:08.4496780Z >>> # tensor to tensor comparison 2025-09-07T06:49:08.4496910Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T06:49:08.4497047Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T06:49:08.4497185Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4497274Z 2025-09-07T06:49:08.4497317Z >>> # scalar to scalar comparison 2025-09-07T06:49:08.4497430Z >>> import math 2025-09-07T06:49:08.4497529Z >>> expected = math.sqrt(2.0) 2025-09-07T06:49:08.4497728Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T06:49:08.4497860Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4497948Z 2025-09-07T06:49:08.4499000Z >>> # numpy array to numpy array comparison 2025-09-07T06:49:08.4499129Z >>> import numpy as np 2025-09-07T06:49:08.4499241Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T06:49:08.4499370Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T06:49:08.4499503Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4499590Z 2025-09-07T06:49:08.4499635Z >>> # sequence to sequence comparison 2025-09-07T06:49:08.4499755Z >>> import numpy as np 2025-09-07T06:49:08.4499910Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T06:49:08.4500088Z >>> # length and their elements have to match. 2025-09-07T06:49:08.4500230Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T06:49:08.4500371Z >>> actual = tuple(expected) 2025-09-07T06:49:08.4501485Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4501579Z 2025-09-07T06:49:08.4501625Z >>> # mapping to mapping comparison 2025-09-07T06:49:08.4501751Z >>> from collections import OrderedDict 2025-09-07T06:49:08.4501871Z >>> import numpy as np 2025-09-07T06:49:08.4501978Z >>> foo = torch.tensor(1.0) 2025-09-07T06:49:08.4502086Z >>> bar = 2.0 2025-09-07T06:49:08.4502181Z >>> baz = np.array(3.0) 2025-09-07T06:49:08.4502338Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T06:49:08.4502537Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T06:49:08.4502717Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T06:49:08.4502879Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T06:49:08.4503994Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4504090Z 2025-09-07T06:49:08.4504142Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T06:49:08.4504270Z >>> actual = expected.clone() 2025-09-07T06:49:08.4504465Z >>> # By default, directly related instances can be compared 2025-09-07T06:49:08.4504641Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T06:49:08.4504825Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T06:49:08.4504975Z >>> torch.testing.assert_close( 2025-09-07T06:49:08.4505124Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T06:49:08.4505264Z ... ) 2025-09-07T06:49:08.4505356Z Traceback (most recent call last): 2025-09-07T06:49:08.4505467Z ... 2025-09-07T06:49:08.4506572Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T06:49:08.4506763Z and . 2025-09-07T06:49:08.4507012Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T06:49:08.4507190Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T06:49:08.4507332Z Traceback (most recent call last): 2025-09-07T06:49:08.4507438Z ... 2025-09-07T06:49:08.4507586Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T06:49:08.4507763Z and . 2025-09-07T06:49:08.4507927Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T06:49:08.4508100Z >>> # their type if check_dtype=False. 2025-09-07T06:49:08.4508244Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T06:49:08.4509348Z 2025-09-07T06:49:08.4509391Z >>> # NaN != NaN by default. 2025-09-07T06:49:08.4509512Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T06:49:08.4509683Z >>> actual = expected.clone() 2025-09-07T06:49:08.4509817Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:08.4509948Z Traceback (most recent call last): 2025-09-07T06:49:08.4510055Z ... 2025-09-07T06:49:08.4510145Z AssertionError: Scalars are not close! 2025-09-07T06:49:08.4510259Z 2025-09-07T06:49:08.4510353Z Expected nan but got nan. 2025-09-07T06:49:08.4510478Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T06:49:08.4510618Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T06:49:08.4511832Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T06:49:08.4511943Z 2025-09-07T06:49:08.4511993Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T06:49:08.4512119Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T06:49:08.4512253Z >>> # The default error message can be overwritten. 2025-09-07T06:49:08.4512390Z >>> torch.testing.assert_close( 2025-09-07T06:49:08.4512539Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T06:49:08.4512674Z ... ) 2025-09-07T06:49:08.4512763Z Traceback (most recent call last): 2025-09-07T06:49:08.4512869Z ... 2025-09-07T06:49:08.4512967Z AssertionError: Argh, the tensors are not close! 2025-09-07T06:49:08.4513132Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T06:49:08.4514269Z >>> # extra information 2025-09-07T06:49:08.4514438Z >>> torch.testing.assert_close( 2025-09-07T06:49:08.4514586Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T06:49:08.4514724Z ... ) 2025-09-07T06:49:08.4514814Z Traceback (most recent call last): 2025-09-07T06:49:08.4514919Z ... 2025-09-07T06:49:08.4515003Z AssertionError: Header 2025-09-07T06:49:08.4515102Z 2025-09-07T06:49:08.4515198Z Tensor-likes are not close! 2025-09-07T06:49:08.4515309Z 2025-09-07T06:49:08.4515402Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T06:49:08.4516544Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T06:49:08.4516741Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T06:49:08.4516889Z 2025-09-07T06:49:08.4516972Z Footer 2025-09-07T06:49:08.4517050Z 2025-09-07T06:49:08.4517181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:08.4517303Z 2025-09-07T06:49:09.1739197Z 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. 2025-09-07T06:49:09.1740290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.1740672Z 2025-09-07T06:49:09.1740949Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T06:49:09.1741769Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T06:49:09.1742323Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T06:49:09.1742849Z its samples are on simplex, and are reparametrizable. 2025-09-07T06:49:09.1743119Z 2025-09-07T06:49:09.1743239Z Example:: 2025-09-07T06:49:09.1743375Z 2025-09-07T06:49:09.1743530Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:09.1743890Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T06:49:09.1744156Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T06:49:09.1744593Z >>> m.sample() 2025-09-07T06:49:09.1744783Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T06:49:09.1744938Z 2025-09-07T06:49:09.1744999Z Args: 2025-09-07T06:49:09.1745185Z temperature (Tensor): relaxation temperature 2025-09-07T06:49:09.1745547Z probs (Tensor): event probabilities 2025-09-07T06:49:09.1745834Z logits (Tensor): unnormalized log probability for each event 2025-09-07T06:49:09.1746024Z 2025-09-07T06:49:09.1746195Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.1746419Z 2025-09-07T06:49:09.1746950Z 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. 2025-09-07T06:49:09.1747600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.1747837Z 2025-09-07T06:49:09.1747999Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T06:49:09.1748376Z distribution where all component are from different parameterizations of 2025-09-07T06:49:09.1748757Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T06:49:09.1749105Z "selecting distribution" (over `k` component) and a component 2025-09-07T06:49:09.1749446Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T06:49:09.1749765Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T06:49:09.1749934Z 2025-09-07T06:49:09.1750002Z Examples:: 2025-09-07T06:49:09.1750098Z 2025-09-07T06:49:09.1750185Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.1750466Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T06:49:09.1756268Z >>> # weighted normal distributions 2025-09-07T06:49:09.1756447Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T06:49:09.1756640Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T06:49:09.1756828Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:09.1756937Z 2025-09-07T06:49:09.1757043Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T06:49:09.1757252Z >>> # weighted bivariate normal distributions 2025-09-07T06:49:09.1757419Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T06:49:09.1757582Z >>> comp = D.Independent(D.Normal( 2025-09-07T06:49:09.1757756Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T06:49:09.1757927Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:09.1758034Z 2025-09-07T06:49:09.1758126Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T06:49:09.1758353Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T06:49:09.1758550Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T06:49:09.1758707Z >>> comp = D.Independent(D.Normal( 2025-09-07T06:49:09.1758883Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T06:49:09.1759059Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:09.1759161Z 2025-09-07T06:49:09.1759204Z Args: 2025-09-07T06:49:09.1759360Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T06:49:09.1759594Z instance. Manages the probability of selecting component. 2025-09-07T06:49:09.1761815Z The number of categories must match the rightmost batch 2025-09-07T06:49:09.1762138Z dimension of the `component_distribution`. Must have either 2025-09-07T06:49:09.1762346Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T06:49:09.1762531Z `component_distribution.batch_shape[:-1]` 2025-09-07T06:49:09.1762745Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T06:49:09.1762981Z instance. Right-most batch dimension indexes component. 2025-09-07T06:49:09.1763113Z 2025-09-07T06:49:09.1763231Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.1763395Z 2025-09-07T06:49:09.1896802Z 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. 2025-09-07T06:49:09.1897148Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.1897362Z 2025-09-07T06:49:09.1897437Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T06:49:09.1899340Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T06:49:09.1899537Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T06:49:09.1899717Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T06:49:09.1899820Z 2025-09-07T06:49:09.1899858Z Example:: 2025-09-07T06:49:09.1899906Z 2025-09-07T06:49:09.1899963Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:09.1900100Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T06:49:09.1900233Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T06:49:09.1900360Z >>> m.sample() 2025-09-07T06:49:09.1900461Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T06:49:09.1900544Z 2025-09-07T06:49:09.1900576Z Args: 2025-09-07T06:49:09.1900671Z temperature (Tensor): relaxation temperature 2025-09-07T06:49:09.1902059Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T06:49:09.1902229Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T06:49:09.1902324Z 2025-09-07T06:49:09.1902414Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.1902537Z 2025-09-07T06:49:09.2011999Z 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. 2025-09-07T06:49:09.2012803Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2013098Z 2025-09-07T06:49:09.2013311Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T06:49:09.2013778Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T06:49:09.2013984Z 2025-09-07T06:49:09.2014150Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T06:49:09.2014554Z 2025-09-07T06:49:09.2014637Z Example: 2025-09-07T06:49:09.2014886Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T06:49:09.2021365Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:09.2021637Z >>> m = LowRankMultivariateNormal( 2025-09-07T06:49:09.2021907Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T06:49:09.2022151Z ... ) 2025-09-07T06:49:09.2022407Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T06:49:09.2022723Z tensor([-0.2102, -0.5429]) 2025-09-07T06:49:09.2022841Z 2025-09-07T06:49:09.2022895Z Args: 2025-09-07T06:49:09.2023124Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T06:49:09.2023493Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T06:49:09.2023795Z `batch_shape + event_shape + (rank,)` 2025-09-07T06:49:09.2024081Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T06:49:09.2027219Z `batch_shape + event_shape` 2025-09-07T06:49:09.2027373Z 2025-09-07T06:49:09.2027432Z Note: 2025-09-07T06:49:09.2027662Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T06:49:09.2028031Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T06:49:09.2028384Z `_ and 2025-09-07T06:49:09.2028658Z `matrix determinant lemma `_. 2025-09-07T06:49:09.2028948Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T06:49:09.2029170Z the small size "capacitance" matrix:: 2025-09-07T06:49:09.2029276Z 2025-09-07T06:49:09.2029366Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T06:49:09.2029498Z 2025-09-07T06:49:09.2029614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2029903Z 2025-09-07T06:49:09.2617147Z 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. 2025-09-07T06:49:09.2626910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2627154Z 2025-09-07T06:49:09.2627246Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T06:49:09.2627373Z 2025-09-07T06:49:09.2627440Z The test is performed with ``actual == desired``. 2025-09-07T06:49:09.2627544Z 2025-09-07T06:49:09.2627584Z Parameters 2025-09-07T06:49:09.2627672Z ---------- 2025-09-07T06:49:09.2627769Z test_string : str 2025-09-07T06:49:09.2627884Z The message supplied to AssertionError. 2025-09-07T06:49:09.2628014Z actual : object 2025-09-07T06:49:09.2628138Z The object to test for equality against `desired`. 2025-09-07T06:49:09.2628288Z desired : object 2025-09-07T06:49:09.2628421Z The expected result. 2025-09-07T06:49:09.2628498Z 2025-09-07T06:49:09.2628551Z Examples 2025-09-07T06:49:09.2628648Z -------- 2025-09-07T06:49:09.2628770Z >>> np.testing.print_assert_equal( 2025-09-07T06:49:09.2632723Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T06:49:09.2632890Z ... ) # doctest: +SKIP 2025-09-07T06:49:09.2633006Z >>> np.testing.print_assert_equal( 2025-09-07T06:49:09.2633149Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T06:49:09.2633277Z ... ) # doctest: +SKIP 2025-09-07T06:49:09.2633387Z Traceback (most recent call last): 2025-09-07T06:49:09.2633499Z ... 2025-09-07T06:49:09.2633599Z AssertionError: Test XYZ of func xyz failed 2025-09-07T06:49:09.2633722Z ACTUAL: 2025-09-07T06:49:09.2633814Z [0, 1] 2025-09-07T06:49:09.2633900Z DESIRED: 2025-09-07T06:49:09.2633984Z [0, 2] 2025-09-07T06:49:09.2634028Z 2025-09-07T06:49:09.2634030Z 2025-09-07T06:49:09.2634128Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2634272Z 2025-09-07T06:49:09.2634641Z 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. 2025-09-07T06:49:09.2634985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2635139Z 2025-09-07T06:49:09.2635225Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T06:49:09.2635384Z precision. 2025-09-07T06:49:09.2635438Z 2025-09-07T06:49:09.2635536Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:09.2635735Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:09.2635944Z instead of this function for more consistent floating point 2025-09-07T06:49:09.2636115Z comparisons. 2025-09-07T06:49:09.2636200Z 2025-09-07T06:49:09.2636294Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T06:49:09.2636431Z 2025-09-07T06:49:09.2636507Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T06:49:09.2636984Z 2025-09-07T06:49:09.2637085Z That is a looser test than originally documented, but agrees with what the 2025-09-07T06:49:09.2637302Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T06:49:09.2637524Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T06:49:09.2637703Z delegates to assert_array_almost_equal 2025-09-07T06:49:09.2639506Z 2025-09-07T06:49:09.2639547Z Parameters 2025-09-07T06:49:09.2639627Z ---------- 2025-09-07T06:49:09.2639708Z actual : array_like 2025-09-07T06:49:09.2639801Z The object to check. 2025-09-07T06:49:09.2639902Z desired : array_like 2025-09-07T06:49:09.2639996Z The expected object. 2025-09-07T06:49:09.2640092Z decimal : int, optional 2025-09-07T06:49:09.2640196Z Desired precision, default is 7. 2025-09-07T06:49:09.2640310Z err_msg : str, optional 2025-09-07T06:49:09.2640422Z The error message to be printed in case of failure. 2025-09-07T06:49:09.2640656Z verbose : bool, optional 2025-09-07T06:49:09.2641975Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:09.2642083Z 2025-09-07T06:49:09.2642116Z Raises 2025-09-07T06:49:09.2642188Z ------ 2025-09-07T06:49:09.2642265Z AssertionError 2025-09-07T06:49:09.2642387Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:09.2642493Z 2025-09-07T06:49:09.2642525Z See Also 2025-09-07T06:49:09.2642599Z -------- 2025-09-07T06:49:09.2642727Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:09.2642897Z relative and/or absolute precision. 2025-09-07T06:49:09.2643055Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:09.2644736Z 2025-09-07T06:49:09.2644775Z Examples 2025-09-07T06:49:09.2644853Z -------- 2025-09-07T06:49:09.2644964Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T06:49:09.2645112Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T06:49:09.2645259Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T06:49:09.2645400Z Traceback (most recent call last): 2025-09-07T06:49:09.2645504Z ... 2025-09-07T06:49:09.2645582Z AssertionError: 2025-09-07T06:49:09.2645683Z Arrays are not almost equal to 10 decimals 2025-09-07T06:49:09.2645798Z ACTUAL: 2.3333333333333 2025-09-07T06:49:09.2645891Z DESIRED: 2.33333334 2025-09-07T06:49:09.2645948Z 2025-09-07T06:49:09.2645986Z >>> assert_almost_equal( 2025-09-07T06:49:09.2646118Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T06:49:09.2646254Z ... ) 2025-09-07T06:49:09.2646340Z Traceback (most recent call last): 2025-09-07T06:49:09.2646441Z ... 2025-09-07T06:49:09.2646517Z AssertionError: 2025-09-07T06:49:09.2646614Z Arrays are not almost equal to 9 decimals 2025-09-07T06:49:09.2646724Z 2025-09-07T06:49:09.2646811Z Mismatched elements: 1 / 2 (50%) 2025-09-07T06:49:09.2649342Z Max absolute difference: 6.666699636781459e-09 2025-09-07T06:49:09.2649481Z Max relative difference: 2.8571569790287484e-09 2025-09-07T06:49:09.2649616Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T06:49:09.2649750Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T06:49:09.2649835Z 2025-09-07T06:49:09.2649838Z 2025-09-07T06:49:09.2649929Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2650052Z 2025-09-07T06:49:09.2650248Z 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. 2025-09-07T06:49:09.2650563Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2650691Z 2025-09-07T06:49:09.2650773Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T06:49:09.2650917Z digits. 2025-09-07T06:49:09.2650961Z 2025-09-07T06:49:09.2651030Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:09.2651264Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:09.2652571Z instead of this function for more consistent floating point 2025-09-07T06:49:09.2652751Z comparisons. 2025-09-07T06:49:09.2652812Z 2025-09-07T06:49:09.2652882Z Given two numbers, check that they are approximately equal. 2025-09-07T06:49:09.2653061Z Approximately equal is defined as the number of significant digits 2025-09-07T06:49:09.2653206Z that agree. 2025-09-07T06:49:09.2653254Z 2025-09-07T06:49:09.2653287Z Parameters 2025-09-07T06:49:09.2653364Z ---------- 2025-09-07T06:49:09.2653443Z actual : scalar 2025-09-07T06:49:09.2653533Z The object to check. 2025-09-07T06:49:09.2653627Z desired : scalar 2025-09-07T06:49:09.2654875Z The expected object. 2025-09-07T06:49:09.2654982Z significant : int, optional 2025-09-07T06:49:09.2655095Z Desired precision, default is 7. 2025-09-07T06:49:09.2655261Z err_msg : str, optional 2025-09-07T06:49:09.2655382Z The error message to be printed in case of failure. 2025-09-07T06:49:09.2655510Z verbose : bool, optional 2025-09-07T06:49:09.2655644Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:09.2655755Z 2025-09-07T06:49:09.2655786Z Raises 2025-09-07T06:49:09.2655860Z ------ 2025-09-07T06:49:09.2655935Z AssertionError 2025-09-07T06:49:09.2656058Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:09.2656162Z 2025-09-07T06:49:09.2657264Z See Also 2025-09-07T06:49:09.2657344Z -------- 2025-09-07T06:49:09.2657472Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:09.2657643Z relative and/or absolute precision. 2025-09-07T06:49:09.2657801Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:09.2657913Z 2025-09-07T06:49:09.2657946Z Examples 2025-09-07T06:49:09.2658020Z -------- 2025-09-07T06:49:09.2658112Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:09.2658234Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T06:49:09.2658344Z ... ) # doctest: +SKIP 2025-09-07T06:49:09.2658446Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:09.2659575Z ... 0.12345670e-20, 2025-09-07T06:49:09.2659679Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T06:49:09.2659790Z ... significant=8, 2025-09-07T06:49:09.2659878Z ... ) 2025-09-07T06:49:09.2659963Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:09.2660069Z ... 0.12345670e-20, 2025-09-07T06:49:09.2660165Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T06:49:09.2660273Z ... significant=8, 2025-09-07T06:49:09.2660359Z ... ) 2025-09-07T06:49:09.2660443Z Traceback (most recent call last): 2025-09-07T06:49:09.2660547Z ... 2025-09-07T06:49:09.2661631Z AssertionError: 2025-09-07T06:49:09.2661737Z Items are not equal to 8 significant digits: 2025-09-07T06:49:09.2661854Z ACTUAL: 1.234567e-21 2025-09-07T06:49:09.2661951Z DESIRED: 1.2345672e-21 2025-09-07T06:49:09.2662010Z 2025-09-07T06:49:09.2662073Z the evaluated condition that raises the exception is 2025-09-07T06:49:09.2662167Z 2025-09-07T06:49:09.2662245Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T06:49:09.2662380Z True 2025-09-07T06:49:09.2662423Z 2025-09-07T06:49:09.2662425Z 2025-09-07T06:49:09.2662514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2662637Z 2025-09-07T06:49:09.2662829Z 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. 2025-09-07T06:49:09.2663142Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2663268Z 2025-09-07T06:49:09.2663345Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T06:49:09.2664553Z 2025-09-07T06:49:09.2664634Z Given two array_like objects, check that the shape is equal and all 2025-09-07T06:49:09.2664868Z elements of these objects are equal (but see the Notes for the special 2025-09-07T06:49:09.2665054Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T06:49:09.2665241Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T06:49:09.2665428Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T06:49:09.2665577Z NaNs in the same positions. 2025-09-07T06:49:09.2665645Z 2025-09-07T06:49:09.2665728Z The usual caution for verifying equality with floating point numbers is 2025-09-07T06:49:09.2665870Z advised. 2025-09-07T06:49:09.2665916Z 2025-09-07T06:49:09.2665950Z Parameters 2025-09-07T06:49:09.2666028Z ---------- 2025-09-07T06:49:09.2666106Z x : array_like 2025-09-07T06:49:09.2667236Z The actual object to check. 2025-09-07T06:49:09.2667338Z y : array_like 2025-09-07T06:49:09.2667430Z The desired, expected object. 2025-09-07T06:49:09.2667538Z err_msg : str, optional 2025-09-07T06:49:09.2667702Z The error message to be printed in case of failure. 2025-09-07T06:49:09.2667834Z verbose : bool, optional 2025-09-07T06:49:09.2667970Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:09.2668111Z strict : bool, optional 2025-09-07T06:49:09.2668237Z If True, raise an AssertionError when either the shape or the data 2025-09-07T06:49:09.2668406Z type of the array_like objects does not match. The special 2025-09-07T06:49:09.2668577Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T06:49:09.2669708Z 2025-09-07T06:49:09.2669740Z Raises 2025-09-07T06:49:09.2669816Z ------ 2025-09-07T06:49:09.2669893Z AssertionError 2025-09-07T06:49:09.2669993Z If actual and desired objects are not equal. 2025-09-07T06:49:09.2670078Z 2025-09-07T06:49:09.2670110Z See Also 2025-09-07T06:49:09.2670184Z -------- 2025-09-07T06:49:09.2670310Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:09.2670481Z relative and/or absolute precision. 2025-09-07T06:49:09.2670639Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:09.2670748Z 2025-09-07T06:49:09.2670779Z Notes 2025-09-07T06:49:09.2670851Z ----- 2025-09-07T06:49:09.2672058Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T06:49:09.2672237Z function checks that each element of the array_like object is equal to 2025-09-07T06:49:09.2672432Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T06:49:09.2672548Z 2025-09-07T06:49:09.2672580Z Examples 2025-09-07T06:49:09.2672655Z -------- 2025-09-07T06:49:09.2672747Z The first assert does not raise an exception: 2025-09-07T06:49:09.2672832Z 2025-09-07T06:49:09.2672876Z >>> np.testing.assert_array_equal( 2025-09-07T06:49:09.2673006Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T06:49:09.2673124Z ... ) 2025-09-07T06:49:09.2673166Z 2025-09-07T06:49:09.2673252Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T06:49:09.2673410Z functions for these cases instead: 2025-09-07T06:49:09.2674573Z 2025-09-07T06:49:09.2674618Z >>> np.testing.assert_allclose( 2025-09-07T06:49:09.2674763Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T06:49:09.2674901Z ... ) 2025-09-07T06:49:09.2674943Z 2025-09-07T06:49:09.2675020Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T06:49:09.2675205Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T06:49:09.2675318Z 2025-09-07T06:49:09.2675360Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T06:49:09.2675477Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T06:49:09.2675554Z 2025-09-07T06:49:09.2675629Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T06:49:09.2675767Z array: 2025-09-07T06:49:09.2675811Z 2025-09-07T06:49:09.2675868Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T06:49:09.2676000Z Traceback (most recent call last): 2025-09-07T06:49:09.2677175Z ... 2025-09-07T06:49:09.2677254Z AssertionError: 2025-09-07T06:49:09.2677343Z Arrays are not equal 2025-09-07T06:49:09.2677432Z 2025-09-07T06:49:09.2677517Z (shapes (2, 5), () mismatch) 2025-09-07T06:49:09.2677622Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T06:49:09.2677729Z [3, 3, 3, 3, 3]]) 2025-09-07T06:49:09.2677822Z y: torch.ndarray(3) 2025-09-07T06:49:09.2677880Z 2025-09-07T06:49:09.2677958Z The `strict` parameter also ensures that the array data types match: 2025-09-07T06:49:09.2678069Z 2025-09-07T06:49:09.2678107Z >>> x = np.array([2, 2, 2]) 2025-09-07T06:49:09.2678222Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T06:49:09.2679428Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T06:49:09.2679559Z Traceback (most recent call last): 2025-09-07T06:49:09.2679662Z ... 2025-09-07T06:49:09.2679739Z AssertionError: 2025-09-07T06:49:09.2679884Z Arrays are not equal 2025-09-07T06:49:09.2679971Z 2025-09-07T06:49:09.2680073Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T06:49:09.2680201Z x: torch.ndarray([2, 2, 2]) 2025-09-07T06:49:09.2680305Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T06:49:09.2680372Z 2025-09-07T06:49:09.2680461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2680585Z 2025-09-07T06:49:09.2681809Z 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. 2025-09-07T06:49:09.2682132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2682259Z 2025-09-07T06:49:09.2682335Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T06:49:09.2682477Z precision. 2025-09-07T06:49:09.2682526Z 2025-09-07T06:49:09.2682591Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:09.2682762Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:09.2682930Z instead of this function for more consistent floating point 2025-09-07T06:49:09.2683066Z comparisons. 2025-09-07T06:49:09.2683126Z 2025-09-07T06:49:09.2683210Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T06:49:09.2683362Z ``desired`` satisfy. 2025-09-07T06:49:09.2683420Z 2025-09-07T06:49:09.2683469Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T06:49:09.2683555Z 2025-09-07T06:49:09.2684724Z That is a looser test than originally documented, but agrees with what the 2025-09-07T06:49:09.2684927Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T06:49:09.2685125Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T06:49:09.2685316Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T06:49:09.2685477Z objects have NaNs in the same positions. 2025-09-07T06:49:09.2685555Z 2025-09-07T06:49:09.2685592Z Parameters 2025-09-07T06:49:09.2685671Z ---------- 2025-09-07T06:49:09.2685748Z x : array_like 2025-09-07T06:49:09.2685836Z The actual object to check. 2025-09-07T06:49:09.2685935Z y : array_like 2025-09-07T06:49:09.2686026Z The desired, expected object. 2025-09-07T06:49:09.2687151Z decimal : int, optional 2025-09-07T06:49:09.2687259Z Desired precision, default is 6. 2025-09-07T06:49:09.2687370Z err_msg : str, optional 2025-09-07T06:49:09.2687488Z The error message to be printed in case of failure. 2025-09-07T06:49:09.2687616Z verbose : bool, optional 2025-09-07T06:49:09.2687752Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:09.2687858Z 2025-09-07T06:49:09.2687890Z Raises 2025-09-07T06:49:09.2687963Z ------ 2025-09-07T06:49:09.2688039Z AssertionError 2025-09-07T06:49:09.2688158Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:09.2688265Z 2025-09-07T06:49:09.2688296Z See Also 2025-09-07T06:49:09.2689373Z -------- 2025-09-07T06:49:09.2689550Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:09.2689718Z relative and/or absolute precision. 2025-09-07T06:49:09.2689874Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:09.2689983Z 2025-09-07T06:49:09.2690016Z Examples 2025-09-07T06:49:09.2690090Z -------- 2025-09-07T06:49:09.2690182Z the first assert does not raise an exception 2025-09-07T06:49:09.2690264Z 2025-09-07T06:49:09.2690354Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T06:49:09.2690479Z 2025-09-07T06:49:09.2690527Z >>> np.testing.assert_array_almost_equal( 2025-09-07T06:49:09.2690665Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T06:49:09.2690788Z ... ) 2025-09-07T06:49:09.2691892Z Traceback (most recent call last): 2025-09-07T06:49:09.2692043Z ... 2025-09-07T06:49:09.2692120Z AssertionError: 2025-09-07T06:49:09.2692219Z Arrays are not almost equal to 5 decimals 2025-09-07T06:49:09.2692328Z 2025-09-07T06:49:09.2692415Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T06:49:09.2692534Z Max absolute difference: 5.999999999994898e-05 2025-09-07T06:49:09.2692664Z Max relative difference: 2.5713661239633743e-05 2025-09-07T06:49:09.2692807Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T06:49:09.2692960Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T06:49:09.2693053Z 2025-09-07T06:49:09.2693099Z >>> np.testing.assert_array_almost_equal( 2025-09-07T06:49:09.2694256Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T06:49:09.2694471Z ... ) 2025-09-07T06:49:09.2694555Z Traceback (most recent call last): 2025-09-07T06:49:09.2694657Z ... 2025-09-07T06:49:09.2694733Z AssertionError: 2025-09-07T06:49:09.2694826Z Arrays are not almost equal to 5 decimals 2025-09-07T06:49:09.2694939Z 2025-09-07T06:49:09.2695024Z x and y nan location mismatch: 2025-09-07T06:49:09.2695154Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T06:49:09.2695304Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T06:49:09.2695395Z 2025-09-07T06:49:09.2695397Z 2025-09-07T06:49:09.2695486Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2696647Z 2025-09-07T06:49:09.2696849Z 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. 2025-09-07T06:49:09.2697167Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.2697370Z Context manager that resets warning registry for catching warnings 2025-09-07T06:49:09.2697481Z 2025-09-07T06:49:09.2697566Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T06:49:09.2697763Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T06:49:09.2697961Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T06:49:09.2698161Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T06:49:09.2698331Z as a keyword argument to its constructor and: 2025-09-07T06:49:09.2698417Z 2025-09-07T06:49:09.2698497Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T06:49:09.2698643Z on entry; 2025-09-07T06:49:09.2698761Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T06:49:09.2699893Z 2025-09-07T06:49:09.2699975Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T06:49:09.2700157Z manager without disturbing the state of warnings outside. 2025-09-07T06:49:09.2700257Z 2025-09-07T06:49:09.2700341Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T06:49:09.2700493Z keyword-only. 2025-09-07T06:49:09.2700547Z 2025-09-07T06:49:09.2700582Z Parameters 2025-09-07T06:49:09.2700709Z ---------- 2025-09-07T06:49:09.2700798Z record : bool, optional 2025-09-07T06:49:09.2700933Z Specifies whether warnings should be captured by a custom 2025-09-07T06:49:09.2701118Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T06:49:09.2701308Z returned by the context manager. Otherwise None is returned by the 2025-09-07T06:49:09.2701493Z context manager. The objects appended to the list are arguments whose 2025-09-07T06:49:09.2702697Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T06:49:09.2702837Z modules : sequence, optional 2025-09-07T06:49:09.2702984Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T06:49:09.2703165Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T06:49:09.2703308Z filter by one of these modules. 2025-09-07T06:49:09.2703426Z 2025-09-07T06:49:09.2703460Z Examples 2025-09-07T06:49:09.2703545Z -------- 2025-09-07T06:49:09.2703627Z >>> import warnings 2025-09-07T06:49:09.2703756Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T06:49:09.2703901Z ... modules=[np.core.fromnumeric] 2025-09-07T06:49:09.2704010Z ... ): 2025-09-07T06:49:09.2705220Z ... warnings.simplefilter("always") 2025-09-07T06:49:09.2705380Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T06:49:09.2705558Z ... # do something that raises a warning but ignore those in 2025-09-07T06:49:09.2705697Z ... # np.core.fromnumeric 2025-09-07T06:49:09.2705797Z 2025-09-07T06:49:09.2705927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.2706047Z 2025-09-07T06:49:09.3763987Z 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=701. 2025-09-07T06:49:09.3764568Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:09.3764704Z 2025-09-07T06:49:09.3764806Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T06:49:09.3765039Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T06:49:09.3769266Z ``mesh_dim_names`` 2025-09-07T06:49:09.3769325Z 2025-09-07T06:49:09.3769359Z Args: 2025-09-07T06:49:09.3769492Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T06:49:09.3769683Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T06:49:09.3769819Z Returns: 2025-09-07T06:49:09.3769909Z A :class:`DeviceMesh` object 2025-09-07T06:49:09.3769979Z 2025-09-07T06:49:09.3770082Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T06:49:09.3770261Z In the first example: 2025-09-07T06:49:09.3770415Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T06:49:09.3770627Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T06:49:09.3770826Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T06:49:09.3771023Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T06:49:09.3771216Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T06:49:09.3771408Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T06:49:09.3771525Z 2025-09-07T06:49:09.3771567Z In the second example: 2025-09-07T06:49:09.3773172Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T06:49:09.3773387Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T06:49:09.3773816Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T06:49:09.3774025Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T06:49:09.3774147Z 2025-09-07T06:49:09.3774204Z Example:: 2025-09-07T06:49:09.3774252Z 2025-09-07T06:49:09.3774296Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:09.3774532Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T06:49:09.3774661Z >>> 2025-09-07T06:49:09.3774775Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T06:49:09.3774935Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T06:49:09.3775112Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T06:49:09.3776510Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T06:49:09.3776616Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T06:49:09.3776822Z >>> 2025-09-07T06:49:09.3776908Z >>> # Initialize a 3D mesh. 2025-09-07T06:49:09.3777073Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T06:49:09.3777308Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T06:49:09.3777489Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T06:49:09.3777608Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T06:49:09.3777683Z 2025-09-07T06:49:09.3777909Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-09-07T06:49:09.3778164Z 2025-09-07T06:49:09.3778253Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T06:49:09.3779530Z ^ 2025-09-07T06:49:09.3928815Z 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=2705. 2025-09-07T06:49:09.3929144Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.3929271Z 2025-09-07T06:49:09.3929365Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T06:49:09.3929485Z 2025-09-07T06:49:09.3929573Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T06:49:09.3929767Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T06:49:09.3929883Z 2025-09-07T06:49:09.3929917Z Args: 2025-09-07T06:49:09.3930043Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T06:49:09.3930236Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T06:49:09.3930427Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T06:49:09.3930574Z remote end. 2025-09-07T06:49:09.3932089Z 2025-09-07T06:49:09.3932135Z Returns: 2025-09-07T06:49:09.3932267Z A list of distributed request objects returned by calling the corresponding 2025-09-07T06:49:09.3932424Z op in the op_list. 2025-09-07T06:49:09.3932485Z 2025-09-07T06:49:09.3932519Z Examples: 2025-09-07T06:49:09.3932607Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:09.3932753Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T06:49:09.3932915Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T06:49:09.3933077Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T06:49:09.3933228Z >>> recv_op = dist.P2POp( 2025-09-07T06:49:09.3933367Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T06:49:09.3935306Z ... ) 2025-09-07T06:49:09.3935410Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T06:49:09.3935542Z >>> for req in reqs: 2025-09-07T06:49:09.3935710Z >>> req.wait() 2025-09-07T06:49:09.3935802Z >>> recv_tensor 2025-09-07T06:49:09.3935892Z tensor([2, 3]) # Rank 0 2025-09-07T06:49:09.3935994Z tensor([0, 1]) # Rank 1 2025-09-07T06:49:09.3936059Z 2025-09-07T06:49:09.3936148Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T06:49:09.3936348Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T06:49:09.3936502Z lead to unexpected hang issues. 2025-09-07T06:49:09.3936582Z 2025-09-07T06:49:09.3936657Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T06:49:09.3936845Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T06:49:09.3937034Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T06:49:09.3937224Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T06:49:09.3937451Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T06:49:09.3937557Z 2025-09-07T06:49:09.3937648Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.3939049Z 2025-09-07T06:49:09.3939254Z 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=2837. 2025-09-07T06:49:09.3939566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.3939692Z 2025-09-07T06:49:09.3939786Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T06:49:09.3939914Z 2025-09-07T06:49:09.3939991Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T06:49:09.3940106Z 2025-09-07T06:49:09.3940150Z Complex tensors are supported. 2025-09-07T06:49:09.3940219Z 2025-09-07T06:49:09.3940250Z Args: 2025-09-07T06:49:09.3940372Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T06:49:09.3940523Z operates in-place. 2025-09-07T06:49:09.3940632Z op (optional): One of the values from 2025-09-07T06:49:09.3940757Z ``torch.distributed.ReduceOp`` 2025-09-07T06:49:09.3942213Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T06:49:09.3942397Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.3942569Z the default process group will be used. 2025-09-07T06:49:09.3942722Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T06:49:09.3942826Z 2025-09-07T06:49:09.3942859Z Returns: 2025-09-07T06:49:09.3942954Z Async work handle, if async_op is set to True. 2025-09-07T06:49:09.3943094Z None, if not async_op or if not part of the group 2025-09-07T06:49:09.3943184Z 2025-09-07T06:49:09.3943217Z Examples: 2025-09-07T06:49:09.3943302Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:09.3943432Z >>> # All tensors below are of torch.int64 type. 2025-09-07T06:49:09.3943562Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.3945324Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T06:49:09.3945488Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T06:49:09.3945638Z >>> tensor 2025-09-07T06:49:09.3945733Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3945851Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3945975Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T06:49:09.3946088Z >>> tensor 2025-09-07T06:49:09.3946178Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3946292Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3946367Z 2025-09-07T06:49:09.3946420Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T06:49:09.3946546Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.3947739Z >>> tensor = torch.tensor( 2025-09-07T06:49:09.3947923Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T06:49:09.3948051Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T06:49:09.3948147Z >>> tensor 2025-09-07T06:49:09.3948249Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3948386Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3948518Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T06:49:09.3948630Z >>> tensor 2025-09-07T06:49:09.3948727Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3948857Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3948939Z 2025-09-07T06:49:09.3948941Z 2025-09-07T06:49:09.3950063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.3950192Z 2025-09-07T06:49:09.3950401Z 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=3201. 2025-09-07T06:49:09.3950766Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.3950952Z 2025-09-07T06:49:09.3951032Z Gathers picklable objects from the whole group in a single process. 2025-09-07T06:49:09.3951146Z 2025-09-07T06:49:09.3951229Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T06:49:09.3951400Z object must be picklable in order to be gathered. 2025-09-07T06:49:09.3951487Z 2025-09-07T06:49:09.3951520Z Args: 2025-09-07T06:49:09.3951613Z obj (Any): Input object. Must be picklable. 2025-09-07T06:49:09.3951769Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T06:49:09.3951947Z should be correctly sized as the size of the group for this 2025-09-07T06:49:09.3952123Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T06:49:09.3953330Z ranks. (default is ``None``) 2025-09-07T06:49:09.3953522Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T06:49:09.3953736Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T06:49:09.3953921Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.3954103Z the default process group will be used. Default is ``None``. 2025-09-07T06:49:09.3954321Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T06:49:09.3954539Z 2025-09-07T06:49:09.3954573Z Returns: 2025-09-07T06:49:09.3954684Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T06:49:09.3954822Z output of the collective. 2025-09-07T06:49:09.3954892Z 2025-09-07T06:49:09.3954969Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T06:49:09.3955150Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T06:49:09.3956334Z call. 2025-09-07T06:49:09.3956378Z 2025-09-07T06:49:09.3956462Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T06:49:09.3956653Z of objects must be moved to the GPU device before communication takes 2025-09-07T06:49:09.3956817Z place. In this case, the device used is given by 2025-09-07T06:49:09.3956981Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T06:49:09.3957165Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T06:49:09.3957315Z ``torch.cuda.set_device()``. 2025-09-07T06:49:09.3957385Z 2025-09-07T06:49:09.3957420Z .. warning:: 2025-09-07T06:49:09.3957547Z Object collectives have a number of serious performance and scalability 2025-09-07T06:49:09.3957727Z limitations. See :ref:`object_collectives` for details. 2025-09-07T06:49:09.3957827Z 2025-09-07T06:49:09.3959111Z .. warning:: 2025-09-07T06:49:09.3959237Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T06:49:09.3959481Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T06:49:09.3959668Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T06:49:09.3959823Z function with data you trust. 2025-09-07T06:49:09.3959896Z 2025-09-07T06:49:09.3959929Z .. warning:: 2025-09-07T06:49:09.3960051Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T06:49:09.3960239Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T06:49:09.3960415Z pickled. Please consider using :func:`gather` instead. 2025-09-07T06:49:09.3960511Z 2025-09-07T06:49:09.3960545Z Example:: 2025-09-07T06:49:09.3960643Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T06:49:09.3960796Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T06:49:09.3962043Z >>> import torch.distributed as dist 2025-09-07T06:49:09.3962214Z >>> # Assumes world_size of 3. 2025-09-07T06:49:09.3962357Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T06:49:09.3962503Z >>> output = [None for _ in gather_objects] 2025-09-07T06:49:09.3962622Z >>> dist.gather_object( 2025-09-07T06:49:09.3962731Z ... gather_objects[dist.get_rank()], 2025-09-07T06:49:09.3962859Z ... output if dist.get_rank() == 0 else None, 2025-09-07T06:49:09.3962977Z ... dst=0 2025-09-07T06:49:09.3963058Z ... ) 2025-09-07T06:49:09.3963134Z >>> # On rank 0 2025-09-07T06:49:09.3963219Z >>> output 2025-09-07T06:49:09.3964352Z ['foo', 12, {1: 2}] 2025-09-07T06:49:09.3964486Z 2025-09-07T06:49:09.3964574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.3964697Z 2025-09-07T06:49:09.3964896Z 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=3849. 2025-09-07T06:49:09.3965210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.3965342Z 2025-09-07T06:49:09.3965395Z Gathers tensors from the whole group in a list. 2025-09-07T06:49:09.3965480Z 2025-09-07T06:49:09.3965533Z Complex and uneven sized tensors are supported. 2025-09-07T06:49:09.3965619Z 2025-09-07T06:49:09.3965650Z Args: 2025-09-07T06:49:09.3965760Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T06:49:09.3965934Z correctly-sized tensors to be used for output of the collective. 2025-09-07T06:49:09.3966089Z Uneven sized tensors are supported. 2025-09-07T06:49:09.3966235Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T06:49:09.3967456Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.3967618Z the default process group will be used. 2025-09-07T06:49:09.3967769Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T06:49:09.3967880Z 2025-09-07T06:49:09.3967913Z Returns: 2025-09-07T06:49:09.3968010Z Async work handle, if async_op is set to True. 2025-09-07T06:49:09.3968150Z None, if not async_op or if not part of the group 2025-09-07T06:49:09.3968238Z 2025-09-07T06:49:09.3968272Z Examples: 2025-09-07T06:49:09.3968368Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T06:49:09.3968502Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T06:49:09.3968628Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.3968750Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T06:49:09.3969883Z >>> tensor_list = [ 2025-09-07T06:49:09.3970017Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T06:49:09.3970159Z ... ] 2025-09-07T06:49:09.3970234Z >>> tensor_list 2025-09-07T06:49:09.3970357Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T06:49:09.3970529Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T06:49:09.3970760Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T06:49:09.3970908Z >>> tensor 2025-09-07T06:49:09.3971000Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3971116Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3971234Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T06:49:09.3972378Z >>> tensor_list 2025-09-07T06:49:09.3972497Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T06:49:09.3972666Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T06:49:09.3972771Z 2025-09-07T06:49:09.3972825Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T06:49:09.3972954Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.3973064Z >>> tensor_list = [ 2025-09-07T06:49:09.3973197Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T06:49:09.3973379Z ... ] 2025-09-07T06:49:09.3973455Z >>> tensor_list 2025-09-07T06:49:09.3973596Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T06:49:09.3973800Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T06:49:09.3975080Z >>> tensor = torch.tensor( 2025-09-07T06:49:09.3975206Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T06:49:09.3975334Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T06:49:09.3975428Z >>> tensor 2025-09-07T06:49:09.3975530Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T06:49:09.3975666Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T06:49:09.3975794Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T06:49:09.3975902Z >>> tensor_list 2025-09-07T06:49:09.3976036Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T06:49:09.3976241Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T06:49:09.3976358Z 2025-09-07T06:49:09.3976361Z 2025-09-07T06:49:09.3976448Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.3977604Z 2025-09-07T06:49:09.3982553Z 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=4555. 2025-09-07T06:49:09.3982893Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.3983021Z 2025-09-07T06:49:09.3983110Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T06:49:09.3983233Z 2025-09-07T06:49:09.3983321Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T06:49:09.3983491Z and returned as a single output tensor. 2025-09-07T06:49:09.3983572Z 2025-09-07T06:49:09.3983622Z Complex tensors are supported. 2025-09-07T06:49:09.3983692Z 2025-09-07T06:49:09.3983726Z Args: 2025-09-07T06:49:09.3983833Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T06:49:09.3983973Z input (Tensor): Input tensor to scatter. 2025-09-07T06:49:09.3985599Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T06:49:09.3985802Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T06:49:09.3985964Z equally by ``world_size``. 2025-09-07T06:49:09.3986112Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T06:49:09.3986294Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T06:49:09.3986437Z equally by ``world_size``. 2025-09-07T06:49:09.3986588Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.3986750Z the default process group will be used. 2025-09-07T06:49:09.3986903Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T06:49:09.3987009Z 2025-09-07T06:49:09.3987103Z Returns: 2025-09-07T06:49:09.3987200Z Async work handle, if async_op is set to True. 2025-09-07T06:49:09.3987341Z None, if not async_op or if not part of the group. 2025-09-07T06:49:09.3987431Z 2025-09-07T06:49:09.3987467Z .. warning:: 2025-09-07T06:49:09.3987578Z `all_to_all_single` is experimental and subject to change. 2025-09-07T06:49:09.3987675Z 2025-09-07T06:49:09.3987709Z Examples: 2025-09-07T06:49:09.3987802Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T06:49:09.3987929Z >>> input = torch.arange(4) + rank * 4 2025-09-07T06:49:09.3989809Z >>> input 2025-09-07T06:49:09.3989909Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T06:49:09.3990020Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T06:49:09.3990126Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T06:49:09.3990230Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T06:49:09.3990354Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T06:49:09.3990541Z >>> dist.all_to_all_single(output, input) 2025-09-07T06:49:09.3990653Z >>> output 2025-09-07T06:49:09.3990737Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T06:49:09.3990847Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T06:49:09.3991009Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T06:49:09.3992359Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T06:49:09.3992429Z 2025-09-07T06:49:09.3992496Z >>> # Essentially, it is similar to following operation: 2025-09-07T06:49:09.3992642Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T06:49:09.3992776Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T06:49:09.3992901Z >>> for i in range(world_size): 2025-09-07T06:49:09.3993056Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T06:49:09.3993174Z 2025-09-07T06:49:09.3993221Z >>> # Another example with uneven split 2025-09-07T06:49:09.3993336Z >>> input 2025-09-07T06:49:09.3993445Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T06:49:09.3993594Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T06:49:09.3993741Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T06:49:09.3993886Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T06:49:09.3994013Z >>> input_splits 2025-09-07T06:49:09.3994115Z [2, 2, 1, 1] # Rank 0 2025-09-07T06:49:09.3994241Z [3, 2, 2, 2] # Rank 1 2025-09-07T06:49:09.3995559Z [2, 1, 1, 1] # Rank 2 2025-09-07T06:49:09.3995684Z [2, 2, 2, 1] # Rank 3 2025-09-07T06:49:09.3995800Z >>> output_splits 2025-09-07T06:49:09.3995902Z [2, 3, 2, 2] # Rank 0 2025-09-07T06:49:09.3996032Z [2, 2, 1, 2] # Rank 1 2025-09-07T06:49:09.3996156Z [1, 2, 1, 2] # Rank 2 2025-09-07T06:49:09.3996280Z [1, 2, 1, 1] # Rank 3 2025-09-07T06:49:09.3996394Z >>> output = ... 2025-09-07T06:49:09.3996520Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T06:49:09.3996660Z >>> output 2025-09-07T06:49:09.3996764Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T06:49:09.3997943Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T06:49:09.3998089Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T06:49:09.3998233Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T06:49:09.3998327Z 2025-09-07T06:49:09.3998329Z 2025-09-07T06:49:09.3998436Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T06:49:09.3998572Z >>> input = torch.tensor( 2025-09-07T06:49:09.3998693Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T06:49:09.3998818Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T06:49:09.3998915Z >>> input 2025-09-07T06:49:09.3999027Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T06:49:09.3999184Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T06:49:09.4000368Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T06:49:09.4000535Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T06:49:09.4000686Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T06:49:09.4000818Z >>> dist.all_to_all_single(output, input) 2025-09-07T06:49:09.4000927Z >>> output 2025-09-07T06:49:09.4001079Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T06:49:09.4001239Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T06:49:09.4001397Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T06:49:09.4001553Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T06:49:09.4001651Z 2025-09-07T06:49:09.4001738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4001858Z 2025-09-07T06:49:09.4002064Z 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=4697. 2025-09-07T06:49:09.4003417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4003547Z 2025-09-07T06:49:09.4003672Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T06:49:09.4003835Z 2025-09-07T06:49:09.4003883Z Complex tensors are supported. 2025-09-07T06:49:09.4003953Z 2025-09-07T06:49:09.4003985Z Args: 2025-09-07T06:49:09.4004108Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T06:49:09.4004254Z per rank. 2025-09-07T06:49:09.4004444Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T06:49:09.4004640Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.4004803Z the default process group will be used. 2025-09-07T06:49:09.4004955Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T06:49:09.4005060Z 2025-09-07T06:49:09.4005093Z Returns: 2025-09-07T06:49:09.4006223Z Async work handle, if async_op is set to True. 2025-09-07T06:49:09.4006366Z None, if not async_op or if not part of the group. 2025-09-07T06:49:09.4006454Z 2025-09-07T06:49:09.4006490Z .. warning:: 2025-09-07T06:49:09.4006597Z `all_to_all` is experimental and subject to change. 2025-09-07T06:49:09.4006685Z 2025-09-07T06:49:09.4006721Z Examples: 2025-09-07T06:49:09.4006812Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T06:49:09.4006932Z >>> input = torch.arange(4) + rank * 4 2025-09-07T06:49:09.4007048Z >>> input = list(input.chunk(4)) 2025-09-07T06:49:09.4007153Z >>> input 2025-09-07T06:49:09.4007265Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T06:49:09.4007420Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T06:49:09.4008627Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T06:49:09.4008782Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T06:49:09.4008945Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T06:49:09.4009089Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:09.4009195Z >>> output 2025-09-07T06:49:09.4009305Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T06:49:09.4009513Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T06:49:09.4009664Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T06:49:09.4009813Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T06:49:09.4009906Z 2025-09-07T06:49:09.4011744Z >>> # Essentially, it is similar to following operation: 2025-09-07T06:49:09.4011896Z >>> scatter_list = input 2025-09-07T06:49:09.4011999Z >>> gather_list = output 2025-09-07T06:49:09.4012104Z >>> for i in range(world_size): 2025-09-07T06:49:09.4012257Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T06:49:09.4012375Z 2025-09-07T06:49:09.4012407Z >>> input 2025-09-07T06:49:09.4012511Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T06:49:09.4012665Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T06:49:09.4012872Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T06:49:09.4013021Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T06:49:09.4013148Z >>> input_splits 2025-09-07T06:49:09.4013251Z [2, 2, 1, 1] # Rank 0 2025-09-07T06:49:09.4013376Z [3, 2, 2, 2] # Rank 1 2025-09-07T06:49:09.4013502Z [2, 1, 1, 1] # Rank 2 2025-09-07T06:49:09.4013626Z [2, 2, 2, 1] # Rank 3 2025-09-07T06:49:09.4013747Z >>> output_splits 2025-09-07T06:49:09.4013849Z [2, 3, 2, 2] # Rank 0 2025-09-07T06:49:09.4013975Z [2, 2, 1, 2] # Rank 1 2025-09-07T06:49:09.4014107Z [1, 2, 1, 2] # Rank 2 2025-09-07T06:49:09.4014238Z [1, 2, 1, 1] # Rank 3 2025-09-07T06:49:09.4014447Z >>> input = list(input.split(input_splits)) 2025-09-07T06:49:09.4014568Z >>> input 2025-09-07T06:49:09.4015992Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T06:49:09.4016171Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T06:49:09.4016346Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T06:49:09.4016520Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T06:49:09.4016658Z >>> output = ... 2025-09-07T06:49:09.4016758Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:09.4016866Z >>> output 2025-09-07T06:49:09.4016984Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T06:49:09.4017161Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T06:49:09.4017338Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T06:49:09.4019051Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T06:49:09.4019168Z 2025-09-07T06:49:09.4019232Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T06:49:09.4019369Z >>> input = torch.tensor( 2025-09-07T06:49:09.4019491Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T06:49:09.4019615Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T06:49:09.4019725Z >>> input = list(input.chunk(4)) 2025-09-07T06:49:09.4019831Z >>> input 2025-09-07T06:49:09.4019956Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T06:49:09.4020137Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T06:49:09.4020363Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T06:49:09.4020547Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T06:49:09.4021764Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T06:49:09.4021914Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:09.4022024Z >>> output 2025-09-07T06:49:09.4022146Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T06:49:09.4022326Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T06:49:09.4022503Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T06:49:09.4022680Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T06:49:09.4022788Z 2025-09-07T06:49:09.4022829Z 2025-09-07T06:49:09.4022927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4023049Z 2025-09-07T06:49:09.4023232Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2025-09-07T06:49:09.4023516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4023645Z 2025-09-07T06:49:09.4023691Z Module ``torch.distributed.launch``. 2025-09-07T06:49:09.4024890Z 2025-09-07T06:49:09.4024983Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T06:49:09.4025163Z training processes on each of the training nodes. 2025-09-07T06:49:09.4025255Z 2025-09-07T06:49:09.4025291Z .. warning:: 2025-09-07T06:49:09.4025341Z 2025-09-07T06:49:09.4025434Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T06:49:09.4025561Z 2025-09-07T06:49:09.4025646Z The utility can be used for single-node distributed training, in which one or 2025-09-07T06:49:09.4025855Z more processes per node will be spawned. The utility can be used for either 2025-09-07T06:49:09.4026050Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T06:49:09.4026245Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T06:49:09.4026446Z well-improved single-node training performance. It can also be used in 2025-09-07T06:49:09.4026653Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T06:49:09.4026860Z for well-improved multi-node distributed training performance as well. 2025-09-07T06:49:09.4028082Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T06:49:09.4028286Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T06:49:09.4028452Z aggregated communication bandwidth. 2025-09-07T06:49:09.4028528Z 2025-09-07T06:49:09.4028611Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T06:49:09.4028817Z training, this utility will launch the given number of processes per node 2025-09-07T06:49:09.4029016Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T06:49:09.4029210Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T06:49:09.4029395Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T06:49:09.4029549Z GPU (nproc_per_node - 1)*. 2025-09-07T06:49:09.4029616Z 2025-09-07T06:49:09.4029655Z **How to use this module:** 2025-09-07T06:49:09.4029721Z 2025-09-07T06:49:09.4029778Z 1. Single-Node multi-process distributed training 2025-09-07T06:49:09.4030915Z 2025-09-07T06:49:09.4030949Z :: 2025-09-07T06:49:09.4030997Z 2025-09-07T06:49:09.4031086Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:09.4031272Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T06:49:09.4031426Z arguments of your training script) 2025-09-07T06:49:09.4031554Z 2025-09-07T06:49:09.4031633Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T06:49:09.4031746Z 2025-09-07T06:49:09.4031748Z 2025-09-07T06:49:09.4031805Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T06:49:09.4031894Z 2025-09-07T06:49:09.4031926Z :: 2025-09-07T06:49:09.4031969Z 2025-09-07T06:49:09.4032053Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:09.4032231Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T06:49:09.4032400Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T06:49:09.4033579Z and all other arguments of your training script) 2025-09-07T06:49:09.4033677Z 2025-09-07T06:49:09.4033712Z Node 2: 2025-09-07T06:49:09.4033756Z 2025-09-07T06:49:09.4033789Z :: 2025-09-07T06:49:09.4033832Z 2025-09-07T06:49:09.4033961Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:09.4034138Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T06:49:09.4034302Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T06:49:09.4034542Z and all other arguments of your training script) 2025-09-07T06:49:09.4034636Z 2025-09-07T06:49:09.4034697Z 3. To look up what optional arguments this module offers: 2025-09-07T06:49:09.4034790Z 2025-09-07T06:49:09.4034821Z :: 2025-09-07T06:49:09.4034863Z 2025-09-07T06:49:09.4034916Z python -m torch.distributed.launch --help 2025-09-07T06:49:09.4035001Z 2025-09-07T06:49:09.4035003Z 2025-09-07T06:49:09.4035040Z **Important Notices:** 2025-09-07T06:49:09.4035103Z 2025-09-07T06:49:09.4036188Z 1. This utility and multi-process distributed (single-node or 2025-09-07T06:49:09.4036382Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T06:49:09.4036594Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T06:49:09.4036757Z use for GPU training. 2025-09-07T06:49:09.4036819Z 2025-09-07T06:49:09.4036897Z 2. In your training program, you must parse the command-line argument: 2025-09-07T06:49:09.4037087Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T06:49:09.4037282Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T06:49:09.4037467Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T06:49:09.4037574Z 2025-09-07T06:49:09.4037617Z Parsing the local_rank argument 2025-09-07T06:49:09.4037687Z 2025-09-07T06:49:09.4037719Z :: 2025-09-07T06:49:09.4037760Z 2025-09-07T06:49:09.4037800Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4038897Z >>> import argparse 2025-09-07T06:49:09.4039013Z >>> parser = argparse.ArgumentParser() 2025-09-07T06:49:09.4039166Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T06:49:09.4039314Z >>> args = parser.parse_args() 2025-09-07T06:49:09.4039391Z 2025-09-07T06:49:09.4039438Z Set your device to local rank using either 2025-09-07T06:49:09.4039522Z 2025-09-07T06:49:09.4039552Z :: 2025-09-07T06:49:09.4039594Z 2025-09-07T06:49:09.4039668Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T06:49:09.4039775Z 2025-09-07T06:49:09.4039805Z or 2025-09-07T06:49:09.4039846Z 2025-09-07T06:49:09.4039876Z :: 2025-09-07T06:49:09.4039919Z 2025-09-07T06:49:09.4039970Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T06:49:09.4040093Z >>> # your code to run 2025-09-07T06:49:09.4041186Z >>> ... 2025-09-07T06:49:09.4041238Z 2025-09-07T06:49:09.4041280Z .. versionchanged:: 2.0.0 2025-09-07T06:49:09.4041345Z 2025-09-07T06:49:09.4041433Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T06:49:09.4041640Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T06:49:09.4041812Z previously used underscored ``--local_rank``. 2025-09-07T06:49:09.4041940Z 2025-09-07T06:49:09.4042025Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T06:49:09.4042234Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T06:49:09.4042437Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T06:49:09.4042634Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T06:49:09.4042835Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T06:49:09.4043009Z including ``"--local-rank"`` should be sufficient. 2025-09-07T06:49:09.4044107Z 2025-09-07T06:49:09.4044193Z 3. In your training program, you are supposed to call the following function 2025-09-07T06:49:09.4044463Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T06:49:09.4044714Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T06:49:09.4044900Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T06:49:09.4045004Z 2025-09-07T06:49:09.4045036Z :: 2025-09-07T06:49:09.4045078Z 2025-09-07T06:49:09.4045155Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T06:49:09.4045325Z >>> init_method='env://') 2025-09-07T06:49:09.4045414Z 2025-09-07T06:49:09.4045498Z 4. In your training program, you can either use regular distributed functions 2025-09-07T06:49:09.4045702Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T06:49:09.4045897Z training program uses GPUs for training and you would like to use 2025-09-07T06:49:09.4046076Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T06:49:09.4047248Z here is how to configure it. 2025-09-07T06:49:09.4047318Z 2025-09-07T06:49:09.4047350Z :: 2025-09-07T06:49:09.4047396Z 2025-09-07T06:49:09.4047471Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T06:49:09.4047628Z >>> device_ids=[args.local_rank], 2025-09-07T06:49:09.4047770Z >>> output_device=args.local_rank) 2025-09-07T06:49:09.4047858Z 2025-09-07T06:49:09.4047943Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T06:49:09.4048143Z that your code will be operating on. This is generally the local rank of the 2025-09-07T06:49:09.4048340Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T06:49:09.4048536Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T06:49:09.4048678Z utility 2025-09-07T06:49:09.4048723Z 2025-09-07T06:49:09.4049807Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T06:49:09.4050009Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T06:49:09.4050204Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T06:49:09.4050388Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T06:49:09.4050558Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T06:49:09.4050657Z 2025-09-07T06:49:09.4050691Z .. warning:: 2025-09-07T06:49:09.4050744Z 2025-09-07T06:49:09.4050817Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T06:49:09.4050992Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T06:49:09.4051137Z write to a networked filesystem. See 2025-09-07T06:49:09.4051296Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T06:49:09.4051469Z how things can go wrong if you don't do this correctly. 2025-09-07T06:49:09.4051564Z 2025-09-07T06:49:09.4051566Z 2025-09-07T06:49:09.4051567Z 2025-09-07T06:49:09.4051572Z 2025-09-07T06:49:09.4052701Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4052829Z 2025-09-07T06:49:09.4452293Z 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. 2025-09-07T06:49:09.4453270Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4453643Z 2025-09-07T06:49:09.4453876Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T06:49:09.4454533Z reduces losses of multiple microbatches into one value. 2025-09-07T06:49:09.4454816Z 2025-09-07T06:49:09.4454914Z Example: 2025-09-07T06:49:09.4455148Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4455441Z >>> sum_reducer = _CustomReducer( 2025-09-07T06:49:09.4455755Z >>> torch.tensor(0.0), 2025-09-07T06:49:09.4456037Z >>> lambda a, b: a + b 2025-09-07T06:49:09.4456290Z >>> ) 2025-09-07T06:49:09.4460277Z 2025-09-07T06:49:09.4460383Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4460515Z 2025-09-07T06:49:09.4473209Z 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. 2025-09-07T06:49:09.4473582Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4473722Z 2025-09-07T06:49:09.4473803Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T06:49:09.4473983Z function can be called multiple times to incrementally add 2025-09-07T06:49:09.4474130Z device placement configurations. 2025-09-07T06:49:09.4474204Z 2025-09-07T06:49:09.4474237Z Args: 2025-09-07T06:49:09.4474320Z to (str): Callee name. 2025-09-07T06:49:09.4474550Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T06:49:09.4474724Z mappings from this worker to the callee. This map must be 2025-09-07T06:49:09.4476652Z invertible. 2025-09-07T06:49:09.4476719Z 2025-09-07T06:49:09.4476760Z Example: 2025-09-07T06:49:09.4476855Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.4476973Z >>> # both workers 2025-09-07T06:49:09.4477066Z >>> def add(x, y): 2025-09-07T06:49:09.4477177Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T06:49:09.4477308Z >>> return x + y, (x + y).to(2) 2025-09-07T06:49:09.4477416Z >>> 2025-09-07T06:49:09.4477492Z >>> # on worker 0 2025-09-07T06:49:09.4477602Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T06:49:09.4477729Z >>> num_worker_threads=8, 2025-09-07T06:49:09.4479060Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T06:49:09.4479189Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T06:49:09.4479304Z >>> ) 2025-09-07T06:49:09.4479401Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T06:49:09.4479530Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T06:49:09.4479648Z >>> 2025-09-07T06:49:09.4479726Z >>> rpc.init_rpc( 2025-09-07T06:49:09.4479823Z >>> "worker0", 2025-09-07T06:49:09.4479912Z >>> rank=0, 2025-09-07T06:49:09.4481395Z >>> world_size=2, 2025-09-07T06:49:09.4481518Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T06:49:09.4481652Z >>> rpc_backend_options=options 2025-09-07T06:49:09.4481789Z >>> ) 2025-09-07T06:49:09.4481864Z >>> 2025-09-07T06:49:09.4481943Z >>> x = torch.ones(2) 2025-09-07T06:49:09.4482068Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T06:49:09.4482233Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T06:49:09.4482402Z >>> # sending the return value back, it will follow the invert of 2025-09-07T06:49:09.4482568Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T06:49:09.4482702Z >>> # cuda:1 on worker0 2025-09-07T06:49:09.4482823Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T06:49:09.4483045Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T06:49:09.4483138Z 2025-09-07T06:49:09.4483228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4483353Z 2025-09-07T06:49:09.4483547Z 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. 2025-09-07T06:49:09.4483862Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4483992Z 2025-09-07T06:49:09.4484076Z A decorator for a function indicating that the return value of the function 2025-09-07T06:49:09.4484273Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T06:49:09.4484571Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T06:49:09.4484775Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T06:49:09.4485021Z function and installs subsequent processing steps as a callback to that 2025-09-07T06:49:09.4485222Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T06:49:09.4485415Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T06:49:09.4485595Z value back as the RPC response. That also means the returned 2025-09-07T06:49:09.4485780Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T06:49:09.4485978Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T06:49:09.4486175Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T06:49:09.4486374Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T06:49:09.4486503Z 2025-09-07T06:49:09.4486590Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T06:49:09.4486803Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T06:49:09.4487024Z attributes installed by this decorator, it knows that this function 2025-09-07T06:49:09.4487225Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T06:49:09.4487424Z However, this does not mean this decorator has to be outmost one when 2025-09-07T06:49:09.4489143Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T06:49:09.4489342Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T06:49:09.4489527Z inner decorator to allow the target function be recognized as a static 2025-09-07T06:49:09.4489719Z or class function. This target function can still execute asynchronously 2025-09-07T06:49:09.4489913Z because, when accessed, the static or class method preserves attributes 2025-09-07T06:49:09.4490089Z installed by ``@rpc.functions.async_execution``. 2025-09-07T06:49:09.4490181Z 2025-09-07T06:49:09.4490183Z 2025-09-07T06:49:09.4490219Z Example:: 2025-09-07T06:49:09.4490342Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T06:49:09.4490499Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T06:49:09.4490664Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T06:49:09.4490843Z constructor. The example below shows directly using the 2025-09-07T06:49:09.4492070Z :class:`~torch.futures.Future` returned by 2025-09-07T06:49:09.4492203Z :meth:`~torch.futures.Future.then`. 2025-09-07T06:49:09.4492284Z 2025-09-07T06:49:09.4492330Z >>> from torch.distributed import rpc 2025-09-07T06:49:09.4492440Z >>> 2025-09-07T06:49:09.4492526Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:09.4492632Z >>> 2025-09-07T06:49:09.4492671Z >>> # On all workers 2025-09-07T06:49:09.4492717Z >>> @rpc.functions.async_execution 2025-09-07T06:49:09.4492762Z >>> def async_add_chained(to, x, y, z): 2025-09-07T06:49:09.4492833Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T06:49:09.4492955Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T06:49:09.4493024Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T06:49:09.4493085Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T06:49:09.4493153Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T06:49:09.4493219Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T06:49:09.4493283Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T06:49:09.4493346Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:09.4494496Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:09.4494531Z >>> ) 2025-09-07T06:49:09.4494563Z >>> 2025-09-07T06:49:09.4494597Z >>> # On worker0 2025-09-07T06:49:09.4494637Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4494727Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:09.4494763Z >>> "worker1", 2025-09-07T06:49:09.4494808Z >>> async_add_chained, 2025-09-07T06:49:09.4494856Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T06:49:09.4494887Z >>> ) 2025-09-07T06:49:09.4494936Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T06:49:09.4494938Z 2025-09-07T06:49:09.4495016Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T06:49:09.4495051Z outmost one. 2025-09-07T06:49:09.4495053Z 2025-09-07T06:49:09.4495095Z >>> from torch import Tensor 2025-09-07T06:49:09.4495140Z >>> from torch.futures import Future 2025-09-07T06:49:09.4495185Z >>> from torch.distributed import rpc 2025-09-07T06:49:09.4495216Z >>> 2025-09-07T06:49:09.4495259Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:09.4495290Z >>> 2025-09-07T06:49:09.4495327Z >>> # On all workers 2025-09-07T06:49:09.4495365Z >>> @torch.jit.script 2025-09-07T06:49:09.4496484Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T06:49:09.4496526Z >>> return x + y 2025-09-07T06:49:09.4496559Z >>> 2025-09-07T06:49:09.4496604Z >>> @rpc.functions.async_execution 2025-09-07T06:49:09.4496642Z >>> @torch.jit.script 2025-09-07T06:49:09.4496712Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T06:49:09.4496769Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T06:49:09.4496800Z >>> 2025-09-07T06:49:09.4496834Z >>> # On worker0 2025-09-07T06:49:09.4496872Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:09.4496907Z >>> "worker1", 2025-09-07T06:49:09.4496941Z >>> async_add, 2025-09-07T06:49:09.4496987Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T06:49:09.4497019Z >>> ) 2025-09-07T06:49:09.4497065Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T06:49:09.4497067Z 2025-09-07T06:49:09.4497144Z When combined with static or class method, this decorator must be the 2025-09-07T06:49:09.4497180Z inner one. 2025-09-07T06:49:09.4497182Z 2025-09-07T06:49:09.4497227Z >>> from torch.distributed import rpc 2025-09-07T06:49:09.4497258Z >>> 2025-09-07T06:49:09.4497300Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:09.4497331Z >>> 2025-09-07T06:49:09.4497367Z >>> # On all workers 2025-09-07T06:49:09.4498422Z >>> class AsyncExecutionClass: 2025-09-07T06:49:09.4498458Z >>> 2025-09-07T06:49:09.4498495Z >>> @staticmethod 2025-09-07T06:49:09.4498540Z >>> @rpc.functions.async_execution 2025-09-07T06:49:09.4498585Z >>> def static_async_add(to, x, y, z): 2025-09-07T06:49:09.4498651Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:09.4498696Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:09.4498729Z >>> ) 2025-09-07T06:49:09.4498759Z >>> 2025-09-07T06:49:09.4498795Z >>> @classmethod 2025-09-07T06:49:09.4498839Z >>> @rpc.functions.async_execution 2025-09-07T06:49:09.4498891Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T06:49:09.4499000Z >>> ret_fut = torch.futures.Future() 2025-09-07T06:49:09.4499058Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:09.4499117Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T06:49:09.4499149Z >>> ) 2025-09-07T06:49:09.4499188Z >>> return ret_fut 2025-09-07T06:49:09.4499219Z >>> 2025-09-07T06:49:09.4499264Z >>> @rpc.functions.async_execution 2025-09-07T06:49:09.4499312Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T06:49:09.4499374Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:09.4499417Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:09.4500466Z >>> ) 2025-09-07T06:49:09.4500499Z >>> 2025-09-07T06:49:09.4500533Z >>> # On worker0 2025-09-07T06:49:09.4500571Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:09.4500648Z >>> "worker1", 2025-09-07T06:49:09.4500704Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T06:49:09.4500750Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:09.4500782Z >>> ) 2025-09-07T06:49:09.4500828Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:09.4500859Z >>> 2025-09-07T06:49:09.4500897Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:09.4500932Z >>> "worker1", 2025-09-07T06:49:09.4500981Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T06:49:09.4501025Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:09.4501057Z >>> ) 2025-09-07T06:49:09.4501102Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:09.4501104Z 2025-09-07T06:49:09.4501165Z This decorator also works with RRef helpers, i.e., . 2025-09-07T06:49:09.4501218Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T06:49:09.4501278Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T06:49:09.4501333Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T06:49:09.4501338Z 2025-09-07T06:49:09.4501383Z >>> from torch.distributed import rpc 2025-09-07T06:49:09.4502420Z >>> 2025-09-07T06:49:09.4502476Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T06:49:09.4502537Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:09.4502613Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:09.4502658Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:09.4502689Z >>> 2025-09-07T06:49:09.4502747Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:09.4502831Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T06:49:09.4502876Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:09.4502907Z >>> 2025-09-07T06:49:09.4502963Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:09.4503047Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T06:49:09.4503094Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:09.4503096Z 2025-09-07T06:49:09.4503185Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4503187Z 2025-09-07T06:49:09.4554350Z 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. 2025-09-07T06:49:09.4555732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4555771Z 2025-09-07T06:49:09.4555930Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T06:49:09.4556127Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T06:49:09.4556134Z 2025-09-07T06:49:09.4556327Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T06:49:09.4563590Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T06:49:09.4563753Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T06:49:09.4563838Z only report runtime of PyTorch functions. 2025-09-07T06:49:09.4563984Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T06:49:09.4563990Z 2025-09-07T06:49:09.4564041Z Args: 2025-09-07T06:49:09.4564196Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T06:49:09.4564258Z Default: ``True``. 2025-09-07T06:49:09.4564260Z 2025-09-07T06:49:09.4564520Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T06:49:09.4564651Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T06:49:09.4564717Z Default: ``False`` 2025-09-07T06:49:09.4564719Z 2025-09-07T06:49:09.4564855Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T06:49:09.4565147Z about input dimensions will be collected. This allows one to see which 2025-09-07T06:49:09.4565272Z dimensions have been used under the hood and further group by them 2025-09-07T06:49:09.4565386Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T06:49:09.4565513Z shape recording might skew your profiling data. It is recommended to 2025-09-07T06:49:09.4565640Z use separate runs with and without shape recording to validate the timing. 2025-09-07T06:49:09.4565768Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T06:49:09.4565900Z of nested function calls). But for higher level functions the total 2025-09-07T06:49:09.4566007Z self cpu time might be artificially increased because of the shape 2025-09-07T06:49:09.4566064Z collection. 2025-09-07T06:49:09.4566069Z 2025-09-07T06:49:09.4566215Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T06:49:09.4566226Z 2025-09-07T06:49:09.4566322Z .. warning:: 2025-09-07T06:49:09.4566450Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T06:49:09.4566453Z 2025-09-07T06:49:09.4566514Z .. warning:: 2025-09-07T06:49:09.4566670Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T06:49:09.4566796Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T06:49:09.4566923Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T06:49:09.4567038Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T06:49:09.4567042Z 2025-09-07T06:49:09.4567104Z Example: 2025-09-07T06:49:09.4567180Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4567247Z >>> # On worker 0: 2025-09-07T06:49:09.4569410Z >>> import torch 2025-09-07T06:49:09.4569489Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.4569588Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:09.4569664Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T06:49:09.4569733Z >>> outer_profile_rref = rpc.remote( 2025-09-07T06:49:09.4569832Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T06:49:09.4569881Z ... ) 2025-09-07T06:49:09.4569947Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T06:49:09.4570041Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T06:49:09.4570101Z >>> inner_profile_rref = rpc.remote( 2025-09-07T06:49:09.4570193Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T06:49:09.4570241Z ... ) 2025-09-07T06:49:09.4570306Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T06:49:09.4570394Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T06:49:09.4570490Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T06:49:09.4570585Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T06:49:09.4570750Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T06:49:09.4570890Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4571062Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T06:49:09.4571181Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4571290Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T06:49:09.4571394Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T06:49:09.4573031Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4573131Z Self CPU time total: 89.667us 2025-09-07T06:49:09.4573213Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T06:49:09.4573307Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4573426Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T06:49:09.4573520Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4573591Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T06:49:09.4573672Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T06:49:09.4573743Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T06:49:09.4573837Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:09.4573896Z Self CPU time total: 213.926us 2025-09-07T06:49:09.4573941Z >>> rpc.shutdown() 2025-09-07T06:49:09.4573943Z 2025-09-07T06:49:09.4573986Z >>> # On worker 1: 2025-09-07T06:49:09.4574043Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.4574106Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:09.4574182Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T06:49:09.4574220Z >>> rpc.shutdown() 2025-09-07T06:49:09.4574223Z 2025-09-07T06:49:09.4574329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4574333Z 2025-09-07T06:49:09.4617330Z 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. 2025-09-07T06:49:09.4617448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4617656Z 2025-09-07T06:49:09.4621439Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T06:49:09.4621456Z 2025-09-07T06:49:09.4621700Z The sharing is done as described by `ZeRO `_. 2025-09-07T06:49:09.4621705Z 2025-09-07T06:49:09.4621820Z The local optimizer instance in each rank is only 2025-09-07T06:49:09.4621984Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T06:49:09.4622132Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T06:49:09.4622290Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T06:49:09.4622424Z all other peers to keep all model replicas in the same state. 2025-09-07T06:49:09.4622555Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T06:49:09.4622733Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T06:49:09.4622820Z memory consumption. 2025-09-07T06:49:09.4623117Z 2025-09-07T06:49:09.4623287Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T06:49:09.4623431Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T06:49:09.4623586Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T06:49:09.4623674Z the parameter registration or usage order. 2025-09-07T06:49:09.4623679Z 2025-09-07T06:49:09.4623751Z Arguments: 2025-09-07T06:49:09.4623876Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T06:49:09.4624004Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T06:49:09.4624069Z across ranks. 2025-09-07T06:49:09.4624073Z 2025-09-07T06:49:09.4624141Z Keyword Args: 2025-09-07T06:49:09.4624278Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T06:49:09.4628497Z optimizer. 2025-09-07T06:49:09.4628644Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T06:49:09.4628765Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T06:49:09.4628860Z :meth:`torch.distributed.init_process_group`). 2025-09-07T06:49:09.4628994Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T06:49:09.4629118Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T06:49:09.4629236Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T06:49:09.4629353Z each individual parameter is communicated separately, and each 2025-09-07T06:49:09.4629464Z ``params.data`` stays intact (default: ``False``). 2025-09-07T06:49:09.4629582Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T06:49:09.4629701Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T06:49:09.4629829Z synchronization; this requires (1) either a functional optimizer 2025-09-07T06:49:09.4629939Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T06:49:09.4630047Z equivalent and (2) registering a DDP communication hook 2025-09-07T06:49:09.4630163Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T06:49:09.4630263Z parameters are packed into buckets matching those in 2025-09-07T06:49:09.4630360Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T06:49:09.4630448Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T06:49:09.4630560Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T06:49:09.4630621Z (per normal). 2025-09-07T06:49:09.4630693Z (default: ``False``) 2025-09-07T06:49:09.4632972Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T06:49:09.4633039Z optimizer. 2025-09-07T06:49:09.4633047Z 2025-09-07T06:49:09.4633121Z Example:: 2025-09-07T06:49:09.4633125Z 2025-09-07T06:49:09.4633194Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4633264Z >>> import torch.nn as nn 2025-09-07T06:49:09.4633386Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T06:49:09.4633486Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T06:49:09.4633593Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T06:49:09.4633655Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T06:49:09.4633714Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T06:49:09.4633768Z >>> ddp.parameters(), 2025-09-07T06:49:09.4633829Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T06:49:09.4633871Z >>> lr=0.01 2025-09-07T06:49:09.4633912Z >>> ) 2025-09-07T06:49:09.4633965Z >>> ddp(inputs).sum().backward() 2025-09-07T06:49:09.4634011Z >>> opt.step() 2025-09-07T06:49:09.4634013Z 2025-09-07T06:49:09.4634058Z .. warning:: 2025-09-07T06:49:09.4634245Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T06:49:09.4634318Z passed-in parameters are the same dense type. 2025-09-07T06:49:09.4634321Z 2025-09-07T06:49:09.4634463Z .. warning:: 2025-09-07T06:49:09.4634562Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T06:49:09.4634659Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T06:49:09.4636212Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T06:49:09.4636319Z two or three training iterations do not perform parameter updates in 2025-09-07T06:49:09.4636412Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T06:49:09.4636500Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T06:49:09.4636588Z information about the gradient bucketing strategy used by 2025-09-07T06:49:09.4636688Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T06:49:09.4636844Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T06:49:09.4636940Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T06:49:09.4636996Z is to prepend dummy inputs. 2025-09-07T06:49:09.4636999Z 2025-09-07T06:49:09.4637112Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T06:49:09.4637115Z 2025-09-07T06:49:09.4637228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4637231Z 2025-09-07T06:49:09.4645268Z 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. 2025-09-07T06:49:09.4646100Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4646121Z 2025-09-07T06:49:09.4646410Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T06:49:09.4646557Z This optimizer runs local optimizer at every step. 2025-09-07T06:49:09.4646764Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T06:49:09.4646769Z 2025-09-07T06:49:09.4646824Z Args: 2025-09-07T06:49:09.4646906Z optim: The local optimizer. 2025-09-07T06:49:09.4647044Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T06:49:09.4647047Z 2025-09-07T06:49:09.4651543Z Example:: 2025-09-07T06:49:09.4651547Z 2025-09-07T06:49:09.4651650Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.4651709Z >>> import torch 2025-09-07T06:49:09.4651800Z >>> import torch.distributed as dist 2025-09-07T06:49:09.4651960Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T06:49:09.4652032Z >>> import torch.nn as nn 2025-09-07T06:49:09.4652155Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T06:49:09.4652317Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T06:49:09.4652413Z >>> PostLocalSGDState, 2025-09-07T06:49:09.4652506Z >>> post_localSGD_hook, 2025-09-07T06:49:09.4652572Z >>> ) 2025-09-07T06:49:09.4652625Z >>> 2025-09-07T06:49:09.4652720Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:09.4652802Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:09.4652847Z >>> ) 2025-09-07T06:49:09.4652891Z >>> 2025-09-07T06:49:09.4652975Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:09.4653126Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T06:49:09.4653216Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:09.4653256Z >>> 2025-09-07T06:49:09.4653365Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T06:49:09.4653804Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T06:49:09.4655889Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:09.4656009Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T06:49:09.4656080Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T06:49:09.4656142Z >>> optim=local_optim, 2025-09-07T06:49:09.4656271Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T06:49:09.4656317Z >>> ) 2025-09-07T06:49:09.4656361Z >>> 2025-09-07T06:49:09.4656474Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T06:49:09.4656624Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T06:49:09.4656811Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T06:49:09.4656995Z >>> for step in range(0, 200): 2025-09-07T06:49:09.4657056Z >>> opt.zero_grad() 2025-09-07T06:49:09.4657125Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:09.4657183Z >>> loss.backward() 2025-09-07T06:49:09.4657233Z >>> opt.step() 2025-09-07T06:49:09.4657237Z 2025-09-07T06:49:09.4657369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4657372Z 2025-09-07T06:49:09.4680327Z 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. 2025-09-07T06:49:09.4680455Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4680501Z 2025-09-07T06:49:09.4680689Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T06:49:09.4680822Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T06:49:09.4680826Z 2025-09-07T06:49:09.4683359Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T06:49:09.4683539Z to retrieve the gradients for specific parameters. 2025-09-07T06:49:09.4683542Z 2025-09-07T06:49:09.4683589Z Concurrent calls to 2025-09-07T06:49:09.4683691Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T06:49:09.4683755Z either from the same or different clients, will 2025-09-07T06:49:09.4683857Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T06:49:09.4683938Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T06:49:09.4684044Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T06:49:09.4684129Z at a time. This means that the gradients being applied may not correspond 2025-09-07T06:49:09.4684222Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T06:49:09.4684274Z guaranteed ordering across workers. 2025-09-07T06:49:09.4684277Z 2025-09-07T06:49:09.4684459Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T06:49:09.4684552Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T06:49:09.4684655Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T06:49:09.4684746Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T06:49:09.4684853Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T06:49:09.4684903Z for your own custom optimizers. 2025-09-07T06:49:09.4684908Z 2025-09-07T06:49:09.4684955Z Args: 2025-09-07T06:49:09.4685038Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T06:49:09.4685109Z instantiate on each worker. 2025-09-07T06:49:09.4685196Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T06:49:09.4686812Z to optimize. 2025-09-07T06:49:09.4686897Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T06:49:09.4687076Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T06:49:09.4687079Z 2025-09-07T06:49:09.4687116Z Example:: 2025-09-07T06:49:09.4687164Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.4687229Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T06:49:09.4687280Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.4687321Z >>> from torch import optim 2025-09-07T06:49:09.4687395Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T06:49:09.4687428Z >>> 2025-09-07T06:49:09.4687483Z >>> with dist_autograd.context() as context_id: 2025-09-07T06:49:09.4687523Z >>> # Forward pass. 2025-09-07T06:49:09.4687599Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T06:49:09.4687671Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T06:49:09.4687720Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T06:49:09.4687831Z >>> 2025-09-07T06:49:09.4687874Z >>> # Backward pass. 2025-09-07T06:49:09.4687934Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T06:49:09.4687966Z >>> 2025-09-07T06:49:09.4688004Z >>> # Optimizer. 2025-09-07T06:49:09.4688055Z >>> dist_optim = DistributedOptimizer( 2025-09-07T06:49:09.4689218Z >>> optim.SGD, 2025-09-07T06:49:09.4689264Z >>> [rref1, rref2], 2025-09-07T06:49:09.4689300Z >>> lr=0.05, 2025-09-07T06:49:09.4689334Z >>> ) 2025-09-07T06:49:09.4689381Z >>> dist_optim.step(context_id) 2025-09-07T06:49:09.4689383Z 2025-09-07T06:49:09.4689447Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T06:49:09.4689449Z 2025-09-07T06:49:09.4689544Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4689547Z 2025-09-07T06:49:09.4699023Z 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. 2025-09-07T06:49:09.4699565Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4699606Z 2025-09-07T06:49:09.4699783Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T06:49:09.4699916Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T06:49:09.4699921Z 2025-09-07T06:49:09.4700030Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T06:49:09.4700038Z 2025-09-07T06:49:09.4700106Z .. warning:: 2025-09-07T06:49:09.4700219Z Current implementation only supports loading Tensors. 2025-09-07T06:49:09.4700225Z 2025-09-07T06:49:09.4700306Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4700381Z >>> sd = {"mode": model} 2025-09-07T06:49:09.4700437Z >>> dcp.load( 2025-09-07T06:49:09.4700497Z >>> sd, 2025-09-07T06:49:09.4700587Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T06:49:09.4700691Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T06:49:09.4700771Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T06:49:09.4704171Z >>> ) 2025-09-07T06:49:09.4704176Z 2025-09-07T06:49:09.4704323Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4704327Z 2025-09-07T06:49:09.4704773Z 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. 2025-09-07T06:49:09.4704907Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4704913Z 2025-09-07T06:49:09.4705090Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T06:49:09.4705247Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T06:49:09.4705331Z metadata file, like Torch Save files. 2025-09-07T06:49:09.4705336Z 2025-09-07T06:49:09.4705639Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T06:49:09.4705642Z 2025-09-07T06:49:09.4705697Z .. warning:: 2025-09-07T06:49:09.4705791Z Current implementation only supports loading Tensors. 2025-09-07T06:49:09.4705793Z 2025-09-07T06:49:09.4705853Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4705907Z >>> sd = {"mode": model} 2025-09-07T06:49:09.4705956Z >>> dcp.load( 2025-09-07T06:49:09.4706001Z >>> sd, 2025-09-07T06:49:09.4706084Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T06:49:09.4706151Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T06:49:09.4706210Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T06:49:09.4706255Z >>> ) 2025-09-07T06:49:09.4706258Z 2025-09-07T06:49:09.4706381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4706386Z 2025-09-07T06:49:09.4719971Z 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=97. 2025-09-07T06:49:09.4722563Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4722568Z 2025-09-07T06:49:09.4722629Z Save a distributed model in SPMD style. 2025-09-07T06:49:09.4722633Z 2025-09-07T06:49:09.4722734Z This function is different from ``torch.save()`` as it handles 2025-09-07T06:49:09.4722853Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T06:49:09.4722862Z 2025-09-07T06:49:09.4723039Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T06:49:09.4723104Z save will call ``state_dict`` before serialization. 2025-09-07T06:49:09.4723108Z 2025-09-07T06:49:09.4723149Z .. warning:: 2025-09-07T06:49:09.4723247Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T06:49:09.4723298Z for saved state_dicts. 2025-09-07T06:49:09.4723306Z 2025-09-07T06:49:09.4723341Z .. warning:: 2025-09-07T06:49:09.4723436Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T06:49:09.4723525Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T06:49:09.4723527Z 2025-09-07T06:49:09.4723568Z .. note:: 2025-09-07T06:49:09.4723678Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T06:49:09.4723787Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T06:49:09.4723839Z group needs to be passed in. 2025-09-07T06:49:09.4723842Z 2025-09-07T06:49:09.4723883Z .. note:: 2025-09-07T06:49:09.4723995Z If no process group is available, this function assumes the intention is to save the 2025-09-07T06:49:09.4724061Z state_dict in the local process. 2025-09-07T06:49:09.4724065Z 2025-09-07T06:49:09.4724105Z .. note: 2025-09-07T06:49:09.4726278Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T06:49:09.4726285Z 2025-09-07T06:49:09.4726287Z 2025-09-07T06:49:09.4726334Z Args: 2025-09-07T06:49:09.4726416Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T06:49:09.4726481Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:09.4726576Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:09.4726666Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:09.4726749Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:09.4726795Z (Default: ``None``) 2025-09-07T06:49:09.4726854Z storage_writer (Optional[StorageWriter]): 2025-09-07T06:49:09.4726942Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T06:49:09.4727027Z specified, DCP will automatically infer the writer based on the 2025-09-07T06:49:09.4727112Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:09.4727165Z be raised. (Default: ``None``) 2025-09-07T06:49:09.4727299Z planner (Optional[SavePlanner]): 2025-09-07T06:49:09.4727383Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T06:49:09.4727442Z planner will be used. (Default: ``None``) 2025-09-07T06:49:09.4727501Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:09.4727580Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:09.4727626Z (Default: ``None``) 2025-09-07T06:49:09.4727668Z no_dist (bool): 2025-09-07T06:49:09.4727742Z If ``True``, this function will assume the intent is to load 2025-09-07T06:49:09.4727808Z a checkpoint on a single rank/process. 2025-09-07T06:49:09.4727867Z (Default: ``False``) 2025-09-07T06:49:09.4727975Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T06:49:09.4728063Z a checkpoint without using cross-rank synchronization. 2025-09-07T06:49:09.4728175Z (Default: ``True``) 2025-09-07T06:49:09.4728284Z This configuration is experimental and should be used with caution. 2025-09-07T06:49:09.4728401Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T06:49:09.4728403Z 2025-09-07T06:49:09.4728453Z Returns: 2025-09-07T06:49:09.4728533Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T06:49:09.4728535Z 2025-09-07T06:49:09.4728589Z Example: 2025-09-07T06:49:09.4728639Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4728700Z >>> my_model = MyModule() 2025-09-07T06:49:09.4728703Z 2025-09-07T06:49:09.4728760Z >>> state_dict = {"model": my_model} 2025-09-07T06:49:09.4728762Z 2025-09-07T06:49:09.4728877Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T06:49:09.4730420Z ... "/checkpoint/1" 2025-09-07T06:49:09.4730462Z ... ) 2025-09-07T06:49:09.4730523Z >>> torch.distributed.checkpoint.save( 2025-09-07T06:49:09.4730579Z >>> state_dict=state_dict, 2025-09-07T06:49:09.4730639Z >>> storage_writer=fs_storage_writer, 2025-09-07T06:49:09.4730678Z >>> ) 2025-09-07T06:49:09.4730680Z 2025-09-07T06:49:09.4730715Z .. note:: 2025-09-07T06:49:09.4730808Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T06:49:09.4730899Z For NCCL-based process groups, internal tensor representations of 2025-09-07T06:49:09.4730994Z objects must be moved to the GPU device before communication takes place. 2025-09-07T06:49:09.4731085Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T06:49:09.4731170Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T06:49:09.4731255Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T06:49:09.4731257Z 2025-09-07T06:49:09.4731360Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4731366Z 2025-09-07T06:49:09.4731623Z 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=230. 2025-09-07T06:49:09.4731731Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4731839Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T06:49:09.4731959Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T06:49:09.4731962Z 2025-09-07T06:49:09.4732003Z .. warning:: 2025-09-07T06:49:09.4732074Z This feature is experimental and subject to change. 2025-09-07T06:49:09.4732138Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T06:49:09.4732140Z 2025-09-07T06:49:09.4733408Z Args: 2025-09-07T06:49:09.4733478Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T06:49:09.4733542Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:09.4733669Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:09.4733750Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:09.4733812Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:09.4733853Z (Default: ``None``) 2025-09-07T06:49:09.4733903Z storage_writer (Optional[StorageWriter]): 2025-09-07T06:49:09.4733978Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T06:49:09.4734061Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T06:49:09.4734134Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:09.4734178Z be raised. (Default: ``None``) 2025-09-07T06:49:09.4734223Z planner (Optional[SavePlanner]): 2025-09-07T06:49:09.4734293Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T06:49:09.4734440Z planner will be used. (Default: ``None``) 2025-09-07T06:49:09.4734496Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:09.4734565Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:09.4734604Z (Default: ``None``) 2025-09-07T06:49:09.4734666Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T06:49:09.4734730Z whether to do checkpoint in separate thread or process 2025-09-07T06:49:09.4734787Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T06:49:09.4734830Z async_stager (AsyncStager): 2025-09-07T06:49:09.4735946Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T06:49:09.4736026Z and async_stager is provided, async_stager will be used for staging 2025-09-07T06:49:09.4736065Z no_dist (bool): 2025-09-07T06:49:09.4736128Z If ``True``, this function will assume the intent is to save 2025-09-07T06:49:09.4736183Z a checkpoint on a single rank/process. 2025-09-07T06:49:09.4736228Z (Default: ``False``) 2025-09-07T06:49:09.4736336Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T06:49:09.4736415Z This configuration is experimental and should be used with caution. 2025-09-07T06:49:09.4736509Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T06:49:09.4736512Z 2025-09-07T06:49:09.4736545Z Returns: 2025-09-07T06:49:09.4736619Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T06:49:09.4736623Z 2025-09-07T06:49:09.4736656Z Example: 2025-09-07T06:49:09.4736696Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4736737Z >>> my_model = MyModule() 2025-09-07T06:49:09.4736740Z 2025-09-07T06:49:09.4736785Z >>> state_dict = {"model": my_model} 2025-09-07T06:49:09.4736789Z 2025-09-07T06:49:09.4736876Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T06:49:09.4736914Z ... "/checkpoint/1" 2025-09-07T06:49:09.4736947Z ... ) 2025-09-07T06:49:09.4737023Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T06:49:09.4737065Z >>> state_dict=state_dict, 2025-09-07T06:49:09.4737111Z >>> storage_writer=fs_storage_writer, 2025-09-07T06:49:09.4738131Z >>> ) 2025-09-07T06:49:09.4738166Z >>> 2025-09-07T06:49:09.4738208Z >>> # ... do some work ... 2025-09-07T06:49:09.4738239Z >>> 2025-09-07T06:49:09.4738283Z >>> checkpoint_future.result() 2025-09-07T06:49:09.4738285Z 2025-09-07T06:49:09.4738315Z 2025-09-07T06:49:09.4738405Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4738407Z 2025-09-07T06:49:09.4793202Z 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. 2025-09-07T06:49:09.4793411Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4793415Z 2025-09-07T06:49:09.4793531Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T06:49:09.4793535Z 2025-09-07T06:49:09.4793638Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T06:49:09.4793640Z 2025-09-07T06:49:09.4793736Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T06:49:09.4793783Z will be visible to the whole process. 2025-09-07T06:49:09.4793785Z 2025-09-07T06:49:09.4793883Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T06:49:09.4793885Z 2025-09-07T06:49:09.4793939Z 1) set_up_planner - called on all ranks. 2025-09-07T06:49:09.4793989Z Signals the start of a checkpoint save. 2025-09-07T06:49:09.4794032Z 2025-09-07T06:49:09.4794079Z 2) create_local_plan - called on all ranks. 2025-09-07T06:49:09.4794186Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T06:49:09.4794188Z 2025-09-07T06:49:09.4794257Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T06:49:09.4796598Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T06:49:09.4796602Z 2025-09-07T06:49:09.4796659Z 4) finish_plan - called on all ranks. 2025-09-07T06:49:09.4796753Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T06:49:09.4796755Z 2025-09-07T06:49:09.4796817Z 5) resolve_data - called multiple times on each rank 2025-09-07T06:49:09.4796902Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T06:49:09.4796904Z 2025-09-07T06:49:09.4797017Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T06:49:09.4797099Z most changes can be expressed by changes in a single method. 2025-09-07T06:49:09.4797128Z 2025-09-07T06:49:09.4797193Z There are 3 usual patterns of extension: 2025-09-07T06:49:09.4797196Z 2025-09-07T06:49:09.4797289Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T06:49:09.4797371Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T06:49:09.4797373Z 2025-09-07T06:49:09.4797417Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4797470Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T06:49:09.4797512Z >>> def set_up_planner( 2025-09-07T06:49:09.4797546Z >>> self, 2025-09-07T06:49:09.4797589Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T06:49:09.4797639Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T06:49:09.4797682Z >>> is_coordinator: bool, 2025-09-07T06:49:09.4797717Z >>> ) -> None: 2025-09-07T06:49:09.4797761Z >>> # prefix all keys with `foo_`` 2025-09-07T06:49:09.4797878Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T06:49:09.4797880Z 2025-09-07T06:49:09.4797998Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T06:49:09.4798001Z 2025-09-07T06:49:09.4799602Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4799660Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T06:49:09.4799706Z >>> def create_local_plan(self): 2025-09-07T06:49:09.4799758Z >>> plan = super().create_local_plan() 2025-09-07T06:49:09.4799796Z >>> for p in plan: 2025-09-07T06:49:09.4799846Z >>> if p.tensor_data is not None: 2025-09-07T06:49:09.4799919Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T06:49:09.4799961Z >>> return plan 2025-09-07T06:49:09.4799992Z >>> 2025-09-07T06:49:09.4800041Z >>> def resolve_data(self, write_item): 2025-09-07T06:49:09.4800090Z >>> item = super().resolve_data(write_item) 2025-09-07T06:49:09.4800263Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T06:49:09.4800266Z 2025-09-07T06:49:09.4800383Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T06:49:09.4800385Z 2025-09-07T06:49:09.4800429Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4800473Z >>> from itertools import zip_longest 2025-09-07T06:49:09.4800516Z >>> from dataclasses import replace 2025-09-07T06:49:09.4800580Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T06:49:09.4800678Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T06:49:09.4800729Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T06:49:09.4800779Z >>> def create_global_plan(self, all_plans): 2025-09-07T06:49:09.4800837Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T06:49:09.4800918Z >>> items_per_rank = [ 2025-09-07T06:49:09.4800973Z >>> [item for item in items if item is not None] 2025-09-07T06:49:09.4801035Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T06:49:09.4801069Z >>> ] 2025-09-07T06:49:09.4801108Z >>> all_plans = [ 2025-09-07T06:49:09.4801152Z >>> replace(plan, items=items) 2025-09-07T06:49:09.4801224Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T06:49:09.4801256Z >>> ] 2025-09-07T06:49:09.4801311Z >>> return super().create_global_plan(all_plans) 2025-09-07T06:49:09.4801314Z 2025-09-07T06:49:09.4801408Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T06:49:09.4801503Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T06:49:09.4801549Z the global planner aggregate them: 2025-09-07T06:49:09.4801551Z 2025-09-07T06:49:09.4801595Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4801664Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T06:49:09.4801712Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T06:49:09.4801770Z >>> plan = super().create_local_plan() 2025-09-07T06:49:09.4803062Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T06:49:09.4803096Z >>> 2025-09-07T06:49:09.4803207Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T06:49:09.4803279Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T06:49:09.4803340Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T06:49:09.4803404Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T06:49:09.4803449Z >>> return global_plan, metadata 2025-09-07T06:49:09.4803451Z 2025-09-07T06:49:09.4803540Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4803548Z 2025-09-07T06:49:09.4803759Z 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. 2025-09-07T06:49:09.4803851Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4803853Z 2025-09-07T06:49:09.4803952Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T06:49:09.4803954Z 2025-09-07T06:49:09.4804052Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T06:49:09.4804054Z 2025-09-07T06:49:09.4804150Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T06:49:09.4804195Z will be visible to the whole process. 2025-09-07T06:49:09.4804197Z 2025-09-07T06:49:09.4804293Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T06:49:09.4804295Z 2025-09-07T06:49:09.4804343Z 1) set_up_planner - called on all ranks. 2025-09-07T06:49:09.4804555Z Signals the start of loading a checkpoint. 2025-09-07T06:49:09.4804557Z 2025-09-07T06:49:09.4804605Z 2) create_local_plan - called on all ranks. 2025-09-07T06:49:09.4804710Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T06:49:09.4804712Z 2025-09-07T06:49:09.4804778Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T06:49:09.4804850Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T06:49:09.4804852Z 2025-09-07T06:49:09.4805974Z 4) load_bytes - called multiple times on each rank 2025-09-07T06:49:09.4806046Z This is called once per non-tensor value in state_dict. 2025-09-07T06:49:09.4806048Z 2025-09-07T06:49:09.4806128Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T06:49:09.4806196Z They are called in pair for each Tensor value in state_dict. 2025-09-07T06:49:09.4806198Z 2025-09-07T06:49:09.4806350Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T06:49:09.4806424Z most changes can be expressed by changes in a single method. 2025-09-07T06:49:09.4806426Z 2025-09-07T06:49:09.4806475Z There are two usual patterns of extension: 2025-09-07T06:49:09.4806478Z 2025-09-07T06:49:09.4806566Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T06:49:09.4806653Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T06:49:09.4806734Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T06:49:09.4806781Z we need to be able to perform it in place 2025-09-07T06:49:09.4806784Z 2025-09-07T06:49:09.4806827Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4806877Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T06:49:09.4806918Z >>> def set_up_planner( 2025-09-07T06:49:09.4806952Z >>> self, 2025-09-07T06:49:09.4806997Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T06:49:09.4807038Z >>> metadata: Metadata, 2025-09-07T06:49:09.4807084Z >>> is_coordinator: bool, 2025-09-07T06:49:09.4807119Z >>> ) -> None: 2025-09-07T06:49:09.4807168Z >>> self.original_state_dict = state_dict 2025-09-07T06:49:09.4807236Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T06:49:09.4808321Z >>> 2025-09-07T06:49:09.4808372Z >>> if self.flatten_sharded_tensors: 2025-09-07T06:49:09.4808432Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T06:49:09.4808463Z >>> 2025-09-07T06:49:09.4808505Z >>> if self.flatten_state_dict: 2025-09-07T06:49:09.4808572Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T06:49:09.4808603Z >>> 2025-09-07T06:49:09.4808644Z >>> self.state_dict = state_dict 2025-09-07T06:49:09.4808685Z >>> self.metadata = metadata 2025-09-07T06:49:09.4808731Z >>> self.is_coordinator = is_coordinator 2025-09-07T06:49:09.4808767Z >>> 2025-09-07T06:49:09.4808819Z >>> def load_bytes(self, read_item, value): 2025-09-07T06:49:09.4808860Z >>> # Remove the "foo_" prefix 2025-09-07T06:49:09.4808974Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T06:49:09.4808976Z 2025-09-07T06:49:09.4808978Z 2025-09-07T06:49:09.4809068Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T06:49:09.4809071Z 2025-09-07T06:49:09.4809114Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:09.4809171Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T06:49:09.4809217Z >>> def resolve_tensor(self, read_item): 2025-09-07T06:49:09.4809267Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T06:49:09.4809325Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T06:49:09.4809356Z >>> 2025-09-07T06:49:09.4809405Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T06:49:09.4810450Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T06:49:09.4810486Z 2025-09-07T06:49:09.4810580Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4810582Z 2025-09-07T06:49:09.4866973Z 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. 2025-09-07T06:49:09.4867111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4867115Z 2025-09-07T06:49:09.4867185Z Return the model state_dict and optimizers state_dict. 2025-09-07T06:49:09.4867187Z 2025-09-07T06:49:09.4867270Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T06:49:09.4867362Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T06:49:09.4867451Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T06:49:09.4867623Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T06:49:09.4867701Z with a different number of trainers and/or different parallelisms. 2025-09-07T06:49:09.4867792Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T06:49:09.4867828Z these APIs. 2025-09-07T06:49:09.4867877Z 3.) sanity checking the result state_dict. 2025-09-07T06:49:09.4867879Z 2025-09-07T06:49:09.4867959Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T06:49:09.4868047Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T06:49:09.4868129Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T06:49:09.4868206Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T06:49:09.4868282Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T06:49:09.4868371Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T06:49:09.4868454Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T06:49:09.4870440Z canonical FQNs when calling this API. 2025-09-07T06:49:09.4870444Z 2025-09-07T06:49:09.4870541Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T06:49:09.4870630Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T06:49:09.4870693Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T06:49:09.4870695Z 2025-09-07T06:49:09.4870729Z Example: 2025-09-07T06:49:09.4870770Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4870807Z >>> import torch 2025-09-07T06:49:09.4870940Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:09.4871016Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T06:49:09.4871096Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T06:49:09.4871104Z 2025-09-07T06:49:09.4871156Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T06:49:09.4871228Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T06:49:09.4871277Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T06:49:09.4871344Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T06:49:09.4871346Z 2025-09-07T06:49:09.4871348Z 2025-09-07T06:49:09.4871433Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T06:49:09.4871499Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T06:49:09.4871544Z ... fsdp_model, fsdp_optim 2025-09-07T06:49:09.4871577Z ... ) 2025-09-07T06:49:09.4871579Z 2025-09-07T06:49:09.4871659Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T06:49:09.4871701Z >>> # the asserts will fail. 2025-09-07T06:49:09.4871751Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T06:49:09.4873049Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T06:49:09.4873052Z 2025-09-07T06:49:09.4873121Z 2025-09-07T06:49:09.4873157Z Args: 2025-09-07T06:49:09.4873212Z model (nn.Module): the nn.Module to the model. 2025-09-07T06:49:09.4873284Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T06:49:09.4873345Z The optimizers that are used to optimize ``model``. 2025-09-07T06:49:09.4873445Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T06:49:09.4873489Z that belong to the submodules. 2025-09-07T06:49:09.4873554Z options (StateDictOptions): the options to control how 2025-09-07T06:49:09.4873629Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T06:49:09.4873677Z `StateDictOptions` for the details. 2025-09-07T06:49:09.4873679Z 2025-09-07T06:49:09.4873711Z Returns: 2025-09-07T06:49:09.4873783Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T06:49:09.4873826Z 2025-09-07T06:49:09.4873912Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T06:49:09.4873915Z 2025-09-07T06:49:09.4874004Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4874006Z 2025-09-07T06:49:09.4947792Z 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=69. 2025-09-07T06:49:09.4947905Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.4947910Z 2025-09-07T06:49:09.4948033Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T06:49:09.4948035Z 2025-09-07T06:49:09.4948138Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T06:49:09.4948221Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T06:49:09.4950402Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T06:49:09.4950449Z costs). 2025-09-07T06:49:09.4950452Z 2025-09-07T06:49:09.4950530Z Each rank will try to read the least amount of data necessary 2025-09-07T06:49:09.4950614Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T06:49:09.4950702Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T06:49:09.4950704Z 2025-09-07T06:49:09.4950799Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T06:49:09.4950939Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T06:49:09.4951005Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T06:49:09.4951098Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T06:49:09.4951157Z it in the ``state_dict`` with the deserialized object. 2025-09-07T06:49:09.4951159Z 2025-09-07T06:49:09.4951198Z .. warning:: 2025-09-07T06:49:09.4951265Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T06:49:09.4951329Z destination device *prior to* calling this function. 2025-09-07T06:49:09.4951331Z 2025-09-07T06:49:09.4951415Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T06:49:09.4951459Z on state_dict. 2025-09-07T06:49:09.4951462Z 2025-09-07T06:49:09.4951495Z .. warning:: 2025-09-07T06:49:09.4951576Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T06:49:09.4951649Z pos-processing and non-tensor data properly propagates. 2025-09-07T06:49:09.4951651Z 2025-09-07T06:49:09.4951689Z .. note: 2025-09-07T06:49:09.4951767Z If no process group is initialized, this function will assume the intent 2025-09-07T06:49:09.4951846Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T06:49:09.4951933Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T06:49:09.4953305Z or ShardedTensor) 2025-09-07T06:49:09.4953308Z 2025-09-07T06:49:09.4953412Z .. note: 2025-09-07T06:49:09.4953469Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T06:49:09.4953471Z 2025-09-07T06:49:09.4953502Z Args: 2025-09-07T06:49:09.4953579Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T06:49:09.4953634Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:09.4953709Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:09.4953781Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:09.4953846Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:09.4953886Z (Default: ``None``) 2025-09-07T06:49:09.4953937Z storage_reader (Optional[StorageReader]): 2025-09-07T06:49:09.4954018Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T06:49:09.4954090Z specified, DCP will automatically infer the reader based on the 2025-09-07T06:49:09.4954209Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:09.4954253Z be raised. (Default: ``None``) 2025-09-07T06:49:09.4954299Z planner (Optional[LoadPlanner]): 2025-09-07T06:49:09.4954441Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T06:49:09.4954491Z planner will be used. (Default: ``None``) 2025-09-07T06:49:09.4954541Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:09.4954609Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:09.4954648Z (Default: ``None``) 2025-09-07T06:49:09.4955869Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T06:49:09.4955966Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T06:49:09.4955999Z Returns: 2025-09-07T06:49:09.4956032Z None. 2025-09-07T06:49:09.4956034Z 2025-09-07T06:49:09.4956072Z Examples 2025-09-07T06:49:09.4956112Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.4956159Z >>> my_model = MyModule() 2025-09-07T06:49:09.4956212Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T06:49:09.4956260Z >>> model_state_dict = my_model.state_dict() 2025-09-07T06:49:09.4956347Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T06:49:09.4956385Z ... "/checkpoint/1" 2025-09-07T06:49:09.4956417Z ... ) 2025-09-07T06:49:09.4956419Z 2025-09-07T06:49:09.4956479Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T06:49:09.4956524Z >>> state_dict=model_state_dict, 2025-09-07T06:49:09.4956569Z >>> storage_reader=fs_storage_reader, 2025-09-07T06:49:09.4956599Z >>> ) 2025-09-07T06:49:09.4956602Z 2025-09-07T06:49:09.4956673Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T06:49:09.4956720Z >>> # to flush the state_dict, must call it to 2025-09-07T06:49:09.4956765Z >>> # ensure correct behavior. 2025-09-07T06:49:09.4956816Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T06:49:09.4956818Z 2025-09-07T06:49:09.4956852Z .. note:: 2025-09-07T06:49:09.4956929Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T06:49:09.4958083Z For NCCL-based process groups, internal tensor representations of 2025-09-07T06:49:09.4958164Z objects must be moved to the GPU device before communication takes place. 2025-09-07T06:49:09.4958243Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T06:49:09.4958322Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T06:49:09.4958390Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T06:49:09.4958393Z 2025-09-07T06:49:09.4958481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.4958484Z 2025-09-07T06:49:09.5018289Z 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. 2025-09-07T06:49:09.5018405Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5018512Z 2025-09-07T06:49:09.5018868Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T06:49:09.5018881Z 2025-09-07T06:49:09.5019023Z This is the current recommended way to checkpoint FSDP. 2025-09-07T06:49:09.5019105Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.5019237Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T06:49:09.5019309Z >>> # Save 2025-09-07T06:49:09.5019389Z >>> model: torch.nn.Model 2025-09-07T06:49:09.5019497Z >>> optim_params = model.parameters() 2025-09-07T06:49:09.5019606Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T06:49:09.5019680Z >>> # Save 2025-09-07T06:49:09.5019842Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T06:49:09.5020144Z >>> state_dict = { 2025-09-07T06:49:09.5023829Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T06:49:09.5023923Z >>> "model": model.state_dict() 2025-09-07T06:49:09.5023983Z >>> } 2025-09-07T06:49:09.5024073Z >>> dist_cp.save_state_dict( 2025-09-07T06:49:09.5024146Z >>> state_dict=optim_state, 2025-09-07T06:49:09.5024274Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T06:49:09.5024500Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T06:49:09.5024562Z >>> ) 2025-09-07T06:49:09.5024616Z >>> 2025-09-07T06:49:09.5024673Z >>> # Load 2025-09-07T06:49:09.5024823Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T06:49:09.5024913Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T06:49:09.5024975Z >>> checkpoint = { 2025-09-07T06:49:09.5025052Z >>> "model": model_state_dict 2025-09-07T06:49:09.5025106Z >>> } 2025-09-07T06:49:09.5025181Z >>> dist_cp.load_state_dict( 2025-09-07T06:49:09.5025262Z >>> state_dict=checkpoint, 2025-09-07T06:49:09.5025383Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T06:49:09.5025473Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T06:49:09.5025525Z >>> ) 2025-09-07T06:49:09.5025630Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T06:49:09.5027801Z >>> 2025-09-07T06:49:09.5027932Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T06:49:09.5028000Z >>> model_state_dict, 2025-09-07T06:49:09.5028083Z >>> optimizer_key="optimizer", 2025-09-07T06:49:09.5028194Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T06:49:09.5028249Z >>> ) 2025-09-07T06:49:09.5028303Z >>> 2025-09-07T06:49:09.5028404Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:09.5028494Z >>> model, optim, optim_state["optimizer"] 2025-09-07T06:49:09.5028551Z >>> ) 2025-09-07T06:49:09.5028603Z >>> 2025-09-07T06:49:09.5028692Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T06:49:09.5028697Z 2025-09-07T06:49:09.5028854Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5028857Z 2025-09-07T06:49:09.5396170Z 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. 2025-09-07T06:49:09.5396511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5396519Z 2025-09-07T06:49:09.5396707Z This configures FSDP-native mixed precision training. 2025-09-07T06:49:09.5396713Z 2025-09-07T06:49:09.5396816Z Attributes: 2025-09-07T06:49:09.5397067Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T06:49:09.5397280Z parameters during forward and backward and thus the dtype for 2025-09-07T06:49:09.5397500Z forward and backward computation. Outside forward and backward, the 2025-09-07T06:49:09.5402965Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T06:49:09.5403229Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T06:49:09.5403405Z always saved in full precision. (Default: ``None``) 2025-09-07T06:49:09.5403622Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T06:49:09.5403848Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T06:49:09.5404031Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T06:49:09.5404222Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T06:49:09.5404334Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T06:49:09.5404550Z to force gradient reduction to run in full precision. (Default: 2025-09-07T06:49:09.5404599Z ``None``) 2025-09-07T06:49:09.5404762Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T06:49:09.5404868Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T06:49:09.5404968Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T06:49:09.5405078Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T06:49:09.5405171Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T06:49:09.5405220Z ``None``) 2025-09-07T06:49:09.5405315Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T06:49:09.5405420Z gradients to full precision after the backward pass in preparation 2025-09-07T06:49:09.5405517Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T06:49:09.5405612Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T06:49:09.5405711Z using a custom optimizer that supports running in low precision. 2025-09-07T06:49:09.5405784Z (Default: ``False``) 2025-09-07T06:49:09.5405894Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T06:49:09.5405998Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T06:49:09.5406100Z that parameter and input dtypes match for forward computation, as 2025-09-07T06:49:09.5406210Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T06:49:09.5406318Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T06:49:09.5406427Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T06:49:09.5406484Z (Default: ``False``) 2025-09-07T06:49:09.5406598Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T06:49:09.5406698Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T06:49:09.5406798Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T06:49:09.5408770Z this does not do anything. (Default: ``True``) 2025-09-07T06:49:09.5408891Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T06:49:09.5408985Z module classes to ignore for mixed precision when using an 2025-09-07T06:49:09.5409076Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T06:49:09.5409175Z applied to them separately with mixed precision disabled (meaning 2025-09-07T06:49:09.5409271Z that the final FSDP construction would deviate from the specified 2025-09-07T06:49:09.5409362Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T06:49:09.5409455Z not do anything. This API is experimental and subject to change. 2025-09-07T06:49:09.5409513Z (Default: ``(_BatchNorm,)``) 2025-09-07T06:49:09.5409517Z 2025-09-07T06:49:09.5409607Z .. note:: This API is experimental and subject to change. 2025-09-07T06:49:09.5409615Z 2025-09-07T06:49:09.5409788Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T06:49:09.5409791Z 2025-09-07T06:49:09.5409883Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T06:49:09.5409943Z precision, but buffers are not. 2025-09-07T06:49:09.5409946Z 2025-09-07T06:49:09.5410045Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T06:49:09.5410145Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T06:49:09.5410253Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T06:49:09.5410355Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T06:49:09.5410468Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T06:49:09.5410569Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T06:49:09.5410646Z to still apply mixed precision to those modules. 2025-09-07T06:49:09.5410690Z 2025-09-07T06:49:09.5410793Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T06:49:09.5412369Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T06:49:09.5412482Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T06:49:09.5412571Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T06:49:09.5412574Z 2025-09-07T06:49:09.5412673Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T06:49:09.5412774Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T06:49:09.5412859Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T06:49:09.5412948Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T06:49:09.5413048Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T06:49:09.5413153Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T06:49:09.5413268Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T06:49:09.5413361Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T06:49:09.5413364Z 2025-09-07T06:49:09.5413464Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T06:49:09.5413575Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T06:49:09.5413672Z values to configure casting inputs or not before each instance's 2025-09-07T06:49:09.5413768Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T06:49:09.5413873Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T06:49:09.5413981Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T06:49:09.5414079Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T06:49:09.5414082Z 2025-09-07T06:49:09.5414134Z Example:: 2025-09-07T06:49:09.5414137Z 2025-09-07T06:49:09.5414224Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5414313Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T06:49:09.5414453Z >>> model[1] = FSDP( 2025-09-07T06:49:09.5415644Z >>> model[1], 2025-09-07T06:49:09.5415762Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T06:49:09.5415798Z >>> ) 2025-09-07T06:49:09.5415838Z >>> model = FSDP( 2025-09-07T06:49:09.5415875Z >>> model, 2025-09-07T06:49:09.5415986Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T06:49:09.5416020Z >>> ) 2025-09-07T06:49:09.5416022Z 2025-09-07T06:49:09.5416102Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T06:49:09.5416181Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T06:49:09.5416322Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T06:49:09.5416404Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T06:49:09.5416438Z ones. 2025-09-07T06:49:09.5416441Z 2025-09-07T06:49:09.5416443Z 2025-09-07T06:49:09.5416538Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5416541Z 2025-09-07T06:49:09.5416753Z 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. 2025-09-07T06:49:09.5416852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5416854Z 2025-09-07T06:49:09.5416933Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T06:49:09.5417009Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T06:49:09.5417085Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T06:49:09.5417213Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T06:49:09.5417287Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T06:49:09.5417324Z follows: 2025-09-07T06:49:09.5417326Z 2025-09-07T06:49:09.5418476Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5418571Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:09.5418623Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T06:49:09.5418703Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T06:49:09.5418784Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T06:49:09.5418831Z >>> state = fsdp.state_dict() 2025-09-07T06:49:09.5418912Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T06:49:09.5419002Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T06:49:09.5419103Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T06:49:09.5419149Z >>> if dist.get_rank() == 0: 2025-09-07T06:49:09.5419220Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T06:49:09.5419280Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T06:49:09.5419331Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:09.5419418Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T06:49:09.5419506Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T06:49:09.5419545Z >>> fsdp = FSDP( 2025-09-07T06:49:09.5419581Z ... model, 2025-09-07T06:49:09.5419636Z ... device_id=torch.cuda.current_device(), 2025-09-07T06:49:09.5419680Z ... auto_wrap_policy=..., 2025-09-07T06:49:09.5419723Z ... sync_module_states=True, 2025-09-07T06:49:09.5419761Z ... ) 2025-09-07T06:49:09.5419846Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T06:49:09.5420955Z 2025-09-07T06:49:09.5420994Z Attributes: 2025-09-07T06:49:09.5421071Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T06:49:09.5421149Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T06:49:09.5421213Z ranks save the full state dict. (Default: ``False``) 2025-09-07T06:49:09.5421217Z 2025-09-07T06:49:09.5421310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5421313Z 2025-09-07T06:49:09.5488514Z 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=634. 2025-09-07T06:49:09.5488628Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5488759Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T06:49:09.5488855Z 2025-09-07T06:49:09.5488953Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T06:49:09.5489030Z The target module does not have to be a FSDP module. If the target 2025-09-07T06:49:09.5489110Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T06:49:09.5489112Z 2025-09-07T06:49:09.5489190Z .. note:: This API should be called for only the top-level (root) 2025-09-07T06:49:09.5489225Z module. 2025-09-07T06:49:09.5489228Z 2025-09-07T06:49:09.5489307Z .. note:: This API enables users to transparently use the conventional 2025-09-07T06:49:09.5489378Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T06:49:09.5489452Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T06:49:09.5489524Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T06:49:09.5489656Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T06:49:09.5489692Z for FSDP: 2025-09-07T06:49:09.5489695Z 2025-09-07T06:49:09.5491613Z Example:: 2025-09-07T06:49:09.5491616Z 2025-09-07T06:49:09.5491680Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5491741Z >>> model = DDP(FSDP(...)) 2025-09-07T06:49:09.5491785Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:09.5491827Z >>> model, 2025-09-07T06:49:09.5491886Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T06:49:09.5491976Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T06:49:09.5492060Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T06:49:09.5492096Z >>> ) 2025-09-07T06:49:09.5492147Z >>> param_state_dict = model.state_dict() 2025-09-07T06:49:09.5492227Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T06:49:09.5492229Z 2025-09-07T06:49:09.5492262Z Args: 2025-09-07T06:49:09.5492311Z module (torch.nn.Module): Root module. 2025-09-07T06:49:09.5492393Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T06:49:09.5492475Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T06:49:09.5492520Z target ``state_dict_type``. 2025-09-07T06:49:09.5492608Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T06:49:09.5492654Z for the optimizer state dict. 2025-09-07T06:49:09.5492656Z 2025-09-07T06:49:09.5492690Z Returns: 2025-09-07T06:49:09.5492769Z A StateDictSettings that include the previous state_dict type and 2025-09-07T06:49:09.5492834Z configuration for the module. 2025-09-07T06:49:09.5494176Z 2025-09-07T06:49:09.5494277Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5494279Z 2025-09-07T06:49:09.5494637Z 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=792. 2025-09-07T06:49:09.5494729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5494815Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T06:49:09.5494817Z 2025-09-07T06:49:09.5494927Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T06:49:09.5494978Z :meth:`set_state_dict_type` for the detail. 2025-09-07T06:49:09.5494980Z 2025-09-07T06:49:09.5495016Z Example:: 2025-09-07T06:49:09.5495019Z 2025-09-07T06:49:09.5495071Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5495171Z >>> model = DDP(FSDP(...)) 2025-09-07T06:49:09.5495215Z >>> with FSDP.state_dict_type( 2025-09-07T06:49:09.5495253Z >>> model, 2025-09-07T06:49:09.5495303Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T06:49:09.5495335Z >>> ): 2025-09-07T06:49:09.5495383Z >>> checkpoint = model.state_dict() 2025-09-07T06:49:09.5495385Z 2025-09-07T06:49:09.5495419Z Args: 2025-09-07T06:49:09.5495466Z module (torch.nn.Module): Root module. 2025-09-07T06:49:09.5495549Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T06:49:09.5495630Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T06:49:09.5495692Z configuration for the target ``state_dict_type``. 2025-09-07T06:49:09.5495774Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T06:49:09.5496991Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T06:49:09.5497026Z 2025-09-07T06:49:09.5497118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5497121Z 2025-09-07T06:49:09.5513726Z 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=1805. 2025-09-07T06:49:09.5513840Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5513865Z 2025-09-07T06:49:09.5513958Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T06:49:09.5513962Z 2025-09-07T06:49:09.5514036Z The given state-dict can be transformed to one of three types: 2025-09-07T06:49:09.5514151Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T06:49:09.5514161Z 2025-09-07T06:49:09.5514255Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T06:49:09.5514344Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T06:49:09.5514462Z avoid OOM. 2025-09-07T06:49:09.5514465Z 2025-09-07T06:49:09.5514548Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T06:49:09.5514626Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T06:49:09.5514686Z memory. 2025-09-07T06:49:09.5514689Z 2025-09-07T06:49:09.5514766Z For local state_dict, no transformation will be performed. But a state 2025-09-07T06:49:09.5514850Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T06:49:09.5514895Z nature (this is not supported yet). 2025-09-07T06:49:09.5514898Z 2025-09-07T06:49:09.5514935Z Example:: 2025-09-07T06:49:09.5514937Z 2025-09-07T06:49:09.5514988Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5517261Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:09.5517334Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T06:49:09.5517411Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T06:49:09.5517489Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T06:49:09.5517538Z >>> # Save a checkpoint 2025-09-07T06:49:09.5517578Z >>> model, optim = ... 2025-09-07T06:49:09.5517621Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:09.5517659Z >>> model, 2025-09-07T06:49:09.5517710Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:09.5517767Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5517832Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5517868Z >>> ) 2025-09-07T06:49:09.5517911Z >>> state_dict = model.state_dict() 2025-09-07T06:49:09.5517974Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T06:49:09.5518032Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T06:49:09.5518148Z >>> # Load a checkpoint 2025-09-07T06:49:09.5518187Z >>> model, optim = ... 2025-09-07T06:49:09.5518244Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T06:49:09.5518286Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:09.5518320Z >>> model, 2025-09-07T06:49:09.5518365Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:09.5518415Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5518470Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5518502Z >>> ) 2025-09-07T06:49:09.5518546Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:09.5518604Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:09.5518648Z >>> model, optim, optim_state_dict 2025-09-07T06:49:09.5518680Z >>> ) 2025-09-07T06:49:09.5518728Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T06:49:09.5518893Z 2025-09-07T06:49:09.5518925Z Args: 2025-09-07T06:49:09.5519003Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T06:49:09.5519078Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T06:49:09.5520459Z were passed into the optimizer ``optim``. 2025-09-07T06:49:09.5520532Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T06:49:09.5520569Z parameters. 2025-09-07T06:49:09.5520646Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T06:49:09.5520723Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T06:49:09.5520761Z Default: ``None``) 2025-09-07T06:49:09.5520847Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T06:49:09.5520916Z are sharded or ``None`` if using the default process group. ( 2025-09-07T06:49:09.5520953Z Default: ``None``) 2025-09-07T06:49:09.5520961Z 2025-09-07T06:49:09.5520993Z Returns: 2025-09-07T06:49:09.5521071Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T06:49:09.5521134Z ``model``. The sharding of the optimizer state is based on 2025-09-07T06:49:09.5521173Z ``state_dict_type``. 2025-09-07T06:49:09.5521175Z 2025-09-07T06:49:09.5521264Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5521266Z 2025-09-07T06:49:09.5521551Z 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=1903. 2025-09-07T06:49:09.5521642Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.5521644Z 2025-09-07T06:49:09.5521768Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T06:49:09.5521770Z 2025-09-07T06:49:09.5521837Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T06:49:09.5521915Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T06:49:09.5521989Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T06:49:09.5522061Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T06:49:09.5522063Z 2025-09-07T06:49:09.5523153Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.5523243Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:09.5523303Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T06:49:09.5523369Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T06:49:09.5523440Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T06:49:09.5523480Z >>> # Save a checkpoint 2025-09-07T06:49:09.5523517Z >>> model, optim = ... 2025-09-07T06:49:09.5523561Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:09.5523598Z >>> model, 2025-09-07T06:49:09.5523680Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:09.5523730Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5523785Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5523817Z >>> ) 2025-09-07T06:49:09.5523859Z >>> state_dict = model.state_dict() 2025-09-07T06:49:09.5523905Z >>> original_osd = optim.state_dict() 2025-09-07T06:49:09.5523953Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T06:49:09.5523987Z >>> model, 2025-09-07T06:49:09.5524019Z >>> optim, 2025-09-07T06:49:09.5524062Z >>> optim_state_dict=original_osd 2025-09-07T06:49:09.5524092Z >>> ) 2025-09-07T06:49:09.5524147Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T06:49:09.5524184Z >>> # Load a checkpoint 2025-09-07T06:49:09.5525344Z >>> model, optim = ... 2025-09-07T06:49:09.5525405Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T06:49:09.5525531Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:09.5525570Z >>> model, 2025-09-07T06:49:09.5525615Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:09.5525663Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5525717Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:09.5525748Z >>> ) 2025-09-07T06:49:09.5525791Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:09.5525846Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:09.5525890Z >>> model, optim, optim_state_dict 2025-09-07T06:49:09.5525921Z >>> ) 2025-09-07T06:49:09.5525969Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T06:49:09.5525972Z 2025-09-07T06:49:09.5526003Z Args: 2025-09-07T06:49:09.5526074Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T06:49:09.5526148Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T06:49:09.5526200Z were passed into the optimizer ``optim``. 2025-09-07T06:49:09.5526273Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T06:49:09.5526307Z parameters. 2025-09-07T06:49:09.5526384Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T06:49:09.5526456Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T06:49:09.5527530Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T06:49:09.5527598Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T06:49:09.5527669Z load_directly (bool): If this is set to True, this API will also 2025-09-07T06:49:09.5527740Z call optim.load_state_dict(result) before returning the result. 2025-09-07T06:49:09.5527819Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T06:49:09.5527859Z (Default: ``False``) 2025-09-07T06:49:09.5527944Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T06:49:09.5528021Z are sharded or ``None`` if using the default process group. ( 2025-09-07T06:49:09.5528060Z Default: ``None``) 2025-09-07T06:49:09.5528062Z 2025-09-07T06:49:09.5528150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.5528152Z 2025-09-07T06:49:09.7537614Z 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. 2025-09-07T06:49:09.7538890Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7539261Z 2025-09-07T06:49:09.7539633Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T06:49:09.7540070Z 2025-09-07T06:49:09.7540382Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T06:49:09.7541022Z by using different periods concurrently after the warm-up stage. 2025-09-07T06:49:09.7541972Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T06:49:09.7542820Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T06:49:09.7543550Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T06:49:09.7544195Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T06:49:09.7544793Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T06:49:09.7545111Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T06:49:09.7545294Z 2025-09-07T06:49:09.7545341Z Args: 2025-09-07T06:49:09.7545544Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T06:49:09.7545909Z process group size, used for initializing process groups of 2025-09-07T06:49:09.7546182Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T06:49:09.7546461Z Particularly, at each iteration, there will be at most a single 2025-09-07T06:49:09.7546736Z process group that runs averaging -- the period of such group should 2025-09-07T06:49:09.7547006Z have the largest period which the current step can be divided by. 2025-09-07T06:49:09.7547254Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T06:49:09.7547491Z then this means totally three process groups will be created to 2025-09-07T06:49:09.7547747Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T06:49:09.7547994Z At the 4th iteration, only the second process group will run 2025-09-07T06:49:09.7548241Z averaging, because the first process group should be a 2025-09-07T06:49:09.7548490Z subset of the second process group, and no need to execute the first 2025-09-07T06:49:09.7552452Z process group redundantly. 2025-09-07T06:49:09.7552682Z On the other hand, the third process group can only be triggered 2025-09-07T06:49:09.7552932Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T06:49:09.7553230Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T06:49:09.7553627Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T06:49:09.7553968Z If ``None``, the default process group, which is created 2025-09-07T06:49:09.7554212Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T06:49:09.7554569Z (default: ``None``) 2025-09-07T06:49:09.7554684Z 2025-09-07T06:49:09.7554740Z Example:: 2025-09-07T06:49:09.7554863Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T06:49:09.7555027Z >>> from collections import OrderedDict 2025-09-07T06:49:09.7555176Z >>> import torch 2025-09-07T06:49:09.7555290Z >>> import torch.distributed as dist 2025-09-07T06:49:09.7555475Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T06:49:09.7555660Z >>> PostLocalSGDState, 2025-09-07T06:49:09.7557282Z >>> post_localSGD_hook, 2025-09-07T06:49:09.7557397Z >>> ) 2025-09-07T06:49:09.7557579Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T06:49:09.7557793Z >>> import torch.nn as nn 2025-09-07T06:49:09.7557902Z >>> 2025-09-07T06:49:09.7558091Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T06:49:09.7558244Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:09.7558379Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T06:49:09.7558533Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:09.7558688Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:09.7558813Z >>> ) 2025-09-07T06:49:09.7560109Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:09.7560309Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T06:49:09.7560496Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T06:49:09.7560696Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T06:49:09.7560912Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:09.7561045Z >>> 2025-09-07T06:49:09.7561249Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T06:49:09.7561442Z >>> # the 16 processes every 16 iterations. 2025-09-07T06:49:09.7561601Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T06:49:09.7561797Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T06:49:09.7562040Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:09.7563424Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T06:49:09.7563622Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T06:49:09.7563764Z >>> for step in range(0, 200): 2025-09-07T06:49:09.7563882Z >>> optimizer.zero_grad() 2025-09-07T06:49:09.7563995Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:09.7564113Z >>> loss.backward() 2025-09-07T06:49:09.7564221Z >>> optimizer.step() 2025-09-07T06:49:09.7564348Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T06:49:09.7564630Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T06:49:09.7564822Z >>> averager.average_parameters(model.parameters()) 2025-09-07T06:49:09.7564917Z 2025-09-07T06:49:09.7564953Z .. warning :: 2025-09-07T06:49:09.7566154Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T06:49:09.7566363Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T06:49:09.7566588Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T06:49:09.7566731Z 2025-09-07T06:49:09.7566765Z .. warning :: 2025-09-07T06:49:09.7566895Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T06:49:09.7567011Z 2025-09-07T06:49:09.7567101Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7567229Z 2025-09-07T06:49:09.7567498Z 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. 2025-09-07T06:49:09.7567870Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7568000Z 2025-09-07T06:49:09.7568070Z Averages parameters periodically after the warm-up stage. 2025-09-07T06:49:09.7568172Z 2025-09-07T06:49:09.7568264Z This can be used for running `post-local SGD `_, 2025-09-07T06:49:09.7568460Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T06:49:09.7569721Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T06:49:09.7569843Z 2025-09-07T06:49:09.7569874Z Args: 2025-09-07T06:49:09.7569982Z period (int): The number of steps per model averaging. 2025-09-07T06:49:09.7570232Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T06:49:09.7570428Z Otherwise, only DDP needs to be used. 2025-09-07T06:49:09.7570587Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T06:49:09.7570744Z model averaging is skipped. 2025-09-07T06:49:09.7570897Z process_group: The process group to be used for all-reduce. 2025-09-07T06:49:09.7571054Z If ``None``, the default process group, which 2025-09-07T06:49:09.7571211Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T06:49:09.7572801Z will be used. (default: ``None``) 2025-09-07T06:49:09.7572896Z 2025-09-07T06:49:09.7572932Z Example:: 2025-09-07T06:49:09.7572979Z 2025-09-07T06:49:09.7573028Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:09.7573145Z >>> import torch 2025-09-07T06:49:09.7573319Z >>> import torch.distributed as dist 2025-09-07T06:49:09.7573514Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T06:49:09.7575581Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T06:49:09.7575753Z >>> import torch.nn as nn 2025-09-07T06:49:09.7575852Z >>> 2025-09-07T06:49:09.7575962Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T06:49:09.7577521Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:09.7577652Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T06:49:09.7577796Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:09.7577944Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:09.7578060Z >>> ) 2025-09-07T06:49:09.7578160Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:09.7578350Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T06:49:09.7578549Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:09.7578678Z >>> 2025-09-07T06:49:09.7578813Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T06:49:09.7579015Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T06:49:09.7579215Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:09.7579448Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T06:49:09.7579613Z >>> for step in range(0, 200): 2025-09-07T06:49:09.7580885Z >>> optimizer.zero_grad() 2025-09-07T06:49:09.7580999Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:09.7581115Z >>> loss.backward() 2025-09-07T06:49:09.7581216Z >>> optimizer.step() 2025-09-07T06:49:09.7581349Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T06:49:09.7581527Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T06:49:09.7581683Z >>> # the initial ``warmup_steps`` period. 2025-09-07T06:49:09.7581823Z >>> averager.average_parameters(model.parameters()) 2025-09-07T06:49:09.7581918Z 2025-09-07T06:49:09.7582007Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7582128Z 2025-09-07T06:49:09.7582387Z 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. 2025-09-07T06:49:09.7582754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7584826Z 2025-09-07T06:49:09.7584876Z Run post-localSGD algorithm. 2025-09-07T06:49:09.7584951Z 2025-09-07T06:49:09.7585036Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T06:49:09.7585215Z by combining with a model averaging component (e.g., 2025-09-07T06:49:09.7585489Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T06:49:09.7585687Z that runs after the optimizer step. 2025-09-07T06:49:09.7585766Z 2025-09-07T06:49:09.7585798Z Args: 2025-09-07T06:49:09.7585922Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T06:49:09.7586132Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T06:49:09.7586411Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T06:49:09.7586678Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T06:49:09.7586861Z only exactly one tensor is stored in this bucket. 2025-09-07T06:49:09.7588059Z 2025-09-07T06:49:09.7588092Z Returns: 2025-09-07T06:49:09.7588224Z Future handler of the communication, which updates the gradients in place. 2025-09-07T06:49:09.7588375Z 2025-09-07T06:49:09.7588411Z Example:: 2025-09-07T06:49:09.7588501Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.7588650Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T06:49:09.7588900Z start_localSGD_iter=10) 2025-09-07T06:49:09.7589048Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:09.7589262Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T06:49:09.7589533Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T06:49:09.7589687Z 2025-09-07T06:49:09.7589779Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7589902Z 2025-09-07T06:49:09.7597593Z 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. 2025-09-07T06:49:09.7597992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7598119Z 2025-09-07T06:49:09.7598165Z Implement PowerSGD algorithm. 2025-09-07T06:49:09.7598235Z 2025-09-07T06:49:09.7598315Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T06:49:09.7598517Z algorithm described in the `paper `_. 2025-09-07T06:49:09.7598718Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T06:49:09.7598874Z compression as follows: 2025-09-07T06:49:09.7598935Z 2025-09-07T06:49:09.7599088Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T06:49:09.7599269Z 2025-09-07T06:49:09.7599410Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T06:49:09.7599588Z 2025-09-07T06:49:09.7599730Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T06:49:09.7599903Z 2025-09-07T06:49:09.7601308Z 2. Handles uncompressed tensors: 2025-09-07T06:49:09.7601393Z 2025-09-07T06:49:09.7601566Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T06:49:09.7601766Z 2025-09-07T06:49:09.7601884Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T06:49:09.7602033Z 2025-09-07T06:49:09.7602116Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T06:49:09.7602231Z 2025-09-07T06:49:09.7602318Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T06:49:09.7602558Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T06:49:09.7602703Z 2025-09-07T06:49:09.7602837Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T06:49:09.7602928Z 2025-09-07T06:49:09.7602973Z 3.3. Allreduces Ps as a batch; 2025-09-07T06:49:09.7603049Z 2025-09-07T06:49:09.7603096Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T06:49:09.7603171Z 2025-09-07T06:49:09.7603243Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T06:49:09.7603348Z 2025-09-07T06:49:09.7603389Z 3.6. Allreduces Qs as a batch; 2025-09-07T06:49:09.7604684Z 2025-09-07T06:49:09.7604795Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T06:49:09.7604933Z 2025-09-07T06:49:09.7605070Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T06:49:09.7605339Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T06:49:09.7605613Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T06:49:09.7605827Z 2025-09-07T06:49:09.7605860Z Args: 2025-09-07T06:49:09.7606076Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T06:49:09.7606374Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T06:49:09.7606569Z and ``min_compression_rate``. 2025-09-07T06:49:09.7606784Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T06:49:09.7607044Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T06:49:09.7607222Z only exactly one tensor is stored in this bucket. 2025-09-07T06:49:09.7608374Z 2025-09-07T06:49:09.7608408Z Returns: 2025-09-07T06:49:09.7608539Z Future handler of the communication, which updates the gradients in place. 2025-09-07T06:49:09.7608662Z 2025-09-07T06:49:09.7608702Z Example:: 2025-09-07T06:49:09.7608786Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.7608944Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T06:49:09.7609137Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T06:49:09.7609290Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T06:49:09.7609381Z 2025-09-07T06:49:09.7609470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7609591Z 2025-09-07T06:49:09.7664643Z 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. 2025-09-07T06:49:09.7665027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7666820Z 2025-09-07T06:49:09.7666918Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T06:49:09.7667098Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T06:49:09.7667189Z 2025-09-07T06:49:09.7667223Z Args: 2025-09-07T06:49:09.7667362Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T06:49:09.7667556Z of shards that represent the local shards on this rank. 2025-09-07T06:49:09.7667733Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T06:49:09.7667893Z shape of the overall sharded tensor. 2025-09-07T06:49:09.7667972Z 2025-09-07T06:49:09.7668006Z Keyword args: 2025-09-07T06:49:09.7668148Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:09.7668330Z the default process group will be used. 2025-09-07T06:49:09.7668479Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T06:49:09.7668662Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T06:49:09.7668906Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T06:49:09.7669048Z Default: ``False``. 2025-09-07T06:49:09.7669113Z 2025-09-07T06:49:09.7669146Z Returns: 2025-09-07T06:49:09.7669248Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T06:49:09.7669339Z 2025-09-07T06:49:09.7669341Z 2025-09-07T06:49:09.7669374Z Examples: 2025-09-07T06:49:09.7669507Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T06:49:09.7670984Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T06:49:09.7671088Z 2025-09-07T06:49:09.7671122Z on rank 0: 2025-09-07T06:49:09.7671219Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T06:49:09.7671343Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T06:49:09.7671462Z >>> shard_offsets=[0, 0], 2025-09-07T06:49:09.7671569Z >>> shard_lengths=[5, 5], 2025-09-07T06:49:09.7671711Z >>> placement="rank:0/cuda:0" 2025-09-07T06:49:09.7671816Z >>> ) 2025-09-07T06:49:09.7671936Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T06:49:09.7672130Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T06:49:09.7672233Z 2025-09-07T06:49:09.7673333Z on rank 1: 2025-09-07T06:49:09.7673434Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T06:49:09.7673555Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T06:49:09.7673671Z >>> shard_offsets=[5, 0], 2025-09-07T06:49:09.7673777Z >>> shard_lengths=[5, 5], 2025-09-07T06:49:09.7673886Z >>> placement="rank:1/cuda:1" 2025-09-07T06:49:09.7673988Z >>> ) 2025-09-07T06:49:09.7674100Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T06:49:09.7674271Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T06:49:09.7674478Z 2025-09-07T06:49:09.7674566Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7674690Z 2025-09-07T06:49:09.7739526Z 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. 2025-09-07T06:49:09.7741429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7741697Z 2025-09-07T06:49:09.7742030Z Representation of a sharding plan, describes how to shard a module 2025-09-07T06:49:09.7742442Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T06:49:09.7742846Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T06:49:09.7743231Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T06:49:09.7743435Z 2025-09-07T06:49:09.7743507Z Args: 2025-09-07T06:49:09.7743764Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T06:49:09.7744132Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T06:49:09.7744559Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T06:49:09.7744948Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T06:49:09.7748738Z a parameter to a `ShardingSpec`. 2025-09-07T06:49:09.7749031Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T06:49:09.7749307Z to a `Sharder` object. 2025-09-07T06:49:09.7749614Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T06:49:09.7750003Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T06:49:09.7750340Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T06:49:09.7750590Z Default: `None` 2025-09-07T06:49:09.7751275Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T06:49:09.7751614Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T06:49:09.7751951Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T06:49:09.7752211Z root module). 2025-09-07T06:49:09.7754248Z Default: None 2025-09-07T06:49:09.7754469Z Example: 2025-09-07T06:49:09.7754660Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T06:49:09.7754956Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T06:49:09.7755126Z 2025-09-07T06:49:09.7755213Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T06:49:09.7755389Z >>> class MyModule(nn.Module): 2025-09-07T06:49:09.7755539Z >>> def __init__(self) -> None: 2025-09-07T06:49:09.7755747Z >>> super().__init__() 2025-09-07T06:49:09.7755890Z >>> self.fc1 = nn.Linear() 2025-09-07T06:49:09.7756030Z >>> self.gelu = nn.GELU() 2025-09-07T06:49:09.7756227Z >>> self.fc2 = nn.Linear() 2025-09-07T06:49:09.7757934Z >>> self.relu = nn.Linear() 2025-09-07T06:49:09.7758068Z >>> 2025-09-07T06:49:09.7758170Z >>> def forward(self, input): 2025-09-07T06:49:09.7758344Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T06:49:09.7758471Z 2025-09-07T06:49:09.7758474Z 2025-09-07T06:49:09.7758538Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T06:49:09.7758698Z >>> sharding_plan = ShardingPlan( 2025-09-07T06:49:09.7758835Z >>> plan={ 2025-09-07T06:49:09.7758954Z >>> "fc1.weight": spec1, 2025-09-07T06:49:09.7759096Z >>> "fc2.weight": spec2 2025-09-07T06:49:09.7759223Z >>> }, 2025-09-07T06:49:09.7759344Z >>> output_plan={ 2025-09-07T06:49:09.7760969Z >>> "fc2": output_spec 2025-09-07T06:49:09.7761101Z >>> }, 2025-09-07T06:49:09.7761218Z >>> return_local_tensor=["fc2"] 2025-09-07T06:49:09.7761349Z >>> ) 2025-09-07T06:49:09.7761412Z 2025-09-07T06:49:09.7761527Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7761685Z 2025-09-07T06:49:09.7787595Z 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. 2025-09-07T06:49:09.7787991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7788117Z 2025-09-07T06:49:09.7788207Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T06:49:09.7788373Z size and sharding spec on each rank. 2025-09-07T06:49:09.7788449Z 2025-09-07T06:49:09.7788480Z Args: 2025-09-07T06:49:09.7788602Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T06:49:09.7790375Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T06:49:09.7790596Z The specification describing how to shard the Tensor. 2025-09-07T06:49:09.7790757Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T06:49:09.7791018Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T06:49:09.7791184Z Default: None 2025-09-07T06:49:09.7791314Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T06:49:09.7791494Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T06:49:09.7791671Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T06:49:09.7791811Z Default: ``False``. 2025-09-07T06:49:09.7791879Z 2025-09-07T06:49:09.7791911Z Returns: 2025-09-07T06:49:09.7793408Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T06:49:09.7793587Z tensor stored in the current rank. 2025-09-07T06:49:09.7793669Z 2025-09-07T06:49:09.7793779Z Examples: 2025-09-07T06:49:09.7793865Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.7793983Z >>> # All tensors below are of torch.int64 type. 2025-09-07T06:49:09.7794115Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.7794258Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T06:49:09.7794540Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T06:49:09.7794680Z >>> local_tensor 2025-09-07T06:49:09.7794774Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T06:49:09.7794891Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T06:49:09.7794996Z >>> sharding_dim = 0 2025-09-07T06:49:09.7795110Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T06:49:09.7795246Z dim=sharding_dim, 2025-09-07T06:49:09.7795366Z placements=[ 2025-09-07T06:49:09.7795486Z "rank:0/cuda:0", 2025-09-07T06:49:09.7795633Z "rank:1/cuda:1", 2025-09-07T06:49:09.7796960Z ], 2025-09-07T06:49:09.7797046Z ) 2025-09-07T06:49:09.7797139Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T06:49:09.7797297Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T06:49:09.7797408Z ... ) 2025-09-07T06:49:09.7797482Z >>> st 2025-09-07T06:49:09.7797559Z ShardedTensor( 2025-09-07T06:49:09.7797653Z ShardedTensorMetadata( 2025-09-07T06:49:09.7797759Z shards_metadata=[ 2025-09-07T06:49:09.7797918Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T06:49:09.7798135Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T06:49:09.7799346Z ], 2025-09-07T06:49:09.7799436Z size=torch.Size([2, 4]) 2025-09-07T06:49:09.7799538Z ) 2025-09-07T06:49:09.7799617Z >>> st.local_tensor() 2025-09-07T06:49:09.7799715Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T06:49:09.7799822Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T06:49:09.7799897Z 2025-09-07T06:49:09.7799991Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T06:49:09.7800209Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T06:49:09.7800405Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T06:49:09.7800551Z sharding spec. 2025-09-07T06:49:09.7800612Z 2025-09-07T06:49:09.7800700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7801846Z 2025-09-07T06:49:09.7802078Z 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. 2025-09-07T06:49:09.7802427Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.7802554Z 2025-09-07T06:49:09.7802645Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T06:49:09.7802803Z single local shard. 2025-09-07T06:49:09.7802858Z 2025-09-07T06:49:09.7802941Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T06:49:09.7803136Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T06:49:09.7803293Z we swap local shards directly. 2025-09-07T06:49:09.7803449Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T06:49:09.7803660Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T06:49:09.7803781Z 2025-09-07T06:49:09.7803812Z Args: 2025-09-07T06:49:09.7805114Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T06:49:09.7805320Z specification describing how the tensor is sharded. 2025-09-07T06:49:09.7805417Z 2025-09-07T06:49:09.7805450Z Returns: 2025-09-07T06:49:09.7805568Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T06:49:09.7805740Z 2025-09-07T06:49:09.7805774Z Examples: 2025-09-07T06:49:09.7805857Z >>> # xdoctest: +SKIP 2025-09-07T06:49:09.7805964Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:09.7806107Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T06:49:09.7806252Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T06:49:09.7806360Z >>> tensor 2025-09-07T06:49:09.7806454Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T06:49:09.7807620Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T06:49:09.7807748Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T06:49:09.7807870Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T06:49:09.7807987Z >>> sharding_dim = 0 2025-09-07T06:49:09.7808090Z >>> spec = ChunkShardingSpec( 2025-09-07T06:49:09.7808200Z dim=sharding_dim, 2025-09-07T06:49:09.7808328Z placements=[ 2025-09-07T06:49:09.7808424Z "rank:0/cuda:0", 2025-09-07T06:49:09.7808530Z "rank:1/cuda:1", 2025-09-07T06:49:09.7808629Z "rank:2/cuda:2", 2025-09-07T06:49:09.7808743Z "rank:3/cuda:3", 2025-09-07T06:49:09.7809871Z ], 2025-09-07T06:49:09.7809957Z ) 2025-09-07T06:49:09.7810039Z >>> current_offsets = [0] * 2 2025-09-07T06:49:09.7810147Z >>> current_offsets[0] = rank * 2 2025-09-07T06:49:09.7810262Z >>> shard_metadata = ShardMetadata( 2025-09-07T06:49:09.7810393Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T06:49:09.7810522Z shard_sizes=tensor.size(), 2025-09-07T06:49:09.7810642Z placement=spec.placements[rank], 2025-09-07T06:49:09.7810751Z ) 2025-09-07T06:49:09.7810828Z >>> local_shards = [ 2025-09-07T06:49:09.7810918Z Shard( 2025-09-07T06:49:09.7812018Z tensor=tensor, 2025-09-07T06:49:09.7812128Z metadata=shard_metadata, 2025-09-07T06:49:09.7812238Z ) 2025-09-07T06:49:09.7812320Z ] 2025-09-07T06:49:09.7812441Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T06:49:09.7812592Z >>> sharding_dim = 1 2025-09-07T06:49:09.7812696Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T06:49:09.7812812Z dim=sharding_dim, 2025-09-07T06:49:09.7812913Z placements=[ 2025-09-07T06:49:09.7813006Z "rank:0/cuda:0", 2025-09-07T06:49:09.7813106Z "rank:1/cuda:1", 2025-09-07T06:49:09.7814225Z "rank:2/cuda:2", 2025-09-07T06:49:09.7814328Z "rank:3/cuda:3", 2025-09-07T06:49:09.7814519Z ], 2025-09-07T06:49:09.7814597Z ) 2025-09-07T06:49:09.7814680Z >>> st.reshard(resharding_spec) 2025-09-07T06:49:09.7814796Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T06:49:09.7814903Z >>> tensor 2025-09-07T06:49:09.7815004Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T06:49:09.7815148Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T06:49:09.7815283Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T06:49:09.7815423Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T06:49:09.7815512Z 2025-09-07T06:49:09.7816634Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.7816766Z 2025-09-07T06:49:09.8265176Z 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. 2025-09-07T06:49:09.8266259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8266626Z 2025-09-07T06:49:09.8266906Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T06:49:09.8267513Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T06:49:09.8268283Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T06:49:09.8268868Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T06:49:09.8269471Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T06:49:09.8269835Z 2025-09-07T06:49:09.8269926Z Args: 2025-09-07T06:49:09.8270192Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T06:49:09.8275486Z An op or a list of ops to register the customized sharding function. 2025-09-07T06:49:09.8275632Z 2025-09-07T06:49:09.8275673Z Returns: 2025-09-07T06:49:09.8275819Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T06:49:09.8276045Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T06:49:09.8276270Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T06:49:09.8276577Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T06:49:09.8276801Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T06:49:09.8277035Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T06:49:09.8277259Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T06:49:09.8277432Z corresponding input placements. 2025-09-07T06:49:09.8277512Z 2025-09-07T06:49:09.8277548Z Example: 2025-09-07T06:49:09.8277634Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.8277763Z >>> @register_sharding(aten._softmax.default) 2025-09-07T06:49:09.8277908Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T06:49:09.8279805Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T06:49:09.8279943Z >>> acceptable_shardings = [] 2025-09-07T06:49:09.8280053Z >>> 2025-09-07T06:49:09.8280166Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T06:49:09.8280321Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T06:49:09.8280457Z >>> 2025-09-07T06:49:09.8280543Z >>> for sharding_dim in range(x.ndim): 2025-09-07T06:49:09.8280666Z >>> if sharding_dim != softmax_dim: 2025-09-07T06:49:09.8280781Z >>> all_sharded = ( 2025-09-07T06:49:09.8280894Z >>> [Shard(sharding_dim)], 2025-09-07T06:49:09.8281016Z >>> [Shard(sharding_dim), None, None], 2025-09-07T06:49:09.8281131Z >>> ) 2025-09-07T06:49:09.8281241Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T06:49:09.8283100Z >>> 2025-09-07T06:49:09.8283188Z >>> return acceptable_shardings 2025-09-07T06:49:09.8283265Z 2025-09-07T06:49:09.8283348Z .. note:: This API is currently experimental and subject to change 2025-09-07T06:49:09.8283453Z 2025-09-07T06:49:09.8283545Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8283669Z 2025-09-07T06:49:09.8283900Z 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. 2025-09-07T06:49:09.8284236Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8284442Z 2025-09-07T06:49:09.8284537Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T06:49:09.8284762Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T06:49:09.8284988Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T06:49:09.8285174Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T06:49:09.8285267Z 2025-09-07T06:49:09.8286412Z Args: 2025-09-07T06:49:09.8286536Z func (Callable): the function to be applied on each local shard of 2025-09-07T06:49:09.8286742Z :class:`DTensor` s. 2025-09-07T06:49:09.8286889Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T06:49:09.8287095Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T06:49:09.8287303Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T06:49:09.8287507Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T06:49:09.8287713Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T06:49:09.8287880Z mapping to the flattened ``output``. 2025-09-07T06:49:09.8288031Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T06:49:09.8288236Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T06:49:09.8289430Z should be `None`. 2025-09-07T06:49:09.8289615Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T06:49:09.8289810Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T06:49:09.8290028Z should ignore the desired placements because the function is not running with 2025-09-07T06:49:09.8290188Z :class:`DTensor` s. 2025-09-07T06:49:09.8290314Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T06:49:09.8290504Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T06:49:09.8290716Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T06:49:09.8290913Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T06:49:09.8291093Z placements or not. If the placements are not the same and 2025-09-07T06:49:09.8291278Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T06:49:09.8292490Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T06:49:09.8292701Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T06:49:09.8292903Z The only exception is when required placements are not ``None`` and the 2025-09-07T06:49:09.8293099Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T06:49:09.8293291Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T06:49:09.8293477Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T06:49:09.8293625Z Default: None 2025-09-07T06:49:09.8293751Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T06:49:09.8293924Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T06:49:09.8294107Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T06:49:09.8294290Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T06:49:09.8295542Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T06:49:09.8295727Z If not specified, we will assume the gradient layout of the local 2025-09-07T06:49:09.8295907Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T06:49:09.8296078Z and use that for gradient computation. Default: None. 2025-09-07T06:49:09.8296226Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T06:49:09.8296387Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T06:49:09.8296588Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T06:49:09.8296750Z mesh. Default: None. 2025-09-07T06:49:09.8296816Z 2025-09-07T06:49:09.8296851Z Keyword Args: 2025-09-07T06:49:09.8296945Z redistribute_inputs (bool, optional): 2025-09-07T06:49:09.8297160Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T06:49:09.8298364Z their placements are different from the required input placements. If this 2025-09-07T06:49:09.8298570Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T06:49:09.8298738Z an exception will be raised. Default: False. 2025-09-07T06:49:09.8298824Z 2025-09-07T06:49:09.8298857Z Returns: 2025-09-07T06:49:09.8298993Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T06:49:09.8299201Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T06:49:09.8299320Z 2025-09-07T06:49:09.8299351Z Raises: 2025-09-07T06:49:09.8299479Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T06:49:09.8299684Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T06:49:09.8299876Z if this is not the case. 2025-09-07T06:49:09.8299952Z 2025-09-07T06:49:09.8300043Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T06:49:09.8301243Z a redistribution according to ``in_placements``. 2025-09-07T06:49:09.8301333Z 2025-09-07T06:49:09.8301367Z Example: 2025-09-07T06:49:09.8301411Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.8301466Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T06:49:09.8301515Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T06:49:09.8301603Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T06:49:09.8301644Z >>> return reduced_tensor 2025-09-07T06:49:09.8301676Z >>> 2025-09-07T06:49:09.8301724Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T06:49:09.8301772Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T06:49:09.8301809Z >>> Y = torch.mm(W, X) 2025-09-07T06:49:09.8301889Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T06:49:09.8301954Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T06:49:09.8301989Z >>> 2025-09-07T06:49:09.8302085Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T06:49:09.8302132Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T06:49:09.8302172Z >>> mm_allreduce_forward, 2025-09-07T06:49:09.8302217Z >>> out_placements=[Replicate()], 2025-09-07T06:49:09.8302262Z >>> in_placements=[col_wise, row_wise], 2025-09-07T06:49:09.8303268Z >>> device_mesh=device_mesh, 2025-09-07T06:49:09.8303303Z >>> ) 2025-09-07T06:49:09.8303334Z >>> 2025-09-07T06:49:09.8303374Z >>> W_dt = distribute_tensor( 2025-09-07T06:49:09.8303416Z ... W, device_mesh, (col_wise) 2025-09-07T06:49:09.8303459Z ... ) # col-wisely sharded W tensor 2025-09-07T06:49:09.8303498Z >>> X_dt = distribute_tensor( 2025-09-07T06:49:09.8303543Z ... X, device_mesh, (row_wise) 2025-09-07T06:49:09.8303583Z ... ) # row-wisely sharded X tensor 2025-09-07T06:49:09.8303630Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T06:49:09.8303671Z ... device_mesh, W_dt, X_dt 2025-09-07T06:49:09.8303731Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T06:49:09.8303733Z 2025-09-07T06:49:09.8303808Z .. note:: This API is currently experimental and subject to change 2025-09-07T06:49:09.8303811Z 2025-09-07T06:49:09.8303901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8303903Z 2025-09-07T06:49:09.8417790Z 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. 2025-09-07T06:49:09.8418560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8418613Z 2025-09-07T06:49:09.8419295Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T06:49:09.8419486Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T06:49:09.8419503Z 2025-09-07T06:49:09.8419579Z Keyword Args: 2025-09-07T06:49:09.8419705Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:09.8425301Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T06:49:09.8425524Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T06:49:09.8425602Z as a placeholder. default: None. 2025-09-07T06:49:09.8425737Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:09.8425950Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:09.8426286Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T06:49:09.8426449Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T06:49:09.8426654Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T06:49:09.8426746Z default: None 2025-09-07T06:49:09.8426847Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T06:49:09.8427068Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:09.8427174Z have the desired DTensor layouts. default: None. 2025-09-07T06:49:09.8427240Z use_local_output (bool, optional): 2025-09-07T06:49:09.8427413Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T06:49:09.8427462Z Returns: 2025-09-07T06:49:09.8427616Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T06:49:09.8427619Z 2025-09-07T06:49:09.8427690Z Example:: 2025-09-07T06:49:09.8427751Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:09.8427897Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T06:49:09.8428006Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:09.8428061Z >>> ... 2025-09-07T06:49:09.8428215Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:09.8428293Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:09.8428340Z >>> 2025-09-07T06:49:09.8430394Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T06:49:09.8430495Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T06:49:09.8430553Z >>> parallelize_module( 2025-09-07T06:49:09.8430634Z >>> block, # this can be a submodule or module 2025-09-07T06:49:09.8430681Z >>> tp_mesh, 2025-09-07T06:49:09.8430741Z >>> parallelize_plan={ 2025-09-07T06:49:09.8430804Z >>> "attn": PrepareModuleInput( 2025-09-07T06:49:09.8430952Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T06:49:09.8431040Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T06:49:09.8431082Z >>> ), 2025-09-07T06:49:09.8431123Z >>> } 2025-09-07T06:49:09.8431168Z >>> ) 2025-09-07T06:49:09.8431170Z 2025-09-07T06:49:09.8431297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8431302Z 2025-09-07T06:49:09.8431604Z 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. 2025-09-07T06:49:09.8431728Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8431788Z 2025-09-07T06:49:09.8431976Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T06:49:09.8432134Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T06:49:09.8432137Z 2025-09-07T06:49:09.8432183Z Keyword Args: 2025-09-07T06:49:09.8432267Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:09.8432426Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T06:49:09.8432599Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T06:49:09.8434265Z ``None`` need to be specified as a placeholder. 2025-09-07T06:49:09.8434451Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:09.8434664Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T06:49:09.8434729Z have the desired DTensor layouts. 2025-09-07T06:49:09.8434810Z use_local_output (bool, optional): 2025-09-07T06:49:09.8434977Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T06:49:09.8435017Z Returns: 2025-09-07T06:49:09.8435160Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T06:49:09.8435163Z 2025-09-07T06:49:09.8435211Z Example:: 2025-09-07T06:49:09.8435267Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:09.8435417Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T06:49:09.8435515Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:09.8435560Z >>> ... 2025-09-07T06:49:09.8435701Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:09.8435771Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:09.8435819Z >>> 2025-09-07T06:49:09.8435994Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T06:49:09.8436072Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T06:49:09.8436125Z >>> parallelize_module( 2025-09-07T06:49:09.8436190Z >>> block, # this can be a submodule or module 2025-09-07T06:49:09.8436236Z >>> tp_mesh, 2025-09-07T06:49:09.8436308Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T06:49:09.8436375Z >>> output_layouts=Replicate(), 2025-09-07T06:49:09.8437777Z >>> desired_output_layouts=Shard(0) 2025-09-07T06:49:09.8437815Z >>> ) 2025-09-07T06:49:09.8437847Z >>> ) 2025-09-07T06:49:09.8437849Z 2025-09-07T06:49:09.8437945Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8437950Z 2025-09-07T06:49:09.8438200Z 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. 2025-09-07T06:49:09.8438301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8438303Z 2025-09-07T06:49:09.8438461Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T06:49:09.8438613Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T06:49:09.8438751Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T06:49:09.8438836Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T06:49:09.8438838Z 2025-09-07T06:49:09.8438874Z Keyword Args: 2025-09-07T06:49:09.8438994Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:09.8439114Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T06:49:09.8439249Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T06:49:09.8439297Z as a placeholder. default: None. 2025-09-07T06:49:09.8439382Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:09.8439518Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:09.8439660Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T06:49:09.8439714Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T06:49:09.8439845Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T06:49:09.8439933Z default: None 2025-09-07T06:49:09.8439995Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T06:49:09.8441251Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:09.8441313Z have the desired DTensor layouts. default: None. 2025-09-07T06:49:09.8441362Z use_local_input (bool, optional): 2025-09-07T06:49:09.8441492Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T06:49:09.8441555Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:09.8441674Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T06:49:09.8441814Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T06:49:09.8441879Z ``None`` need to be specified as a placeholder. 2025-09-07T06:49:09.8441957Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:09.8442095Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T06:49:09.8442143Z have the desired DTensor layouts. 2025-09-07T06:49:09.8442188Z use_local_output (bool, optional): 2025-09-07T06:49:09.8442316Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T06:49:09.8442348Z Returns: 2025-09-07T06:49:09.8442482Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T06:49:09.8442484Z 2025-09-07T06:49:09.8442521Z Example:: 2025-09-07T06:49:09.8442565Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:09.8442693Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T06:49:09.8442773Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:09.8442805Z >>> ... 2025-09-07T06:49:09.8442917Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:09.8444029Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:09.8444067Z >>> 2025-09-07T06:49:09.8444186Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T06:49:09.8444315Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T06:49:09.8444454Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T06:49:09.8444499Z >>> parallelize_module( 2025-09-07T06:49:09.8444554Z >>> block, # this can be a submodule or module 2025-09-07T06:49:09.8444592Z >>> tp_mesh, 2025-09-07T06:49:09.8444638Z >>> parallelize_plan={ 2025-09-07T06:49:09.8444740Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T06:49:09.8444798Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T06:49:09.8444866Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T06:49:09.8444915Z >>> output_layouts=Replicate(), 2025-09-07T06:49:09.8444966Z >>> desired_output_layouts=Shard(0), 2025-09-07T06:49:09.8445003Z >>> ), 2025-09-07T06:49:09.8445038Z >>> } 2025-09-07T06:49:09.8445069Z >>> ) 2025-09-07T06:49:09.8445071Z 2025-09-07T06:49:09.8445167Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8445169Z 2025-09-07T06:49:09.8665494Z 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. 2025-09-07T06:49:09.8665910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.8665931Z 2025-09-07T06:49:09.8666150Z Initialize rendezvous event object and record its operations. 2025-09-07T06:49:09.8666209Z 2025-09-07T06:49:09.8671769Z Args: 2025-09-07T06:49:09.8671943Z run_id (str): The run id of the rendezvous. 2025-09-07T06:49:09.8672114Z message (str): The message describing the event. 2025-09-07T06:49:09.8672381Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T06:49:09.8672588Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T06:49:09.8672714Z hostname (str): Hostname of the node. 2025-09-07T06:49:09.8672873Z pid (Optional[int]): The process id of the node. 2025-09-07T06:49:09.8673121Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T06:49:09.8673394Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T06:49:09.8673572Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T06:49:09.8673672Z Returns: 2025-09-07T06:49:09.8673763Z None 2025-09-07T06:49:09.8673855Z Example: 2025-09-07T06:49:09.8673996Z >>> # See DynamicRendezvousHandler class 2025-09-07T06:49:09.8674096Z >>> def _record( 2025-09-07T06:49:09.8674192Z ... self, 2025-09-07T06:49:09.8674296Z ... message: str, 2025-09-07T06:49:09.8674819Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T06:49:09.8674945Z ... rank: Optional[int] = None, 2025-09-07T06:49:09.8675048Z ... ) -> None: 2025-09-07T06:49:09.8675174Z ... construct_and_record_rdzv_event( 2025-09-07T06:49:09.8677670Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T06:49:09.8677725Z ... run_id=self._settings.run_id, 2025-09-07T06:49:09.8677768Z ... message=message, 2025-09-07T06:49:09.8677814Z ... node_state=node_state, 2025-09-07T06:49:09.8677864Z ... hostname=self._this_node.addr, 2025-09-07T06:49:09.8677909Z ... pid=self._this_node.pid, 2025-09-07T06:49:09.8677957Z ... local_id=self._this_node.local_id, 2025-09-07T06:49:09.8677998Z ... rank=rank, 2025-09-07T06:49:09.8678031Z ... ) 2025-09-07T06:49:09.8678034Z 2025-09-07T06:49:09.8678127Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.8678129Z 2025-09-07T06:49:09.9053692Z 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. 2025-09-07T06:49:09.9053805Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.9053810Z 2025-09-07T06:49:09.9058447Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T06:49:09.9058451Z 2025-09-07T06:49:09.9058539Z It creates a user-specified module on a specified remote node. 2025-09-07T06:49:09.9058821Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T06:49:09.9058869Z executed on the remote node. 2025-09-07T06:49:09.9058964Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T06:49:09.9059023Z gradients back to the corresponding remote module. 2025-09-07T06:49:09.9059150Z It can be shared across processors using `RPC framework `__, 2025-09-07T06:49:09.9059237Z without incurring any overheads of copying the actual module, 2025-09-07T06:49:09.9059312Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T06:49:09.9059357Z pointing to the remote module. 2025-09-07T06:49:09.9059359Z 2025-09-07T06:49:09.9059435Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T06:49:09.9059510Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T06:49:09.9059513Z 2025-09-07T06:49:09.9059657Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T06:49:09.9059661Z 2025-09-07T06:49:09.9059751Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T06:49:09.9059924Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T06:49:09.9059966Z Hybrid Example: 2025-09-07T06:49:09.9060012Z >>> class HybridModel(nn.Module): 2025-09-07T06:49:09.9060060Z >>> def __init__(self) -> None: 2025-09-07T06:49:09.9060105Z >>> nn.Module.__init__(self) 2025-09-07T06:49:09.9060160Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T06:49:09.9060211Z >>> self.local_linear = nn.Linear(...) 2025-09-07T06:49:09.9060214Z 2025-09-07T06:49:09.9060290Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T06:49:09.9060384Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T06:49:09.9060469Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T06:49:09.9060533Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T06:49:09.9060602Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T06:49:09.9060604Z 2025-09-07T06:49:09.9060671Z .. note:: 2025-09-07T06:49:09.9060732Z If the remote module is placed on a cuda device, 2025-09-07T06:49:09.9060829Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T06:49:09.9060971Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T06:49:09.9060974Z 2025-09-07T06:49:09.9061017Z Args: 2025-09-07T06:49:09.9061130Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:09.9061245Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T06:49:09.9061285Z formats: 2025-09-07T06:49:09.9061287Z 2025-09-07T06:49:09.9061360Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T06:49:09.9061426Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T06:49:09.9061431Z 2025-09-07T06:49:09.9061566Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:09.9061625Z module_cls (nn.Module): For example, 2025-09-07T06:49:09.9061675Z >>> class MyModule(nn.Module): 2025-09-07T06:49:09.9061732Z >>> def forward(input): 2025-09-07T06:49:09.9061778Z >>> return input + 1 2025-09-07T06:49:09.9061827Z >>> 2025-09-07T06:49:09.9061875Z >>> module_cls = MyModule 2025-09-07T06:49:09.9061962Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T06:49:09.9062041Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T06:49:09.9062150Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T06:49:09.9062273Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T06:49:09.9062368Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T06:49:09.9062455Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T06:49:09.9062457Z 2025-09-07T06:49:09.9062505Z Returns: 2025-09-07T06:49:09.9062595Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:09.9062693Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T06:49:09.9065361Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:09.9065429Z on the user-provided module on the remote side. 2025-09-07T06:49:09.9065432Z 2025-09-07T06:49:09.9065468Z Example:: 2025-09-07T06:49:09.9065532Z Run the following code in two different processes: 2025-09-07T06:49:09.9065571Z 2025-09-07T06:49:09.9065616Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.9065658Z >>> # On worker 0: 2025-09-07T06:49:09.9065693Z >>> import torch 2025-09-07T06:49:09.9065762Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9065807Z >>> from torch import nn, Tensor 2025-09-07T06:49:09.9065889Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:09.9065927Z >>> 2025-09-07T06:49:09.9065982Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:09.9066029Z >>> remote_linear_module = RemoteModule( 2025-09-07T06:49:09.9066079Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:09.9066111Z >>> ) 2025-09-07T06:49:09.9066152Z >>> input = torch.randn(128, 20) 2025-09-07T06:49:09.9066216Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T06:49:09.9066258Z >>> ret = ret_fut.wait() 2025-09-07T06:49:09.9066296Z >>> rpc.shutdown() 2025-09-07T06:49:09.9066301Z 2025-09-07T06:49:09.9066334Z >>> # On worker 1: 2025-09-07T06:49:09.9066372Z >>> import torch 2025-09-07T06:49:09.9066419Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9066453Z >>> 2025-09-07T06:49:09.9066506Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:09.9066541Z >>> rpc.shutdown() 2025-09-07T06:49:09.9066543Z 2025-09-07T06:49:09.9066632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.9066635Z 2025-09-07T06:49:09.9066875Z 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. 2025-09-07T06:49:09.9066965Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.9066967Z 2025-09-07T06:49:09.9067078Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T06:49:09.9067084Z 2025-09-07T06:49:09.9067194Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T06:49:09.9068732Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T06:49:09.9068738Z 2025-09-07T06:49:09.9068845Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T06:49:09.9068910Z which is not supported. The recommended way is as follows: 2025-09-07T06:49:09.9068912Z 2025-09-07T06:49:09.9068958Z 1. the sender creates a RemoteModule; 2025-09-07T06:49:09.9069012Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T06:49:09.9069127Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T06:49:09.9069130Z 2025-09-07T06:49:09.9069164Z Example:: 2025-09-07T06:49:09.9069221Z Run the following code in two different processes: 2025-09-07T06:49:09.9069223Z 2025-09-07T06:49:09.9069269Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.9069304Z >>> # On worker 0: 2025-09-07T06:49:09.9069391Z >>> import torch 2025-09-07T06:49:09.9069438Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9069480Z >>> from torch import nn, Tensor 2025-09-07T06:49:09.9069561Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:09.9069592Z >>> 2025-09-07T06:49:09.9069645Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:09.9069687Z >>> remote_module = RemoteModule( 2025-09-07T06:49:09.9069736Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:09.9069768Z >>> ) 2025-09-07T06:49:09.9069798Z >>> 2025-09-07T06:49:09.9069841Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T06:49:09.9069881Z >>> "worker1/cpu", 2025-09-07T06:49:09.9071249Z >>> RemoteModule.init_from_module_rref, 2025-09-07T06:49:09.9071310Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T06:49:09.9071367Z >>> ) 2025-09-07T06:49:09.9071403Z >>> rpc.shutdown() 2025-09-07T06:49:09.9071410Z 2025-09-07T06:49:09.9071444Z >>> # On worker 1: 2025-09-07T06:49:09.9071506Z >>> import torch 2025-09-07T06:49:09.9071554Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9071584Z >>> 2025-09-07T06:49:09.9071637Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:09.9071671Z >>> rpc.shutdown() 2025-09-07T06:49:09.9071673Z 2025-09-07T06:49:09.9071704Z Args: 2025-09-07T06:49:09.9071809Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:09.9071909Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T06:49:09.9071941Z formats: 2025-09-07T06:49:09.9071944Z 2025-09-07T06:49:09.9072000Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T06:49:09.9072057Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T06:49:09.9072060Z 2025-09-07T06:49:09.9072149Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:09.9072237Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T06:49:09.9072280Z the created remote module. 2025-09-07T06:49:09.9072375Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T06:49:09.9072459Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T06:49:09.9073699Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T06:49:09.9073787Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T06:49:09.9073790Z 2025-09-07T06:49:09.9073821Z Returns: 2025-09-07T06:49:09.9073907Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:09.9073988Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T06:49:09.9074088Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:09.9074144Z on the user-provided module on the remote side. 2025-09-07T06:49:09.9074146Z 2025-09-07T06:49:09.9074234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.9074236Z 2025-09-07T06:49:09.9074539Z 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. 2025-09-07T06:49:09.9074629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:09.9074631Z 2025-09-07T06:49:09.9074711Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T06:49:09.9074714Z 2025-09-07T06:49:09.9074785Z It creates a user-specified module on a specified remote node. 2025-09-07T06:49:09.9074866Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T06:49:09.9074954Z executed on the remote node. 2025-09-07T06:49:09.9075035Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T06:49:09.9075095Z gradients back to the corresponding remote module. 2025-09-07T06:49:09.9075097Z 2025-09-07T06:49:09.9075173Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T06:49:09.9075248Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T06:49:09.9075333Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T06:49:09.9075404Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T06:49:09.9075447Z returned by the ``module_cls``. 2025-09-07T06:49:09.9075449Z 2025-09-07T06:49:09.9076669Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T06:49:09.9076765Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T06:49:09.9076873Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T06:49:09.9076876Z 2025-09-07T06:49:09.9076943Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T06:49:09.9077006Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T06:49:09.9077008Z 2025-09-07T06:49:09.9077039Z Args: 2025-09-07T06:49:09.9077144Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:09.9077263Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T06:49:09.9077318Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T06:49:09.9077403Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:09.9077490Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T06:49:09.9077494Z 2025-09-07T06:49:09.9077536Z >>> class MyModule(nn.Module): 2025-09-07T06:49:09.9077580Z >>> def forward(input): 2025-09-07T06:49:09.9077620Z >>> return input + 1 2025-09-07T06:49:09.9077653Z >>> 2025-09-07T06:49:09.9077693Z >>> module_cls = MyModule 2025-09-07T06:49:09.9077702Z 2025-09-07T06:49:09.9077777Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T06:49:09.9077846Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T06:49:09.9077848Z 2025-09-07T06:49:09.9077881Z Returns: 2025-09-07T06:49:09.9077965Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:09.9078047Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T06:49:09.9078139Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:09.9079329Z on the user-provided module on the remote side. 2025-09-07T06:49:09.9079334Z 2025-09-07T06:49:09.9079368Z Example:: 2025-09-07T06:49:09.9079428Z Run the following code in two different processes: 2025-09-07T06:49:09.9079430Z 2025-09-07T06:49:09.9079476Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:09.9079512Z >>> # On worker 0: 2025-09-07T06:49:09.9079547Z >>> import torch 2025-09-07T06:49:09.9079594Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9079635Z >>> from torch import nn, Tensor 2025-09-07T06:49:09.9079717Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:09.9079747Z >>> 2025-09-07T06:49:09.9079803Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:09.9079848Z >>> remote_linear_module = RemoteModule( 2025-09-07T06:49:09.9079900Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:09.9079931Z >>> ) 2025-09-07T06:49:09.9079972Z >>> input = torch.randn(128, 20) 2025-09-07T06:49:09.9080030Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T06:49:09.9080070Z >>> ret = ret_fut.wait() 2025-09-07T06:49:09.9080141Z >>> rpc.shutdown() 2025-09-07T06:49:09.9080144Z 2025-09-07T06:49:09.9080199Z >>> # On worker 1: 2025-09-07T06:49:09.9080244Z >>> import torch 2025-09-07T06:49:09.9082177Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:09.9082217Z >>> 2025-09-07T06:49:09.9082286Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:09.9082325Z >>> rpc.shutdown() 2025-09-07T06:49:09.9082328Z 2025-09-07T06:49:09.9082401Z Furthermore, a more practical example that is combined with 2025-09-07T06:49:09.9082574Z `DistributedDataParallel `__ (DDP) 2025-09-07T06:49:09.9082689Z can be found in this `tutorial `__. 2025-09-07T06:49:09.9082691Z 2025-09-07T06:49:09.9082783Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:09.9082811Z 2025-09-07T06:49:10.6821995Z 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. 2025-09-07T06:49:10.6823255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.6823850Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T06:49:10.6824176Z 2025-09-07T06:49:10.6824300Z The values are as follows: 2025-09-07T06:49:10.6824614Z 2025-09-07T06:49:10.6824754Z ================= ==================================================== 2025-09-07T06:49:10.6833992Z nonlinearity gain 2025-09-07T06:49:10.6834210Z ================= ==================================================== 2025-09-07T06:49:10.6834549Z Linear / Identity :math:`1` 2025-09-07T06:49:10.6834718Z Conv{1,2,3}D :math:`1` 2025-09-07T06:49:10.6834866Z Sigmoid :math:`1` 2025-09-07T06:49:10.6835037Z Tanh :math:`\frac{5}{3}` 2025-09-07T06:49:10.6835214Z ReLU :math:`\sqrt{2}` 2025-09-07T06:49:10.6835430Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T06:49:10.6835652Z SELU :math:`\frac{3}{4}` 2025-09-07T06:49:10.6835827Z ================= ==================================================== 2025-09-07T06:49:10.6835958Z 2025-09-07T06:49:10.6836044Z .. warning:: 2025-09-07T06:49:10.6836232Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T06:49:10.6836504Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T06:49:10.6836774Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T06:49:10.6837006Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T06:49:10.6837225Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T06:49:10.6837429Z effect for more stable gradient flow in rectangular layers. 2025-09-07T06:49:10.6837549Z 2025-09-07T06:49:10.6837586Z Args: 2025-09-07T06:49:10.6837728Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T06:49:10.6837925Z param: optional parameter for the non-linear function 2025-09-07T06:49:10.6838038Z 2025-09-07T06:49:10.6838079Z Examples: 2025-09-07T06:49:10.6838193Z >>> gain = nn.init.calculate_gain( 2025-09-07T06:49:10.6838334Z ... "leaky_relu", 0.2 2025-09-07T06:49:10.6838474Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T06:49:10.6838568Z 2025-09-07T06:49:10.6838782Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T06:49:10.6839044Z 2025-09-07T06:49:10.6839199Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.6839334Z 2025-09-07T06:49:10.7165676Z 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. 2025-09-07T06:49:10.7166106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7166257Z 2025-09-07T06:49:10.7166361Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T06:49:10.7166497Z 2025-09-07T06:49:10.7166596Z This context manager will keep track of already-joined DDP processes, 2025-09-07T06:49:10.7166816Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T06:49:10.7167035Z communication operations to match with the ones created by non-joined 2025-09-07T06:49:10.7167266Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T06:49:10.7167496Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T06:49:10.7167706Z would otherwise happen when training with uneven inputs across 2025-09-07T06:49:10.7167993Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T06:49:10.7168223Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T06:49:10.7168477Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T06:49:10.7168652Z according to application logic. 2025-09-07T06:49:10.7168741Z 2025-09-07T06:49:10.7168830Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T06:49:10.7169054Z the model corresponding to the last joined process to all processes to 2025-09-07T06:49:10.7169253Z ensure the model is the same across all processes 2025-09-07T06:49:10.7169411Z (which is guaranteed by DDP). 2025-09-07T06:49:10.7169493Z 2025-09-07T06:49:10.7169581Z To use this to enable training with uneven inputs across processes, 2025-09-07T06:49:10.7169795Z simply wrap this context manager around your training loop. No further 2025-09-07T06:49:10.7170002Z modifications to the model or data loading is required. 2025-09-07T06:49:10.7170117Z 2025-09-07T06:49:10.7170162Z .. warning:: 2025-09-07T06:49:10.7170310Z If the model or training loop this context manager is wrapped around 2025-09-07T06:49:10.7170511Z has additional distributed collective operations, such as 2025-09-07T06:49:10.7170703Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T06:49:10.7170904Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T06:49:10.7171111Z context manager is not aware of non-DDP collective communication. 2025-09-07T06:49:10.7171310Z This flag will cause all ranks to throw when any one rank 2025-09-07T06:49:10.7171507Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T06:49:10.7171678Z from across all ranks. 2025-09-07T06:49:10.7171751Z 2025-09-07T06:49:10.7171789Z Args: 2025-09-07T06:49:10.7171916Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T06:49:10.7172113Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T06:49:10.7172310Z with. If ``False``, will compute the effective world size 2025-09-07T06:49:10.7172494Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T06:49:10.7172677Z divide gradients by that during allreduce. Set 2025-09-07T06:49:10.7172854Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T06:49:10.7173053Z sample including the uneven inputs have equal weight in terms of 2025-09-07T06:49:10.7173250Z how much they contribute to the global gradient. This is 2025-09-07T06:49:10.7173431Z achieved by always dividing the gradient by the initial 2025-09-07T06:49:10.7173620Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T06:49:10.7173805Z this to ``False``, we divide the gradient by the remaining 2025-09-07T06:49:10.7173994Z number of nodes. This ensures parity with training on a smaller 2025-09-07T06:49:10.7174189Z ``world_size`` although it also means the uneven inputs would 2025-09-07T06:49:10.7174532Z contribute more towards the global gradient. Typically, you 2025-09-07T06:49:10.7174727Z would want to set this to ``True`` for cases where the last few 2025-09-07T06:49:10.7174919Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T06:49:10.7175109Z there is a large discrepancy in the number of inputs, setting 2025-09-07T06:49:10.7175285Z this to ``False`` might provide better results. 2025-09-07T06:49:10.7175472Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T06:49:10.7175666Z in ``enable=False`` to disable in cases where you know that 2025-09-07T06:49:10.7175852Z inputs are even across participating processes. Default is 2025-09-07T06:49:10.7176020Z ``True``. 2025-09-07T06:49:10.7176157Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T06:49:10.7176377Z or continue training when at least one rank has exhausted 2025-09-07T06:49:10.7176569Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T06:49:10.7176784Z of data. If ``False``, will continue training with a smaller 2025-09-07T06:49:10.7176974Z effective world size until all ranks are joined. Note that if 2025-09-07T06:49:10.7177147Z this flag is specified, then the flag 2025-09-07T06:49:10.7177316Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T06:49:10.7180935Z is ``False``. 2025-09-07T06:49:10.7181003Z 2025-09-07T06:49:10.7181005Z 2025-09-07T06:49:10.7181041Z Example:: 2025-09-07T06:49:10.7181091Z 2025-09-07T06:49:10.7181139Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T06:49:10.7181257Z >>> import torch 2025-09-07T06:49:10.7181360Z >>> import torch.distributed as dist 2025-09-07T06:49:10.7181473Z >>> import os 2025-09-07T06:49:10.7181573Z >>> import torch.multiprocessing as mp 2025-09-07T06:49:10.7181699Z >>> import torch.nn as nn 2025-09-07T06:49:10.7181813Z >>> # On each spawned worker 2025-09-07T06:49:10.7181921Z >>> def worker(rank): 2025-09-07T06:49:10.7182056Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T06:49:10.7182201Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:10.7183957Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T06:49:10.7184119Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T06:49:10.7184273Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T06:49:10.7184584Z >>> ) 2025-09-07T06:49:10.7184681Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T06:49:10.7184832Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T06:49:10.7184976Z >>> with model.join(): 2025-09-07T06:49:10.7185079Z >>> for _ in range(5): 2025-09-07T06:49:10.7185188Z >>> for inp in inputs: 2025-09-07T06:49:10.7185307Z >>> loss = model(inp).sum() 2025-09-07T06:49:10.7185427Z >>> loss.backward() 2025-09-07T06:49:10.7186842Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T06:49:10.7187009Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T06:49:10.7187143Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T06:49:10.7187226Z 2025-09-07T06:49:10.7187316Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7187440Z 2025-09-07T06:49:10.7187696Z 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. 2025-09-07T06:49:10.7188056Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7188184Z 2025-09-07T06:49:10.7188288Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T06:49:10.7188430Z 2025-09-07T06:49:10.7188569Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T06:49:10.7188751Z parameter will run immediately when that parameter's gradient is 2025-09-07T06:49:10.7188931Z finished with reduction, instead of waiting for all parameters' 2025-09-07T06:49:10.7189116Z gradients to finish reduction. This can result in a training speedup 2025-09-07T06:49:10.7190539Z depending on your workload since the optimizer can run while gradient 2025-09-07T06:49:10.7190733Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T06:49:10.7191003Z the potential to reduce peak memory consumption during training, as it 2025-09-07T06:49:10.7191188Z only needs to load the per-parameter optimizer states of a single 2025-09-07T06:49:10.7191369Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T06:49:10.7191512Z states at once. 2025-09-07T06:49:10.7191564Z 2025-09-07T06:49:10.7191630Z Args: 2025-09-07T06:49:10.7191749Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T06:49:10.7191890Z as a fused optimizer. 2025-09-07T06:49:10.7192033Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T06:49:10.7192202Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T06:49:10.7193598Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T06:49:10.7193785Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T06:49:10.7193925Z optimized. 2025-09-07T06:49:10.7194047Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T06:49:10.7194153Z 2025-09-07T06:49:10.7194189Z .. warning :: 2025-09-07T06:49:10.7194312Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T06:49:10.7194578Z and registering multiple fused optimizers for the same DDP model 2025-09-07T06:49:10.7194734Z is not currently supported. Please ping 2025-09-07T06:49:10.7194904Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:10.7195057Z for your use case. 2025-09-07T06:49:10.7195119Z 2025-09-07T06:49:10.7195152Z .. warning :: 2025-09-07T06:49:10.7196442Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T06:49:10.7196623Z compose together, meaning that custom DDP communication hooks are 2025-09-07T06:49:10.7196795Z not supported with overlapped optimizers. Please ping 2025-09-07T06:49:10.7196969Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:10.7197118Z for your use case. 2025-09-07T06:49:10.7197178Z 2025-09-07T06:49:10.7197211Z .. warning :: 2025-09-07T06:49:10.7197337Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T06:49:10.7197494Z with overlapped optimizer. Please ping 2025-09-07T06:49:10.7197650Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:10.7197800Z for your use case. 2025-09-07T06:49:10.7197859Z 2025-09-07T06:49:10.7197894Z Example:: 2025-09-07T06:49:10.7199073Z 2025-09-07T06:49:10.7199130Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T06:49:10.7199320Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T06:49:10.7199534Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T06:49:10.7199675Z >>> lr = 1e-2 2025-09-07T06:49:10.7199763Z >>> betas = (0.9, 0.99) 2025-09-07T06:49:10.7199858Z >>> eps = 1e-6 2025-09-07T06:49:10.7199989Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T06:49:10.7200151Z >>> # Example with subset of parameters 2025-09-07T06:49:10.7200278Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T06:49:10.7200406Z >>> net._register_fused_optim( 2025-09-07T06:49:10.7200558Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T06:49:10.7201839Z ... ) 2025-09-07T06:49:10.7201936Z 2025-09-07T06:49:10.7202027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7202151Z 2025-09-07T06:49:10.7592541Z 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. 2025-09-07T06:49:10.7593420Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7594080Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T06:49:10.7594651Z 2025-09-07T06:49:10.7594971Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T06:49:10.7595496Z and with 2D inputs, this class 2025-09-07T06:49:10.7595698Z 2025-09-07T06:49:10.7596000Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T06:49:10.7596895Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T06:49:10.7600991Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T06:49:10.7601142Z 2025-09-07T06:49:10.7601262Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T06:49:10.7601453Z operations. 2025-09-07T06:49:10.7601508Z 2025-09-07T06:49:10.7601606Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T06:49:10.7601813Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T06:49:10.7602018Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T06:49:10.7602226Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T06:49:10.7602388Z :attr:`per_sample_weights`. 2025-09-07T06:49:10.7602462Z 2025-09-07T06:49:10.7602494Z Args: 2025-09-07T06:49:10.7602609Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T06:49:10.7602777Z embedding_dim (int): the size of each embedding vector 2025-09-07T06:49:10.7602982Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T06:49:10.7603185Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T06:49:10.7603404Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T06:49:10.7603673Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T06:49:10.7603886Z the words in the mini-batch. Default ``False``. 2025-09-07T06:49:10.7604063Z Note: this option is not supported when ``mode="max"``. 2025-09-07T06:49:10.7604264Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T06:49:10.7607738Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T06:49:10.7607951Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T06:49:10.7608150Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T06:49:10.7608327Z Default: ``"mean"`` 2025-09-07T06:49:10.7608537Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T06:49:10.7608771Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T06:49:10.7608948Z supported when ``mode="max"``. 2025-09-07T06:49:10.7609282Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T06:49:10.7609524Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T06:49:10.7609750Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T06:49:10.7609993Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T06:49:10.7611431Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T06:49:10.7611650Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T06:49:10.7611861Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T06:49:10.7612097Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T06:49:10.7612260Z reduction. 2025-09-07T06:49:10.7612363Z 2025-09-07T06:49:10.7612399Z Attributes: 2025-09-07T06:49:10.7612563Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T06:49:10.7612765Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T06:49:10.7612852Z 2025-09-07T06:49:10.7612905Z Examples:: 2025-09-07T06:49:10.7612956Z 2025-09-07T06:49:10.7613020Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T06:49:10.7613175Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T06:49:10.7614478Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T06:49:10.7614637Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T06:49:10.7614799Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T06:49:10.7614945Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:10.7615077Z >>> embedding_sum(input, offsets) 2025-09-07T06:49:10.7615208Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T06:49:10.7615323Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T06:49:10.7615397Z 2025-09-07T06:49:10.7615440Z >>> # Example with padding_idx 2025-09-07T06:49:10.7615587Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T06:49:10.7615760Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T06:49:10.7615914Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T06:49:10.7617098Z >>> embedding_sum(input, offsets) 2025-09-07T06:49:10.7617219Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T06:49:10.7617332Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T06:49:10.7617404Z 2025-09-07T06:49:10.7617471Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T06:49:10.7617624Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T06:49:10.7617772Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T06:49:10.7617903Z embedding.weight, 2025-09-07T06:49:10.7618021Z padding_idx=embedding.padding_idx, 2025-09-07T06:49:10.7618142Z mode='sum') 2025-09-07T06:49:10.7618235Z 2025-09-07T06:49:10.7618367Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7619523Z 2025-09-07T06:49:10.7623694Z 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. 2025-09-07T06:49:10.7624995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7625527Z Take in and process masked source/target sequences. 2025-09-07T06:49:10.7625770Z 2025-09-07T06:49:10.7625870Z .. note:: 2025-09-07T06:49:10.7626006Z 2025-09-07T06:49:10.7626603Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T06:49:10.7627117Z not allowed to participate in the attention, 2025-09-07T06:49:10.7627499Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T06:49:10.7627900Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T06:49:10.7628138Z 2025-09-07T06:49:10.7628226Z Args: 2025-09-07T06:49:10.7634023Z src: the sequence to the encoder (required). 2025-09-07T06:49:10.7634314Z tgt: the sequence to the decoder (required). 2025-09-07T06:49:10.7634795Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T06:49:10.7635092Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T06:49:10.7635489Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T06:49:10.7635829Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T06:49:10.7636259Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T06:49:10.7636615Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T06:49:10.7636966Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T06:49:10.7637262Z Default: ``None``; try to detect a causal mask. 2025-09-07T06:49:10.7637489Z Warning: 2025-09-07T06:49:10.7637717Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T06:49:10.7638020Z the causal mask. Providing incorrect hints can result in 2025-09-07T06:49:10.7638321Z incorrect execution, including forward and backward 2025-09-07T06:49:10.7638584Z compatibility. 2025-09-07T06:49:10.7641237Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T06:49:10.7641534Z Default: ``None``; try to detect a causal mask. 2025-09-07T06:49:10.7641762Z Warning: 2025-09-07T06:49:10.7641973Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T06:49:10.7642251Z the causal mask. Providing incorrect hints can result in 2025-09-07T06:49:10.7642532Z incorrect execution, including forward and backward 2025-09-07T06:49:10.7642767Z compatibility. 2025-09-07T06:49:10.7642999Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T06:49:10.7643242Z ``memory_mask``. 2025-09-07T06:49:10.7643431Z Default: ``False``. 2025-09-07T06:49:10.7643617Z Warning: 2025-09-07T06:49:10.7645431Z ``memory_is_causal`` provides a hint that 2025-09-07T06:49:10.7645630Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T06:49:10.7645836Z hints can result in incorrect execution, including 2025-09-07T06:49:10.7646023Z forward and backward compatibility. 2025-09-07T06:49:10.7646133Z 2025-09-07T06:49:10.7646180Z Shape: 2025-09-07T06:49:10.7646371Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T06:49:10.7646591Z `(N, S, E)` if `batch_first=True`. 2025-09-07T06:49:10.7646804Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T06:49:10.7647016Z `(N, T, E)` if `batch_first=True`. 2025-09-07T06:49:10.7647209Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T06:49:10.7647429Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T06:49:10.7648994Z - memory_mask: :math:`(T, S)`. 2025-09-07T06:49:10.7649279Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T06:49:10.7649546Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T06:49:10.7649817Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T06:49:10.7649981Z 2025-09-07T06:49:10.7650127Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T06:49:10.7650414Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T06:49:10.7650676Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T06:49:10.7650922Z is provided, it will be added to the attention weight. 2025-09-07T06:49:10.7651187Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T06:49:10.7651493Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T06:49:10.7651784Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T06:49:10.7653357Z 2025-09-07T06:49:10.7653484Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T06:49:10.7653706Z `(N, T, E)` if `batch_first=True`. 2025-09-07T06:49:10.7653813Z 2025-09-07T06:49:10.7653924Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T06:49:10.7654191Z the output sequence length of a transformer is same as the input sequence 2025-09-07T06:49:10.7654570Z (i.e. target) length of the decoder. 2025-09-07T06:49:10.7654676Z 2025-09-07T06:49:10.7654823Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T06:49:10.7655081Z batch size, :math:`E` is the feature number 2025-09-07T06:49:10.7655197Z 2025-09-07T06:49:10.7655248Z Examples: 2025-09-07T06:49:10.7655352Z >>> # xdoctest: +SKIP 2025-09-07T06:49:10.7655476Z >>> output = transformer_model( 2025-09-07T06:49:10.7656724Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T06:49:10.7656860Z ... ) 2025-09-07T06:49:10.7656946Z 2025-09-07T06:49:10.7657085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7657215Z 2025-09-07T06:49:10.7703052Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=410. 2025-09-07T06:49:10.7703393Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7703589Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T06:49:10.7703683Z 2025-09-07T06:49:10.7703774Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T06:49:10.7703907Z 2025-09-07T06:49:10.7703991Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T06:49:10.7704191Z including the indices of the maximal values and computes a partial inverse 2025-09-07T06:49:10.7706308Z in which all non-maximal values are set to zero. 2025-09-07T06:49:10.7706410Z 2025-09-07T06:49:10.7706448Z Note: 2025-09-07T06:49:10.7706603Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T06:49:10.7706883Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T06:49:10.7707053Z 2025-09-07T06:49:10.7707141Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T06:49:10.7707327Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T06:49:10.7707497Z To accommodate this, you can provide the needed output size 2025-09-07T06:49:10.7707768Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T06:49:10.7707925Z See the Inputs and Example below. 2025-09-07T06:49:10.7708008Z 2025-09-07T06:49:10.7708041Z Args: 2025-09-07T06:49:10.7708154Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T06:49:10.7708320Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T06:49:10.7708470Z It is set to :attr:`kernel_size` by default. 2025-09-07T06:49:10.7708625Z padding (int or tuple): Padding that was added to the input 2025-09-07T06:49:10.7708728Z 2025-09-07T06:49:10.7708763Z Inputs: 2025-09-07T06:49:10.7710095Z - `input`: the input Tensor to invert 2025-09-07T06:49:10.7710259Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T06:49:10.7710429Z - `output_size` (optional): the targeted output size 2025-09-07T06:49:10.7710557Z 2025-09-07T06:49:10.7710591Z Shape: 2025-09-07T06:49:10.7710710Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T06:49:10.7710967Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T06:49:10.7711102Z 2025-09-07T06:49:10.7711138Z .. math:: 2025-09-07T06:49:10.7711290Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T06:49:10.7711423Z 2025-09-07T06:49:10.7711457Z .. math:: 2025-09-07T06:49:10.7711596Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T06:49:10.7711721Z 2025-09-07T06:49:10.7711784Z or as given by :attr:`output_size` in the call operator 2025-09-07T06:49:10.7711882Z 2025-09-07T06:49:10.7712998Z Example:: 2025-09-07T06:49:10.7713056Z 2025-09-07T06:49:10.7713118Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T06:49:10.7713264Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T06:49:10.7713404Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T06:49:10.7713535Z [ 5., 6., 7., 8.], 2025-09-07T06:49:10.7713655Z [ 9., 10., 11., 12.], 2025-09-07T06:49:10.7713776Z [13., 14., 15., 16.]]]]) 2025-09-07T06:49:10.7713898Z >>> output, indices = pool(input) 2025-09-07T06:49:10.7714018Z >>> unpool(output, indices) 2025-09-07T06:49:10.7714133Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T06:49:10.7714244Z [ 0., 6., 0., 8.], 2025-09-07T06:49:10.7715472Z [ 0., 0., 0., 0.], 2025-09-07T06:49:10.7715587Z [ 0., 14., 0., 16.]]]]) 2025-09-07T06:49:10.7715732Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T06:49:10.7715897Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T06:49:10.7716034Z [ 6., 7., 8., 9., 10.], 2025-09-07T06:49:10.7716156Z [11., 12., 13., 14., 15.], 2025-09-07T06:49:10.7716280Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T06:49:10.7716402Z >>> output, indices = pool(input) 2025-09-07T06:49:10.7716543Z >>> # This call will not work without specifying output_size 2025-09-07T06:49:10.7716699Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T06:49:10.7716832Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T06:49:10.7717967Z [ 0., 7., 0., 9., 0.], 2025-09-07T06:49:10.7718079Z [ 0., 0., 0., 0., 0.], 2025-09-07T06:49:10.7718190Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T06:49:10.7718264Z 2025-09-07T06:49:10.7718266Z 2025-09-07T06:49:10.7718297Z 2025-09-07T06:49:10.7718427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7718552Z 2025-09-07T06:49:10.7851293Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1798. 2025-09-07T06:49:10.7851654Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7851854Z Creates a criterion that measures the triplet loss given input 2025-09-07T06:49:10.7852036Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T06:49:10.7852219Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T06:49:10.7854542Z real-valued function ("distance function") used to compute the relationship 2025-09-07T06:49:10.7854776Z between the anchor and positive example ("positive distance") and the 2025-09-07T06:49:10.7854959Z anchor and negative example ("negative distance"). 2025-09-07T06:49:10.7855052Z 2025-09-07T06:49:10.7855186Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T06:49:10.7855349Z can be described as: 2025-09-07T06:49:10.7855417Z 2025-09-07T06:49:10.7855477Z .. math:: 2025-09-07T06:49:10.7855584Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T06:49:10.7855733Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T06:49:10.7855853Z 2025-09-07T06:49:10.7855950Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T06:49:10.7856176Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T06:49:10.7856402Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T06:49:10.7856613Z between the positive and negative distances that is required for the loss to 2025-09-07T06:49:10.7856814Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T06:49:10.7856989Z that the distance function can handle. 2025-09-07T06:49:10.7857085Z 2025-09-07T06:49:10.7859001Z If :attr:`reduction` is not ``'none'`` 2025-09-07T06:49:10.7859135Z (default ``'mean'``), then: 2025-09-07T06:49:10.7859215Z 2025-09-07T06:49:10.7859257Z .. math:: 2025-09-07T06:49:10.7859343Z \ell(x, y) = 2025-09-07T06:49:10.7859440Z \begin{cases} 2025-09-07T06:49:10.7859574Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T06:49:10.7859749Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T06:49:10.7859884Z \end{cases} 2025-09-07T06:49:10.7859938Z 2025-09-07T06:49:10.7860022Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T06:49:10.7860232Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T06:49:10.7860370Z 2025-09-07T06:49:10.7860402Z Args: 2025-09-07T06:49:10.7860542Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T06:49:10.7860749Z quantifies the closeness of two tensors. If not specified, 2025-09-07T06:49:10.7860917Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T06:49:10.7861110Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T06:49:10.7861334Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T06:49:10.7861567Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T06:49:10.7861783Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T06:49:10.7861993Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T06:49:10.7862224Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T06:49:10.7862441Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T06:49:10.7862717Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T06:49:10.7862919Z the loss computation. Default: ``False``. 2025-09-07T06:49:10.7863119Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T06:49:10.7864734Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T06:49:10.7864912Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T06:49:10.7865095Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T06:49:10.7865217Z 2025-09-07T06:49:10.7865218Z 2025-09-07T06:49:10.7865252Z Shape: 2025-09-07T06:49:10.7865387Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T06:49:10.7865555Z as supported by the distance function. 2025-09-07T06:49:10.7865769Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T06:49:10.7865928Z otherwise. 2025-09-07T06:49:10.7866142Z 2025-09-07T06:49:10.7866176Z Examples: 2025-09-07T06:49:10.7866228Z 2025-09-07T06:49:10.7866269Z >>> # Initialize embeddings 2025-09-07T06:49:10.7866382Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T06:49:10.7867569Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:10.7867704Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:10.7867834Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:10.7867959Z >>> anchor = embedding(anchor_ids) 2025-09-07T06:49:10.7868079Z >>> positive = embedding(positive_ids) 2025-09-07T06:49:10.7868202Z >>> negative = embedding(negative_ids) 2025-09-07T06:49:10.7868310Z >>> 2025-09-07T06:49:10.7868394Z >>> # Built-in Distance Function 2025-09-07T06:49:10.7868505Z >>> triplet_loss = \ 2025-09-07T06:49:10.7868668Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T06:49:10.7868860Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:10.7870011Z >>> output.backward() 2025-09-07T06:49:10.7870110Z >>> 2025-09-07T06:49:10.7870195Z >>> # Custom Distance Function 2025-09-07T06:49:10.7870308Z >>> def l_infinity(x1, x2): 2025-09-07T06:49:10.7870435Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T06:49:10.7870557Z >>> 2025-09-07T06:49:10.7870669Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T06:49:10.7870810Z >>> triplet_loss = ( 2025-09-07T06:49:10.7871050Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T06:49:10.7871240Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:10.7871369Z >>> output.backward() 2025-09-07T06:49:10.7872484Z >>> 2025-09-07T06:49:10.7872577Z >>> # Custom Distance Function (Lambda) 2025-09-07T06:49:10.7872695Z >>> triplet_loss = ( 2025-09-07T06:49:10.7872807Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T06:49:10.7872970Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T06:49:10.7873136Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:10.7873262Z >>> output.backward() 2025-09-07T06:49:10.7873322Z 2025-09-07T06:49:10.7873356Z Reference: 2025-09-07T06:49:10.7873512Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T06:49:10.7873736Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T06:49:10.7873880Z 2025-09-07T06:49:10.7875117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T06:49:10.7875248Z 2025-09-07T06:49:10.7875420Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1933. 2025-09-07T06:49:10.7875761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.7875943Z The Connectionist Temporal Classification loss. 2025-09-07T06:49:10.7876034Z 2025-09-07T06:49:10.7876163Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T06:49:10.7876450Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T06:49:10.7876720Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T06:49:10.7876974Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T06:49:10.7877114Z 2025-09-07T06:49:10.7877147Z Args: 2025-09-07T06:49:10.7877254Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T06:49:10.7877435Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T06:49:10.7878715Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T06:49:10.7878922Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T06:49:10.7879120Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T06:49:10.7879285Z Default: ``'mean'`` 2025-09-07T06:49:10.7879394Z zero_infinity (bool, optional): 2025-09-07T06:49:10.7879543Z Whether to zero infinite losses and the associated gradients. 2025-09-07T06:49:10.7879686Z Default: ``False`` 2025-09-07T06:49:10.7879820Z Infinite losses mainly occur when the inputs are too short 2025-09-07T06:49:10.7879963Z to be aligned to the targets. 2025-09-07T06:49:10.7880038Z 2025-09-07T06:49:10.7880072Z Shape: 2025-09-07T06:49:10.7880188Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T06:49:10.7881381Z where :math:`T = \text{input length}`, 2025-09-07T06:49:10.7881511Z :math:`N = \text{batch size}`, and 2025-09-07T06:49:10.7881650Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T06:49:10.7881820Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T06:49:10.7881984Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T06:49:10.7882118Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T06:49:10.7882260Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T06:49:10.7882399Z where :math:`N = \text{batch size}` and 2025-09-07T06:49:10.7882540Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T06:49:10.7882704Z It represents the target sequences. Each element in the target 2025-09-07T06:49:10.7882891Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T06:49:10.7884095Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T06:49:10.7884242Z length of the longest sequence, and stacked. 2025-09-07T06:49:10.7884479Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T06:49:10.7884634Z the targets are assumed to be un-padded and 2025-09-07T06:49:10.7884761Z concatenated within 1 dimension. 2025-09-07T06:49:10.7884911Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T06:49:10.7885089Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T06:49:10.7885266Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T06:49:10.7885450Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T06:49:10.7885606Z are padded to equal lengths. 2025-09-07T06:49:10.7885753Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T06:49:10.7887030Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T06:49:10.7887218Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T06:49:10.7887415Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T06:49:10.7887599Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T06:49:10.7887782Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T06:49:10.7887971Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T06:49:10.7888151Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T06:49:10.7888351Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T06:49:10.7888539Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T06:49:10.7888747Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T06:49:10.7889974Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T06:49:10.7890111Z 2025-09-07T06:49:10.7890147Z Examples: 2025-09-07T06:49:10.7890198Z 2025-09-07T06:49:10.7890242Z >>> # Target are to be padded 2025-09-07T06:49:10.7890360Z >>> T = 50 # Input sequence length 2025-09-07T06:49:10.7890487Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:10.7890608Z >>> N = 16 # Batch size 2025-09-07T06:49:10.7890754Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T06:49:10.7890932Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T06:49:10.7891062Z >>> 2025-09-07T06:49:10.7891177Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T06:49:10.7891357Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T06:49:10.7892543Z >>> 2025-09-07T06:49:10.7892660Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:10.7892843Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T06:49:10.7892985Z >>> 2025-09-07T06:49:10.7893106Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T06:49:10.7893259Z >>> target_lengths = torch.randint( 2025-09-07T06:49:10.7893372Z ... low=S_min, 2025-09-07T06:49:10.7893467Z ... high=S, 2025-09-07T06:49:10.7893560Z ... size=(N,), 2025-09-07T06:49:10.7893660Z ... dtype=torch.long, 2025-09-07T06:49:10.7893760Z ... ) 2025-09-07T06:49:10.7894966Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:10.7895111Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:10.7895253Z >>> loss.backward() 2025-09-07T06:49:10.7895347Z >>> 2025-09-07T06:49:10.7895423Z >>> 2025-09-07T06:49:10.7895511Z >>> # Target are to be un-padded 2025-09-07T06:49:10.7895629Z >>> T = 50 # Input sequence length 2025-09-07T06:49:10.7895753Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:10.7895874Z >>> N = 16 # Batch size 2025-09-07T06:49:10.7895971Z >>> 2025-09-07T06:49:10.7896084Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T06:49:10.7897296Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T06:49:10.7897484Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T06:49:10.7897625Z >>> 2025-09-07T06:49:10.7897735Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:10.7897930Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T06:49:10.7898091Z >>> target = torch.randint( 2025-09-07T06:49:10.7898250Z ... low=1, 2025-09-07T06:49:10.7898342Z ... high=C, 2025-09-07T06:49:10.7898442Z ... size=(sum(target_lengths),), 2025-09-07T06:49:10.7898558Z ... dtype=torch.long, 2025-09-07T06:49:10.7898658Z ... ) 2025-09-07T06:49:10.7899801Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:10.7899943Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:10.7900082Z >>> loss.backward() 2025-09-07T06:49:10.7900175Z >>> 2025-09-07T06:49:10.7900249Z >>> 2025-09-07T06:49:10.7900361Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T06:49:10.7900504Z >>> T = 50 # Input sequence length 2025-09-07T06:49:10.7900628Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:10.7900740Z >>> 2025-09-07T06:49:10.7900851Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T06:49:10.7901037Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T06:49:10.7902235Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T06:49:10.7902442Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T06:49:10.7902563Z >>> 2025-09-07T06:49:10.7902674Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:10.7902858Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T06:49:10.7903016Z >>> target = torch.randint( 2025-09-07T06:49:10.7903120Z ... low=1, 2025-09-07T06:49:10.7903209Z ... high=C, 2025-09-07T06:49:10.7903306Z ... size=(target_lengths,), 2025-09-07T06:49:10.7903418Z ... dtype=torch.long, 2025-09-07T06:49:10.7903518Z ... ) 2025-09-07T06:49:10.7904732Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:10.7904872Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:10.7905016Z >>> loss.backward() 2025-09-07T06:49:10.7905079Z 2025-09-07T06:49:10.7905117Z Reference: 2025-09-07T06:49:10.7905232Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T06:49:10.7905405Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T06:49:10.7905579Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T06:49:10.7905673Z 2025-09-07T06:49:10.7905706Z Note: 2025-09-07T06:49:10.7905835Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T06:49:10.7906041Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T06:49:10.7906242Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T06:49:10.7907468Z dtype :attr:`torch.int32`. 2025-09-07T06:49:10.7907545Z 2025-09-07T06:49:10.7907641Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T06:49:10.7907770Z 2025-09-07T06:49:10.7907772Z 2025-09-07T06:49:10.7907805Z Note: 2025-09-07T06:49:10.7907937Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T06:49:10.7908144Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T06:49:10.7908353Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T06:49:10.7908554Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T06:49:10.7908702Z True``. 2025-09-07T06:49:10.7908824Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T06:49:10.7908959Z 2025-09-07T06:49:10.7909089Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.7909211Z 2025-09-07T06:49:10.8002932Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-09-07T06:49:10.8003313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8004345Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T06:49:10.8004898Z 2025-09-07T06:49:10.8005272Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T06:49:10.8005956Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T06:49:10.8006532Z Internal Covariate Shift `__ . 2025-09-07T06:49:10.8006865Z 2025-09-07T06:49:10.8006979Z .. math:: 2025-09-07T06:49:10.8007133Z 2025-09-07T06:49:10.8007395Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T06:49:10.8007720Z 2025-09-07T06:49:10.8007978Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T06:49:10.8008862Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T06:49:10.8016198Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T06:49:10.8016614Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T06:49:10.8016907Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T06:49:10.8017237Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T06:49:10.8017517Z `torch.var(input, unbiased=False)`. 2025-09-07T06:49:10.8017650Z 2025-09-07T06:49:10.8017788Z Also by default, during training this layer keeps running estimates of its 2025-09-07T06:49:10.8018108Z computed mean and variance, which are then used for normalization during 2025-09-07T06:49:10.8018436Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T06:49:10.8018682Z of 0.1. 2025-09-07T06:49:10.8018765Z 2025-09-07T06:49:10.8018905Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T06:49:10.8019215Z keep running estimates, and batch statistics are instead used during 2025-09-07T06:49:10.8019492Z evaluation time as well. 2025-09-07T06:49:10.8019619Z 2025-09-07T06:49:10.8019682Z .. note:: 2025-09-07T06:49:10.8019912Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T06:49:10.8020241Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T06:49:10.8020524Z update rule for running statistics here is 2025-09-07T06:49:10.8020841Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T06:49:10.8021184Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T06:49:10.8021441Z new observed value. 2025-09-07T06:49:10.8021556Z 2025-09-07T06:49:10.8021744Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T06:49:10.8024184Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T06:49:10.8024493Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T06:49:10.8024614Z 2025-09-07T06:49:10.8024681Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T06:49:10.8024914Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T06:49:10.8025175Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T06:49:10.8025402Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T06:49:10.8025578Z Network with DDP. 2025-09-07T06:49:10.8025655Z 2025-09-07T06:49:10.8025695Z Args: 2025-09-07T06:49:10.8025828Z num_features: :math:`C` from an expected input of size 2025-09-07T06:49:10.8025994Z :math:`(N, C, +)` 2025-09-07T06:49:10.8026158Z eps: a value added to the denominator for numerical stability. 2025-09-07T06:49:10.8027822Z Default: ``1e-5`` 2025-09-07T06:49:10.8027992Z momentum: the value used for the running_mean and running_var 2025-09-07T06:49:10.8028211Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T06:49:10.8028402Z (i.e. simple average). Default: 0.1 2025-09-07T06:49:10.8028592Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T06:49:10.8028791Z learnable affine parameters. Default: ``True`` 2025-09-07T06:49:10.8028995Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T06:49:10.8029228Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T06:49:10.8029466Z this module does not track such statistics, and initializes statistics 2025-09-07T06:49:10.8029691Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T06:49:10.8031387Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T06:49:10.8031617Z in both training and eval modes. Default: ``True`` 2025-09-07T06:49:10.8031870Z process_group: synchronization of stats happen within each process group 2025-09-07T06:49:10.8032112Z individually. Default behavior is synchronization across the whole 2025-09-07T06:49:10.8032291Z world 2025-09-07T06:49:10.8032357Z 2025-09-07T06:49:10.8032398Z Shape: 2025-09-07T06:49:10.8032510Z - Input: :math:`(N, C, +)` 2025-09-07T06:49:10.8032672Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T06:49:10.8032782Z 2025-09-07T06:49:10.8032823Z .. note:: 2025-09-07T06:49:10.8032985Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T06:49:10.8033219Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T06:49:10.8034737Z ``self.training`` is otherwise ``False``. 2025-09-07T06:49:10.8034830Z 2025-09-07T06:49:10.8034868Z Examples:: 2025-09-07T06:49:10.8034925Z 2025-09-07T06:49:10.8034967Z >>> # xdoctest: +SKIP 2025-09-07T06:49:10.8035080Z >>> # With Learnable Parameters 2025-09-07T06:49:10.8035199Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T06:49:10.8035321Z >>> # creating process group (optional) 2025-09-07T06:49:10.8035455Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T06:49:10.8035583Z >>> ranks = list(range(8)) 2025-09-07T06:49:10.8035694Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T06:49:10.8035824Z >>> # Note: every rank calls into new_group for every 2025-09-07T06:49:10.8035968Z >>> # process group created, even if that rank is not 2025-09-07T06:49:10.8037134Z >>> # part of the group. 2025-09-07T06:49:10.8037300Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T06:49:10.8037498Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T06:49:10.8037653Z >>> # Without Learnable Parameters 2025-09-07T06:49:10.8037804Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T06:49:10.8037963Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T06:49:10.8038082Z >>> output = m(input) 2025-09-07T06:49:10.8038147Z 2025-09-07T06:49:10.8038193Z >>> # network is nn.BatchNorm layer 2025-09-07T06:49:10.8038367Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T06:49:10.8038558Z >>> # only single gpu per process is currently supported 2025-09-07T06:49:10.8038728Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T06:49:10.8039895Z >>> sync_bn_network, 2025-09-07T06:49:10.8040030Z >>> device_ids=[args.local_rank], 2025-09-07T06:49:10.8040169Z >>> output_device=args.local_rank) 2025-09-07T06:49:10.8040284Z 2025-09-07T06:49:10.8040462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8040590Z 2025-09-07T06:49:10.8040811Z 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=830. 2025-09-07T06:49:10.8041149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8041383Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T06:49:10.8041525Z 2025-09-07T06:49:10.8041558Z Args: 2025-09-07T06:49:10.8041694Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T06:49:10.8041894Z process_group (optional): process group to scope synchronization, 2025-09-07T06:49:10.8043072Z default is the whole world 2025-09-07T06:49:10.8043178Z 2025-09-07T06:49:10.8043212Z Returns: 2025-09-07T06:49:10.8043358Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T06:49:10.8043574Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T06:49:10.8043760Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T06:49:10.8043901Z instead. 2025-09-07T06:49:10.8043959Z 2025-09-07T06:49:10.8043995Z Example:: 2025-09-07T06:49:10.8044047Z 2025-09-07T06:49:10.8044094Z >>> # Network with nn.BatchNorm layer 2025-09-07T06:49:10.8044226Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:10.8044357Z >>> module = torch.nn.Sequential( 2025-09-07T06:49:10.8044539Z >>> torch.nn.Linear(20, 100), 2025-09-07T06:49:10.8045701Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T06:49:10.8045824Z >>> ).cuda() 2025-09-07T06:49:10.8045944Z >>> # creating process group (optional) 2025-09-07T06:49:10.8046081Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T06:49:10.8046211Z >>> ranks = list(range(8)) 2025-09-07T06:49:10.8046327Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T06:49:10.8046458Z >>> # Note: every rank calls into new_group for every 2025-09-07T06:49:10.8046604Z >>> # process group created, even if that rank is not 2025-09-07T06:49:10.8046733Z >>> # part of the group. 2025-09-07T06:49:10.8046850Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:10.8047016Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T06:49:10.8048225Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T06:49:10.8048436Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T06:49:10.8048575Z 2025-09-07T06:49:10.8048607Z 2025-09-07T06:49:10.8048737Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8048865Z 2025-09-07T06:49:10.8067480Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=66. 2025-09-07T06:49:10.8067812Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8067938Z 2025-09-07T06:49:10.8068051Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T06:49:10.8068190Z 2025-09-07T06:49:10.8068289Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T06:49:10.8068500Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T06:49:10.8068614Z 2025-09-07T06:49:10.8068724Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T06:49:10.8070620Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T06:49:10.8070809Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T06:49:10.8070958Z 2025-09-07T06:49:10.8070990Z Shape: 2025-09-07T06:49:10.8071115Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T06:49:10.8071319Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T06:49:10.8071523Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T06:49:10.8071695Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T06:49:10.8071783Z 2025-09-07T06:49:10.8071814Z Args: 2025-09-07T06:49:10.8071914Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T06:49:10.8072122Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T06:49:10.8072296Z 2025-09-07T06:49:10.8072331Z Examples: 2025-09-07T06:49:10.8072425Z >>> input = torch.randn(2, 50) 2025-09-07T06:49:10.8072536Z >>> # With tuple of ints 2025-09-07T06:49:10.8072774Z >>> m = nn.Sequential( 2025-09-07T06:49:10.8074136Z >>> nn.Linear(50, 50), 2025-09-07T06:49:10.8074250Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T06:49:10.8074353Z >>> ) 2025-09-07T06:49:10.8074505Z >>> output = m(input) 2025-09-07T06:49:10.8074604Z >>> output.size() 2025-09-07T06:49:10.8074699Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:10.8074799Z >>> # With torch.Size 2025-09-07T06:49:10.8074894Z >>> m = nn.Sequential( 2025-09-07T06:49:10.8074995Z >>> nn.Linear(50, 50), 2025-09-07T06:49:10.8075103Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T06:49:10.8075213Z >>> ) 2025-09-07T06:49:10.8076327Z >>> output = m(input) 2025-09-07T06:49:10.8076427Z >>> output.size() 2025-09-07T06:49:10.8076523Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:10.8076632Z >>> # With namedshape (tuple of tuples) 2025-09-07T06:49:10.8076770Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T06:49:10.8076936Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T06:49:10.8077085Z >>> output = unflatten(input) 2025-09-07T06:49:10.8077190Z >>> output.size() 2025-09-07T06:49:10.8077281Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:10.8077344Z 2025-09-07T06:49:10.8077431Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8077551Z 2025-09-07T06:49:10.8190621Z 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. 2025-09-07T06:49:10.8193218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8193412Z Register a parametrization to a tensor in a module. 2025-09-07T06:49:10.8193519Z 2025-09-07T06:49:10.8193622Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T06:49:10.8193849Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T06:49:10.8194076Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T06:49:10.8194312Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T06:49:10.8194541Z 2025-09-07T06:49:10.8194651Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T06:49:10.8194879Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T06:49:10.8194999Z 2025-09-07T06:49:10.8195096Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T06:49:10.8195272Z ``module.parametrizations.weight``. 2025-09-07T06:49:10.8197065Z 2025-09-07T06:49:10.8197131Z The original tensor will be accessible under 2025-09-07T06:49:10.8197370Z ``module.parametrizations.weight.original``. 2025-09-07T06:49:10.8197460Z 2025-09-07T06:49:10.8197559Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T06:49:10.8197727Z on the same attribute. 2025-09-07T06:49:10.8197793Z 2025-09-07T06:49:10.8197876Z The training mode of a registered parametrization is updated on registration 2025-09-07T06:49:10.8198043Z to match the training mode of the host module 2025-09-07T06:49:10.8198134Z 2025-09-07T06:49:10.8198238Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T06:49:10.8198425Z using the context manager :func:`cached`. 2025-09-07T06:49:10.8198505Z 2025-09-07T06:49:10.8198588Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T06:49:10.8198705Z 2025-09-07T06:49:10.8198750Z .. code-block:: python 2025-09-07T06:49:10.8198838Z 2025-09-07T06:49:10.8198920Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T06:49:10.8199033Z 2025-09-07T06:49:10.8199125Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T06:49:10.8199347Z is registered to compute the initial value of the original tensor. 2025-09-07T06:49:10.8199558Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T06:49:10.8199697Z 2025-09-07T06:49:10.8199805Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T06:49:10.8200044Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T06:49:10.8200178Z 2025-09-07T06:49:10.8200257Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T06:49:10.8200456Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T06:49:10.8201985Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T06:49:10.8202109Z 2025-09-07T06:49:10.8202224Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T06:49:10.8202425Z with names ``original0``, ``original1``,... 2025-09-07T06:49:10.8202507Z 2025-09-07T06:49:10.8202542Z .. note:: 2025-09-07T06:49:10.8202592Z 2025-09-07T06:49:10.8202687Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T06:49:10.8202874Z once to perform a number of consistency checks. 2025-09-07T06:49:10.8203054Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T06:49:10.8203228Z and nothing will be called otherwise. 2025-09-07T06:49:10.8203307Z 2025-09-07T06:49:10.8203339Z .. note:: 2025-09-07T06:49:10.8203387Z 2025-09-07T06:49:10.8203460Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T06:49:10.8203614Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T06:49:10.8204976Z `right inverse `_). 2025-09-07T06:49:10.8205199Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T06:49:10.8205358Z to relax this. 2025-09-07T06:49:10.8205417Z 2025-09-07T06:49:10.8205453Z .. warning:: 2025-09-07T06:49:10.8205504Z 2025-09-07T06:49:10.8205600Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T06:49:10.8205816Z will register a number of new parameters. If such parametrization is registered 2025-09-07T06:49:10.8206035Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T06:49:10.8206236Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T06:49:10.8206340Z 2025-09-07T06:49:10.8206372Z Args: 2025-09-07T06:49:10.8206493Z module (nn.Module): module on which to register the parametrization 2025-09-07T06:49:10.8206728Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T06:49:10.8207943Z the parametrization 2025-09-07T06:49:10.8208087Z parametrization (nn.Module): the parametrization to register 2025-09-07T06:49:10.8208225Z Keyword args: 2025-09-07T06:49:10.8208354Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T06:49:10.8208531Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T06:49:10.8208724Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T06:49:10.8208894Z Enable this flag at your own risk. 2025-09-07T06:49:10.8208973Z 2025-09-07T06:49:10.8209006Z Raises: 2025-09-07T06:49:10.8209151Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T06:49:10.8209320Z 2025-09-07T06:49:10.8209354Z Examples: 2025-09-07T06:49:10.8210492Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T06:49:10.8210625Z >>> import torch 2025-09-07T06:49:10.8210753Z >>> import torch.nn as nn 2025-09-07T06:49:10.8210876Z >>> import torch.nn.utils.parametrize as P 2025-09-07T06:49:10.8210991Z >>> 2025-09-07T06:49:10.8211078Z >>> class Symmetric(nn.Module): 2025-09-07T06:49:10.8211193Z >>> def forward(self, X): 2025-09-07T06:49:10.8211330Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T06:49:10.8211459Z >>> 2025-09-07T06:49:10.8211543Z >>> def right_inverse(self, A): 2025-09-07T06:49:10.8211657Z >>> return A.triu() 2025-09-07T06:49:10.8212770Z >>> 2025-09-07T06:49:10.8212859Z >>> m = nn.Linear(5, 5) 2025-09-07T06:49:10.8212989Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T06:49:10.8213175Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T06:49:10.8213331Z True 2025-09-07T06:49:10.8213418Z >>> A = torch.rand(5, 5) 2025-09-07T06:49:10.8213531Z >>> A = A + A.T # A is now symmetric 2025-09-07T06:49:10.8213676Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T06:49:10.8213832Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T06:49:10.8213940Z True 2025-09-07T06:49:10.8213988Z 2025-09-07T06:49:10.8214028Z >>> class RankOne(nn.Module): 2025-09-07T06:49:10.8215217Z >>> def forward(self, x, y): 2025-09-07T06:49:10.8215353Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T06:49:10.8215492Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T06:49:10.8215607Z >>> 2025-09-07T06:49:10.8215692Z >>> def right_inverse(self, Z): 2025-09-07T06:49:10.8215812Z >>> # Project Z onto the rank 1 matrices 2025-09-07T06:49:10.8215952Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T06:49:10.8216085Z >>> # Return rescaled singular vectors 2025-09-07T06:49:10.8216213Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T06:49:10.8216347Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T06:49:10.8216465Z >>> 2025-09-07T06:49:10.8217577Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T06:49:10.8217721Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T06:49:10.8217834Z ... ) 2025-09-07T06:49:10.8217954Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T06:49:10.8218092Z 1 2025-09-07T06:49:10.8218136Z 2025-09-07T06:49:10.8218167Z 2025-09-07T06:49:10.8218296Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8218417Z 2025-09-07T06:49:10.8242006Z 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. 2025-09-07T06:49:10.8242405Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8242615Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T06:49:10.8245131Z 2025-09-07T06:49:10.8245263Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T06:49:10.8245497Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T06:49:10.8245719Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T06:49:10.8245953Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T06:49:10.8246095Z 2025-09-07T06:49:10.8246133Z .. note:: 2025-09-07T06:49:10.8246273Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T06:49:10.8246527Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T06:49:10.8246731Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T06:49:10.8246951Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T06:49:10.8247141Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T06:49:10.8247332Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T06:49:10.8247512Z even in cases where we have to apply permutation to input tensors. 2025-09-07T06:49:10.8247619Z 2025-09-07T06:49:10.8247697Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T06:49:10.8247852Z channels_last. This ensures that; 2025-09-07T06:49:10.8248008Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T06:49:10.8248199Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T06:49:10.8248398Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T06:49:10.8248556Z from memory_format conversion. 2025-09-07T06:49:10.8248633Z 2025-09-07T06:49:10.8248712Z The optimal case is that, layers between convolution layers are channels 2025-09-07T06:49:10.8248904Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T06:49:10.8249121Z encounters the first convolution layer and stay in that memory format. 2025-09-07T06:49:10.8249335Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T06:49:10.8249458Z 2025-09-07T06:49:10.8251147Z In case where a channels last incompatible layer is between convolution 2025-09-07T06:49:10.8251347Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T06:49:10.8251532Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T06:49:10.8251725Z contiguous format and be permuted to channels last when it encounters 2025-09-07T06:49:10.8251912Z another convolution layer. There's no point in propagating that 2025-09-07T06:49:10.8252098Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T06:49:10.8252247Z ``memory_format``. 2025-09-07T06:49:10.8252311Z 2025-09-07T06:49:10.8252392Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T06:49:10.8252581Z there might have been a better spot to fuse the permutation other than 2025-09-07T06:49:10.8252737Z immediately before a convolution. 2025-09-07T06:49:10.8252817Z 2025-09-07T06:49:10.8252849Z Args: 2025-09-07T06:49:10.8254073Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T06:49:10.8254227Z ``nn.Module`` 2025-09-07T06:49:10.8254359Z memory_format: user specified ``memory_format``, 2025-09-07T06:49:10.8254642Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T06:49:10.8254744Z 2025-09-07T06:49:10.8254779Z Returns: 2025-09-07T06:49:10.8254882Z The original module with updated ``nn.Conv2d`` 2025-09-07T06:49:10.8254970Z 2025-09-07T06:49:10.8255004Z Example: 2025-09-07T06:49:10.8255104Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:10.8255246Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T06:49:10.8255381Z >>> input = torch.randint( 2025-09-07T06:49:10.8255511Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T06:49:10.8256706Z ... ) 2025-09-07T06:49:10.8256799Z >>> model = nn.Sequential( 2025-09-07T06:49:10.8256915Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T06:49:10.8257033Z >>> # This is identical to: 2025-09-07T06:49:10.8257189Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T06:49:10.8257405Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T06:49:10.8257544Z ... model, torch.channels_last 2025-09-07T06:49:10.8257679Z ... ) 2025-09-07T06:49:10.8257766Z >>> out = model(input) 2025-09-07T06:49:10.8257862Z 2025-09-07T06:49:10.8257991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8258115Z 2025-09-07T06:49:10.8259366Z 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. 2025-09-07T06:49:10.8259702Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8259902Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T06:49:10.8260106Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T06:49:10.8260336Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T06:49:10.8260552Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T06:49:10.8260785Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T06:49:10.8260928Z 2025-09-07T06:49:10.8260962Z .. note:: 2025-09-07T06:49:10.8261095Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T06:49:10.8261294Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T06:49:10.8261490Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T06:49:10.8262736Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T06:49:10.8262933Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T06:49:10.8263126Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T06:49:10.8263314Z even in cases where we have to apply permutation to input tensors. 2025-09-07T06:49:10.8263421Z 2025-09-07T06:49:10.8263502Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T06:49:10.8263659Z channels_last_3d. This ensures that; 2025-09-07T06:49:10.8263809Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T06:49:10.8264011Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T06:49:10.8264208Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T06:49:10.8264365Z from memory_format conversion. 2025-09-07T06:49:10.8264509Z 2025-09-07T06:49:10.8266216Z The optimal case is that, layers between convolution layers are channels 2025-09-07T06:49:10.8266420Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T06:49:10.8266616Z encounters the first convolution layer and stay in that memory format. 2025-09-07T06:49:10.8266868Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T06:49:10.8266988Z 2025-09-07T06:49:10.8267063Z In case where a channels last incompatible layer is between convolution 2025-09-07T06:49:10.8267245Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T06:49:10.8267433Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T06:49:10.8267626Z contiguous format and be permuted to channels last when it encounters 2025-09-07T06:49:10.8267817Z another convolution layer. There's no point in propagating that 2025-09-07T06:49:10.8267998Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T06:49:10.8268146Z ``memory_format``. 2025-09-07T06:49:10.8268218Z 2025-09-07T06:49:10.8268298Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T06:49:10.8268509Z there might have been a better spot to fuse the permutation other than 2025-09-07T06:49:10.8268665Z immediately before a convolution. 2025-09-07T06:49:10.8268764Z 2025-09-07T06:49:10.8269995Z Args: 2025-09-07T06:49:10.8270120Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T06:49:10.8270267Z ``nn.Module`` 2025-09-07T06:49:10.8270397Z memory_format: user specified ``memory_format``, 2025-09-07T06:49:10.8270550Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T06:49:10.8270649Z 2025-09-07T06:49:10.8270681Z Returns: 2025-09-07T06:49:10.8270781Z The original module with updated ``nn.Conv3d`` 2025-09-07T06:49:10.8270932Z 2025-09-07T06:49:10.8270964Z Example: 2025-09-07T06:49:10.8271064Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:10.8271207Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T06:49:10.8271348Z >>> input = torch.randint( 2025-09-07T06:49:10.8273049Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T06:49:10.8273182Z ... ) 2025-09-07T06:49:10.8273272Z >>> model = nn.Sequential( 2025-09-07T06:49:10.8273390Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T06:49:10.8273508Z >>> # This is identical to: 2025-09-07T06:49:10.8273669Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T06:49:10.8273853Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T06:49:10.8273993Z ... model, torch.channels_last_3d 2025-09-07T06:49:10.8274101Z ... ) 2025-09-07T06:49:10.8274187Z >>> out = model(input) 2025-09-07T06:49:10.8274283Z 2025-09-07T06:49:10.8275512Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8275644Z 2025-09-07T06:49:10.8275834Z 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=350. 2025-09-07T06:49:10.8276138Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8276318Z Pad a packed batch of variable length sequences. 2025-09-07T06:49:10.8276407Z 2025-09-07T06:49:10.8276473Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T06:49:10.8276575Z 2025-09-07T06:49:10.8276675Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T06:49:10.8276892Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T06:49:10.8277052Z sequence and ``B`` is the batch size. 2025-09-07T06:49:10.8277130Z 2025-09-07T06:49:10.8277164Z Example: 2025-09-07T06:49:10.8277296Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T06:49:10.8277473Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T06:49:10.8278676Z >>> lens = [2, 1, 3] 2025-09-07T06:49:10.8278790Z >>> packed = pack_padded_sequence( 2025-09-07T06:49:10.8278925Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T06:49:10.8279046Z ... ) 2025-09-07T06:49:10.8279126Z >>> packed 2025-09-07T06:49:10.8279263Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T06:49:10.8279459Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T06:49:10.8279658Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T06:49:10.8279820Z >>> seq_unpacked 2025-09-07T06:49:10.8279917Z tensor([[1, 2, 0], 2025-09-07T06:49:10.8280011Z [3, 0, 0], 2025-09-07T06:49:10.8281114Z [4, 5, 6]]) 2025-09-07T06:49:10.8281239Z >>> lens_unpacked 2025-09-07T06:49:10.8281334Z tensor([2, 1, 3]) 2025-09-07T06:49:10.8281398Z 2025-09-07T06:49:10.8281433Z .. note:: 2025-09-07T06:49:10.8281490Z :attr:`total_length` is useful to implement the 2025-09-07T06:49:10.8281593Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T06:49:10.8281673Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T06:49:10.8281759Z See :ref:`this FAQ section ` for 2025-09-07T06:49:10.8281792Z details. 2025-09-07T06:49:10.8281794Z 2025-09-07T06:49:10.8281826Z Args: 2025-09-07T06:49:10.8281877Z sequence (PackedSequence): batch to pad 2025-09-07T06:49:10.8281963Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T06:49:10.8282007Z format, ``T x B x *`` otherwise. 2025-09-07T06:49:10.8282080Z padding_value (float, optional): values for padded elements. 2025-09-07T06:49:10.8282171Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T06:49:10.8282254Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T06:49:10.8282322Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T06:49:10.8282362Z :attr:`sequence`. 2025-09-07T06:49:10.8282364Z 2025-09-07T06:49:10.8283390Z Returns: 2025-09-07T06:49:10.8283464Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T06:49:10.8283535Z containing the list of lengths of each sequence in the batch. 2025-09-07T06:49:10.8283615Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T06:49:10.8283689Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T06:49:10.8283721Z 2025-09-07T06:49:10.8283809Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8283813Z 2025-09-07T06:49:10.8322274Z 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. 2025-09-07T06:49:10.8323272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8323521Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T06:49:10.8323528Z 2025-09-07T06:49:10.8323649Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T06:49:10.8323779Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T06:49:10.8323878Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T06:49:10.8323992Z Modifies module in place (and also return the modified module) 2025-09-07T06:49:10.8324046Z by: 2025-09-07T06:49:10.8324052Z 2025-09-07T06:49:10.8324180Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:10.8324748Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:10.8324860Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:10.8329938Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:10.8329995Z ``name+'_orig'``. 2025-09-07T06:49:10.8330002Z 2025-09-07T06:49:10.8330054Z Args: 2025-09-07T06:49:10.8330148Z module (nn.Module): module containing the tensor to prune 2025-09-07T06:49:10.8330239Z name (str): parameter name within ``module`` on which pruning 2025-09-07T06:49:10.8330288Z will act. 2025-09-07T06:49:10.8330379Z amount (int or float): quantity of parameters to prune. 2025-09-07T06:49:10.8330759Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T06:49:10.8330857Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T06:49:10.8331007Z absolute number of parameters to prune. 2025-09-07T06:49:10.8331142Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T06:49:10.8331215Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T06:49:10.8331472Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T06:49:10.8331583Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T06:49:10.8331674Z shape as module parameter) used to compute mask for pruning. 2025-09-07T06:49:10.8331775Z The values in this tensor indicate the importance of the corresponding 2025-09-07T06:49:10.8331835Z elements in the parameter being pruned. 2025-09-07T06:49:10.8331949Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T06:49:10.8331958Z 2025-09-07T06:49:10.8332001Z Returns: 2025-09-07T06:49:10.8332096Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T06:49:10.8332107Z 2025-09-07T06:49:10.8332147Z Examples: 2025-09-07T06:49:10.8332227Z >>> from torch.nn.utils import prune 2025-09-07T06:49:10.8332322Z >>> m = prune.ln_structured( 2025-09-07T06:49:10.8332403Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T06:49:10.8332449Z ... ) 2025-09-07T06:49:10.8332488Z 2025-09-07T06:49:10.8332599Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8332601Z 2025-09-07T06:49:10.8332815Z 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. 2025-09-07T06:49:10.8332931Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8332933Z 2025-09-07T06:49:10.8333103Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T06:49:10.8333108Z 2025-09-07T06:49:10.8333177Z Modifies modules in place by: 2025-09-07T06:49:10.8333187Z 2025-09-07T06:49:10.8333273Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:10.8333371Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:10.8333460Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:10.8335291Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:10.8335329Z ``name+'_orig'``. 2025-09-07T06:49:10.8335333Z 2025-09-07T06:49:10.8335368Z Args: 2025-09-07T06:49:10.8335452Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T06:49:10.8335528Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T06:49:10.8335610Z weights prior to deciding which ones to prune. module must be of 2025-09-07T06:49:10.8335673Z type :class:`nn.Module`, and name must be a string. 2025-09-07T06:49:10.8335764Z pruning_method (function): a valid pruning function from this module, 2025-09-07T06:49:10.8335895Z or a custom one implemented by the user that satisfies the 2025-09-07T06:49:10.8335986Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T06:49:10.8336076Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T06:49:10.8336160Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T06:49:10.8336239Z should be the same shape as the parameter, and is used for computing 2025-09-07T06:49:10.8336281Z mask for pruning. 2025-09-07T06:49:10.8336361Z If unspecified or None, the parameter will be used in place of its 2025-09-07T06:49:10.8336401Z importance scores. 2025-09-07T06:49:10.8336453Z kwargs: other keyword arguments such as: 2025-09-07T06:49:10.8336532Z amount (int or float): quantity of parameters to prune across the 2025-09-07T06:49:10.8336602Z specified parameters. 2025-09-07T06:49:10.8336677Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T06:49:10.8336752Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T06:49:10.8338109Z absolute number of parameters to prune. 2025-09-07T06:49:10.8338112Z 2025-09-07T06:49:10.8338149Z Raises: 2025-09-07T06:49:10.8338216Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T06:49:10.8338218Z 2025-09-07T06:49:10.8338250Z Note: 2025-09-07T06:49:10.8338335Z Since global structured pruning doesn't make much sense unless the 2025-09-07T06:49:10.8338415Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T06:49:10.8338474Z scope of global pruning to unstructured methods. 2025-09-07T06:49:10.8338477Z 2025-09-07T06:49:10.8338513Z Examples: 2025-09-07T06:49:10.8338561Z >>> from torch.nn.utils import prune 2025-09-07T06:49:10.8338610Z >>> from collections import OrderedDict 2025-09-07T06:49:10.8338656Z >>> net = nn.Sequential( 2025-09-07T06:49:10.8338703Z ... OrderedDict( 2025-09-07T06:49:10.8338740Z ... [ 2025-09-07T06:49:10.8338790Z ... ("first", nn.Linear(10, 4)), 2025-09-07T06:49:10.8338841Z ... ("second", nn.Linear(4, 1)), 2025-09-07T06:49:10.8338877Z ... ] 2025-09-07T06:49:10.8338908Z ... ) 2025-09-07T06:49:10.8338943Z ... ) 2025-09-07T06:49:10.8338989Z >>> parameters_to_prune = ( 2025-09-07T06:49:10.8339035Z ... (net.first, "weight"), 2025-09-07T06:49:10.8339079Z ... (net.second, "weight"), 2025-09-07T06:49:10.8339110Z ... ) 2025-09-07T06:49:10.8340294Z >>> prune.global_unstructured( 2025-09-07T06:49:10.8340342Z ... parameters_to_prune, 2025-09-07T06:49:10.8340396Z ... pruning_method=prune.L1Unstructured, 2025-09-07T06:49:10.8340435Z ... amount=10, 2025-09-07T06:49:10.8340469Z ... ) 2025-09-07T06:49:10.8340554Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T06:49:10.8340593Z tensor(10) 2025-09-07T06:49:10.8340598Z 2025-09-07T06:49:10.8340601Z 2025-09-07T06:49:10.8340698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8340703Z 2025-09-07T06:49:10.8340898Z 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. 2025-09-07T06:49:10.8340997Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:10.8341145Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T06:49:10.8341148Z 2025-09-07T06:49:10.8341232Z Modifies module in place (and also return the modified module) by: 2025-09-07T06:49:10.8341234Z 2025-09-07T06:49:10.8341311Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:10.8341395Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:10.8341517Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:10.8341602Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:10.8341650Z ``name+'_orig'``. 2025-09-07T06:49:10.8341652Z 2025-09-07T06:49:10.8341688Z Args: 2025-09-07T06:49:10.8341760Z module (nn.Module): module containing the tensor to prune 2025-09-07T06:49:10.8341832Z name (str): parameter name within ``module`` on which pruning 2025-09-07T06:49:10.8341870Z will act. 2025-09-07T06:49:10.8342988Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T06:49:10.8342991Z 2025-09-07T06:49:10.8343026Z Returns: 2025-09-07T06:49:10.8343105Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T06:49:10.8343107Z 2025-09-07T06:49:10.8343140Z Examples: 2025-09-07T06:49:10.8343186Z >>> from torch.nn.utils import prune 2025-09-07T06:49:10.8343251Z >>> m = prune.custom_from_mask( 2025-09-07T06:49:10.8343322Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T06:49:10.8343368Z ... ) 2025-09-07T06:49:10.8343411Z >>> print(m.bias_mask) 2025-09-07T06:49:10.8343448Z tensor([0., 1., 0.]) 2025-09-07T06:49:10.8343450Z 2025-09-07T06:49:10.8343481Z 2025-09-07T06:49:10.8343568Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:10.8343570Z 2025-09-07T06:49:10.8477569Z gathering tests 2025-09-07T06:49:10.8485380Z running 732 test(s) 2025-09-07T06:49:10.8488500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file 2025-09-07T06:49:10.8495594Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2025-09-07T06:49:10.8495841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file 2025-09-07T06:49:10.8497686Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2025-09-07T06:49:10.8499680Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file 2025-09-07T06:49:10.8500421Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2025-09-07T06:49:10.8507590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1252 <- wrt source file 2025-09-07T06:49:10.8507839Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-09-07T06:49:10.8508082Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file 2025-09-07T06:49:10.8508339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2025-09-07T06:49:10.8508601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file 2025-09-07T06:49:10.8508815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-09-07T06:49:10.8509028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2568 <- wrt source file 2025-09-07T06:49:10.8509204Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2025-09-07T06:49:10.8509459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2841 <- wrt source file 2025-09-07T06:49:10.8509997Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-09-07T06:49:10.8510232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-09-07T06:49:10.8510421Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2025-09-07T06:49:10.8510633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-09-07T06:49:10.8510810Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2025-09-07T06:49:10.8511108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-09-07T06:49:10.8783833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-09-07T06:49:10.8784140Z * 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 2025-09-07T06:49:10.8784609Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2025-09-07T06:49:10.8784874Z * 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 2025-09-07T06:49:10.8785105Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2025-09-07T06:49:10.8788747Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-09-07T06:49:10.8788972Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2025-09-07T06:49:10.8789176Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 473 <- wrt source file 2025-09-07T06:49:10.8789348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2025-09-07T06:49:10.8789546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 533 <- wrt source file 2025-09-07T06:49:10.8789705Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2025-09-07T06:49:10.8789935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-09-07T06:49:10.8813260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-09-07T06:49:10.8813521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-09-07T06:49:10.8819366Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-09-07T06:49:10.8819653Z * 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 2025-09-07T06:49:10.8823451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-09-07T06:49:10.8823701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-09-07T06:49:10.8866074Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-09-07T06:49:10.8866468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-09-07T06:49:10.8867517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-09-07T06:49:10.8867753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-09-07T06:49:10.8870428Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2025-09-07T06:49:10.8870683Z * 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 2025-09-07T06:49:10.8872998Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-09-07T06:49:10.8875652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-09-07T06:49:10.8876201Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2025-09-07T06:49:10.8876436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-09-07T06:49:10.8877812Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2025-09-07T06:49:10.8878037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 144 <- wrt source file 2025-09-07T06:49:10.8882421Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2025-09-07T06:49:10.8883069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 258 <- wrt source file 2025-09-07T06:49:10.8890653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2025-09-07T06:49:10.8890928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 992 <- wrt source file 2025-09-07T06:49:10.8895419Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-09-07T06:49:10.8895657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1267 <- wrt source file 2025-09-07T06:49:10.8902314Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2025-09-07T06:49:10.8902553Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1351 <- wrt source file 2025-09-07T06:49:10.8905261Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2025-09-07T06:49:10.8905492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1385 <- wrt source file 2025-09-07T06:49:10.8909653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2025-09-07T06:49:10.8909884Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1441 <- wrt source file 2025-09-07T06:49:10.8915488Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2025-09-07T06:49:10.8915724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1482 <- wrt source file 2025-09-07T06:49:10.8923762Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2025-09-07T06:49:10.8924024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1520 <- wrt source file 2025-09-07T06:49:10.8927118Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2025-09-07T06:49:10.8927355Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1560 <- wrt source file 2025-09-07T06:49:10.8933960Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2025-09-07T06:49:10.8934182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1735 <- wrt source file 2025-09-07T06:49:10.8944725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2025-09-07T06:49:10.8945021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1903 <- wrt source file 2025-09-07T06:49:10.8954160Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2025-09-07T06:49:10.8954452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2003 <- wrt source file 2025-09-07T06:49:10.8954643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2025-09-07T06:49:10.8954860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2104 <- wrt source file 2025-09-07T06:49:10.8955056Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2025-09-07T06:49:10.8955302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-09-07T06:49:10.8955507Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2025-09-07T06:49:10.8955740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-09-07T06:49:10.8956128Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2025-09-07T06:49:10.8956356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-09-07T06:49:10.8956546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0 2025-09-07T06:49:10.8956787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-09-07T06:49:10.8960779Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2025-09-07T06:49:10.8961011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-09-07T06:49:10.8961190Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2025-09-07T06:49:10.8961414Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file 2025-09-07T06:49:10.8961593Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2025-09-07T06:49:10.8961847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 247 <- wrt source file 2025-09-07T06:49:10.8967001Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-09-07T06:49:10.8967224Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file 2025-09-07T06:49:10.8968877Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2025-09-07T06:49:10.8969092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 512 <- wrt source file 2025-09-07T06:49:10.8975254Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2025-09-07T06:49:10.8975463Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 618 <- wrt source file 2025-09-07T06:49:10.8981897Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2025-09-07T06:49:10.8982182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file 2025-09-07T06:49:10.8982388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2025-09-07T06:49:10.8982617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file 2025-09-07T06:49:10.8982805Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0 2025-09-07T06:49:10.8983044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file 2025-09-07T06:49:10.9020451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2025-09-07T06:49:10.9020709Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file 2025-09-07T06:49:10.9092707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2025-09-07T06:49:10.9092985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-09-07T06:49:10.9094502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2025-09-07T06:49:10.9095734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 423 <- wrt source file 2025-09-07T06:49:10.9113426Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2025-09-07T06:49:10.9113770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1578 <- wrt source file 2025-09-07T06:49:10.9114362Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2025-09-07T06:49:10.9114722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1713 <- wrt source file 2025-09-07T06:49:10.9115798Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2025-09-07T06:49:10.9116102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1961 <- wrt source file 2025-09-07T06:49:10.9128675Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-09-07T06:49:10.9129241Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1980 <- wrt source file 2025-09-07T06:49:10.9134522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2025-09-07T06:49:10.9134815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-09-07T06:49:10.9135050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-09-07T06:49:10.9135305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-09-07T06:49:10.9135517Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2025-09-07T06:49:10.9135883Z * 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 2025-09-07T06:49:10.9140725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-09-07T06:49:10.9145030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-09-07T06:49:10.9145261Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py::_compile_kernel:0 2025-09-07T06:49:10.9145498Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 349 <- wrt source file 2025-09-07T06:49:10.9145691Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2025-09-07T06:49:10.9145926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 419 <- wrt source file 2025-09-07T06:49:10.9146114Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2025-09-07T06:49:10.9146370Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 576 <- wrt source file 2025-09-07T06:49:10.9146588Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2025-09-07T06:49:10.9146831Z * 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 2025-09-07T06:49:10.9435499Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-09-07T06:49:10.9435959Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-09-07T06:49:10.9436361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-09-07T06:49:10.9436790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-09-07T06:49:10.9437172Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2025-09-07T06:49:10.9437642Z * 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 2025-09-07T06:49:10.9438325Z W0907 06:49:10.941000 11422 site-packages/torch/fx/_symbolic_trace.py:52] is_fx_tracing will return true for both fx.symbolic_trace and torch.export. Please use is_fx_tracing_symbolic_tracing() for specifically fx.symbolic_trace or torch.compiler.is_compiling() for specifically torch.export/compile. 2025-09-07T06:49:10.9438845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-09-07T06:49:10.9439287Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-09-07T06:49:10.9446540Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0 2025-09-07T06:49:10.9446851Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 127 <- wrt source file 2025-09-07T06:49:10.9453224Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-09-07T06:49:10.9453513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 183 <- wrt source file 2025-09-07T06:49:11.0672749Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-09-07T06:49:11.0673103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 413 <- wrt source file 2025-09-07T06:49:11.0673305Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-09-07T06:49:11.0673543Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 445 <- wrt source file 2025-09-07T06:49:11.0675827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-09-07T06:49:11.0676106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 466 <- wrt source file 2025-09-07T06:49:11.0677295Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-09-07T06:49:11.0677588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0, line 484 <- wrt source file 2025-09-07T06:49:11.0678576Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-09-07T06:49:11.0679142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 499 <- wrt source file 2025-09-07T06:49:11.0679417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-09-07T06:49:11.0679682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 514 <- wrt source file 2025-09-07T06:49:11.0679916Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-09-07T06:49:11.0680152Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-09-07T06:49:11.0680337Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2025-09-07T06:49:11.0680592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 197 <- wrt source file 2025-09-07T06:49:11.0680818Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-09-07T06:49:11.0683387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 261 <- wrt source file 2025-09-07T06:49:11.0683785Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-09-07T06:49:11.0684033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-09-07T06:49:11.0684235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2025-09-07T06:49:11.0684665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2025-09-07T06:49:11.0689385Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2025-09-07T06:49:11.0689651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-09-07T06:49:11.1955570Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-09-07T06:49:11.1957016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-09-07T06:49:11.1958236Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0 2025-09-07T06:49:11.1959401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 320 <- wrt source file 2025-09-07T06:49:11.1960602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-09-07T06:49:11.1961755Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-09-07T06:49:11.1963867Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2025-09-07T06:49:11.1964320Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 768 <- wrt source file 2025-09-07T06:49:11.2138822Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-09-07T06:49:11.2139410Z * 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 2025-09-07T06:49:11.2139991Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-09-07T06:49:11.2140533Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-09-07T06:49:11.2158588Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-09-07T06:49:11.2159098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-09-07T06:49:11.2185236Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-09-07T06:49:11.2185734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-09-07T06:49:11.2600183Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-09-07T06:49:11.2600987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-09-07T06:49:11.2629543Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-09-07T06:49:11.2630093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-09-07T06:49:11.2637775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-09-07T06:49:11.2638405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-09-07T06:49:11.2638951Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-09-07T06:49:11.2639481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-09-07T06:49:11.2727181Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-09-07T06:49:11.2727753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2025-09-07T06:49:11.2728594Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-09-07T06:49:11.2729099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-09-07T06:49:11.2729578Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-09-07T06:49:11.2733521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1131 <- wrt source file 2025-09-07T06:49:11.2734103Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-09-07T06:49:11.2734774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0, line 1186 <- wrt source file 2025-09-07T06:49:11.2735313Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0 2025-09-07T06:49:11.2735804Z * 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 2025-09-07T06:49:11.2736264Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-09-07T06:49:11.2736741Z * 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 2025-09-07T06:49:11.2737225Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-09-07T06:49:11.2737700Z * 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 2025-09-07T06:49:11.2738176Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-09-07T06:49:11.2738658Z * 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 2025-09-07T06:49:11.2739272Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-09-07T06:49:11.2739778Z * 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 2025-09-07T06:49:11.2740300Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-09-07T06:49:11.2740814Z * 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 2025-09-07T06:49:11.2741434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-09-07T06:49:11.2741968Z * 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 2025-09-07T06:49:11.2745565Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-09-07T06:49:11.2746112Z * 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 2025-09-07T06:49:11.2746555Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-09-07T06:49:11.2746997Z * 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 2025-09-07T06:49:11.2747454Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-09-07T06:49:11.2747908Z * 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 2025-09-07T06:49:11.2748374Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-09-07T06:49:11.2748823Z * 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 2025-09-07T06:49:11.2749277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-09-07T06:49:11.2749744Z * 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 2025-09-07T06:49:11.2751924Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-09-07T06:49:11.2752434Z * 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 2025-09-07T06:49:11.2752965Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-09-07T06:49:11.2753445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 172 <- wrt source file 2025-09-07T06:49:11.2753889Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-09-07T06:49:11.2754330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 544 <- wrt source file 2025-09-07T06:49:11.2754871Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-09-07T06:49:11.2755389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 566 <- wrt source file 2025-09-07T06:49:11.2755853Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-09-07T06:49:11.2756304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 580 <- wrt source file 2025-09-07T06:49:11.2756757Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-09-07T06:49:11.2757218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 602 <- wrt source file 2025-09-07T06:49:11.2757703Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-09-07T06:49:11.2759519Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 729 <- wrt source file 2025-09-07T06:49:11.2760004Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-09-07T06:49:11.2760457Z * 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 2025-09-07T06:49:11.2760926Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-09-07T06:49:11.2761385Z * 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 2025-09-07T06:49:11.2761869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-09-07T06:49:11.2762326Z * 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 2025-09-07T06:49:11.2762779Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-09-07T06:49:11.2763265Z * 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 2025-09-07T06:49:11.2763795Z * 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 2025-09-07T06:49:11.2764313Z * 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 189 <- wrt source file 2025-09-07T06:49:11.2766064Z * 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 2025-09-07T06:49:11.2766586Z * 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 2025-09-07T06:49:11.2767116Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-09-07T06:49:11.2767632Z * 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 2025-09-07T06:49:11.2768157Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-09-07T06:49:11.2768750Z * 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 2025-09-07T06:49:11.2769288Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-09-07T06:49:11.2769787Z * 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 2025-09-07T06:49:11.2770281Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-09-07T06:49:11.2770816Z * 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 2025-09-07T06:49:11.2772611Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-09-07T06:49:11.2773170Z * 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 2025-09-07T06:49:11.2773667Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-09-07T06:49:11.2774137Z * 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 2025-09-07T06:49:11.3237092Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-09-07T06:49:11.3265127Z * 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 2025-09-07T06:49:11.3830159Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-09-07T06:49:11.3830733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 30 <- wrt source file 2025-09-07T06:49:11.3831408Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-09-07T06:49:11.3831982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 24 <- wrt source file 2025-09-07T06:49:11.3836302Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-09-07T06:49:11.3836893Z * 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 2025-09-07T06:49:11.3837468Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-09-07T06:49:11.3838002Z * 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 2025-09-07T06:49:11.3838558Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-09-07T06:49:11.3839093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 142 <- wrt source file 2025-09-07T06:49:11.3839818Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-09-07T06:49:11.3840304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 211 <- wrt source file 2025-09-07T06:49:11.3840758Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-09-07T06:49:11.3841193Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 283 <- wrt source file 2025-09-07T06:49:11.3841654Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-09-07T06:49:11.3842104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 359 <- wrt source file 2025-09-07T06:49:11.3842642Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-09-07T06:49:11.3843155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2025-09-07T06:49:11.3845582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-09-07T06:49:11.3846040Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2025-09-07T06:49:11.3846522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-09-07T06:49:11.3846981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 515 <- wrt source file 2025-09-07T06:49:11.3847465Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-09-07T06:49:11.3847923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2025-09-07T06:49:11.3848393Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-09-07T06:49:11.3848854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1206 <- wrt source file 2025-09-07T06:49:11.3849334Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-09-07T06:49:11.3849805Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1273 <- wrt source file 2025-09-07T06:49:11.3850286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-09-07T06:49:11.3850755Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1326 <- wrt source file 2025-09-07T06:49:11.3851231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-09-07T06:49:11.3852986Z * 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 2025-09-07T06:49:11.3853481Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-09-07T06:49:11.3854019Z * 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 2025-09-07T06:49:11.3854583Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-09-07T06:49:11.3855051Z * 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 2025-09-07T06:49:11.3855529Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-09-07T06:49:11.3855992Z * 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 2025-09-07T06:49:11.3856463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-09-07T06:49:11.3856973Z * 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 2025-09-07T06:49:11.3857510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-09-07T06:49:11.3858019Z * 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 2025-09-07T06:49:11.3859689Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-09-07T06:49:11.3860194Z * 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 2025-09-07T06:49:11.3860710Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-09-07T06:49:11.3861192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2025-09-07T06:49:11.3861663Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-09-07T06:49:11.3862135Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 111 <- wrt source file 2025-09-07T06:49:11.3923827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-09-07T06:49:11.3955385Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 275 <- wrt source file 2025-09-07T06:49:11.3955930Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-09-07T06:49:11.3956456Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 23 <- wrt source file 2025-09-07T06:49:11.3958988Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-09-07T06:49:11.3959523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 176 <- wrt source file 2025-09-07T06:49:11.3960047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-09-07T06:49:11.3960636Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2025-09-07T06:49:11.3961098Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-09-07T06:49:11.3961541Z * 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 2025-09-07T06:49:11.3961992Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-09-07T06:49:11.3962439Z * 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 2025-09-07T06:49:11.3962891Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-09-07T06:49:11.3988472Z * 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 2025-09-07T06:49:11.3992336Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-09-07T06:49:11.3993023Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2025-09-07T06:49:11.3993512Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-09-07T06:49:11.3993983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2025-09-07T06:49:11.3994590Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-09-07T06:49:11.3995040Z * 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 2025-09-07T06:49:11.3995459Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-09-07T06:49:11.3995856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 283 <- wrt source file 2025-09-07T06:49:11.3996284Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-09-07T06:49:11.3996701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 391 <- wrt source file 2025-09-07T06:49:11.3999092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-09-07T06:49:11.3999535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 494 <- wrt source file 2025-09-07T06:49:11.3999951Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-09-07T06:49:11.4000351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 550 <- wrt source file 2025-09-07T06:49:11.4000768Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-09-07T06:49:11.4001171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 611 <- wrt source file 2025-09-07T06:49:11.4001585Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-09-07T06:49:11.4002064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 686 <- wrt source file 2025-09-07T06:49:11.4002469Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-09-07T06:49:11.4002871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 776 <- wrt source file 2025-09-07T06:49:11.4003297Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-09-07T06:49:11.4003711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 974 <- wrt source file 2025-09-07T06:49:11.4004133Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-09-07T06:49:11.4007241Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1065 <- wrt source file 2025-09-07T06:49:11.4007711Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-09-07T06:49:11.4008152Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1137 <- wrt source file 2025-09-07T06:49:11.4008616Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-09-07T06:49:11.4009101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1806 <- wrt source file 2025-09-07T06:49:11.4009623Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-09-07T06:49:11.4010132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1822 <- wrt source file 2025-09-07T06:49:11.4010647Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-09-07T06:49:11.4011136Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 339 <- wrt source file 2025-09-07T06:49:11.4011636Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2025-09-07T06:49:11.4012087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-09-07T06:49:11.4012520Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-09-07T06:49:11.4012946Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-09-07T06:49:11.4013378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-09-07T06:49:11.4015392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2025-09-07T06:49:11.4015807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2025-09-07T06:49:11.4016208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2025-09-07T06:49:11.4016618Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-09-07T06:49:11.4017078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 140 <- wrt source file 2025-09-07T06:49:11.4017489Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-09-07T06:49:11.4017913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 173 <- wrt source file 2025-09-07T06:49:11.4018365Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-09-07T06:49:11.4018771Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-09-07T06:49:11.4019159Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0 2025-09-07T06:49:11.4020731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_register_buffer:0, line 42 <- wrt source file 2025-09-07T06:49:11.4021171Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_register_buffer:0 2025-09-07T06:49:11.4021570Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0, line 58 <- wrt source file 2025-09-07T06:49:11.4021982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-09-07T06:49:11.4022359Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-09-07T06:49:11.4022735Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::GdsFile:0 2025-09-07T06:49:11.4023126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file 2025-09-07T06:49:11.4023535Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::Dim:0 2025-09-07T06:49:11.4023954Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file 2025-09-07T06:49:11.4024462Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2025-09-07T06:49:11.4024903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file 2025-09-07T06:49:11.4026480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1 2025-09-07T06:49:11.4026923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0, line 815 <- wrt source file 2025-09-07T06:49:11.4027369Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0 2025-09-07T06:49:11.4027820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 230 <- wrt source file 2025-09-07T06:49:11.4028285Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-09-07T06:49:11.4028706Z * 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 2025-09-07T06:49:11.4243476Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-09-07T06:49:11.4244032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 238 <- wrt source file 2025-09-07T06:49:11.4281306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-09-07T06:49:11.4281779Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 307 <- wrt source file 2025-09-07T06:49:11.4284195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-09-07T06:49:11.4284767Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 541 <- wrt source file 2025-09-07T06:49:11.4356970Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-09-07T06:49:11.4357510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 709 <- wrt source file 2025-09-07T06:49:11.4431762Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-09-07T06:49:11.4432226Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 795 <- wrt source file 2025-09-07T06:49:11.4432701Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-09-07T06:49:11.4433143Z * 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 2025-09-07T06:49:11.4433936Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-09-07T06:49:11.4434447Z * 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 2025-09-07T06:49:11.4464256Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-09-07T06:49:11.4466650Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0, line 102 <- wrt source file 2025-09-07T06:49:11.4467049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0 2025-09-07T06:49:11.4467446Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1873 <- wrt source file 2025-09-07T06:49:11.4467884Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2025-09-07T06:49:11.4468302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1967 <- wrt source file 2025-09-07T06:49:11.4468716Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0 2025-09-07T06:49:11.4469116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file 2025-09-07T06:49:11.4469516Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0 2025-09-07T06:49:11.4469909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file 2025-09-07T06:49:11.4470316Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0 2025-09-07T06:49:11.4472484Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- 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file 2025-09-07T06:49:11.4500174Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0 2025-09-07T06:49:11.4500693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file 2025-09-07T06:49:11.4501218Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0 2025-09-07T06:49:11.4501722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file 2025-09-07T06:49:11.4502238Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0 2025-09-07T06:49:11.4502749Z * DOCTEST : 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source file 2025-09-07T06:49:11.4511992Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2025-09-07T06:49:11.4512570Z * 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 2025-09-07T06:49:11.4513180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2025-09-07T06:49:11.4513759Z * 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 2025-09-07T06:49:11.4514343Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2025-09-07T06:49:11.4514985Z * DOCTEST : 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line 129 <- wrt source file 2025-09-07T06:49:11.4537321Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0 2025-09-07T06:49:11.4537782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0, line 89 <- wrt source file 2025-09-07T06:49:11.4538230Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-09-07T06:49:11.4538657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 99 <- wrt source file 2025-09-07T06:49:11.4539087Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-09-07T06:49:11.4539596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 51 <- wrt source file 2025-09-07T06:49:11.4540182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2025-09-07T06:49:11.4540749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 28 <- wrt source file 2025-09-07T06:49:11.4541198Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-09-07T06:49:11.4543140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-09-07T06:49:11.4543543Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0 2025-09-07T06:49:11.4543951Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 110 <- wrt source file 2025-09-07T06:49:11.4548847Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-09-07T06:49:11.4549277Z * 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 2025-09-07T06:49:11.4555193Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-09-07T06:49:11.4555823Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 283 <- wrt source file 2025-09-07T06:49:11.4556252Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-09-07T06:49:11.4556657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 353 <- wrt source file 2025-09-07T06:49:11.4557060Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-09-07T06:49:11.4560102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 410 <- wrt source file 2025-09-07T06:49:11.4560563Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2025-09-07T06:49:11.4561008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 487 <- wrt source file 2025-09-07T06:49:11.4562934Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2025-09-07T06:49:11.4563400Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 753 <- wrt source file 2025-09-07T06:49:11.4570694Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-09-07T06:49:11.4571146Z * 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 2025-09-07T06:49:11.4571564Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-09-07T06:49:11.4571975Z * 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 2025-09-07T06:49:11.4572402Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-09-07T06:49:11.4574200Z * 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 2025-09-07T06:49:11.4581061Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-09-07T06:49:11.4581563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-09-07T06:49:11.4581978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2025-09-07T06:49:11.4582378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-09-07T06:49:11.4582781Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-09-07T06:49:11.4583177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-09-07T06:49:11.4583593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-09-07T06:49:11.4583990Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-09-07T06:49:11.4584502Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0 2025-09-07T06:49:11.4586459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-09-07T06:49:11.4586870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0 2025-09-07T06:49:11.4587272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-09-07T06:49:11.4587688Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0 2025-09-07T06:49:11.4588090Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-09-07T06:49:11.4588510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2025-09-07T06:49:11.4588906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-09-07T06:49:11.4589310Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2025-09-07T06:49:11.4589702Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-09-07T06:49:11.4590100Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2025-09-07T06:49:11.4590530Z * 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 2025-09-07T06:49:11.4591082Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-09-07T06:49:11.4591547Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 115 <- wrt source file 2025-09-07T06:49:11.4592020Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-09-07T06:49:11.4592473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 167 <- wrt source file 2025-09-07T06:49:11.4592929Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-09-07T06:49:11.4593392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 214 <- wrt source file 2025-09-07T06:49:11.4593953Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-09-07T06:49:11.4595940Z * 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 2025-09-07T06:49:11.4596434Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-09-07T06:49:11.4596871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-09-07T06:49:11.4597278Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2025-09-07T06:49:11.4597670Z * 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 2025-09-07T06:49:11.4598116Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-09-07T06:49:11.4598534Z * 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 2025-09-07T06:49:11.4598956Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-09-07T06:49:11.4599368Z * 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 2025-09-07T06:49:11.4599799Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-09-07T06:49:11.4601603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 246 <- wrt source file 2025-09-07T06:49:11.4602060Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-09-07T06:49:11.4605096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 186 <- wrt source file 2025-09-07T06:49:11.4605575Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-09-07T06:49:11.4606054Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 322 <- wrt source file 2025-09-07T06:49:11.4607785Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-09-07T06:49:11.4608279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 156 <- wrt source file 2025-09-07T06:49:11.4608685Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-09-07T06:49:11.4609099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 474 <- wrt source file 2025-09-07T06:49:11.4609540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-09-07T06:49:11.4609951Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file 2025-09-07T06:49:11.4610368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2025-09-07T06:49:11.4610769Z * 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 2025-09-07T06:49:11.4611196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-09-07T06:49:11.4611612Z * 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 2025-09-07T06:49:11.4612024Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-09-07T06:49:11.4612426Z * 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 2025-09-07T06:49:11.4612843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-09-07T06:49:11.4614513Z * 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 2025-09-07T06:49:11.4614925Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-09-07T06:49:11.4615319Z * 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 2025-09-07T06:49:11.4615738Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-09-07T06:49:11.4616140Z * 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 2025-09-07T06:49:11.4616563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-09-07T06:49:11.4616964Z * 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 2025-09-07T06:49:11.4617367Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-09-07T06:49:11.4617773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 893 <- wrt source file 2025-09-07T06:49:11.4618199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-09-07T06:49:11.4618631Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 555 <- wrt source file 2025-09-07T06:49:11.4620348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2025-09-07T06:49:11.4620804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 757 <- wrt source file 2025-09-07T06:49:11.4621326Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-09-07T06:49:11.4621757Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file 2025-09-07T06:49:11.4622181Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-09-07T06:49:11.4622588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file 2025-09-07T06:49:11.4623004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0 2025-09-07T06:49:11.4623401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1026 <- wrt source file 2025-09-07T06:49:11.4623831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-09-07T06:49:11.4624229Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file 2025-09-07T06:49:11.4625854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2025-09-07T06:49:11.4626236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 93 <- wrt source file 2025-09-07T06:49:11.4628168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-09-07T06:49:11.4628616Z * 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 2025-09-07T06:49:11.4629123Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-09-07T06:49:11.4629630Z * 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 2025-09-07T06:49:11.4630170Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2025-09-07T06:49:11.4630662Z * 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 2025-09-07T06:49:11.4631171Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2025-09-07T06:49:11.4631648Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 724 <- wrt source file 2025-09-07T06:49:11.4836336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-09-07T06:49:11.4836849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 40 <- wrt source file 2025-09-07T06:49:11.4837278Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2025-09-07T06:49:11.4837700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-09-07T06:49:11.4838155Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2025-09-07T06:49:11.4838662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file 2025-09-07T06:49:11.4839085Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2025-09-07T06:49:11.4839497Z * 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 2025-09-07T06:49:11.4839917Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2025-09-07T06:49:11.4840339Z * 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 2025-09-07T06:49:11.4842654Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-09-07T06:49:11.4843152Z * 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 2025-09-07T06:49:11.4843614Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-09-07T06:49:11.4844040Z * 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 2025-09-07T06:49:11.4844605Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2025-09-07T06:49:11.4845060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 268 <- wrt source file 2025-09-07T06:49:11.4845524Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2025-09-07T06:49:11.4846008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2025-09-07T06:49:11.4846520Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-09-07T06:49:11.4847019Z * 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 2025-09-07T06:49:11.4847523Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-09-07T06:49:11.4848031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 29 <- wrt source file 2025-09-07T06:49:11.4848548Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-09-07T06:49:11.4849053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 73 <- wrt source file 2025-09-07T06:49:11.4849557Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-09-07T06:49:11.4850068Z * 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 2025-09-07T06:49:11.4850596Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-09-07T06:49:11.4852638Z * 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 2025-09-07T06:49:11.4853268Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-09-07T06:49:11.4853781Z * 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 2025-09-07T06:49:11.4854270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-09-07T06:49:11.4854837Z * 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 2025-09-07T06:49:11.4855335Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-09-07T06:49:11.4855857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 53 <- wrt source file 2025-09-07T06:49:11.4856385Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2025-09-07T06:49:11.4856891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 201 <- wrt source file 2025-09-07T06:49:11.4857412Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2025-09-07T06:49:11.4857921Z * 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 2025-09-07T06:49:11.4860046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2025-09-07T06:49:11.4860556Z * 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 2025-09-07T06:49:11.4861083Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2025-09-07T06:49:11.4861598Z * 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 2025-09-07T06:49:11.4862154Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-09-07T06:49:11.4862662Z * 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 2025-09-07T06:49:11.4863175Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2025-09-07T06:49:11.4863674Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 304 <- wrt source file 2025-09-07T06:49:11.4864167Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2025-09-07T06:49:11.4864761Z * 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 2025-09-07T06:49:11.4866825Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-09-07T06:49:11.4867427Z * 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 2025-09-07T06:49:11.4867967Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-09-07T06:49:11.4868500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 90 <- wrt source file 2025-09-07T06:49:11.4869048Z * SKIPPED: 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line 216 <- wrt source file 2025-09-07T06:49:11.4871005Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-09-07T06:49:11.4871283Z * 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 2025-09-07T06:49:11.4871526Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-09-07T06:49:11.4871803Z * 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 2025-09-07T06:49:11.4872039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-09-07T06:49:11.4872316Z * 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 2025-09-07T06:49:11.4872551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-09-07T06:49:11.4872828Z * 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 2025-09-07T06:49:11.4873062Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-09-07T06:49:11.4873361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2025-09-07T06:49:11.4873599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-09-07T06:49:11.4873885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2025-09-07T06:49:11.4874132Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-09-07T06:49:11.4874515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2025-09-07T06:49:11.4874749Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-09-07T06:49:11.4875023Z * 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 2025-09-07T06:49:11.4875255Z * SKIPPED: 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2025-09-07T06:49:11.4878184Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-09-07T06:49:11.4878519Z * 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 2025-09-07T06:49:11.4878797Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-09-07T06:49:11.4879110Z * 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 2025-09-07T06:49:11.4879380Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-09-07T06:49:11.4879671Z * 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 2025-09-07T06:49:11.4879921Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-09-07T06:49:11.4880195Z * 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 2025-09-07T06:49:11.4880427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-09-07T06:49:11.4880693Z * 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 2025-09-07T06:49:11.9906111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-09-07T06:49:11.9907515Z * 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 2025-09-07T06:49:11.9909270Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-09-07T06:49:11.9910549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 349 <- wrt source file 2025-09-07T06:49:11.9911914Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-09-07T06:49:11.9913195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 322 <- wrt source file 2025-09-07T06:49:11.9914224Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-09-07T06:49:11.9915084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2025-09-07T06:49:11.9915838Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2025-09-07T06:49:11.9916599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 615 <- wrt source file 2025-09-07T06:49:11.9917356Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2025-09-07T06:49:11.9918100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 736 <- wrt source file 2025-09-07T06:49:11.9918866Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-09-07T06:49:11.9919621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 825 <- wrt source file 2025-09-07T06:49:11.9920381Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-09-07T06:49:11.9921165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4734 <- wrt source file 2025-09-07T06:49:11.9922007Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-09-07T06:49:11.9922839Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4748 <- wrt source file 2025-09-07T06:49:11.9923541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-09-07T06:49:11.9924159Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4778 <- wrt source file 2025-09-07T06:49:11.9924872Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2025-09-07T06:49:11.9925469Z * 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 2025-09-07T06:49:11.9926069Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2025-09-07T06:49:11.9926675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0, line 220 <- wrt source file 2025-09-07T06:49:11.9927373Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 295 <- wrt source file 2025-09-07T06:49:11.9931572Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-09-07T06:49:11.9932167Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 616 <- wrt source file 2025-09-07T06:49:11.9932689Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2025-09-07T06:49:11.9933192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-09-07T06:49:11.9933711Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-09-07T06:49:11.9934230Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 25 <- wrt source file 2025-09-07T06:49:11.9934763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0 2025-09-07T06:49:11.9935180Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file 2025-09-07T06:49:11.9935626Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2025-09-07T06:49:11.9936060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file 2025-09-07T06:49:11.9936523Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-09-07T06:49:11.9936995Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file 2025-09-07T06:49:11.9940764Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2025-09-07T06:49:11.9941196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file 2025-09-07T06:49:11.9941603Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0 2025-09-07T06:49:11.9942024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file 2025-09-07T06:49:11.9942490Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-09-07T06:49:11.9943003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-09-07T06:49:11.9943441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0 2025-09-07T06:49:11.9943892Z * 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 2025-09-07T06:49:11.9944568Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2025-09-07T06:49:11.9945057Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-09-07T06:49:11.9948146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-09-07T06:49:11.9948683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file 2025-09-07T06:49:11.9951414Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2025-09-07T06:49:11.9951860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 23 <- wrt source file 2025-09-07T06:49:11.9953190Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0 2025-09-07T06:49:11.9953636Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 43 <- wrt source file 2025-09-07T06:49:12.0002975Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2025-09-07T06:49:12.0035070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file 2025-09-07T06:49:12.0058548Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2025-09-07T06:49:12.0065930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-09-07T06:49:12.0066380Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2025-09-07T06:49:12.0066808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2025-09-07T06:49:12.0067252Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0 2025-09-07T06:49:12.0067826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 103 <- wrt source file 2025-09-07T06:49:12.0068364Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2025-09-07T06:49:12.0068829Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-09-07T06:49:12.0069267Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2025-09-07T06:49:12.0069714Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file 2025-09-07T06:49:12.0070190Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2025-09-07T06:49:12.0071155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file 2025-09-07T06:49:12.0071636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0 2025-09-07T06:49:12.0072113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1065 <- wrt source file 2025-09-07T06:49:12.0072595Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0 2025-09-07T06:49:12.0073070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1177 <- wrt source file 2025-09-07T06:49:12.0073561Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0 2025-09-07T06:49:12.0074168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1253 <- wrt source file 2025-09-07T06:49:12.0074859Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2025-09-07T06:49:12.0075380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2025-09-07T06:49:12.0075885Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2025-09-07T06:49:12.0076350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 22 <- wrt source file 2025-09-07T06:49:12.0076795Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0 2025-09-07T06:49:12.0077292Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file 2025-09-07T06:49:12.0079562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2025-09-07T06:49:12.0080012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 18 <- wrt source file 2025-09-07T06:49:12.0080416Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0 2025-09-07T06:49:12.0080843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 38 <- wrt source file 2025-09-07T06:49:12.0081319Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0 2025-09-07T06:49:12.0081752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 22 <- wrt source file 2025-09-07T06:49:12.0082182Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0 2025-09-07T06:49:12.0082606Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file 2025-09-07T06:49:12.0083049Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2025-09-07T06:49:12.0083470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0, line 114 <- wrt source file 2025-09-07T06:49:12.0085557Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0 2025-09-07T06:49:12.0086008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file 2025-09-07T06:49:12.0086443Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-09-07T06:49:12.0086852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-09-07T06:49:12.0087277Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0 2025-09-07T06:49:12.0087715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 28 <- wrt source file 2025-09-07T06:49:12.0088192Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2025-09-07T06:49:12.0088657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file 2025-09-07T06:49:12.0089118Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2025-09-07T06:49:12.0089541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file 2025-09-07T06:49:12.0089972Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2025-09-07T06:49:12.0091594Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file 2025-09-07T06:49:12.0092024Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2025-09-07T06:49:12.0092440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0, line 117 <- wrt source file 2025-09-07T06:49:12.0092875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0 2025-09-07T06:49:12.0093284Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2025-09-07T06:49:12.0093695Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2025-09-07T06:49:12.0094115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-09-07T06:49:12.0094625Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2025-09-07T06:49:12.0095053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file 2025-09-07T06:49:12.0095474Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-09-07T06:49:12.0104286Z * 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 2025-09-07T06:49:12.0111225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-09-07T06:49:12.0111696Z * 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 2025-09-07T06:49:12.0113183Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-09-07T06:49:12.0113654Z * 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 2025-09-07T06:49:12.0116174Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-09-07T06:49:12.0116607Z * 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 2025-09-07T06:49:12.0117030Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-09-07T06:49:12.0117443Z * 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 2025-09-07T06:49:12.0119129Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-09-07T06:49:12.0119682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-09-07T06:49:12.0120205Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2025-09-07T06:49:12.0127643Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0, line 1530 <- wrt source file 2025-09-07T06:49:12.0128355Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0 2025-09-07T06:49:12.0128796Z * 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 2025-09-07T06:49:12.0129478Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2025-09-07T06:49:12.0129982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 158 <- wrt source file 2025-09-07T06:49:12.0130514Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-09-07T06:49:12.0143430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3497 <- wrt source file 2025-09-07T06:49:12.0143969Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-09-07T06:49:12.0144534Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3643 <- wrt source file 2025-09-07T06:49:12.0145019Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-09-07T06:49:12.0145505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3766 <- wrt source file 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source file 2025-09-07T06:49:12.0148915Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0 2025-09-07T06:49:12.0149388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 4304 <- wrt source file 2025-09-07T06:49:12.0149856Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0 2025-09-07T06:49:12.0150338Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4442 <- wrt source file 2025-09-07T06:49:12.0152540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0 2025-09-07T06:49:12.0153031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4926 <- wrt source file 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file 2025-09-07T06:49:12.0181537Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0 2025-09-07T06:49:12.0182060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 102 <- wrt source file 2025-09-07T06:49:12.0182596Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0 2025-09-07T06:49:12.0183059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 174 <- wrt source file 2025-09-07T06:49:12.0184973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0 2025-09-07T06:49:12.0185392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 345 <- wrt source file 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file 2025-09-07T06:49:12.0223914Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0 2025-09-07T06:49:12.0224531Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 104 <- wrt source file 2025-09-07T06:49:12.0225104Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0 2025-09-07T06:49:12.0226846Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 125 <- wrt source file 2025-09-07T06:49:12.0227478Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file 2025-09-07T06:49:12.0230958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0 2025-09-07T06:49:12.0231515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 708 <- wrt source file 2025-09-07T06:49:12.0232093Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0 2025-09-07T06:49:12.0232639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2025-09-07T06:49:12.0234319Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2025-09-07T06:49:12.0234969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 31 <- wrt source file 2025-09-07T06:49:12.0235532Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2025-09-07T06:49:12.0236067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file 2025-09-07T06:49:12.0236600Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0 2025-09-07T06:49:12.0237106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 55 <- wrt source file 2025-09-07T06:49:12.0237592Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0 2025-09-07T06:49:12.0238056Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file 2025-09-07T06:49:12.0238524Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0 2025-09-07T06:49:12.0239001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate_with_fsdp.py::replicate:0, line 247 <- wrt source file 2025-09-07T06:49:12.0240728Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate_with_fsdp.py::replicate:0 2025-09-07T06:49:12.0241218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2025-09-07T06:49:12.0241698Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2025-09-07T06:49:12.0242183Z * 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 2025-09-07T06:49:12.0242701Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2025-09-07T06:49:12.0243230Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 294 <- wrt source file 2025-09-07T06:49:12.0243807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0 2025-09-07T06:49:12.0244313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 828 <- wrt source file 2025-09-07T06:49:12.0244860Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0 2025-09-07T06:49:12.0245333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 56 <- wrt source file 2025-09-07T06:49:12.0245840Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0 2025-09-07T06:49:12.0247454Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 56 <- wrt source file 2025-09-07T06:49:12.0247958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0 2025-09-07T06:49:12.0248452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file 2025-09-07T06:49:12.0248965Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0 2025-09-07T06:49:12.0249468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 64 <- wrt source file 2025-09-07T06:49:12.0249973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0 2025-09-07T06:49:12.0250470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 198 <- wrt source file 2025-09-07T06:49:12.0250973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0 2025-09-07T06:49:12.0251468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 350 <- wrt source file 2025-09-07T06:49:12.0251970Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2025-09-07T06:49:12.0252455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 230 <- wrt source file 2025-09-07T06:49:12.0254102Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0 2025-09-07T06:49:12.0254700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 231 <- wrt source file 2025-09-07T06:49:12.0255253Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0 2025-09-07T06:49:12.0255763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 141 <- wrt source file 2025-09-07T06:49:12.0256265Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0 2025-09-07T06:49:12.0256738Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 130 <- wrt source file 2025-09-07T06:49:12.0257226Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2025-09-07T06:49:12.0257689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 374 <- wrt source file 2025-09-07T06:49:12.0258133Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/codecache.py::WritableTempFile:0 2025-09-07T06:49:12.0258596Z * 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 1721 <- wrt source file 2025-09-07T06:49:12.0260203Z * 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 2025-09-07T06:49:12.0260698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3473 <- wrt source file 2025-09-07T06:49:12.0261175Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-09-07T06:49:12.0261669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0, line 54 <- wrt source file 2025-09-07T06:49:12.0262202Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0 2025-09-07T06:49:12.0262643Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-09-07T06:49:12.0262960Z [W907 06:49:12.574079159 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0263207Z [W907 06:49:12.574294854 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0263442Z [W907 06:49:12.574436782 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0263761Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2025-09-07T06:49:12.0264140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-09-07T06:49:12.0265789Z [W907 06:49:12.574661777 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0266137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-09-07T06:49:12.0266514Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-09-07T06:49:12.0266891Z [W907 06:49:12.574868153 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0267145Z [W907 06:49:12.579073533 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0296227Z [W907 06:49:12.579138742 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0296948Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2025-09-07T06:49:12.0297386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-09-07T06:49:12.0297747Z [W907 06:49:12.579336408 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware. 2025-09-07T06:49:12.0298110Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-09-07T06:49:12.0303259Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-09-07T06:49:12.0622139Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2025-09-07T06:49:12.0623212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-09-07T06:49:12.0662486Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-09-07T06:49:12.0663103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-09-07T06:49:12.0663819Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2025-09-07T06:49:12.0664599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-09-07T06:49:12.0665256Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2025-09-07T06:49:12.0665981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-09-07T06:49:12.0696283Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2025-09-07T06:49:12.0696778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-09-07T06:49:12.0697187Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2025-09-07T06:49:12.0697569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-09-07T06:49:12.0697953Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2025-09-07T06:49:12.0703189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-09-07T06:49:12.0703588Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2025-09-07T06:49:12.0703955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-09-07T06:49:12.0704318Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2025-09-07T06:49:12.0704769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-09-07T06:49:12.0705134Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2025-09-07T06:49:12.0705730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-09-07T06:49:12.0706141Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-09-07T06:49:12.0706539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-09-07T06:49:12.0706943Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2025-09-07T06:49:12.0707329Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-09-07T06:49:12.0709337Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-09-07T06:49:12.0709731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-09-07T06:49:12.0710214Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-09-07T06:49:12.0710643Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-09-07T06:49:12.0711132Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2025-09-07T06:49:12.0711502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-09-07T06:49:12.0711871Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2025-09-07T06:49:12.0712303Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 460 <- wrt source file 2025-09-07T06:49:12.0718780Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2025-09-07T06:49:12.0719282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 579 <- wrt source file 2025-09-07T06:49:12.1102445Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-09-07T06:49:12.1115379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2174 <- wrt source file 2025-09-07T06:49:12.1327173Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-09-07T06:49:12.1327607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2478 <- wrt source file 2025-09-07T06:49:12.1330721Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2025-09-07T06:49:12.1331150Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2618 <- wrt source file 2025-09-07T06:49:12.1407783Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2025-09-07T06:49:12.1408204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file 2025-09-07T06:49:12.1521010Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2025-09-07T06:49:12.1521740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file 2025-09-07T06:49:12.1527068Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2025-09-07T06:49:12.1527718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file 2025-09-07T06:49:12.1533682Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2025-09-07T06:49:12.1534365Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3495 <- wrt source file 2025-09-07T06:49:12.1536489Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-09-07T06:49:12.1537278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3565 <- wrt source file 2025-09-07T06:49:12.1567789Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-09-07T06:49:12.1568530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file 2025-09-07T06:49:12.1572788Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2025-09-07T06:49:12.1573315Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 120 <- wrt source file 2025-09-07T06:49:12.1573784Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-09-07T06:49:12.1574261Z * 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 2025-09-07T06:49:12.1575226Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-09-07T06:49:12.1575737Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-09-07T06:49:12.1576276Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-09-07T06:49:12.1576812Z * 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 2025-09-07T06:49:12.1577369Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-09-07T06:49:12.1577941Z * 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 2025-09-07T06:49:12.1578549Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-09-07T06:49:12.1579134Z * 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 2025-09-07T06:49:12.1579721Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-09-07T06:49:12.1580319Z * 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 2025-09-07T06:49:12.1580929Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-09-07T06:49:12.1581649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-09-07T06:49:12.1582095Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-09-07T06:49:12.1582539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-09-07T06:49:12.1583008Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-09-07T06:49:12.1586769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-09-07T06:49:12.1591475Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-09-07T06:49:12.1592041Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-09-07T06:49:12.5792093Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-09-07T06:49:12.6057277Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-09-07T06:49:12.6065527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-09-07T06:49:12.6065984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-09-07T06:49:12.6068703Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-09-07T06:49:12.6069207Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-09-07T06:49:12.6415582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-09-07T06:49:12.6418350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-09-07T06:49:12.6497991Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-09-07T06:49:12.6498748Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-09-07T06:49:12.6627003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-09-07T06:49:12.6627775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-09-07T06:49:12.6696178Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-09-07T06:49:12.6696868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-09-07T06:49:12.6836165Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-09-07T06:49:12.6836851Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-09-07T06:49:12.6976135Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-09-07T06:49:12.6977382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-09-07T06:49:12.9244095Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-09-07T06:49:12.9522906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-09-07T06:49:12.9523425Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-09-07T06:49:12.9523853Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file 2025-09-07T06:49:12.9524272Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-09-07T06:49:12.9525195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file 2025-09-07T06:49:12.9796827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-09-07T06:49:12.9834946Z * 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 2025-09-07T06:49:12.9856182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-09-07T06:49:12.9856892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1039 <- wrt source file 2025-09-07T06:49:12.9857536Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-09-07T06:49:12.9860838Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1290 <- wrt source file 2025-09-07T06:49:12.9861504Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2025-09-07T06:49:12.9862143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2229 <- wrt source file 2025-09-07T06:49:12.9862810Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-09-07T06:49:12.9863468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2670 <- wrt source file 2025-09-07T06:49:12.9864140Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-09-07T06:49:12.9864994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2698 <- wrt source file 2025-09-07T06:49:12.9865698Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-09-07T06:49:12.9866366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2725 <- wrt source file 2025-09-07T06:49:12.9867022Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-09-07T06:49:12.9867671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2752 <- wrt source file 2025-09-07T06:49:12.9868353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-09-07T06:49:12.9869185Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2783 <- wrt source file 2025-09-07T06:49:12.9869878Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-09-07T06:49:12.9870546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2807 <- wrt source file 2025-09-07T06:49:12.9871303Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-09-07T06:49:12.9871967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2845 <- wrt source file 2025-09-07T06:49:12.9874524Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-09-07T06:49:12.9875218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-09-07T06:49:12.9875865Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-09-07T06:49:12.9876478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file 2025-09-07T06:49:12.9877107Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-09-07T06:49:12.9877720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file 2025-09-07T06:49:12.9935270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-09-07T06:49:12.9935904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file 2025-09-07T06:49:12.9956416Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-09-07T06:49:12.9957042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file 2025-09-07T06:49:12.9960049Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-09-07T06:49:12.9960715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file 2025-09-07T06:49:13.0156433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-09-07T06:49:13.0157104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-09-07T06:49:13.0158925Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-09-07T06:49:13.0159548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file 2025-09-07T06:49:13.0161718Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-09-07T06:49:13.0162336Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file 2025-09-07T06:49:13.0165376Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-09-07T06:49:13.0166081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file 2025-09-07T06:49:13.0169859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-09-07T06:49:13.0170513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file 2025-09-07T06:49:13.0171134Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-09-07T06:49:13.0171749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file 2025-09-07T06:49:13.0172394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-09-07T06:49:13.0173033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file 2025-09-07T06:49:13.0173739Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-09-07T06:49:13.0174459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file 2025-09-07T06:49:13.0175081Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-09-07T06:49:13.0175681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file 2025-09-07T06:49:13.0178266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-09-07T06:49:13.0178901Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file 2025-09-07T06:49:13.0179537Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2025-09-07T06:49:13.0180153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file 2025-09-07T06:49:13.0180805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-09-07T06:49:13.0181418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file 2025-09-07T06:49:13.0182045Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2025-09-07T06:49:13.0182638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file 2025-09-07T06:49:13.0184669Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2025-09-07T06:49:13.0185319Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file 2025-09-07T06:49:13.0185857Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2025-09-07T06:49:13.0192185Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file 2025-09-07T06:49:13.0194076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2025-09-07T06:49:13.0194582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file 2025-09-07T06:49:13.0226869Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2025-09-07T06:49:13.0227333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file 2025-09-07T06:49:13.0228162Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-09-07T06:49:13.0228603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file 2025-09-07T06:49:13.0229825Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-09-07T06:49:13.0231194Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file 2025-09-07T06:49:13.0297316Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-09-07T06:49:13.0297772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file 2025-09-07T06:49:13.0298280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-09-07T06:49:13.0299597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file 2025-09-07T06:49:13.0300068Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-09-07T06:49:13.0300541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file 2025-09-07T06:49:13.0303721Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-09-07T06:49:13.0304179Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file 2025-09-07T06:49:13.0304767Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-09-07T06:49:13.0326783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file 2025-09-07T06:49:13.0327224Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-09-07T06:49:13.0327656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file 2025-09-07T06:49:13.0328097Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-09-07T06:49:13.0328516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file 2025-09-07T06:49:13.0365969Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-09-07T06:49:13.0368046Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file 2025-09-07T06:49:13.0436292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-09-07T06:49:13.0436940Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file 2025-09-07T06:49:13.0515925Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-09-07T06:49:13.0517332Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file 2025-09-07T06:49:13.0605793Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-09-07T06:49:13.0606565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-09-07T06:49:13.0607427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-09-07T06:49:13.0607892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-09-07T06:49:13.0608321Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-09-07T06:49:13.0608923Z * 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 2025-09-07T06:49:13.0612153Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-09-07T06:49:13.0612654Z * 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 2025-09-07T06:49:13.0613361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-09-07T06:49:13.0613930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-09-07T06:49:18.0439411Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-09-07T06:49:18.0446027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-09-07T06:49:19.1152363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-09-07T06:49:19.1154105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-09-07T06:49:21.4519645Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-09-07T06:49:21.4527023Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-09-07T06:49:21.6499580Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-09-07T06:49:21.6500153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-09-07T06:49:22.0256562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-09-07T06:49:22.0257606Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-09-07T06:49:22.0317251Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-09-07T06:49:22.0318653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file 2025-09-07T06:49:22.0387697Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-09-07T06:49:22.0388891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-09-07T06:49:22.0400808Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-09-07T06:49:22.0425652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file 2025-09-07T06:49:22.0432332Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-09-07T06:49:22.0432820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file 2025-09-07T06:49:22.0433432Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-09-07T06:49:22.0433941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-09-07T06:49:22.0466484Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-09-07T06:49:22.0466926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file 2025-09-07T06:49:22.0526297Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-09-07T06:49:22.0526768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file 2025-09-07T06:49:22.1359636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-09-07T06:49:22.1386475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file 2025-09-07T06:49:22.1386940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-09-07T06:49:22.1390254Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file 2025-09-07T06:49:22.1938387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-09-07T06:49:22.1939891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file 2025-09-07T06:49:22.2021014Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-09-07T06:49:22.2022480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file 2025-09-07T06:49:22.2117999Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-09-07T06:49:22.2119336Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file 2025-09-07T06:49:22.3685563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-09-07T06:49:22.3689558Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file 2025-09-07T06:49:22.3730782Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-09-07T06:49:22.3732510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file 2025-09-07T06:49:22.4034015Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-09-07T06:49:22.4034986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file 2025-09-07T06:49:22.4239994Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-09-07T06:49:22.4241621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file 2025-09-07T06:49:22.4479860Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-09-07T06:49:22.4480562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file 2025-09-07T06:49:22.6037788Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-09-07T06:49:22.6038655Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file 2025-09-07T06:49:22.6077060Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-09-07T06:49:22.6077782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file 2025-09-07T06:49:22.6276439Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-09-07T06:49:22.6277674Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file 2025-09-07T06:49:22.6496696Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-09-07T06:49:22.6497183Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file 2025-09-07T06:49:22.6567803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-09-07T06:49:22.6568588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file 2025-09-07T06:49:22.6806663Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-09-07T06:49:22.6807380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file 2025-09-07T06:49:22.7006586Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-09-07T06:49:22.7007310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-09-07T06:49:22.7078789Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-09-07T06:49:22.7079451Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-09-07T06:49:22.7084988Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-09-07T06:49:22.7085590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file 2025-09-07T06:49:22.7979614Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-09-07T06:49:22.7980263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file 2025-09-07T06:49:22.7992033Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-09-07T06:49:22.7992981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file 2025-09-07T06:49:22.8003607Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-09-07T06:49:22.8004757Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file 2025-09-07T06:49:22.8201145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-09-07T06:49:22.8202157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file 2025-09-07T06:49:22.8202584Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-09-07T06:49:22.8202984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file 2025-09-07T06:49:22.8205371Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-09-07T06:49:22.8206142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file 2025-09-07T06:49:22.8290231Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-09-07T06:49:22.8290991Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file 2025-09-07T06:49:22.8366862Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-09-07T06:49:22.8368655Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file 2025-09-07T06:49:22.8373719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-09-07T06:49:22.8374910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file 2025-09-07T06:49:22.8699447Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-09-07T06:49:22.8700723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file 2025-09-07T06:49:22.8701940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1 2025-09-07T06:49:22.8702732Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file 2025-09-07T06:49:22.8726039Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-09-07T06:49:22.8727005Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file 2025-09-07T06:49:22.8727438Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-09-07T06:49:22.8727857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file 2025-09-07T06:49:22.8728275Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-09-07T06:49:22.8728698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file 2025-09-07T06:49:22.8730939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-09-07T06:49:22.8731410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-09-07T06:49:22.8731886Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-09-07T06:49:22.8732316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file 2025-09-07T06:49:22.8736260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-09-07T06:49:22.8736678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-09-07T06:49:22.8751425Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-09-07T06:49:22.8752218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file 2025-09-07T06:49:22.9015711Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-09-07T06:49:22.9016277Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file 2025-09-07T06:49:22.9050511Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-09-07T06:49:22.9050960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file 2025-09-07T06:49:22.9051372Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-09-07T06:49:22.9051808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file 2025-09-07T06:49:22.9101602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-09-07T06:49:22.9102416Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file 2025-09-07T06:49:22.9113411Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-09-07T06:49:22.9114139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-09-07T06:49:22.9126687Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-09-07T06:49:22.9127661Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-09-07T06:49:22.9529133Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-09-07T06:49:22.9529628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-09-07T06:49:23.1385042Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-09-07T06:49:23.1385849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-09-07T06:49:23.1386492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2025-09-07T06:49:23.1387113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file 2025-09-07T06:49:23.1427030Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2025-09-07T06:49:23.1427804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file 2025-09-07T06:49:23.1601033Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-09-07T06:49:23.1601719Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file 2025-09-07T06:49:23.1610368Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-09-07T06:49:23.1611052Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file 2025-09-07T06:49:23.1986946Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-09-07T06:49:23.2026094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file 2025-09-07T06:49:23.4139155Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-09-07T06:49:23.4177232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-09-07T06:49:23.5316627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-09-07T06:49:23.5317127Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file 2025-09-07T06:49:23.5486420Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-09-07T06:49:23.5486896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file 2025-09-07T06:49:23.5556142Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-09-07T06:49:23.5556607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file 2025-09-07T06:49:23.5559438Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-09-07T06:49:23.5559879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-09-07T06:49:23.5563071Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-09-07T06:49:23.5563519Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-09-07T06:49:23.5563989Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2025-09-07T06:49:23.5564629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 260 <- wrt source file 2025-09-07T06:49:23.5567753Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-09-07T06:49:23.5568219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 283 <- wrt source file 2025-09-07T06:49:23.5571809Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-09-07T06:49:23.5572330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 314 <- wrt source file 2025-09-07T06:49:23.5575333Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-09-07T06:49:23.5575958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file 2025-09-07T06:49:23.5576419Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-09-07T06:49:23.5576868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file 2025-09-07T06:49:23.5577331Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-09-07T06:49:23.5584149Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file 2025-09-07T06:49:23.5584800Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-09-07T06:49:23.5585242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file 2025-09-07T06:49:23.5585689Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-09-07T06:49:23.5586106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-09-07T06:49:23.5586512Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2025-09-07T06:49:23.5586907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file 2025-09-07T06:49:23.5960668Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-09-07T06:49:23.5961091Z * 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 2025-09-07T06:49:23.5969900Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-09-07T06:49:23.5970365Z * 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 2025-09-07T06:49:23.5973145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-09-07T06:49:23.5973616Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-09-07T06:49:23.5974070Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-09-07T06:49:23.5974589Z * 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 2025-09-07T06:49:23.5975056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-09-07T06:49:23.5975499Z * 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 2025-09-07T06:49:23.5975999Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-09-07T06:49:23.5976432Z * 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 2025-09-07T06:49:23.5983674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2025-09-07T06:49:23.5984119Z * 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 2025-09-07T06:49:23.5986364Z * 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 2025-09-07T06:49:23.5986811Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 439 <- wrt source file 2025-09-07T06:49:23.5987236Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-09-07T06:49:23.5987637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 500 <- wrt source file 2025-09-07T06:49:23.6275064Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-09-07T06:49:23.6304800Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 556 <- wrt source file 2025-09-07T06:49:23.6305292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-09-07T06:49:23.6305781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 584 <- wrt source file 2025-09-07T06:49:23.6306282Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-09-07T06:49:23.6306770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-09-07T06:49:23.6309183Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2025-09-07T06:49:23.6309696Z * 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 2025-09-07T06:49:23.6310227Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-09-07T06:49:23.6310737Z * 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 2025-09-07T06:49:23.6311441Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-09-07T06:49:23.6311944Z * 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 2025-09-07T06:49:23.6312469Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-09-07T06:49:23.6312955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-09-07T06:49:23.6313438Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2025-09-07T06:49:23.6313914Z * 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 2025-09-07T06:49:23.6344839Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-09-07T06:49:23.6345366Z * 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 2025-09-07T06:49:23.6345841Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-09-07T06:49:23.6346273Z * 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 2025-09-07T06:49:23.6346707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-09-07T06:49:23.6347144Z * 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 2025-09-07T06:49:23.6385796Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-09-07T06:49:23.6386403Z * 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 2025-09-07T06:49:23.6411539Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-09-07T06:49:23.6412098Z * 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 2025-09-07T06:49:23.6412712Z * 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 2025-09-07T06:49:23.6413043Z ============ 2025-09-07T06:49:23.6413179Z Finished doctests 2025-09-07T06:49:23.6413318Z 338 / 732 passed 2025-09-07T06:49:23.6413447Z  2025-09-07T06:49:23.6413596Z === Found 146 parse-time warnings === 2025-09-07T06:49:23.6413783Z --- Parse Warning: 1 / 146 --- 2025-09-07T06:49:23.6414154Z /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=565. 2025-09-07T06:49:23.6414633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6414817Z 2025-09-07T06:49:23.6415182Z Load a model from a github repo or a local directory. 2025-09-07T06:49:23.6415328Z 2025-09-07T06:49:23.6415476Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T06:49:23.6415691Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T06:49:23.6415857Z 2025-09-07T06:49:23.6415980Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T06:49:23.6416611Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T06:49:23.6416844Z ref (a tag or a branch). 2025-09-07T06:49:23.6417044Z 2025-09-07T06:49:23.6417215Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T06:49:23.6417419Z path to a local directory. 2025-09-07T06:49:23.6417527Z 2025-09-07T06:49:23.6417610Z Args: 2025-09-07T06:49:23.6417730Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T06:49:23.6417930Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T06:49:23.6418182Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T06:49:23.6418422Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T06:49:23.6418629Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T06:49:23.6418910Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T06:49:23.6419063Z repo/dir's ``hubconf.py``. 2025-09-07T06:49:23.6419279Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T06:49:23.6419460Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T06:49:23.6419637Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T06:49:23.6419851Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T06:49:23.6420057Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T06:49:23.6420227Z only run code from repos that they trust. 2025-09-07T06:49:23.6420344Z 2025-09-07T06:49:23.6420466Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T06:49:23.6420611Z be trusted. 2025-09-07T06:49:23.6420742Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T06:49:23.6420907Z without requiring explicit confirmation. 2025-09-07T06:49:23.6421060Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T06:49:23.6421241Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T06:49:23.6421426Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T06:49:23.6421600Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T06:49:23.6421773Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T06:49:23.6421950Z is only present for backward compatibility and will be removed in 2025-09-07T06:49:23.6422096Z v2.0. 2025-09-07T06:49:23.6422183Z 2025-09-07T06:49:23.6422304Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T06:49:23.6422494Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T06:49:23.6422716Z the github repo unconditionally. Does not have any effect if 2025-09-07T06:49:23.6422875Z ``source = 'local'``. Default is ``False``. 2025-09-07T06:49:23.6423034Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T06:49:23.6423222Z local caches. Note that the message about first download cannot be 2025-09-07T06:49:23.6423398Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T06:49:23.6423538Z Default is ``True``. 2025-09-07T06:49:23.6423713Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T06:49:23.6423955Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T06:49:23.6424186Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T06:49:23.6424464Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T06:49:23.6424702Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T06:49:23.6424850Z 2025-09-07T06:49:23.6424928Z Returns: 2025-09-07T06:49:23.6425049Z The output of the ``model`` callable when called with the given 2025-09-07T06:49:23.6431616Z ``*args`` and ``**kwargs``. 2025-09-07T06:49:23.6431771Z 2025-09-07T06:49:23.6431863Z Example: 2025-09-07T06:49:23.6431976Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:23.6432110Z >>> # from a github repo 2025-09-07T06:49:23.6432228Z >>> repo = "pytorch/vision" 2025-09-07T06:49:23.6432344Z >>> model = torch.hub.load( 2025-09-07T06:49:23.6432498Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T06:49:23.6432643Z ... ) 2025-09-07T06:49:23.6432745Z >>> # from a local directory 2025-09-07T06:49:23.6432933Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T06:49:23.6433070Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6433227Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T06:49:23.6433418Z 2025-09-07T06:49:23.6433552Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6433707Z 2025-09-07T06:49:23.6433792Z warnings.warn(msg) 2025-09-07T06:49:23.6433890Z 2025-09-07T06:49:23.6434027Z --- Parse Warning: 2 / 146 --- 2025-09-07T06:49:23.6434519Z /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=657. 2025-09-07T06:49:23.6434926Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6435087Z 2025-09-07T06:49:23.6435194Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T06:49:23.6435326Z 2025-09-07T06:49:23.6435406Z Args: 2025-09-07T06:49:23.6435526Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T06:49:23.6435673Z ``hubconf.py``. 2025-09-07T06:49:23.6435806Z model (str): name of an entrypoint defined in the directory's 2025-09-07T06:49:23.6435943Z ``hubconf.py``. 2025-09-07T06:49:23.6436099Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T06:49:23.6436296Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T06:49:23.6436438Z 2025-09-07T06:49:23.6436520Z Returns: 2025-09-07T06:49:23.6436632Z a single model with corresponding pretrained weights. 2025-09-07T06:49:23.6436778Z 2025-09-07T06:49:23.6436850Z Example: 2025-09-07T06:49:23.6436965Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T06:49:23.6437105Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T06:49:23.6437247Z >>> model = _load_local( 2025-09-07T06:49:23.6437356Z ... path, 2025-09-07T06:49:23.6437450Z ... "resnet50", 2025-09-07T06:49:23.6437583Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T06:49:23.6437705Z ... ) 2025-09-07T06:49:23.6437782Z 2025-09-07T06:49:23.6437913Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6438066Z 2025-09-07T06:49:23.6438145Z warnings.warn(msg) 2025-09-07T06:49:23.6438236Z 2025-09-07T06:49:23.6438351Z --- Parse Warning: 3 / 146 --- 2025-09-07T06:49:23.6438713Z /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=696. 2025-09-07T06:49:23.6439113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6439297Z Download object at the given URL to a local path. 2025-09-07T06:49:23.6439472Z 2025-09-07T06:49:23.6439544Z Args: 2025-09-07T06:49:23.6439642Z url (str): URL of the object to download 2025-09-07T06:49:23.6439809Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T06:49:23.6440043Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T06:49:23.6440232Z Default: None 2025-09-07T06:49:23.6440387Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T06:49:23.6440570Z Default: True 2025-09-07T06:49:23.6440725Z 2025-09-07T06:49:23.6440798Z Example: 2025-09-07T06:49:23.6440907Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:23.6441047Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T06:49:23.6441192Z >>> torch.hub.download_url_to_file( 2025-09-07T06:49:23.6441416Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T06:49:23.6441578Z ... "/tmp/temporary_file", 2025-09-07T06:49:23.6441713Z ... ) 2025-09-07T06:49:23.6441803Z 2025-09-07T06:49:23.6441876Z 2025-09-07T06:49:23.6442013Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6442166Z 2025-09-07T06:49:23.6442247Z warnings.warn(msg) 2025-09-07T06:49:23.6442346Z 2025-09-07T06:49:23.6442455Z --- Parse Warning: 4 / 146 --- 2025-09-07T06:49:23.6442835Z /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=821. 2025-09-07T06:49:23.6443266Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6449003Z Loads the Torch serialized object at the given URL. 2025-09-07T06:49:23.6449148Z 2025-09-07T06:49:23.6449266Z If downloaded file is a zip file, it will be automatically 2025-09-07T06:49:23.6449428Z decompressed. 2025-09-07T06:49:23.6449528Z 2025-09-07T06:49:23.6449688Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T06:49:23.6449851Z returned. 2025-09-07T06:49:23.6449990Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T06:49:23.6450171Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T06:49:23.6450306Z 2025-09-07T06:49:23.6450378Z Args: 2025-09-07T06:49:23.6450478Z url (str): URL of the object to download 2025-09-07T06:49:23.6450636Z model_dir (str, optional): directory in which to save the object 2025-09-07T06:49:23.6450863Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T06:49:23.6451110Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T06:49:23.6451275Z Default: True 2025-09-07T06:49:23.6451453Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T06:49:23.6451711Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T06:49:23.6451939Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T06:49:23.6452140Z ensure unique names and to verify the contents of the file. 2025-09-07T06:49:23.6452293Z Default: False 2025-09-07T06:49:23.6452481Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T06:49:23.6452741Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T06:49:23.6453090Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T06:49:23.6453244Z 2025-09-07T06:49:23.6453325Z Example: 2025-09-07T06:49:23.6453433Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T06:49:23.6453594Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T06:49:23.6453801Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T06:49:23.6453951Z ... ) 2025-09-07T06:49:23.6454039Z 2025-09-07T06:49:23.6454111Z 2025-09-07T06:49:23.6454247Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6454480Z 2025-09-07T06:49:23.6454557Z warnings.warn(msg) 2025-09-07T06:49:23.6454647Z 2025-09-07T06:49:23.6454769Z --- Parse Warning: 5 / 146 --- 2025-09-07T06:49:23.6455154Z /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. 2025-09-07T06:49:23.6455646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6455817Z 2025-09-07T06:49:23.6455909Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T06:49:23.6456019Z 2025-09-07T06:49:23.6456143Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T06:49:23.6456303Z physical layout of :attr:`self`. 2025-09-07T06:49:23.6456411Z 2025-09-07T06:49:23.6456543Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T06:49:23.6456736Z starting from the outermost to the innermost dimension. 2025-09-07T06:49:23.6456869Z 2025-09-07T06:49:23.6456988Z Note that the dim order may not always be uniquely determined. 2025-09-07T06:49:23.6457249Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T06:49:23.6457568Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T06:49:23.6457838Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T06:49:23.6458084Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T06:49:23.6458283Z Otherwise, it will raise TypeError. 2025-09-07T06:49:23.6458397Z 2025-09-07T06:49:23.6458470Z Args: 2025-09-07T06:49:23.6458642Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T06:49:23.6458822Z 2025-09-07T06:49:23.6458903Z Examples:: 2025-09-07T06:49:23.6458998Z 2025-09-07T06:49:23.6459084Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T06:49:23.6459200Z (0, 1, 2, 3) 2025-09-07T06:49:23.6459325Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T06:49:23.6459452Z (0, 2, 1, 3) 2025-09-07T06:49:23.6459594Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T06:49:23.6459742Z (0, 2, 3, 1) 2025-09-07T06:49:23.6459843Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T06:49:23.6467056Z (0, 1, 2, 3) 2025-09-07T06:49:23.6467182Z >>> try: 2025-09-07T06:49:23.6467308Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T06:49:23.6467474Z ... except RuntimeError as e: 2025-09-07T06:49:23.6467588Z ... print(e) 2025-09-07T06:49:23.6467768Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T06:49:23.6467963Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T06:49:23.6468129Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T06:49:23.6468294Z ... ) # It can be mapped to contiguous format 2025-09-07T06:49:23.6468500Z (0, 1, 2, 3) 2025-09-07T06:49:23.6468585Z >>> try: 2025-09-07T06:49:23.6468704Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T06:49:23.6468861Z ... except TypeError as e: 2025-09-07T06:49:23.6468983Z ... print(e) 2025-09-07T06:49:23.6469132Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T06:49:23.6469278Z 2025-09-07T06:49:23.6469356Z .. warning:: 2025-09-07T06:49:23.6469488Z The dim_order tensor API is experimental and subject to change. 2025-09-07T06:49:23.6469636Z 2025-09-07T06:49:23.6469777Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6469932Z 2025-09-07T06:49:23.6470010Z warnings.warn(msg) 2025-09-07T06:49:23.6470100Z 2025-09-07T06:49:23.6470236Z --- Parse Warning: 6 / 146 --- 2025-09-07T06:49:23.6470656Z /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=397. 2025-09-07T06:49:23.6471196Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6471411Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T06:49:23.6471562Z 2025-09-07T06:49:23.6471669Z This is helpful when you want to visualize data over some 2025-09-07T06:49:23.6471836Z range of inputs. See below for a plotting example. 2025-09-07T06:49:23.6472000Z 2025-09-07T06:49:23.6472115Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T06:49:23.6472288Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T06:49:23.6472472Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T06:49:23.6472643Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T06:49:23.6472804Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T06:49:23.6472948Z to the result shape. 2025-09-07T06:49:23.6473049Z 2025-09-07T06:49:23.6473124Z .. note:: 2025-09-07T06:49:23.6473273Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T06:49:23.6473430Z single element. 2025-09-07T06:49:23.6473530Z 2025-09-07T06:49:23.6473617Z .. warning:: 2025-09-07T06:49:23.6473756Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T06:49:23.6473927Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T06:49:23.6474058Z 2025-09-07T06:49:23.6474162Z In the future `torch.meshgrid` will transition to 2025-09-07T06:49:23.6474300Z `indexing='xy'` as the default. 2025-09-07T06:49:23.6474541Z 2025-09-07T06:49:23.6474651Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T06:49:23.6474816Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T06:49:23.6474946Z 2025-09-07T06:49:23.6475028Z .. seealso:: 2025-09-07T06:49:23.6475158Z 2025-09-07T06:49:23.6475300Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T06:49:23.6475465Z collects the data in a tensor of vectors. 2025-09-07T06:49:23.6475588Z 2025-09-07T06:49:23.6475660Z Args: 2025-09-07T06:49:23.6475808Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T06:49:23.6476003Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T06:49:23.6476128Z 2025-09-07T06:49:23.6476233Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T06:49:23.6476395Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T06:49:23.6476581Z 2025-09-07T06:49:23.6476685Z If "xy" is selected, the first dimension corresponds 2025-09-07T06:49:23.6476838Z to the cardinality of the second input and the second 2025-09-07T06:49:23.6477033Z dimension corresponds to the cardinality of the first 2025-09-07T06:49:23.6477169Z input. 2025-09-07T06:49:23.6477263Z 2025-09-07T06:49:23.6477357Z If "ij" is selected, the dimensions are in the same 2025-09-07T06:49:23.6477502Z order as the cardinality of the inputs. 2025-09-07T06:49:23.6477617Z 2025-09-07T06:49:23.6477693Z Returns: 2025-09-07T06:49:23.6477812Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T06:49:23.6484741Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T06:49:23.6484967Z output will also have :math:`N` tensors, where each tensor 2025-09-07T06:49:23.6485138Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T06:49:23.6485281Z 2025-09-07T06:49:23.6485371Z Example:: 2025-09-07T06:49:23.6485458Z 2025-09-07T06:49:23.6485544Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T06:49:23.6485666Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T06:49:23.6485774Z 2025-09-07T06:49:23.6485882Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T06:49:23.6486054Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T06:49:23.6486182Z cartesian product. 2025-09-07T06:49:23.6486321Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T06:49:23.6486465Z >>> grid_x 2025-09-07T06:49:23.6486578Z tensor([[1, 1, 1], 2025-09-07T06:49:23.6486695Z [2, 2, 2], 2025-09-07T06:49:23.6486805Z [3, 3, 3]]) 2025-09-07T06:49:23.6486921Z >>> grid_y 2025-09-07T06:49:23.6487018Z tensor([[4, 5, 6], 2025-09-07T06:49:23.6487126Z [4, 5, 6], 2025-09-07T06:49:23.6487234Z [4, 5, 6]]) 2025-09-07T06:49:23.6487331Z 2025-09-07T06:49:23.6487444Z This correspondence can be seen when these grids are 2025-09-07T06:49:23.6487586Z stacked properly. 2025-09-07T06:49:23.6487746Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T06:49:23.6487930Z ... torch.cartesian_prod(x, y)) 2025-09-07T06:49:23.6488058Z True 2025-09-07T06:49:23.6488145Z 2025-09-07T06:49:23.6488253Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T06:49:23.6488400Z plotting. 2025-09-07T06:49:23.6488536Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T06:49:23.6488687Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T06:49:23.6488822Z >>> import matplotlib.pyplot as plt 2025-09-07T06:49:23.6488964Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T06:49:23.6489104Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T06:49:23.6489251Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T06:49:23.6489398Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T06:49:23.6489526Z >>> ax = plt.axes(projection='3d') 2025-09-07T06:49:23.6489683Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T06:49:23.6489820Z >>> plt.show() 2025-09-07T06:49:23.6489924Z 2025-09-07T06:49:23.6490012Z .. image:: ../_static/img/meshgrid.png 2025-09-07T06:49:23.6490131Z :width: 512 2025-09-07T06:49:23.6490222Z 2025-09-07T06:49:23.6490295Z 2025-09-07T06:49:23.6490480Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6490639Z 2025-09-07T06:49:23.6490719Z warnings.warn(msg) 2025-09-07T06:49:23.6490811Z 2025-09-07T06:49:23.6490935Z --- Parse Warning: 7 / 146 --- 2025-09-07T06:49:23.6491307Z /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=793. 2025-09-07T06:49:23.6491729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6491999Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T06:49:23.6492199Z 2025-09-07T06:49:23.6492299Z Returns the unique elements of the input tensor. 2025-09-07T06:49:23.6492427Z 2025-09-07T06:49:23.6492577Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T06:49:23.6492816Z this function also eliminates non-consecutive duplicate values. 2025-09-07T06:49:23.6492998Z 2025-09-07T06:49:23.6493122Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T06:49:23.6493338Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T06:49:23.6493569Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T06:49:23.6493769Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T06:49:23.6493899Z 2025-09-07T06:49:23.6493970Z Args: 2025-09-07T06:49:23.6494066Z input (Tensor): the input tensor 2025-09-07T06:49:23.6494227Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T06:49:23.6494513Z before returning as output. 2025-09-07T06:49:23.6494685Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T06:49:23.6494872Z elements in the original input ended up in the returned unique list. 2025-09-07T06:49:23.6495066Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T06:49:23.6495216Z element. 2025-09-07T06:49:23.6495348Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T06:49:23.6495529Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T06:49:23.6495710Z tensors indexed by the given dimension is treated as one of the 2025-09-07T06:49:23.6501916Z elements to apply the unique operation upon. See examples for more 2025-09-07T06:49:23.6502094Z details. Default: ``None`` 2025-09-07T06:49:23.6502201Z 2025-09-07T06:49:23.6502280Z Returns: 2025-09-07T06:49:23.6502456Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T06:49:23.6502627Z 2025-09-07T06:49:23.6502739Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T06:49:23.6502903Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T06:49:23.6503064Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T06:49:23.6503238Z returned tensor (same shape as input) representing the indices 2025-09-07T06:49:23.6503431Z for where elements in the original input map to in the output; 2025-09-07T06:49:23.6503609Z otherwise, this function will only return a single tensor. 2025-09-07T06:49:23.6503760Z - **counts** (*Tensor*): (optional) if 2025-09-07T06:49:23.6503918Z :attr:`return_counts` is True, there will be an additional 2025-09-07T06:49:23.6504092Z returned tensor (same shape as output or output.size(dim), 2025-09-07T06:49:23.6504340Z if dim was specified) representing the number of occurrences 2025-09-07T06:49:23.6504600Z for each unique value or tensor. 2025-09-07T06:49:23.6504728Z 2025-09-07T06:49:23.6504811Z Example:: 2025-09-07T06:49:23.6504901Z 2025-09-07T06:49:23.6505036Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T06:49:23.6505185Z >>> output 2025-09-07T06:49:23.6505288Z tensor([1, 2, 3]) 2025-09-07T06:49:23.6505390Z 2025-09-07T06:49:23.6505489Z >>> output, inverse_indices = torch.unique( 2025-09-07T06:49:23.6505673Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T06:49:23.6505831Z >>> output 2025-09-07T06:49:23.6505930Z tensor([1, 2, 3]) 2025-09-07T06:49:23.6506033Z >>> inverse_indices 2025-09-07T06:49:23.6506172Z tensor([0, 2, 1, 2]) 2025-09-07T06:49:23.6506281Z 2025-09-07T06:49:23.6506372Z >>> output, inverse_indices = torch.unique( 2025-09-07T06:49:23.6506547Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T06:49:23.6506731Z >>> output 2025-09-07T06:49:23.6506821Z tensor([1, 2, 3]) 2025-09-07T06:49:23.6506931Z >>> inverse_indices 2025-09-07T06:49:23.6507043Z tensor([[0, 2], 2025-09-07T06:49:23.6507153Z [1, 2]]) 2025-09-07T06:49:23.6507243Z 2025-09-07T06:49:23.6507332Z >>> a = torch.tensor([ 2025-09-07T06:49:23.6507436Z ... [ 2025-09-07T06:49:23.6507537Z ... [1, 1, 0, 0], 2025-09-07T06:49:23.6507643Z ... [1, 1, 0, 0], 2025-09-07T06:49:23.6507745Z ... [0, 0, 1, 1], 2025-09-07T06:49:23.6507846Z ... ], 2025-09-07T06:49:23.6507933Z ... [ 2025-09-07T06:49:23.6508024Z ... [0, 0, 1, 1], 2025-09-07T06:49:23.6508126Z ... [0, 0, 1, 1], 2025-09-07T06:49:23.6508225Z ... [1, 1, 1, 1], 2025-09-07T06:49:23.6508330Z ... ], 2025-09-07T06:49:23.6508425Z ... [ 2025-09-07T06:49:23.6508514Z ... [1, 1, 0, 0], 2025-09-07T06:49:23.6508614Z ... [1, 1, 0, 0], 2025-09-07T06:49:23.6508721Z ... [0, 0, 1, 1], 2025-09-07T06:49:23.6508830Z ... ], 2025-09-07T06:49:23.6508917Z ... ]) 2025-09-07T06:49:23.6508997Z 2025-09-07T06:49:23.6509116Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T06:49:23.6509310Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T06:49:23.6509475Z >>> # each other, so one of them will be removed. 2025-09-07T06:49:23.6509603Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T06:49:23.6509723Z tensor(True) 2025-09-07T06:49:23.6509840Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T06:49:23.6509965Z >>> a_unique_dim0 2025-09-07T06:49:23.6510067Z tensor([[[0, 0, 1, 1], 2025-09-07T06:49:23.6510170Z [0, 0, 1, 1], 2025-09-07T06:49:23.6510273Z [1, 1, 1, 1]], 2025-09-07T06:49:23.6510382Z [[1, 1, 0, 0], 2025-09-07T06:49:23.6510481Z [1, 1, 0, 0], 2025-09-07T06:49:23.6510579Z [0, 0, 1, 1]]]) 2025-09-07T06:49:23.6510679Z 2025-09-07T06:49:23.6510805Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T06:49:23.6511044Z >>> # `a_unique_dim0`: 2025-09-07T06:49:23.6511168Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T06:49:23.6511286Z tensor(True) 2025-09-07T06:49:23.6511392Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T06:49:23.6511509Z tensor(True) 2025-09-07T06:49:23.6511651Z 2025-09-07T06:49:23.6511766Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T06:49:23.6521774Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T06:49:23.6522012Z >>> # them will be removed. 2025-09-07T06:49:23.6522134Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T06:49:23.6522260Z tensor(True) 2025-09-07T06:49:23.6522369Z >>> torch.unique(a, dim=1) 2025-09-07T06:49:23.6522483Z tensor([[[0, 0, 1, 1], 2025-09-07T06:49:23.6522588Z [1, 1, 0, 0]], 2025-09-07T06:49:23.6522705Z [[1, 1, 1, 1], 2025-09-07T06:49:23.6522817Z [0, 0, 1, 1]], 2025-09-07T06:49:23.6522927Z [[0, 0, 1, 1], 2025-09-07T06:49:23.6523044Z [1, 1, 0, 0]]]) 2025-09-07T06:49:23.6523183Z 2025-09-07T06:49:23.6523306Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T06:49:23.6523484Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T06:49:23.6523674Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T06:49:23.6523841Z >>> # sub-tensors will be removed. 2025-09-07T06:49:23.6523968Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T06:49:23.6524087Z tensor(True) 2025-09-07T06:49:23.6524210Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T06:49:23.6524325Z tensor(True) 2025-09-07T06:49:23.6524497Z >>> torch.unique(a, dim=2) 2025-09-07T06:49:23.6524623Z tensor([[[0, 1], 2025-09-07T06:49:23.6524741Z [0, 1], 2025-09-07T06:49:23.6524851Z [1, 0]], 2025-09-07T06:49:23.6524961Z [[1, 0], 2025-09-07T06:49:23.6525064Z [1, 0], 2025-09-07T06:49:23.6525159Z [1, 1]], 2025-09-07T06:49:23.6525256Z [[0, 1], 2025-09-07T06:49:23.6525350Z [0, 1], 2025-09-07T06:49:23.6525460Z [1, 0]]]) 2025-09-07T06:49:23.6525559Z 2025-09-07T06:49:23.6525695Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6525864Z 2025-09-07T06:49:23.6525945Z warnings.warn(msg) 2025-09-07T06:49:23.6526036Z 2025-09-07T06:49:23.6526178Z --- Parse Warning: 8 / 146 --- 2025-09-07T06:49:23.6526572Z /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. 2025-09-07T06:49:23.6526986Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6527237Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T06:49:23.6527425Z 2025-09-07T06:49:23.6527535Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T06:49:23.6527667Z 2025-09-07T06:49:23.6527791Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T06:49:23.6527998Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T06:49:23.6528197Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T06:49:23.6528394Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T06:49:23.6528607Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T06:49:23.6528802Z set of devices using the :attr:`map_location` argument. 2025-09-07T06:49:23.6528937Z 2025-09-07T06:49:23.6529081Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T06:49:23.6529332Z storage with two arguments: storage and location. The storage argument 2025-09-07T06:49:23.6529535Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T06:49:23.6529730Z Each serialized storage has a location tag associated with it which 2025-09-07T06:49:23.6529931Z identifies the device it was saved from, and this tag is the second 2025-09-07T06:49:23.6530138Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T06:49:23.6530342Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T06:49:23.6530544Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T06:49:23.6530745Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T06:49:23.6530957Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T06:49:23.6531194Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T06:49:23.6531359Z 2025-09-07T06:49:23.6531489Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T06:49:23.6531707Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T06:49:23.6531860Z 2025-09-07T06:49:23.6531994Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T06:49:23.6532205Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T06:49:23.6532361Z storages (values). 2025-09-07T06:49:23.6532459Z 2025-09-07T06:49:23.6532584Z User extensions can register their own location tags and tagging and 2025-09-07T06:49:23.6532797Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T06:49:23.6532957Z 2025-09-07T06:49:23.6533097Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T06:49:23.6533253Z 2025-09-07T06:49:23.6533326Z Args: 2025-09-07T06:49:23.6533498Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T06:49:23.6533718Z or a string or os.PathLike object containing a file name 2025-09-07T06:49:23.6538581Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T06:49:23.6538773Z locations 2025-09-07T06:49:23.6538926Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T06:49:23.6539110Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T06:49:23.6539292Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T06:49:23.6539467Z loading only tensors, primitive types, dictionaries 2025-09-07T06:49:23.6539645Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T06:49:23.6539811Z See :ref:`weights-only` for more details. 2025-09-07T06:49:23.6540015Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T06:49:23.6540289Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T06:49:23.6540560Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T06:49:23.6540833Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T06:49:23.6541099Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T06:49:23.6541306Z will be lazily loaded when their data is accessed. 2025-09-07T06:49:23.6541543Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T06:49:23.6541751Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T06:49:23.6541910Z :attr:`errors=...`. 2025-09-07T06:49:23.6542022Z 2025-09-07T06:49:23.6542099Z .. warning:: 2025-09-07T06:49:23.6542235Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T06:49:23.6542428Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T06:49:23.6542631Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T06:49:23.6542848Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T06:49:23.6543067Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T06:49:23.6543237Z 2025-09-07T06:49:23.6543342Z .. note:: 2025-09-07T06:49:23.6543487Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T06:49:23.6543722Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T06:49:23.6543948Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T06:49:23.6544107Z 2025-09-07T06:49:23.6544180Z .. note:: 2025-09-07T06:49:23.6544321Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T06:49:23.6544596Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T06:49:23.6544790Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T06:49:23.6544991Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T06:49:23.6545211Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T06:49:23.6545418Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T06:49:23.6545620Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T06:49:23.6545766Z 2025-09-07T06:49:23.6545841Z Example: 2025-09-07T06:49:23.6545957Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T06:49:23.6546102Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T06:49:23.6546244Z # Load all tensors onto the CPU 2025-09-07T06:49:23.6546360Z >>> torch.load( 2025-09-07T06:49:23.6546471Z ... "tensors.pt", 2025-09-07T06:49:23.6546592Z ... map_location=torch.device("cpu"), 2025-09-07T06:49:23.6546715Z ... weights_only=True, 2025-09-07T06:49:23.6546821Z ... ) 2025-09-07T06:49:23.6546935Z # Load all tensors onto the CPU, using a function 2025-09-07T06:49:23.6547061Z >>> torch.load( 2025-09-07T06:49:23.6547171Z ... "tensors.pt", 2025-09-07T06:49:23.6547295Z ... map_location=lambda storage, loc: storage, 2025-09-07T06:49:23.6547431Z ... weights_only=True, 2025-09-07T06:49:23.6547535Z ... ) 2025-09-07T06:49:23.6547628Z # Load all tensors onto GPU 1 2025-09-07T06:49:23.6547749Z >>> torch.load( 2025-09-07T06:49:23.6547845Z ... "tensors.pt", 2025-09-07T06:49:23.6547974Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T06:49:23.6548108Z ... weights_only=True, 2025-09-07T06:49:23.6548226Z ... ) # type: ignore[attr-defined] 2025-09-07T06:49:23.6548356Z # Map tensors from GPU 1 to GPU 0 2025-09-07T06:49:23.6548470Z >>> torch.load( 2025-09-07T06:49:23.6548570Z ... "tensors.pt", 2025-09-07T06:49:23.6548688Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T06:49:23.6548813Z ... weights_only=True, 2025-09-07T06:49:23.6548964Z ... ) 2025-09-07T06:49:23.6549061Z # Load tensor from io.BytesIO object 2025-09-07T06:49:23.6549236Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T06:49:23.6549407Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T06:49:23.6549529Z ... buffer = io.BytesIO(f.read()) 2025-09-07T06:49:23.6549663Z >>> torch.load(buffer, weights_only=False) 2025-09-07T06:49:23.6549805Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T06:49:23.6549989Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T06:49:23.6550181Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T06:49:23.6550320Z 2025-09-07T06:49:23.6550453Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6550625Z 2025-09-07T06:49:23.6550705Z warnings.warn(msg) 2025-09-07T06:49:23.6550802Z 2025-09-07T06:49:23.6550999Z --- Parse Warning: 9 / 146 --- 2025-09-07T06:49:23.6551397Z /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=375. 2025-09-07T06:49:23.6551810Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6552020Z Registers the function implementation as the fallback for the given key. 2025-09-07T06:49:23.6552169Z 2025-09-07T06:49:23.6552289Z This function only works for a library with global namespace ("_"). 2025-09-07T06:49:23.6552434Z 2025-09-07T06:49:23.6552508Z Args: 2025-09-07T06:49:23.6561597Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T06:49:23.6561816Z to register a fallthrough. 2025-09-07T06:49:23.6562014Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T06:49:23.6562224Z the dispatch key that the library was created with. 2025-09-07T06:49:23.6562445Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T06:49:23.6562717Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T06:49:23.6562894Z 2025-09-07T06:49:23.6562976Z Example:: 2025-09-07T06:49:23.6563065Z 2025-09-07T06:49:23.6563153Z >>> my_lib = Library("_", "IMPL") 2025-09-07T06:49:23.6563284Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T06:49:23.6565480Z >>> # Handle all autocast ops generically 2025-09-07T06:49:23.6565609Z >>> # ... 2025-09-07T06:49:23.6565730Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T06:49:23.6565852Z 2025-09-07T06:49:23.6566110Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T06:49:23.6566378Z 2025-09-07T06:49:23.6566469Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T06:49:23.6566584Z ^ 2025-09-07T06:49:23.6566664Z warnings.warn(msg) 2025-09-07T06:49:23.6566754Z 2025-09-07T06:49:23.6566882Z --- Parse Warning: 10 / 146 --- 2025-09-07T06:49:23.6568803Z /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=948. 2025-09-07T06:49:23.6569203Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6569487Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T06:49:23.6569634Z 2025-09-07T06:49:23.6569742Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T06:49:23.6569871Z 2025-09-07T06:49:23.6569994Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T06:49:23.6570190Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T06:49:23.6570384Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T06:49:23.6570556Z what the properties of the output Tensors are. 2025-09-07T06:49:23.6570674Z 2025-09-07T06:49:23.6572293Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T06:49:23.6572480Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T06:49:23.6572696Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T06:49:23.6572884Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T06:49:23.6573083Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T06:49:23.6573269Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T06:49:23.6573450Z (and may not directly access the storage or data of any input or 2025-09-07T06:49:23.6573593Z intermediate Tensors). 2025-09-07T06:49:23.6573690Z 2025-09-07T06:49:23.6573790Z This API may be used as a decorator (see examples). 2025-09-07T06:49:23.6575458Z 2025-09-07T06:49:23.6575560Z For a detailed guide on custom ops, please see 2025-09-07T06:49:23.6575732Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T06:49:23.6575878Z 2025-09-07T06:49:23.6575951Z Args: 2025-09-07T06:49:23.6576080Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T06:49:23.6576243Z func: Fake tensor implementation. 2025-09-07T06:49:23.6576393Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T06:49:23.6576565Z allow_override: Flag controlling if we want to override an 2025-09-07T06:49:23.6576728Z existing registered fake impl. This is by default off, 2025-09-07T06:49:23.6576889Z and will error you're trying to register a fake impl to 2025-09-07T06:49:23.6578494Z an operator that already has a fake impl. This also only 2025-09-07T06:49:23.6578660Z applies if the custom operator was not created via 2025-09-07T06:49:23.6578822Z torch.library.custom_op, as overriding and existing fake 2025-09-07T06:49:23.6578972Z impl is already allowed. 2025-09-07T06:49:23.6579082Z 2025-09-07T06:49:23.6579155Z Examples: 2025-09-07T06:49:23.6579246Z >>> import torch 2025-09-07T06:49:23.6579352Z >>> import numpy as np 2025-09-07T06:49:23.6579467Z >>> from torch import Tensor 2025-09-07T06:49:23.6579573Z >>> 2025-09-07T06:49:23.6579690Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T06:49:23.6581291Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T06:49:23.6581488Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T06:49:23.6581667Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T06:49:23.6581799Z >>> 2025-09-07T06:49:23.6581913Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T06:49:23.6582050Z >>> def _(x, weight, bias): 2025-09-07T06:49:23.6582163Z >>> assert x.dim() == 2 2025-09-07T06:49:23.6582280Z >>> assert weight.dim() == 2 2025-09-07T06:49:23.6582443Z >>> assert bias.dim() == 1 2025-09-07T06:49:23.6582568Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T06:49:23.6582702Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T06:49:23.6584282Z >>> assert x.device == weight.device 2025-09-07T06:49:23.6584466Z >>> 2025-09-07T06:49:23.6584559Z >>> return (x @ weight.t()) + bias 2025-09-07T06:49:23.6584668Z >>> 2025-09-07T06:49:23.6584781Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T06:49:23.6584918Z >>> x = torch.randn(2, 3) 2025-09-07T06:49:23.6585032Z >>> w = torch.randn(3, 3) 2025-09-07T06:49:23.6585143Z >>> b = torch.randn(3) 2025-09-07T06:49:23.6585268Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T06:49:23.6585385Z >>> 2025-09-07T06:49:23.6585499Z >>> assert y.shape == (2, 3) 2025-09-07T06:49:23.6587057Z >>> 2025-09-07T06:49:23.6587176Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T06:49:23.6587379Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T06:49:23.6587542Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T06:49:23.6587670Z >>> x_np = x.numpy(force=True) 2025-09-07T06:49:23.6587799Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T06:49:23.6587934Z >>> return torch.tensor(res, device=x.device) 2025-09-07T06:49:23.6588049Z >>> 2025-09-07T06:49:23.6588161Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T06:49:23.6588294Z >>> def _(x): 2025-09-07T06:49:23.6588412Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T06:49:23.6590012Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T06:49:23.6590170Z >>> # we use the ctx object to construct a new symint that 2025-09-07T06:49:23.6590313Z >>> # represents the data-dependent size. 2025-09-07T06:49:23.6590443Z >>> ctx = torch.library.get_ctx() 2025-09-07T06:49:23.6590566Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T06:49:23.6590686Z >>> shape = [nnz, x.dim()] 2025-09-07T06:49:23.6590820Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T06:49:23.6591028Z >>> return result 2025-09-07T06:49:23.6591126Z >>> 2025-09-07T06:49:23.6591240Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T06:49:23.6591369Z >>> 2025-09-07T06:49:23.6592892Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T06:49:23.6593060Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T06:49:23.6593224Z >>> trace.print_readable() 2025-09-07T06:49:23.6593329Z >>> 2025-09-07T06:49:23.6593458Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T06:49:23.6593601Z 2025-09-07T06:49:23.6593673Z 2025-09-07T06:49:23.6593889Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T06:49:23.6594125Z 2025-09-07T06:49:23.6594196Z _._ = None 2025-09-07T06:49:23.6594275Z ^ 2025-09-07T06:49:23.6595866Z warnings.warn(msg) 2025-09-07T06:49:23.6595960Z 2025-09-07T06:49:23.6596078Z --- Parse Warning: 11 / 146 --- 2025-09-07T06:49:23.6596450Z /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=1083. 2025-09-07T06:49:23.6596860Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6597092Z Register a backward formula for this custom op. 2025-09-07T06:49:23.6597211Z 2025-09-07T06:49:23.6597327Z In order for an operator to work with autograd, you need to register 2025-09-07T06:49:23.6597472Z a backward formula: 2025-09-07T06:49:23.6597608Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T06:49:23.6597762Z by providing us a "backward" function. 2025-09-07T06:49:23.6599363Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T06:49:23.6599529Z use `setup_context` to save values for backward. 2025-09-07T06:49:23.6599646Z 2025-09-07T06:49:23.6599768Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T06:49:23.6599956Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T06:49:23.6600107Z the number of outputs of the operator. 2025-09-07T06:49:23.6600289Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T06:49:23.6600486Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T06:49:23.6600683Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T06:49:23.6600805Z 2025-09-07T06:49:23.6600921Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T06:49:23.6602571Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T06:49:23.6602771Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T06:49:23.6602960Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T06:49:23.6603140Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T06:49:23.6603319Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T06:49:23.6603453Z 2025-09-07T06:49:23.6603570Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T06:49:23.6603763Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T06:49:23.6603960Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T06:49:23.6604154Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T06:49:23.6604295Z 2025-09-07T06:49:23.6605937Z If you need different autograd behavior on different devices, then we 2025-09-07T06:49:23.6606130Z recommend creating two different custom operators, one for each device 2025-09-07T06:49:23.6606324Z that needs different behavior, and switching between them at runtime. 2025-09-07T06:49:23.6606465Z 2025-09-07T06:49:23.6606537Z Examples: 2025-09-07T06:49:23.6606630Z >>> import torch 2025-09-07T06:49:23.6606735Z >>> import numpy as np 2025-09-07T06:49:23.6606848Z >>> from torch import Tensor 2025-09-07T06:49:23.6606954Z >>> 2025-09-07T06:49:23.6607080Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T06:49:23.6607235Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T06:49:23.6608797Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:23.6608911Z >>> y_np = np.sin(x_np) 2025-09-07T06:49:23.6609046Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T06:49:23.6609171Z >>> 2025-09-07T06:49:23.6609278Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T06:49:23.6609409Z >>> x, = inputs 2025-09-07T06:49:23.6609518Z >>> ctx.save_for_backward(x) 2025-09-07T06:49:23.6609624Z >>> 2025-09-07T06:49:23.6609711Z >>> def backward(ctx, grad): 2025-09-07T06:49:23.6609825Z >>> x, = ctx.saved_tensors 2025-09-07T06:49:23.6611362Z >>> return grad * x.cos() 2025-09-07T06:49:23.6611470Z >>> 2025-09-07T06:49:23.6611611Z >>> torch.library.register_autograd( 2025-09-07T06:49:23.6611757Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T06:49:23.6611889Z ... ) 2025-09-07T06:49:23.6611970Z >>> 2025-09-07T06:49:23.6612065Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T06:49:23.6612188Z >>> y = numpy_sin(x) 2025-09-07T06:49:23.6612318Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T06:49:23.6612463Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T06:49:23.6612576Z >>> 2025-09-07T06:49:23.6614118Z >>> # Example with a keyword-only arg 2025-09-07T06:49:23.6614277Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T06:49:23.6614505Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T06:49:23.6614666Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:23.6614780Z >>> y_np = x_np * val 2025-09-07T06:49:23.6614911Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T06:49:23.6615053Z >>> 2025-09-07T06:49:23.6615181Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T06:49:23.6615347Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T06:49:23.6615460Z >>> 2025-09-07T06:49:23.6615547Z >>> def backward(ctx, grad): 2025-09-07T06:49:23.6617139Z >>> return grad * ctx.val 2025-09-07T06:49:23.6617246Z >>> 2025-09-07T06:49:23.6617340Z >>> torch.library.register_autograd( 2025-09-07T06:49:23.6617483Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T06:49:23.6617611Z ... ) 2025-09-07T06:49:23.6617691Z >>> 2025-09-07T06:49:23.6617786Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T06:49:23.6617909Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T06:49:23.6618043Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T06:49:23.6618206Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T06:49:23.6618333Z 2025-09-07T06:49:23.6619845Z 2025-09-07T06:49:23.6619981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6620133Z 2025-09-07T06:49:23.6620211Z warnings.warn(msg) 2025-09-07T06:49:23.6620302Z 2025-09-07T06:49:23.6620417Z --- Parse Warning: 12 / 146 --- 2025-09-07T06:49:23.6620785Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1482. 2025-09-07T06:49:23.6621188Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6621389Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T06:49:23.6621526Z 2025-09-07T06:49:23.6621645Z This function retrieves the kernel that would be executed for a given 2025-09-07T06:49:23.6623289Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T06:49:23.6623482Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T06:49:23.6623658Z case for this function is to retrieve the original kernel for a given 2025-09-07T06:49:23.6623843Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T06:49:23.6624016Z that calls into the original kernel for certain cases. 2025-09-07T06:49:23.6624140Z 2025-09-07T06:49:23.6624211Z Args: 2025-09-07T06:49:23.6624330Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T06:49:23.6624620Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T06:49:23.6624899Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T06:49:23.6625096Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T06:49:23.6626702Z 2025-09-07T06:49:23.6626777Z Returns: 2025-09-07T06:49:23.6626913Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T06:49:23.6627072Z call the kernel. 2025-09-07T06:49:23.6627167Z 2025-09-07T06:49:23.6627238Z Raises: 2025-09-07T06:49:23.6627343Z RuntimeError: If the operator does not exist. 2025-09-07T06:49:23.6627459Z 2025-09-07T06:49:23.6627530Z Example: 2025-09-07T06:49:23.6627625Z >>> # Get the CPU kernel for torch.add 2025-09-07T06:49:23.6627776Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T06:49:23.6629407Z >>> 2025-09-07T06:49:23.6629505Z >>> # You can also use DispatchKey enum 2025-09-07T06:49:23.6629683Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T06:49:23.6629865Z >>> 2025-09-07T06:49:23.6629957Z >>> # Or use an OpOverload directly 2025-09-07T06:49:23.6630116Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T06:49:23.6630261Z >>> 2025-09-07T06:49:23.6630386Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T06:49:23.6630546Z >>> # Get the original kernel for torch.sin 2025-09-07T06:49:23.6630705Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T06:49:23.6630929Z >>> 2025-09-07T06:49:23.6632527Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T06:49:23.6632700Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T06:49:23.6632830Z >>> if (x < 0).any(): 2025-09-07T06:49:23.6632966Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T06:49:23.6633104Z >>> else: 2025-09-07T06:49:23.6633208Z >>> return torch.zeros_like(x) 2025-09-07T06:49:23.6633316Z >>> 2025-09-07T06:49:23.6633417Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T06:49:23.6633595Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T06:49:23.6633787Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T06:49:23.6633956Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T06:49:23.6635651Z >>> 2025-09-07T06:49:23.6635746Z >>> # Test the conditional behavior 2025-09-07T06:49:23.6635873Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T06:49:23.6636001Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T06:49:23.6636122Z >>> torch.sin(x_positive) 2025-09-07T06:49:23.6636235Z tensor([0., 0.]) 2025-09-07T06:49:23.6636339Z >>> torch.sin(x_mixed) 2025-09-07T06:49:23.6636448Z tensor([-0.8415, 0.9093]) 2025-09-07T06:49:23.6636549Z 2025-09-07T06:49:23.6636756Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T06:49:23.6636978Z 2025-09-07T06:49:23.6638550Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T06:49:23.6638685Z ^ 2025-09-07T06:49:23.6638767Z warnings.warn(msg) 2025-09-07T06:49:23.6638857Z 2025-09-07T06:49:23.6638974Z --- Parse Warning: 13 / 146 --- 2025-09-07T06:49:23.6639386Z /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=1571. 2025-09-07T06:49:23.6639785Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6639991Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T06:49:23.6640143Z registered correctly. 2025-09-07T06:49:23.6640239Z 2025-09-07T06:49:23.6640353Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T06:49:23.6642079Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T06:49:23.6642280Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T06:49:23.6642472Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T06:49:23.6642643Z ``opcheck`` tests these metadata and properties. 2025-09-07T06:49:23.6642762Z 2025-09-07T06:49:23.6642886Z Concretely, we test the following: 2025-09-07T06:49:23.6642993Z 2025-09-07T06:49:23.6643102Z - test_schema: If the schema matches the implementation of 2025-09-07T06:49:23.6643299Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T06:49:23.6643483Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T06:49:23.6643660Z specifies that we return a new Tensor, then we check that the 2025-09-07T06:49:23.6643840Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T06:49:23.6645534Z a view of an existing one). 2025-09-07T06:49:23.6645683Z - test_autograd_registration: If the operator supports training 2025-09-07T06:49:23.6645857Z (autograd): we check that its autograd formula is registered via 2025-09-07T06:49:23.6646036Z torch.library.register_autograd or a manual registration to one 2025-09-07T06:49:23.6646217Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T06:49:23.6646381Z registrations may lead to undefined behavior. 2025-09-07T06:49:23.6646535Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T06:49:23.6646698Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T06:49:23.6646870Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T06:49:23.6647055Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T06:49:23.6648674Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T06:49:23.6648852Z operator and that it is correct. This test takes the result of 2025-09-07T06:49:23.6649022Z running the operator on real tensors and the result of running 2025-09-07T06:49:23.6649191Z the operator on FakeTensors and checks that they have the same 2025-09-07T06:49:23.6649350Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T06:49:23.6649513Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T06:49:23.6649682Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T06:49:23.6649854Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T06:49:23.6650018Z same under eager-mode PyTorch and torch.compile. 2025-09-07T06:49:23.6650176Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T06:49:23.6650341Z other things it tests are that the operator supports 2025-09-07T06:49:23.6651925Z functionalization and that the backward pass (if it exists) also 2025-09-07T06:49:23.6652094Z supports FakeTensor and functionalization. 2025-09-07T06:49:23.6652207Z 2025-09-07T06:49:23.6652320Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T06:49:23.6652491Z representative set of inputs. If your operator supports 2025-09-07T06:49:23.6652723Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T06:49:23.6652923Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T06:49:23.6653097Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T06:49:23.6653220Z 2025-09-07T06:49:23.6653291Z Args: 2025-09-07T06:49:23.6653405Z op: The operator. Must either be a function decorated with 2025-09-07T06:49:23.6657482Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T06:49:23.6657724Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T06:49:23.6657934Z args: The args to the operator 2025-09-07T06:49:23.6658069Z kwargs: The kwargs to the operator 2025-09-07T06:49:23.6658226Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T06:49:23.6658392Z Example: ("test_schema", "test_faketensor") 2025-09-07T06:49:23.6658608Z raise_exception: If we should raise an exception on the first 2025-09-07T06:49:23.6658783Z error. If False, we will return a dict with information 2025-09-07T06:49:23.6658957Z on if each test passed or not. 2025-09-07T06:49:23.6659129Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T06:49:23.6659311Z If specified ``atol`` must also be specified. 2025-09-07T06:49:23.6662268Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T06:49:23.6662455Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T06:49:23.6662643Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T06:49:23.6662825Z If specified ``rtol`` must also be specified. 2025-09-07T06:49:23.6662984Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T06:49:23.6663159Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T06:49:23.6663290Z 2025-09-07T06:49:23.6663379Z .. warning:: 2025-09-07T06:49:23.6663474Z 2025-09-07T06:49:23.6663603Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T06:49:23.6663799Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T06:49:23.6665776Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T06:49:23.6665980Z mathematically correct. Use both to test custom ops that support 2025-09-07T06:49:23.6666140Z gradient computation. 2025-09-07T06:49:23.6666250Z 2025-09-07T06:49:23.6666329Z Example: 2025-09-07T06:49:23.6666416Z 2025-09-07T06:49:23.6666518Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.6666696Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T06:49:23.6666871Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T06:49:23.6667015Z >>> x_np = x.numpy(force=True) 2025-09-07T06:49:23.6667140Z >>> z_np = x_np * y 2025-09-07T06:49:23.6668932Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T06:49:23.6669068Z >>> 2025-09-07T06:49:23.6669169Z >>> @numpy_mul.register_fake 2025-09-07T06:49:23.6669292Z >>> def _(x, y): 2025-09-07T06:49:23.6669407Z >>> return torch.empty_like(x) 2025-09-07T06:49:23.6669522Z >>> 2025-09-07T06:49:23.6669627Z >>> def setup_context(ctx, inputs, output): 2025-09-07T06:49:23.6669764Z >>> y, = inputs 2025-09-07T06:49:23.6669872Z >>> ctx.y = y 2025-09-07T06:49:23.6669969Z >>> 2025-09-07T06:49:23.6670062Z >>> def backward(ctx, grad): 2025-09-07T06:49:23.6671894Z >>> return grad * ctx.y, None 2025-09-07T06:49:23.6692734Z >>> 2025-09-07T06:49:23.6692970Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T06:49:23.6693127Z >>> 2025-09-07T06:49:23.6693231Z >>> sample_inputs = [ 2025-09-07T06:49:23.6693360Z >>> (torch.randn(3), 3.14), 2025-09-07T06:49:23.6693498Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T06:49:23.6693648Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T06:49:23.6693813Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T06:49:23.6693961Z >>> ] 2025-09-07T06:49:23.6694049Z >>> 2025-09-07T06:49:23.6696732Z >>> for args in sample_inputs: 2025-09-07T06:49:23.6696877Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T06:49:23.6697011Z 2025-09-07T06:49:23.6697086Z 2025-09-07T06:49:23.6697224Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6697427Z 2025-09-07T06:49:23.6697511Z warnings.warn(msg) 2025-09-07T06:49:23.6697607Z 2025-09-07T06:49:23.6697786Z --- Parse Warning: 14 / 146 --- 2025-09-07T06:49:23.6698211Z /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=1877. 2025-09-07T06:49:23.6698687Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6700641Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T06:49:23.6700805Z 2025-09-07T06:49:23.6700883Z Example 2025-09-07T06:49:23.6700971Z ======= 2025-09-07T06:49:23.6701056Z 2025-09-07T06:49:23.6701192Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T06:49:23.6701352Z 2025-09-07T06:49:23.6701443Z >>> t = torch.empty((10, 20)) 2025-09-07T06:49:23.6701618Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T06:49:23.6701795Z 200 2025-09-07T06:49:23.6703561Z 2025-09-07T06:49:23.6703675Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:23.6703807Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T06:49:23.6703954Z >>> size = compute_required_storage_length( 2025-09-07T06:49:23.6704088Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T06:49:23.6704206Z ... ) 2025-09-07T06:49:23.6704294Z >>> size == t.storage().size() 2025-09-07T06:49:23.6704481Z True 2025-09-07T06:49:23.6704557Z 2025-09-07T06:49:23.6704671Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T06:49:23.6704814Z 2025-09-07T06:49:23.6707696Z >>> slice = torch.empty(100)[20:40] 2025-09-07T06:49:23.6707832Z >>> slice.storage().size() 2025-09-07T06:49:23.6728521Z 100 2025-09-07T06:49:23.6728606Z 2025-09-07T06:49:23.6728692Z >>> compute_required_storage_length( 2025-09-07T06:49:23.6728835Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T06:49:23.6728964Z ... ) 2025-09-07T06:49:23.6729041Z 40 2025-09-07T06:49:23.6729114Z 2025-09-07T06:49:23.6729146Z 2025-09-07T06:49:23.6729234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6729264Z 2025-09-07T06:49:23.6729303Z warnings.warn(msg) 2025-09-07T06:49:23.6729332Z 2025-09-07T06:49:23.6730980Z --- Parse Warning: 15 / 146 --- 2025-09-07T06:49:23.6731269Z /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. 2025-09-07T06:49:23.6731367Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6731499Z Return the sum of each row of the given sparse tensor. 2025-09-07T06:49:23.6731531Z 2025-09-07T06:49:23.6731613Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T06:49:23.6731688Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T06:49:23.6731763Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T06:49:23.6731821Z returns a dense tensor instead of a sparse tensor. 2025-09-07T06:49:23.6731851Z 2025-09-07T06:49:23.6731942Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T06:49:23.6732011Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T06:49:23.6732042Z 2025-09-07T06:49:23.6732120Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T06:49:23.6732227Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T06:49:23.6732257Z 2025-09-07T06:49:23.6732290Z Args: 2025-09-07T06:49:23.6732373Z input (Tensor): the input sparse tensor 2025-09-07T06:49:23.6732472Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T06:49:23.6732510Z over all dims. 2025-09-07T06:49:23.6732603Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T06:49:23.6732650Z Default: dtype of :attr:`input`. 2025-09-07T06:49:23.6734065Z 2025-09-07T06:49:23.6734105Z Example:: 2025-09-07T06:49:23.6734135Z 2025-09-07T06:49:23.6734170Z >>> nnz = 3 2025-09-07T06:49:23.6734209Z >>> dims = [5, 5, 2, 3] 2025-09-07T06:49:23.6734272Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T06:49:23.6734343Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T06:49:23.6734465Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T06:49:23.6734508Z >>> size = torch.Size(dims) 2025-09-07T06:49:23.6734566Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.6734618Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T06:49:23.6734651Z >>> S 2025-09-07T06:49:23.6734698Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T06:49:23.6734740Z [2, 4, 1]]), 2025-09-07T06:49:23.6734794Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T06:49:23.6734841Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T06:49:23.6734871Z 2025-09-07T06:49:23.6734915Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T06:49:23.6734958Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T06:49:23.6734990Z 2025-09-07T06:49:23.6735033Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T06:49:23.6736454Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T06:49:23.6736515Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T06:49:23.6736546Z 2025-09-07T06:49:23.6736616Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T06:49:23.6736663Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T06:49:23.6736707Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T06:49:23.6736755Z values=tensor([[-1.4512, 0.4073], 2025-09-07T06:49:23.6736799Z [-0.8901, 0.2017], 2025-09-07T06:49:23.6736843Z [-0.3183, -1.7539]]), 2025-09-07T06:49:23.6736895Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T06:49:23.6736927Z 2025-09-07T06:49:23.6736987Z # when sum over all sparse dim, return a dense tensor 2025-09-07T06:49:23.6737073Z # with summed dims squeezed 2025-09-07T06:49:23.6737121Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T06:49:23.6737162Z tensor([-2.6596, -1.1450]) 2025-09-07T06:49:23.6737194Z 2025-09-07T06:49:23.6737283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6737313Z 2025-09-07T06:49:23.6737351Z warnings.warn(msg) 2025-09-07T06:49:23.6737382Z 2025-09-07T06:49:23.6737455Z --- Parse Warning: 16 / 146 --- 2025-09-07T06:49:23.6737756Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T06:49:23.6739213Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6739309Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T06:49:23.6739340Z 2025-09-07T06:49:23.6739421Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T06:49:23.6739524Z variants that extends the gradcheck function with support to input 2025-09-07T06:49:23.6739588Z functions that operate on or/and return sparse tensors. 2025-09-07T06:49:23.6739618Z 2025-09-07T06:49:23.6739693Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T06:49:23.6739734Z on strided tensors only. 2025-09-07T06:49:23.6739765Z 2025-09-07T06:49:23.6739799Z For example: 2025-09-07T06:49:23.6739829Z 2025-09-07T06:49:23.6739914Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T06:49:23.6739947Z >>> x = ( 2025-09-07T06:49:23.6740008Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T06:49:23.6740046Z ... .to_sparse_coo() 2025-09-07T06:49:23.6740090Z ... .requires_grad_(True) 2025-09-07T06:49:23.6740122Z ... ) 2025-09-07T06:49:23.6740174Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T06:49:23.6740207Z True 2025-09-07T06:49:23.6740238Z 2025-09-07T06:49:23.6740326Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6741725Z 2025-09-07T06:49:23.6741763Z warnings.warn(msg) 2025-09-07T06:49:23.6741794Z 2025-09-07T06:49:23.6741865Z --- Parse Warning: 17 / 146 --- 2025-09-07T06:49:23.6742143Z /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. 2025-09-07T06:49:23.6742230Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6742276Z Retrieves the CUDA runtime API module. 2025-09-07T06:49:23.6742307Z 2025-09-07T06:49:23.6742337Z 2025-09-07T06:49:23.6742426Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T06:49:23.6742508Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T06:49:23.6742585Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T06:49:23.6742616Z 2025-09-07T06:49:23.6742647Z Args: 2025-09-07T06:49:23.6742680Z ``None`` 2025-09-07T06:49:23.6742711Z 2025-09-07T06:49:23.6742742Z Returns: 2025-09-07T06:49:23.6742797Z module: The CUDA runtime API module (_cudart). 2025-09-07T06:49:23.6742827Z 2025-09-07T06:49:23.6742861Z Raises: 2025-09-07T06:49:23.6742942Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T06:49:23.6744525Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T06:49:23.6744559Z 2025-09-07T06:49:23.6744657Z Example of CUDA operations with profiling: 2025-09-07T06:49:23.6744697Z >>> import torch 2025-09-07T06:49:23.6744751Z >>> from torch.cuda import cudart, check_error 2025-09-07T06:49:23.6744788Z >>> import os 2025-09-07T06:49:23.6744821Z >>> 2025-09-07T06:49:23.6744867Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T06:49:23.6744898Z >>> 2025-09-07T06:49:23.6744951Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T06:49:23.6744998Z >>> stream = torch.cuda.Stream() 2025-09-07T06:49:23.6745044Z >>> with torch.cuda.stream(stream): 2025-09-07T06:49:23.6745095Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T06:49:23.6745143Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T06:49:23.6745186Z >>> z = torch.mul(x, y) 2025-09-07T06:49:23.6745242Z >>> return z 2025-09-07T06:49:23.6745273Z >>> 2025-09-07T06:49:23.6745320Z >>> torch.cuda.synchronize() 2025-09-07T06:49:23.6745372Z >>> print("====== Start nsys profiling ======") 2025-09-07T06:49:23.6745444Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T06:49:23.6745498Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T06:49:23.6745556Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T06:49:23.6746992Z >>> print("CUDA operations completed.") 2025-09-07T06:49:23.6747058Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T06:49:23.6747108Z >>> print("====== End nsys profiling ======") 2025-09-07T06:49:23.6747140Z 2025-09-07T06:49:23.6747212Z To run this example and save the profiling information, execute: 2025-09-07T06:49:23.6747342Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T06:49:23.6747374Z 2025-09-07T06:49:23.6747462Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T06:49:23.6747535Z the profiling information to a file named `trace_name.prof`. 2025-09-07T06:49:23.6747621Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T06:49:23.6747676Z after the `cudaProfilerStart` call in the script. 2025-09-07T06:49:23.6747756Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T06:49:23.6747806Z CSV file and print a summary, respectively. 2025-09-07T06:49:23.6747894Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T06:49:23.6747950Z overwrite of the output file if it already exists. 2025-09-07T06:49:23.6747982Z 2025-09-07T06:49:23.6748202Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-09-07T06:49:23.6748236Z 2025-09-07T06:49:23.6748356Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T06:49:23.6748389Z ^ 2025-09-07T06:49:23.6748427Z warnings.warn(msg) 2025-09-07T06:49:23.6749813Z 2025-09-07T06:49:23.6749888Z --- Parse Warning: 18 / 146 --- 2025-09-07T06:49:23.6750183Z /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. 2025-09-07T06:49:23.6750275Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6750306Z 2025-09-07T06:49:23.6750387Z Append the given callback function to this ``Future``, which will be run 2025-09-07T06:49:23.6750463Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T06:49:23.6750571Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T06:49:23.6750641Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T06:49:23.6750713Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T06:49:23.6750786Z is the reference to this ``Future``. The callback function can use the 2025-09-07T06:49:23.6750921Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T06:49:23.6751002Z already completed, the given callback will be run immediately inline. 2025-09-07T06:49:23.6751034Z 2025-09-07T06:49:23.6751104Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T06:49:23.6751184Z callback might be invoked while the async kernels that are populating 2025-09-07T06:49:23.6751262Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T06:49:23.6751351Z callback will be invoked with some dedicated streams set as current 2025-09-07T06:49:23.6751426Z (fetched from a global pool) which will be synchronized with those 2025-09-07T06:49:23.6751521Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T06:49:23.6751595Z will be scheduled on the device after the kernels complete. In other 2025-09-07T06:49:23.6751665Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T06:49:23.6753119Z manipulate the result without any additional synchronization. This is 2025-09-07T06:49:23.6753181Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T06:49:23.6753212Z 2025-09-07T06:49:23.6753285Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T06:49:23.6753355Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T06:49:23.6753433Z these tensors are still running on the device, as long as the callback 2025-09-07T06:49:23.6753509Z didn't change streams during its execution. If one wants to change 2025-09-07T06:49:23.6753584Z streams, one must be careful to re-synchronize them with the original 2025-09-07T06:49:23.6753659Z streams, that is, those that were current when the callback was invoked. 2025-09-07T06:49:23.6753690Z 2025-09-07T06:49:23.6753721Z Args: 2025-09-07T06:49:23.6753794Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T06:49:23.6753839Z the only argument. 2025-09-07T06:49:23.6753870Z 2025-09-07T06:49:23.6753901Z Returns: 2025-09-07T06:49:23.6753966Z A new ``Future`` object that holds the return value of the 2025-09-07T06:49:23.6754032Z ``callback`` and will be marked as completed when the given 2025-09-07T06:49:23.6754074Z ``callback`` finishes. 2025-09-07T06:49:23.6754103Z 2025-09-07T06:49:23.6754169Z .. note:: Note that if the callback function throws, either 2025-09-07T06:49:23.6754246Z through the original future being completed with an exception and 2025-09-07T06:49:23.6755767Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T06:49:23.6755845Z future returned by ``then`` will be marked appropriately with the 2025-09-07T06:49:23.6755916Z encountered error. However, if this callback later completes 2025-09-07T06:49:23.6755990Z additional futures, those futures are not marked as completed with 2025-09-07T06:49:23.6756061Z an error and the user is responsible for handling completion/waiting 2025-09-07T06:49:23.6756105Z on those futures independently. 2025-09-07T06:49:23.6756135Z 2025-09-07T06:49:23.6756170Z Example:: 2025-09-07T06:49:23.6756200Z 2025-09-07T06:49:23.6756259Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T06:49:23.6756298Z >>> def callback(fut): 2025-09-07T06:49:23.6756354Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T06:49:23.6756400Z >>> fut = torch.futures.Future() 2025-09-07T06:49:23.6756513Z >>> # The inserted callback will print the return value when 2025-09-07T06:49:23.6756562Z >>> # receiving the response from "worker1" 2025-09-07T06:49:23.6756604Z >>> cb_fut = fut.then(callback) 2025-09-07T06:49:23.6756643Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T06:49:23.6756703Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T06:49:23.6756734Z ... ) 2025-09-07T06:49:23.6756774Z >>> fut.set_result(5) 2025-09-07T06:49:23.6756812Z RPC return value is 5. 2025-09-07T06:49:23.6756851Z Chained cb done. None 2025-09-07T06:49:23.6758250Z 2025-09-07T06:49:23.6758346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6758379Z 2025-09-07T06:49:23.6758416Z warnings.warn(msg) 2025-09-07T06:49:23.6758447Z 2025-09-07T06:49:23.6758550Z --- Parse Warning: 19 / 146 --- 2025-09-07T06:49:23.6758848Z /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. 2025-09-07T06:49:23.6758959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6758990Z 2025-09-07T06:49:23.6759064Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T06:49:23.6759142Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T06:49:23.6759186Z cannot be marked completed twice. 2025-09-07T06:49:23.6759217Z 2025-09-07T06:49:23.6759292Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T06:49:23.6759369Z called even if the asynchronous kernels that are populating those 2025-09-07T06:49:23.6759445Z tensors haven't yet completed running on the device, provided that the 2025-09-07T06:49:23.6759527Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T06:49:23.6759601Z when this method is called. Put simply, it's safe to call this method 2025-09-07T06:49:23.6759678Z immediately after launching those kernels, without any additional 2025-09-07T06:49:23.6759756Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T06:49:23.6759831Z method will record events on all the relevant current streams and will 2025-09-07T06:49:23.6759901Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T06:49:23.6761316Z ``Future``. 2025-09-07T06:49:23.6761347Z 2025-09-07T06:49:23.6761380Z Args: 2025-09-07T06:49:23.6761442Z result (object): the result object of this ``Future``. 2025-09-07T06:49:23.6761474Z 2025-09-07T06:49:23.6761507Z Example:: 2025-09-07T06:49:23.6761537Z 2025-09-07T06:49:23.6761593Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T06:49:23.6761634Z >>> import threading 2025-09-07T06:49:23.6761671Z >>> import time 2025-09-07T06:49:23.6761716Z >>> def slow_set_future(fut, value): 2025-09-07T06:49:23.6761757Z ... time.sleep(0.5) 2025-09-07T06:49:23.6761797Z ... fut.set_result(value) 2025-09-07T06:49:23.6761842Z >>> fut = torch.futures.Future() 2025-09-07T06:49:23.6761881Z >>> t = threading.Thread( 2025-09-07T06:49:23.6761921Z ... target=slow_set_future, 2025-09-07T06:49:23.6761965Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T06:49:23.6761997Z ... ) 2025-09-07T06:49:23.6762030Z >>> t.start() 2025-09-07T06:49:23.6762070Z >>> print(fut.wait()) 2025-09-07T06:49:23.6762107Z tensor([3., 3.]) 2025-09-07T06:49:23.6763496Z >>> t.join() 2025-09-07T06:49:23.6763527Z 2025-09-07T06:49:23.6763618Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6763650Z 2025-09-07T06:49:23.6763722Z warnings.warn(msg) 2025-09-07T06:49:23.6763753Z 2025-09-07T06:49:23.6763825Z --- Parse Warning: 20 / 146 --- 2025-09-07T06:49:23.6764120Z /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. 2025-09-07T06:49:23.6764207Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6764296Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T06:49:23.6764469Z required drivers are available and at least one device is visible. 2025-09-07T06:49:23.6764527Z See :ref:`accelerator` for details. 2025-09-07T06:49:23.6764557Z 2025-09-07T06:49:23.6764590Z Returns: 2025-09-07T06:49:23.6764689Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T06:49:23.6764746Z 2025-09-07T06:49:23.6764831Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T06:49:23.6764942Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T06:49:23.6765031Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T06:49:23.6765091Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T06:49:23.6765121Z 2025-09-07T06:49:23.6765156Z Example:: 2025-09-07T06:49:23.6766578Z 2025-09-07T06:49:23.6766676Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:23.6766707Z 2025-09-07T06:49:23.6766895Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T06:49:23.6766926Z 2025-09-07T06:49:23.6767017Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:23.6767058Z ^ 2025-09-07T06:49:23.6767097Z warnings.warn(msg) 2025-09-07T06:49:23.6767127Z 2025-09-07T06:49:23.6767200Z --- Parse Warning: 21 / 146 --- 2025-09-07T06:49:23.6767496Z /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. 2025-09-07T06:49:23.6767583Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6767658Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T06:49:23.6767688Z 2025-09-07T06:49:23.6767718Z Args: 2025-09-07T06:49:23.6767829Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T06:49:23.6767913Z the current :ref:`accelerator` device type. If not given, 2025-09-07T06:49:23.6767983Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T06:49:23.6768014Z 2025-09-07T06:49:23.6768122Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T06:49:23.6768152Z 2025-09-07T06:49:23.6769544Z Example:: 2025-09-07T06:49:23.6769575Z 2025-09-07T06:49:23.6769630Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.6769724Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:23.6769780Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T06:49:23.6769836Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T06:49:23.6769879Z >>> start_event.record() 2025-09-07T06:49:23.6770008Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T06:49:23.6770049Z >>> sum = torch.sum(tensor) 2025-09-07T06:49:23.6770091Z >>> end_event.record() 2025-09-07T06:49:23.6770141Z >>> torch.accelerator.synchronize() 2025-09-07T06:49:23.6770205Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T06:49:23.6770237Z 2025-09-07T06:49:23.6770424Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T06:49:23.6770454Z 2025-09-07T06:49:23.6770544Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T06:49:23.6770584Z ^ 2025-09-07T06:49:23.6770622Z warnings.warn(msg) 2025-09-07T06:49:23.6770651Z 2025-09-07T06:49:23.6770722Z --- Parse Warning: 22 / 146 --- 2025-09-07T06:49:23.6771027Z /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. 2025-09-07T06:49:23.6771132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6772569Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T06:49:23.6772630Z defined there from the comfort of Python runtime 2025-09-07T06:49:23.6772665Z Example:: 2025-09-07T06:49:23.6772695Z 2025-09-07T06:49:23.6772747Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T06:49:23.6772795Z >>> lib = torch.mps.compile_shader( 2025-09-07T06:49:23.6772928Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T06:49:23.6772962Z ... ) 2025-09-07T06:49:23.6773009Z >>> x = torch.zeros(16, device="mps") 2025-09-07T06:49:23.6773051Z >>> lib.full(x, 3.14) 2025-09-07T06:49:23.6773082Z 2025-09-07T06:49:23.6773170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6773201Z 2025-09-07T06:49:23.6773237Z warnings.warn(msg) 2025-09-07T06:49:23.6773268Z 2025-09-07T06:49:23.6773336Z --- Parse Warning: 23 / 146 --- 2025-09-07T06:49:23.6773612Z /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. 2025-09-07T06:49:23.6773702Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6773733Z 2025-09-07T06:49:23.6773811Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T06:49:23.6773886Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T06:49:23.6773964Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T06:49:23.6775453Z vectorizing those operations. 2025-09-07T06:49:23.6775484Z 2025-09-07T06:49:23.6775561Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T06:49:23.6775633Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T06:49:23.6775708Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T06:49:23.6775773Z compute batched gradients when composed with autograd. 2025-09-07T06:49:23.6775803Z 2025-09-07T06:49:23.6775836Z .. note:: 2025-09-07T06:49:23.6775906Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T06:49:23.6775955Z convenience. Use whichever one you'd like. 2025-09-07T06:49:23.6775986Z 2025-09-07T06:49:23.6776017Z Args: 2025-09-07T06:49:23.6776094Z func (function): A Python function that takes one or more arguments. 2025-09-07T06:49:23.6776188Z Must return one or more Tensors. 2025-09-07T06:49:23.6776261Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T06:49:23.6776328Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T06:49:23.6776399Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T06:49:23.6776466Z input is None, then that indicates there is no map dimension. 2025-09-07T06:49:23.6776502Z Default: 0. 2025-09-07T06:49:23.6776572Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T06:49:23.6776639Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T06:49:23.6778061Z it should have one element per output. Default: 0. 2025-09-07T06:49:23.6778132Z randomness (str): Specifies whether the randomness in this 2025-09-07T06:49:23.6778232Z vmap should be the same or different across batches. If 'different', 2025-09-07T06:49:23.6778305Z the randomness for each batch will be different. If 'same', the 2025-09-07T06:49:23.6778399Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T06:49:23.6778472Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T06:49:23.6778544Z only applies to random PyTorch operations and does not apply to 2025-09-07T06:49:23.6778596Z Python's random module or numpy randomness. 2025-09-07T06:49:23.6778680Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T06:49:23.6778757Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T06:49:23.6778852Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T06:49:23.6778946Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T06:49:23.6778979Z 2025-09-07T06:49:23.6779012Z Returns: 2025-09-07T06:49:23.6779083Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T06:49:23.6779151Z ``func``, except each input has an extra dimension at the index 2025-09-07T06:49:23.6779223Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T06:49:23.6779290Z ``func``, except each output has an extra dimension at the index 2025-09-07T06:49:23.6779332Z specified by ``out_dims``. 2025-09-07T06:49:23.6779364Z 2025-09-07T06:49:23.6779397Z .. warning: 2025-09-07T06:49:23.6779468Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T06:49:23.6780897Z perform any side-effects in ``func``, with the exception of 2025-09-07T06:49:23.6780981Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T06:49:23.6781063Z Python data structures and assigning values to variables not captured 2025-09-07T06:49:23.6781098Z in ``func``. 2025-09-07T06:49:23.6781129Z 2025-09-07T06:49:23.6781212Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T06:49:23.6781291Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T06:49:23.6781370Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T06:49:23.6781401Z 2025-09-07T06:49:23.6781441Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:23.6781515Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T06:49:23.6781566Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T06:49:23.6781605Z >>> batched_dot(x, y) 2025-09-07T06:49:23.6781638Z 2025-09-07T06:49:23.6781717Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T06:49:23.6781759Z model authoring experience. 2025-09-07T06:49:23.6781790Z 2025-09-07T06:49:23.6781872Z >>> batch_size, feature_size = 3, 5 2025-09-07T06:49:23.6781937Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T06:49:23.6781970Z >>> 2025-09-07T06:49:23.6782010Z >>> def model(feature_vec): 2025-09-07T06:49:23.6783419Z >>> # Very simple linear model with activation 2025-09-07T06:49:23.6783471Z >>> return feature_vec.dot(weights).relu() 2025-09-07T06:49:23.6783503Z >>> 2025-09-07T06:49:23.6783561Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T06:49:23.6783609Z >>> result = torch.vmap(model)(examples) 2025-09-07T06:49:23.6783639Z 2025-09-07T06:49:23.6783727Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T06:49:23.6783808Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T06:49:23.6783890Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T06:49:23.6783991Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T06:49:23.6784079Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T06:49:23.6784175Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T06:49:23.6784216Z call to ``autograd.grad``. 2025-09-07T06:49:23.6784246Z 2025-09-07T06:49:23.6784280Z >>> # Setup 2025-09-07T06:49:23.6784313Z >>> N = 5 2025-09-07T06:49:23.6784351Z >>> f = lambda x: x**2 2025-09-07T06:49:23.6784464Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T06:49:23.6784497Z >>> y = f(x) 2025-09-07T06:49:23.6784535Z >>> I_N = torch.eye(N) 2025-09-07T06:49:23.6784565Z >>> 2025-09-07T06:49:23.6784606Z >>> # Sequential approach 2025-09-07T06:49:23.6786053Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T06:49:23.6786102Z >>> for v in I_N.unbind()] 2025-09-07T06:49:23.6786154Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T06:49:23.6786186Z >>> 2025-09-07T06:49:23.6786233Z >>> # vectorized gradient computation 2025-09-07T06:49:23.6786272Z >>> def get_vjp(v): 2025-09-07T06:49:23.6786320Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T06:49:23.6786367Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T06:49:23.6786397Z 2025-09-07T06:49:23.6786489Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T06:49:23.6786519Z 2025-09-07T06:49:23.6786559Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:23.6786601Z >>> batched_dot = torch.vmap( 2025-09-07T06:49:23.6786641Z ... torch.vmap(torch.dot) 2025-09-07T06:49:23.6786693Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T06:49:23.6786746Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T06:49:23.6786798Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T06:49:23.6786828Z 2025-09-07T06:49:23.6786917Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T06:49:23.6786976Z the dimension that each inputs are batched along as 2025-09-07T06:49:23.6787006Z 2025-09-07T06:49:23.6787045Z >>> torch.dot # [N], [N] -> [] 2025-09-07T06:49:23.6788478Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T06:49:23.6788528Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T06:49:23.6788565Z >>> batched_dot( 2025-09-07T06:49:23.6788598Z ... x, y 2025-09-07T06:49:23.6788670Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T06:49:23.6788700Z 2025-09-07T06:49:23.6788791Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T06:49:23.6788864Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T06:49:23.6788939Z 2025-09-07T06:49:23.6788979Z >>> torch.dot # [D], [D] -> [] 2025-09-07T06:49:23.6789063Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T06:49:23.6789112Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T06:49:23.6789149Z >>> batched_dot( 2025-09-07T06:49:23.6789183Z ... x, y 2025-09-07T06:49:23.6789252Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T06:49:23.6789282Z 2025-09-07T06:49:23.6789364Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T06:49:23.6789408Z matching the shape of the input: 2025-09-07T06:49:23.6789438Z 2025-09-07T06:49:23.6789492Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T06:49:23.6789539Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T06:49:23.6791003Z >>> input = {"x": x, "y": y} 2025-09-07T06:49:23.6791076Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T06:49:23.6791116Z >>> batched_dot(input) 2025-09-07T06:49:23.6791171Z 2025-09-07T06:49:23.6791271Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T06:49:23.6791319Z along any dimension by using ``out_dims`` 2025-09-07T06:49:23.6791350Z 2025-09-07T06:49:23.6791389Z >>> f = lambda x: x**2 2025-09-07T06:49:23.6791428Z >>> x = torch.randn(2, 5) 2025-09-07T06:49:23.6791477Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T06:49:23.6791517Z >>> batched_pow(x) # [5, 2] 2025-09-07T06:49:23.6791546Z 2025-09-07T06:49:23.6791651Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T06:49:23.6791684Z accept kwargs 2025-09-07T06:49:23.6791714Z 2025-09-07T06:49:23.6791755Z >>> x = torch.randn([2, 5]) 2025-09-07T06:49:23.6791794Z >>> def fn(x, scale=4.): 2025-09-07T06:49:23.6791836Z >>> return x * scale 2025-09-07T06:49:23.6791867Z >>> 2025-09-07T06:49:23.6791912Z >>> batched_pow = torch.vmap(fn) 2025-09-07T06:49:23.6791966Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T06:49:23.6792048Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T06:49:23.6793439Z 2025-09-07T06:49:23.6793477Z .. note:: 2025-09-07T06:49:23.6793557Z vmap does not provide general autobatching or handle variable-length 2025-09-07T06:49:23.6793598Z sequences out of the box. 2025-09-07T06:49:23.6793627Z 2025-09-07T06:49:23.6793716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6793745Z 2025-09-07T06:49:23.6793782Z warnings.warn(msg) 2025-09-07T06:49:23.6793811Z 2025-09-07T06:49:23.6793889Z --- Parse Warning: 24 / 146 --- 2025-09-07T06:49:23.6794174Z /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. 2025-09-07T06:49:23.6794268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6794350Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T06:49:23.6794485Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T06:49:23.6794528Z compute higher-order gradients. 2025-09-07T06:49:23.6794558Z 2025-09-07T06:49:23.6794591Z Args: 2025-09-07T06:49:23.6794665Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T06:49:23.6794760Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T06:49:23.6794854Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T06:49:23.6794940Z ``(output, aux)``. 2025-09-07T06:49:23.6796440Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T06:49:23.6796521Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T06:49:23.6796598Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T06:49:23.6796663Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T06:49:23.6796696Z 2025-09-07T06:49:23.6796729Z Returns: 2025-09-07T06:49:23.6796828Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T06:49:23.6796912Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T06:49:23.6797008Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T06:49:23.6797125Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T06:49:23.6797199Z respect to each ``argnums`` value is returned. 2025-09-07T06:49:23.6797230Z 2025-09-07T06:49:23.6797270Z Example of using ``grad``: 2025-09-07T06:49:23.6797301Z 2025-09-07T06:49:23.6797340Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6797385Z >>> from torch.func import grad 2025-09-07T06:49:23.6797423Z >>> x = torch.randn([]) 2025-09-07T06:49:23.6797473Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T06:49:23.6797523Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T06:49:23.6797555Z >>> 2025-09-07T06:49:23.6797598Z >>> # Second-order gradients 2025-09-07T06:49:23.6797655Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T06:49:23.6799062Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T06:49:23.6799096Z 2025-09-07T06:49:23.6799192Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T06:49:23.6799226Z 2025-09-07T06:49:23.6799264Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6799311Z >>> from torch.func import grad, vmap 2025-09-07T06:49:23.6799358Z >>> batch_size, feature_size = 3, 5 2025-09-07T06:49:23.6799389Z >>> 2025-09-07T06:49:23.6799436Z >>> def model(weights, feature_vec): 2025-09-07T06:49:23.6799486Z >>> # Very simple linear model with activation 2025-09-07T06:49:23.6799531Z >>> assert feature_vec.dim() == 1 2025-09-07T06:49:23.6799580Z >>> return feature_vec.dot(weights).relu() 2025-09-07T06:49:23.6799611Z >>> 2025-09-07T06:49:23.6799664Z >>> def compute_loss(weights, example, target): 2025-09-07T06:49:23.6799709Z >>> y = model(weights, example) 2025-09-07T06:49:23.6799763Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T06:49:23.6799796Z >>> 2025-09-07T06:49:23.6799861Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T06:49:23.6799919Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T06:49:23.6799963Z >>> targets = torch.randn(batch_size) 2025-09-07T06:49:23.6800012Z >>> inputs = (weights, examples, targets) 2025-09-07T06:49:23.6800096Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T06:49:23.6801489Z ... *inputs 2025-09-07T06:49:23.6801523Z ... ) 2025-09-07T06:49:23.6801553Z 2025-09-07T06:49:23.6801620Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T06:49:23.6801651Z 2025-09-07T06:49:23.6801689Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6801732Z >>> from torch.func import grad 2025-09-07T06:49:23.6801779Z >>> def my_loss_func(y, y_pred): 2025-09-07T06:49:23.6801865Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T06:49:23.6801913Z >>> loss = loss_per_sample.mean() 2025-09-07T06:49:23.6801964Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T06:49:23.6801996Z >>> 2025-09-07T06:49:23.6802052Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T06:49:23.6802093Z >>> y_true = torch.rand(4) 2025-09-07T06:49:23.6802144Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T06:49:23.6802187Z >>> out = fn(y_true, y_preds) 2025-09-07T06:49:23.6802274Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T06:49:23.6802305Z 2025-09-07T06:49:23.6802338Z .. note:: 2025-09-07T06:49:23.6802402Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T06:49:23.6802448Z 2025-09-07T06:49:23.6803874Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T06:49:23.6803905Z 2025-09-07T06:49:23.6803948Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6804005Z >>> def f(x): 2025-09-07T06:49:23.6804050Z >>> with torch.no_grad(): 2025-09-07T06:49:23.6804088Z >>> c = x ** 2 2025-09-07T06:49:23.6804127Z >>> return x - c 2025-09-07T06:49:23.6804158Z 2025-09-07T06:49:23.6804232Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T06:49:23.6804262Z 2025-09-07T06:49:23.6804328Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T06:49:23.6804360Z 2025-09-07T06:49:23.6804463Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6804505Z >>> with torch.no_grad(): 2025-09-07T06:49:23.6804543Z >>> grad(f)(x) 2025-09-07T06:49:23.6804575Z 2025-09-07T06:49:23.6804661Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T06:49:23.6804739Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T06:49:23.6804818Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T06:49:23.6804849Z 2025-09-07T06:49:23.6804879Z 2025-09-07T06:49:23.6804969Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6806359Z 2025-09-07T06:49:23.6806399Z warnings.warn(msg) 2025-09-07T06:49:23.6806429Z 2025-09-07T06:49:23.6806505Z --- Parse Warning: 25 / 146 --- 2025-09-07T06:49:23.6806857Z /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. 2025-09-07T06:49:23.6806958Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6806988Z 2025-09-07T06:49:23.6807076Z Config object that specifies the supported data types passed as arguments to 2025-09-07T06:49:23.6807161Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T06:49:23.6807198Z weights, and biases. 2025-09-07T06:49:23.6807228Z 2025-09-07T06:49:23.6807289Z For example, consider the following reference model: 2025-09-07T06:49:23.6807319Z 2025-09-07T06:49:23.6807381Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T06:49:23.6807411Z 2025-09-07T06:49:23.6807487Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T06:49:23.6807570Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T06:49:23.6807650Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T06:49:23.6807729Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T06:49:23.6807861Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T06:49:23.6807906Z the second quantize op (quant2). 2025-09-07T06:49:23.6807938Z 2025-09-07T06:49:23.6809382Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T06:49:23.6809458Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T06:49:23.6809537Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T06:49:23.6809609Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T06:49:23.6809686Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T06:49:23.6809760Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T06:49:23.6809834Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T06:49:23.6809894Z the observers. 2025-09-07T06:49:23.6809924Z 2025-09-07T06:49:23.6810003Z These dtypes are matched against the ones specified in the user's 2025-09-07T06:49:23.6810098Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T06:49:23.6810174Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T06:49:23.6810251Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T06:49:23.6810295Z the pattern will not be quantized. 2025-09-07T06:49:23.6810325Z 2025-09-07T06:49:23.6810362Z Example usage:: 2025-09-07T06:49:23.6810391Z 2025-09-07T06:49:23.6810432Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:23.6810474Z >>> dtype_config1 = DTypeConfig( 2025-09-07T06:49:23.6810517Z ... input_dtype=torch.quint8, 2025-09-07T06:49:23.6810560Z ... output_dtype=torch.quint8, 2025-09-07T06:49:23.6811956Z ... weight_dtype=torch.qint8, 2025-09-07T06:49:23.6812001Z ... bias_dtype=torch.float) 2025-09-07T06:49:23.6812034Z 2025-09-07T06:49:23.6812079Z >>> dtype_config2 = DTypeConfig( 2025-09-07T06:49:23.6812131Z ... input_dtype=DTypeWithConstraints( 2025-09-07T06:49:23.6812172Z ... dtype=torch.quint8, 2025-09-07T06:49:23.6812215Z ... quant_min_lower_bound=0, 2025-09-07T06:49:23.6812260Z ... quant_max_upper_bound=255, 2025-09-07T06:49:23.6812293Z ... ), 2025-09-07T06:49:23.6812342Z ... output_dtype=DTypeWithConstraints( 2025-09-07T06:49:23.6812381Z ... dtype=torch.quint8, 2025-09-07T06:49:23.6812422Z ... quant_min_lower_bound=0, 2025-09-07T06:49:23.6812466Z ... quant_max_upper_bound=255, 2025-09-07T06:49:23.6812498Z ... ), 2025-09-07T06:49:23.6812546Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T06:49:23.6812588Z ... dtype=torch.qint8, 2025-09-07T06:49:23.6812634Z ... quant_min_lower_bound=-128, 2025-09-07T06:49:23.6812679Z ... quant_max_upper_bound=127, 2025-09-07T06:49:23.6812711Z ... ), 2025-09-07T06:49:23.6812753Z ... bias_dtype=torch.float) 2025-09-07T06:49:23.6812783Z 2025-09-07T06:49:23.6812825Z >>> dtype_config1.input_dtype 2025-09-07T06:49:23.6814209Z torch.quint8 2025-09-07T06:49:23.6814241Z 2025-09-07T06:49:23.6814283Z >>> dtype_config2.input_dtype 2025-09-07T06:49:23.6814318Z torch.quint8 2025-09-07T06:49:23.6814347Z 2025-09-07T06:49:23.6814470Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T06:49:23.6814655Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T06:49:23.6814686Z 2025-09-07T06:49:23.6814776Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6814808Z 2025-09-07T06:49:23.6814844Z warnings.warn(msg) 2025-09-07T06:49:23.6814873Z 2025-09-07T06:49:23.6814994Z --- Parse Warning: 26 / 146 --- 2025-09-07T06:49:23.6815410Z /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. 2025-09-07T06:49:23.6815504Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6815534Z 2025-09-07T06:49:23.6815629Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T06:49:23.6815658Z 2025-09-07T06:49:23.6815734Z The generated tables are presented in both a list-of-lists format 2025-09-07T06:49:23.6815764Z 2025-09-07T06:49:23.6815838Z The reason for the two tables are that they handle different things: 2025-09-07T06:49:23.6815922Z 1.) the first table handles all tensor level information 2025-09-07T06:49:23.6817395Z 2.) the second table handles and displays all channel based information 2025-09-07T06:49:23.6817448Z 2025-09-07T06:49:23.6817562Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T06:49:23.6817676Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T06:49:23.6817798Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T06:49:23.6817828Z 2025-09-07T06:49:23.6817869Z Tensor table columns: 2025-09-07T06:49:23.6817938Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:23.6818001Z ---- --------- --------- --------- --------- --------- 2025-09-07T06:49:23.6818030Z 2025-09-07T06:49:23.6818073Z Per-Channel table columns: 2025-09-07T06:49:23.6818158Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:23.6818224Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T06:49:23.6818255Z 2025-09-07T06:49:23.6818287Z Args: 2025-09-07T06:49:23.6818379Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:23.6818424Z contain this filter substring 2025-09-07T06:49:23.6818486Z Default = "", results in all the features being printed 2025-09-07T06:49:23.6818577Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:23.6818666Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:23.6818697Z 2025-09-07T06:49:23.6820100Z Returns a dictionary with two keys: 2025-09-07T06:49:23.6820171Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T06:49:23.6820228Z "tensor_level_info", "channel_level_info" 2025-09-07T06:49:23.6820273Z Each key maps to a tuple with: 2025-09-07T06:49:23.6820322Z A list of the headers of each table 2025-09-07T06:49:23.6820387Z A list of lists containing the table information row by row 2025-09-07T06:49:23.6820452Z The 0th index row will contain the headers of the columns 2025-09-07T06:49:23.6820500Z The rest of the rows will contain data 2025-09-07T06:49:23.6820532Z 2025-09-07T06:49:23.6820568Z Example Use: 2025-09-07T06:49:23.6820619Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.6820677Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T06:49:23.6820747Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:23.6820844Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T06:49:23.6820876Z 2025-09-07T06:49:23.6820994Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6821026Z 2025-09-07T06:49:23.6821065Z warnings.warn(msg) 2025-09-07T06:49:23.6821095Z 2025-09-07T06:49:23.6821167Z --- Parse Warning: 27 / 146 --- 2025-09-07T06:49:23.6821583Z /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. 2025-09-07T06:49:23.6821674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6823065Z 2025-09-07T06:49:23.6823162Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T06:49:23.6823193Z 2025-09-07T06:49:23.6824753Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T06:49:23.6824848Z 1.) the first table handles all tensor level information 2025-09-07T06:49:23.6824941Z 2.) the second table handles and displays all channel based information 2025-09-07T06:49:23.6824972Z 2025-09-07T06:49:23.6825081Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T06:49:23.6825193Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T06:49:23.6825311Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T06:49:23.6826863Z 2025-09-07T06:49:23.6826903Z Tensor table columns: 2025-09-07T06:49:23.6826975Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:23.6827035Z ---- --------- --------- --------- --------- --------- 2025-09-07T06:49:23.6827068Z 2025-09-07T06:49:23.6827110Z Per-Channel table columns: 2025-09-07T06:49:23.6827146Z 2025-09-07T06:49:23.6827223Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T06:49:23.6827289Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T06:49:23.6827319Z 2025-09-07T06:49:23.6827350Z Args: 2025-09-07T06:49:23.6827442Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:23.6827486Z contain this filter substring 2025-09-07T06:49:23.6827549Z Default = "", results in all the features being printed 2025-09-07T06:49:23.6827639Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:23.6827730Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:23.6827760Z 2025-09-07T06:49:23.6827795Z Example Use: 2025-09-07T06:49:23.6827846Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.6827911Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T06:49:23.6827983Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:23.6828015Z ... ) 2025-09-07T06:49:23.6828084Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T06:49:23.6828134Z >>> # for all modules in block 1 of the model 2025-09-07T06:49:23.6828164Z 2025-09-07T06:49:23.6828252Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6828282Z 2025-09-07T06:49:23.6828321Z warnings.warn(msg) 2025-09-07T06:49:23.6828351Z 2025-09-07T06:49:23.6828423Z --- Parse Warning: 28 / 146 --- 2025-09-07T06:49:23.6828882Z /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. 2025-09-07T06:49:23.6828977Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6829007Z 2025-09-07T06:49:23.6829089Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T06:49:23.6829123Z 2025-09-07T06:49:23.6829217Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T06:49:23.6829308Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T06:49:23.6829401Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T06:49:23.6829493Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T06:49:23.6829531Z further deep dives. 2025-09-07T06:49:23.6829579Z 2025-09-07T06:49:23.6829611Z Note: 2025-09-07T06:49:23.6829707Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T06:49:23.6829790Z When the tensor information is plotted, it will plot: 2025-09-07T06:49:23.6829845Z idx as the x val, feature value as the y_val 2025-09-07T06:49:23.6829908Z When the channel information is plotted, it will plot: 2025-09-07T06:49:23.6830002Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T06:49:23.6830082Z The reason for this is that we want to be able to compare values across the 2025-09-07T06:49:23.6830165Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T06:49:23.6830229Z This means each module is represented by only 1 x value 2025-09-07T06:49:23.6830262Z Args: 2025-09-07T06:49:23.6830342Z feature_filter (str): Filters the features presented to only those that 2025-09-07T06:49:23.6830387Z contain this filter substring 2025-09-07T06:49:23.6830478Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:23.6830566Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:23.6830597Z 2025-09-07T06:49:23.6830632Z Example Use: 2025-09-07T06:49:23.6830682Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.6830742Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T06:49:23.6830812Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:23.6830880Z ... ) 2025-09-07T06:49:23.6830948Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T06:49:23.6831014Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T06:49:23.6831080Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T06:49:23.6831111Z 2025-09-07T06:49:23.6831201Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6831232Z 2025-09-07T06:49:23.6831270Z warnings.warn(msg) 2025-09-07T06:49:23.6831300Z 2025-09-07T06:49:23.6831371Z --- Parse Warning: 29 / 146 --- 2025-09-07T06:49:23.6831786Z /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. 2025-09-07T06:49:23.6831878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6831908Z 2025-09-07T06:49:23.6832004Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T06:49:23.6832034Z 2025-09-07T06:49:23.6832067Z Note: 2025-09-07T06:49:23.6832189Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T06:49:23.6832282Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T06:49:23.6832370Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T06:49:23.6832458Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T06:49:23.6832488Z 2025-09-07T06:49:23.6832520Z Args: 2025-09-07T06:49:23.6832612Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T06:49:23.6832654Z contain this filter substring 2025-09-07T06:49:23.6832715Z Default = "", results in all the features being printed 2025-09-07T06:49:23.6832802Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T06:49:23.6832888Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T06:49:23.6832991Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T06:49:23.6833077Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T06:49:23.6833107Z 2025-09-07T06:49:23.6833141Z Example Use: 2025-09-07T06:49:23.6833181Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6833284Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T06:49:23.6833354Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T06:49:23.6833386Z ... ) 2025-09-07T06:49:23.6833481Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T06:49:23.6833570Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T06:49:23.6833648Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T06:49:23.6833680Z 2025-09-07T06:49:23.6833767Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6833800Z 2025-09-07T06:49:23.6833837Z warnings.warn(msg) 2025-09-07T06:49:23.6833867Z 2025-09-07T06:49:23.6833936Z --- Parse Warning: 30 / 146 --- 2025-09-07T06:49:23.6834272Z /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. 2025-09-07T06:49:23.6834364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6834485Z 2025-09-07T06:49:23.6834594Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T06:49:23.6834698Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T06:49:23.6834804Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T06:49:23.6834908Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T06:49:23.6835005Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T06:49:23.6835045Z issues a use might have. 2025-09-07T06:49:23.6835076Z 2025-09-07T06:49:23.6835126Z Currently supports generating reports on: 2025-09-07T06:49:23.6836601Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T06:49:23.6836698Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T06:49:23.6836798Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T06:49:23.6836873Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T06:49:23.6836906Z 2025-09-07T06:49:23.6837086Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T06:49:23.6837215Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T06:49:23.6837339Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T06:49:23.6837415Z has the capability to remove all the observers it inserted as well. 2025-09-07T06:49:23.6837445Z 2025-09-07T06:49:23.6837545Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T06:49:23.6837575Z 2025-09-07T06:49:23.6837700Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T06:49:23.6837809Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T06:49:23.6837858Z 2025-09-07T06:49:23.6837959Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T06:49:23.6838057Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T06:49:23.6838088Z 2025-09-07T06:49:23.6838201Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T06:49:23.6838311Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T06:49:23.6838357Z can be removed at the end if desired 2025-09-07T06:49:23.6838388Z 2025-09-07T06:49:23.6839876Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T06:49:23.6839962Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T06:49:23.6839992Z 2025-09-07T06:49:23.6840084Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T06:49:23.6840186Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T06:49:23.6840298Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T06:49:23.6840354Z as long as we haven't removed the observers yet. 2025-09-07T06:49:23.6840385Z 2025-09-07T06:49:23.6840416Z Note: 2025-09-07T06:49:23.6840519Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T06:49:23.6840624Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T06:49:23.6840725Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T06:49:23.6840769Z the QuantizationTracer class. 2025-09-07T06:49:23.6840799Z 2025-09-07T06:49:23.6840841Z General Flow for Fx workflow: 2025-09-07T06:49:23.6840977Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T06:49:23.6841027Z 2.) Prepare your model with prepare_fx 2025-09-07T06:49:23.6841112Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T06:49:23.6841154Z 4.) Calibrate your model with data 2025-09-07T06:49:23.6841273Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T06:49:23.6841306Z Optional 2025-09-07T06:49:23.6842749Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T06:49:23.6842838Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T06:49:23.6842874Z - Table 2025-09-07T06:49:23.6842910Z - Histogram 2025-09-07T06:49:23.6842946Z - Line plot 2025-09-07T06:49:23.6843090Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T06:49:23.6843121Z 2025-09-07T06:49:23.6843166Z Example (with QuantizationTracer): 2025-09-07T06:49:23.6843207Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6843248Z >>> # get the necessary qconfig 2025-09-07T06:49:23.6843294Z >>> config = PrepareCustomConfig() 2025-09-07T06:49:23.6843354Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T06:49:23.6843415Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T06:49:23.6843447Z ... ) 2025-09-07T06:49:23.6843476Z 2025-09-07T06:49:23.6843528Z >>> # initialize our model and get GraphModule 2025-09-07T06:49:23.6843567Z >>> model = SomeModel() 2025-09-07T06:49:23.6843656Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T06:49:23.6843718Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T06:49:23.6843764Z 2025-09-07T06:49:23.6843825Z >>> # get our set of detectors and ModelReport instance 2025-09-07T06:49:23.6843864Z >>> detector_set = set( 2025-09-07T06:49:23.6845339Z ... [ 2025-09-07T06:49:23.6845399Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T06:49:23.6845471Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T06:49:23.6845504Z ... ] 2025-09-07T06:49:23.6845535Z ... ) 2025-09-07T06:49:23.6845610Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T06:49:23.6845640Z 2025-09-07T06:49:23.6845699Z >>> # now we insert the observers and calibrate the model 2025-09-07T06:49:23.6845789Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T06:49:23.6845841Z >>> for i in range(num_callibration_batches): 2025-09-07T06:49:23.6845894Z >>> example_input = get_callibration_input() 2025-09-07T06:49:23.6845953Z >>> tracer_model_with_observers(example_input) 2025-09-07T06:49:23.6845984Z 2025-09-07T06:49:23.6846076Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T06:49:23.6846137Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T06:49:23.6846183Z ... remove_inserted_observers=True 2025-09-07T06:49:23.6846215Z ... ) 2025-09-07T06:49:23.6846244Z 2025-09-07T06:49:23.6846325Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T06:49:23.6846384Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T06:49:23.6846414Z 2025-09-07T06:49:23.6846497Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T06:49:23.6847921Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T06:49:23.6847952Z 2025-09-07T06:49:23.6848057Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T06:49:23.6848130Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T06:49:23.6848164Z 2025-09-07T06:49:23.6848193Z 2025-09-07T06:49:23.6848283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6848312Z 2025-09-07T06:49:23.6848350Z warnings.warn(msg) 2025-09-07T06:49:23.6848379Z 2025-09-07T06:49:23.6848455Z --- Parse Warning: 31 / 146 --- 2025-09-07T06:49:23.6848850Z /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. 2025-09-07T06:49:23.6848943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6848973Z 2025-09-07T06:49:23.6849108Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T06:49:23.6849190Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T06:49:23.6849277Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T06:49:23.6849314Z input by the user. 2025-09-07T06:49:23.6849392Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T06:49:23.6849450Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T06:49:23.6849480Z 2025-09-07T06:49:23.6850876Z Note:: 2025-09-07T06:49:23.6850991Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T06:49:23.6851022Z 2025-09-07T06:49:23.6851053Z Args: 2025-09-07T06:49:23.6851093Z model (nn.Module): 2025-09-07T06:49:23.6851170Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T06:49:23.6851296Z sparsified should be added separately using the register_layer() function 2025-09-07T06:49:23.6851342Z aggregate_fn (Optional, Callable): 2025-09-07T06:49:23.6851454Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T06:49:23.6851516Z specifies how inputs should be aggregated over time. 2025-09-07T06:49:23.6851614Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T06:49:23.6851647Z Example 2025-09-07T06:49:23.6851713Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T06:49:23.6851758Z reduce_fn (Optional, Callable): 2025-09-07T06:49:23.6851844Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T06:49:23.6851933Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T06:49:23.6851981Z calling agg_fn() on all inputs. 2025-09-07T06:49:23.6852019Z Example 2025-09-07T06:49:23.6852090Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T06:49:23.6852136Z mask_fn (Optional, Callable): 2025-09-07T06:49:23.6852241Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T06:49:23.6852330Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T06:49:23.6853729Z register_layer(). 2025-09-07T06:49:23.6853854Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T06:49:23.6853894Z arguments. 2025-09-07T06:49:23.6853937Z features (Optional, list): 2025-09-07T06:49:23.6853992Z default selected features to sparsify. 2025-09-07T06:49:23.6854092Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T06:49:23.6854133Z For example, 2025-09-07T06:49:23.6854222Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T06:49:23.6854270Z feature_dim (Optional, int): 2025-09-07T06:49:23.6854367Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T06:49:23.6854457Z for sparsification. 2025-09-07T06:49:23.6854498Z sparse_config (Dict): 2025-09-07T06:49:23.6854575Z Default configuration for the mask_fn. This config will be passed 2025-09-07T06:49:23.6854617Z with the mask_fn() 2025-09-07T06:49:23.6854647Z 2025-09-07T06:49:23.6854680Z Example: 2025-09-07T06:49:23.6854718Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6854759Z >>> model = SomeModel() 2025-09-07T06:49:23.6854889Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T06:49:23.6854933Z >>> # Initialize aggregate_fn 2025-09-07T06:49:23.6854970Z >>> def agg_fn(x, y): 2025-09-07T06:49:23.6856381Z >>> return x + y 2025-09-07T06:49:23.6856414Z >>> 2025-09-07T06:49:23.6856455Z >>> # Initialize reduce_fn 2025-09-07T06:49:23.6856493Z >>> def reduce_fn(x): 2025-09-07T06:49:23.6856539Z >>> return torch.mean(x, dim=0) 2025-09-07T06:49:23.6856569Z >>> 2025-09-07T06:49:23.6856609Z >>> # Initialize mask_fn 2025-09-07T06:49:23.6856647Z >>> def mask_fn(data): 2025-09-07T06:49:23.6856704Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T06:49:23.6856735Z >>> 2025-09-07T06:49:23.6856765Z >>> 2025-09-07T06:49:23.6856810Z >>> act_sparsifier.register_layer( 2025-09-07T06:49:23.6856873Z ... model.some_layer, 2025-09-07T06:49:23.6856913Z ... aggregate_fn=agg_fn, 2025-09-07T06:49:23.6856957Z ... reduce_fn=reduce_fn, 2025-09-07T06:49:23.6857016Z ... mask_fn=mask_fn, 2025-09-07T06:49:23.6857047Z ... ) 2025-09-07T06:49:23.6857078Z >>> 2025-09-07T06:49:23.6857117Z >>> # start training process 2025-09-07T06:49:23.6857154Z >>> for _ in [...]: 2025-09-07T06:49:23.6857190Z >>> # epoch starts 2025-09-07T06:49:23.6857259Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T06:49:23.6858646Z >>> # epoch ends 2025-09-07T06:49:23.6858691Z >>> act_sparsifier.step() 2025-09-07T06:49:23.6858731Z >>> # end training process 2025-09-07T06:49:23.6858772Z >>> sparsifier.squash_mask() 2025-09-07T06:49:23.6858802Z 2025-09-07T06:49:23.6858893Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6858922Z 2025-09-07T06:49:23.6858962Z warnings.warn(msg) 2025-09-07T06:49:23.6858992Z 2025-09-07T06:49:23.6859070Z --- Parse Warning: 32 / 146 --- 2025-09-07T06:49:23.6859466Z /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. 2025-09-07T06:49:23.6859560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6859591Z 2025-09-07T06:49:23.6859665Z Abstract method that needs to be implemented by the child class. 2025-09-07T06:49:23.6859754Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T06:49:23.6859845Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T06:49:23.6859879Z is called. 2025-09-07T06:49:23.6859909Z 2025-09-07T06:49:23.6859943Z Example: 2025-09-07T06:49:23.6859987Z >>> def get_schedule_param(self): 2025-09-07T06:49:23.6860026Z ... new_param = {} 2025-09-07T06:49:23.6860088Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T06:49:23.6861484Z ... new_param[name] = ( 2025-09-07T06:49:23.6861561Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T06:49:23.6861595Z ... ) 2025-09-07T06:49:23.6861634Z ... return new_param 2025-09-07T06:49:23.6861664Z 2025-09-07T06:49:23.6861782Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T06:49:23.6861817Z would be halved 2025-09-07T06:49:23.6861848Z 2025-09-07T06:49:23.6861935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6861965Z 2025-09-07T06:49:23.6862001Z warnings.warn(msg) 2025-09-07T06:49:23.6862033Z 2025-09-07T06:49:23.6862140Z --- Parse Warning: 33 / 146 --- 2025-09-07T06:49:23.6862493Z /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. 2025-09-07T06:49:23.6862585Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6862651Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T06:49:23.6862680Z 2025-09-07T06:49:23.6862755Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T06:49:23.6862823Z the module will have a `sparse_params` dict attached to it. 2025-09-07T06:49:23.6862853Z 2025-09-07T06:49:23.6862885Z Args: 2025-09-07T06:49:23.6864314Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T06:49:23.6864454Z representing the modules and keys that will have 2025-09-07T06:49:23.6864506Z sparsity parameters saved 2025-09-07T06:49:23.6864611Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T06:49:23.6864668Z saved for specific layers. The keys in the dict 2025-09-07T06:49:23.6864725Z should be the module fqn, while the values should 2025-09-07T06:49:23.6864784Z be a list of strings with the names of the variables 2025-09-07T06:49:23.6864833Z to save in the `sparse_params` 2025-09-07T06:49:23.6864864Z 2025-09-07T06:49:23.6864899Z Examples: 2025-09-07T06:49:23.6864951Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T06:49:23.6864996Z >>> # Don't save any sparse params 2025-09-07T06:49:23.6865044Z >>> sparsifier.squash_mask() 2025-09-07T06:49:23.6865101Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T06:49:23.6865135Z False 2025-09-07T06:49:23.6865166Z 2025-09-07T06:49:23.6865212Z >>> # Keep sparse params per layer 2025-09-07T06:49:23.6865256Z >>> sparsifier.squash_mask( 2025-09-07T06:49:23.6865300Z ... params_to_keep_per_layer={ 2025-09-07T06:49:23.6865354Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T06:49:23.6865403Z ... "submodule2.linear42": ("baz",), 2025-09-07T06:49:23.6865439Z ... } 2025-09-07T06:49:23.6866836Z ... ) 2025-09-07T06:49:23.6866899Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:23.6866939Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:23.6867001Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:23.6867040Z {'baz': 0.1} 2025-09-07T06:49:23.6867072Z 2025-09-07T06:49:23.6867126Z >>> # Keep sparse params for all layers 2025-09-07T06:49:23.6867195Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T06:49:23.6867253Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:23.6867292Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:23.6867350Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:23.6867388Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:23.6867418Z 2025-09-07T06:49:23.6867490Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T06:49:23.6867529Z >>> # some other layers 2025-09-07T06:49:23.6867573Z >>> sparsifier.squash_mask( 2025-09-07T06:49:23.6867620Z ... params_to_keep=("foo", "bar"), 2025-09-07T06:49:23.6867691Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T06:49:23.6867769Z ... ) 2025-09-07T06:49:23.6867828Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T06:49:23.6867867Z {'foo': 42, 'bar': 24} 2025-09-07T06:49:23.6869290Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T06:49:23.6869335Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T06:49:23.6869367Z 2025-09-07T06:49:23.6869456Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6869486Z 2025-09-07T06:49:23.6869524Z warnings.warn(msg) 2025-09-07T06:49:23.6869554Z 2025-09-07T06:49:23.6869627Z --- Parse Warning: 34 / 146 --- 2025-09-07T06:49:23.6869919Z /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. 2025-09-07T06:49:23.6870042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6870091Z A quantized long short-term memory (LSTM). 2025-09-07T06:49:23.6870139Z 2025-09-07T06:49:23.6870236Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T06:49:23.6870267Z 2025-09-07T06:49:23.6870301Z Attributes: 2025-09-07T06:49:23.6870347Z layers : instances of the `_LSTMLayer` 2025-09-07T06:49:23.6870377Z 2025-09-07T06:49:23.6870410Z .. note:: 2025-09-07T06:49:23.6870485Z To access the weights and biases, you need to access them per layer. 2025-09-07T06:49:23.6870550Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T06:49:23.6870580Z 2025-09-07T06:49:23.6870615Z Examples:: 2025-09-07T06:49:23.6872055Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6872101Z >>> custom_module_config = { 2025-09-07T06:49:23.6872162Z ... 'float_to_observed_custom_module_class': { 2025-09-07T06:49:23.6872214Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T06:49:23.6872249Z ... }, 2025-09-07T06:49:23.6872306Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T06:49:23.6872363Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T06:49:23.6872395Z ... } 2025-09-07T06:49:23.6872427Z ... } 2025-09-07T06:49:23.6872505Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T06:49:23.6872580Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T06:49:23.6872610Z 2025-09-07T06:49:23.6872698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6872727Z 2025-09-07T06:49:23.6872764Z warnings.warn(msg) 2025-09-07T06:49:23.6872795Z 2025-09-07T06:49:23.6872867Z --- Parse Warning: 35 / 146 --- 2025-09-07T06:49:23.6873168Z /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. 2025-09-07T06:49:23.6873261Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6873337Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T06:49:23.6873381Z several quantized input planes. 2025-09-07T06:49:23.6873410Z 2025-09-07T06:49:23.6874914Z For details on input arguments, parameters, and implementation see 2025-09-07T06:49:23.6874959Z :class:`~torch.nn.Conv1d`. 2025-09-07T06:49:23.6874989Z 2025-09-07T06:49:23.6875022Z .. note:: 2025-09-07T06:49:23.6875095Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T06:49:23.6875126Z 2025-09-07T06:49:23.6875159Z .. note:: 2025-09-07T06:49:23.6875284Z Only `torch.quint8` is supported for the input data type. 2025-09-07T06:49:23.6875318Z 2025-09-07T06:49:23.6875347Z 2025-09-07T06:49:23.6875380Z Attributes: 2025-09-07T06:49:23.6875455Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T06:49:23.6875498Z parameter. 2025-09-07T06:49:23.6875555Z scale (Tensor): scalar for the output scale 2025-09-07T06:49:23.6875616Z zero_point (Tensor): scalar for the output zero point 2025-09-07T06:49:23.6875647Z 2025-09-07T06:49:23.6875704Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T06:49:23.6875735Z 2025-09-07T06:49:23.6875769Z Examples:: 2025-09-07T06:49:23.6875799Z 2025-09-07T06:49:23.6875854Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T06:49:23.6877289Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T06:49:23.6877344Z >>> input = torch.randn(20, 16, 100) 2025-09-07T06:49:23.6877389Z >>> # quantize input to quint8 2025-09-07T06:49:23.6877452Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6877529Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T06:49:23.6877580Z ... dtype=torch.quint8) 2025-09-07T06:49:23.6877621Z >>> output = m(q_input) 2025-09-07T06:49:23.6877651Z 2025-09-07T06:49:23.6877682Z 2025-09-07T06:49:23.6877770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6877799Z 2025-09-07T06:49:23.6877836Z warnings.warn(msg) 2025-09-07T06:49:23.6877866Z 2025-09-07T06:49:23.6877939Z --- Parse Warning: 36 / 146 --- 2025-09-07T06:49:23.6878234Z /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. 2025-09-07T06:49:23.6878327Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6878454Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T06:49:23.6878484Z 2025-09-07T06:49:23.6878568Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T06:49:23.6878645Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T06:49:23.6878723Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T06:49:23.6878756Z (UAI 2018). 2025-09-07T06:49:23.6880141Z 2025-09-07T06:49:23.6880220Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T06:49:23.6880304Z but using exponential weights instead of equal weights across iterations. 2025-09-07T06:49:23.6880338Z 2025-09-07T06:49:23.6880423Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T06:49:23.6880505Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T06:49:23.6880550Z parameters of the :attr:`model`. 2025-09-07T06:49:23.6880581Z 2025-09-07T06:49:23.6880612Z Args: 2025-09-07T06:49:23.6880671Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T06:49:23.6880753Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T06:49:23.6880798Z stored on the :attr:`device` 2025-09-07T06:49:23.6880870Z avg_fn (function, optional): the averaging function used to update 2025-09-07T06:49:23.6880941Z parameters; the function must take in the current value of the 2025-09-07T06:49:23.6881019Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T06:49:23.6881125Z parameter, and the number of models already averaged; if None, 2025-09-07T06:49:23.6881186Z an equally weighted average is used (default: None) 2025-09-07T06:49:23.6881267Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T06:49:23.6881351Z parameters inplace; the function must take in the current values of the 2025-09-07T06:49:23.6881444Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T06:49:23.6881525Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T06:49:23.6882931Z an equally weighted average is used (default: None) 2025-09-07T06:49:23.6883008Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T06:49:23.6883088Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T06:49:23.6883138Z 2025-09-07T06:49:23.6883172Z Example: 2025-09-07T06:49:23.6883228Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.6883295Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T06:49:23.6883360Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T06:49:23.6883440Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T06:49:23.6883487Z >>> T_max=300) 2025-09-07T06:49:23.6883526Z >>> swa_start = 160 2025-09-07T06:49:23.6883581Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T06:49:23.6883621Z >>> for i in range(300): 2025-09-07T06:49:23.6883667Z >>> for input, target in loader: 2025-09-07T06:49:23.6883712Z >>> optimizer.zero_grad() 2025-09-07T06:49:23.6883764Z >>> loss_fn(model(input), target).backward() 2025-09-07T06:49:23.6883808Z >>> optimizer.step() 2025-09-07T06:49:23.6883853Z >>> if i > swa_start: 2025-09-07T06:49:23.6883903Z >>> swa_model.update_parameters(model) 2025-09-07T06:49:23.6883949Z >>> swa_scheduler.step() 2025-09-07T06:49:23.6883985Z >>> else: 2025-09-07T06:49:23.6884027Z >>> scheduler.step() 2025-09-07T06:49:23.6885470Z >>> 2025-09-07T06:49:23.6885532Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T06:49:23.6885593Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T06:49:23.6885623Z 2025-09-07T06:49:23.6885730Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T06:49:23.6885803Z If no averaging function is provided, the default is to compute 2025-09-07T06:49:23.6885859Z equally-weighted average of the weights (SWA). 2025-09-07T06:49:23.6885892Z 2025-09-07T06:49:23.6885925Z Example: 2025-09-07T06:49:23.6885977Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.6886052Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T06:49:23.6886116Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T06:49:23.6886196Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T06:49:23.6886226Z 2025-09-07T06:49:23.6886260Z .. note:: 2025-09-07T06:49:23.6886338Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T06:49:23.6886412Z need to update the activation statistics for Batch Normalization. 2025-09-07T06:49:23.6886494Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T06:49:23.6886575Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T06:49:23.6886706Z statistics in a post-training step by passing data through the model. The 2025-09-07T06:49:23.6886791Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T06:49:23.6886877Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T06:49:23.6888311Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T06:49:23.6888371Z approach yields the best results in your problem. 2025-09-07T06:49:23.6888403Z 2025-09-07T06:49:23.6888435Z .. note:: 2025-09-07T06:49:23.6888526Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T06:49:23.6888556Z 2025-09-07T06:49:23.6888589Z .. note:: 2025-09-07T06:49:23.6888660Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T06:49:23.6888727Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T06:49:23.6888827Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T06:49:23.6888895Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T06:49:23.6888957Z to update the parameters. 2025-09-07T06:49:23.6888987Z 2025-09-07T06:49:23.6889064Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T06:49:23.6889117Z https://arxiv.org/abs/1803.05407 2025-09-07T06:49:23.6889200Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T06:49:23.6889235Z Average: 2025-09-07T06:49:23.6889281Z https://arxiv.org/abs/1806.05594 2025-09-07T06:49:23.6889355Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T06:49:23.6889402Z https://arxiv.org/abs/1904.11943 2025-09-07T06:49:23.6889479Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T06:49:23.6890872Z Generalizes Well: 2025-09-07T06:49:23.6890924Z https://arxiv.org/abs/2001.02312 2025-09-07T06:49:23.6890962Z .. _Polyak averaging: 2025-09-07T06:49:23.6891033Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T06:49:23.6891065Z 2025-09-07T06:49:23.6891151Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6891182Z 2025-09-07T06:49:23.6891219Z warnings.warn(msg) 2025-09-07T06:49:23.6891250Z 2025-09-07T06:49:23.6891323Z --- Parse Warning: 37 / 146 --- 2025-09-07T06:49:23.6891604Z /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. 2025-09-07T06:49:23.6891696Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6891776Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T06:49:23.6891807Z 2025-09-07T06:49:23.6891889Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T06:49:23.6891965Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T06:49:23.6891996Z 2025-09-07T06:49:23.6892027Z Args: 2025-09-07T06:49:23.6892092Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T06:49:23.6892164Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T06:49:23.6892215Z together or separately for each group. 2025-09-07T06:49:23.6892286Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T06:49:23.6893681Z (default: 10) 2025-09-07T06:49:23.6893760Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T06:49:23.6893836Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T06:49:23.6893910Z (default: "cos") 2025-09-07T06:49:23.6893979Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T06:49:23.6894011Z 2025-09-07T06:49:23.6894079Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T06:49:23.6894155Z schedulers to switch to a constant learning rate late in the training 2025-09-07T06:49:23.6894195Z as in the example below. 2025-09-07T06:49:23.6894227Z 2025-09-07T06:49:23.6894259Z Example: 2025-09-07T06:49:23.6894311Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T06:49:23.6894357Z >>> loader, optimizer, model = ... 2025-09-07T06:49:23.6894458Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T06:49:23.6894537Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T06:49:23.6894580Z >>> lr_lambda=lr_lambda) 2025-09-07T06:49:23.6894669Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T06:49:23.6894735Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T06:49:23.6894792Z >>> swa_start = 160 2025-09-07T06:49:23.6894832Z >>> for i in range(300): 2025-09-07T06:49:23.6894877Z >>> for input, target in loader: 2025-09-07T06:49:23.6896297Z >>> optimizer.zero_grad() 2025-09-07T06:49:23.6896353Z >>> loss_fn(model(input), target).backward() 2025-09-07T06:49:23.6896396Z >>> optimizer.step() 2025-09-07T06:49:23.6896435Z >>> if i > swa_start: 2025-09-07T06:49:23.6896479Z >>> swa_scheduler.step() 2025-09-07T06:49:23.6896514Z >>> else: 2025-09-07T06:49:23.6896556Z >>> scheduler.step() 2025-09-07T06:49:23.6896585Z 2025-09-07T06:49:23.6896664Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T06:49:23.6896716Z https://arxiv.org/abs/1803.05407 2025-09-07T06:49:23.6896747Z 2025-09-07T06:49:23.6896837Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6896867Z 2025-09-07T06:49:23.6896904Z warnings.warn(msg) 2025-09-07T06:49:23.6896934Z 2025-09-07T06:49:23.6897007Z --- Parse Warning: 38 / 146 --- 2025-09-07T06:49:23.6897315Z /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=868. 2025-09-07T06:49:23.6897407Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6897446Z Load the optimizer state. 2025-09-07T06:49:23.6897477Z 2025-09-07T06:49:23.6897508Z Args: 2025-09-07T06:49:23.6897581Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T06:49:23.6898975Z from a call to :meth:`state_dict`. 2025-09-07T06:49:23.6899010Z 2025-09-07T06:49:23.6899047Z .. warning:: 2025-09-07T06:49:23.6899168Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T06:49:23.6899243Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T06:49:23.6899273Z 2025-09-07T06:49:23.6899307Z .. note:: 2025-09-07T06:49:23.6899410Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T06:49:23.6899474Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T06:49:23.6899588Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T06:49:23.6899646Z differ from those initialized in the optimizer), 2025-09-07T06:49:23.6899805Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T06:49:23.6899845Z accordingly. 2025-09-07T06:49:23.6899947Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T06:49:23.6900055Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T06:49:23.6900117Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T06:49:23.6900147Z 2025-09-07T06:49:23.6900181Z Example: 2025-09-07T06:49:23.6900222Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6900270Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T06:49:23.6900335Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T06:49:23.6901752Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T06:49:23.6901810Z ... optim, 2025-09-07T06:49:23.6901857Z ... start_factor=0.1, 2025-09-07T06:49:23.6901910Z ... end_factor=1, 2025-09-07T06:49:23.6901948Z ... total_iters=20, 2025-09-07T06:49:23.6901982Z ... ) 2025-09-07T06:49:23.6902051Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T06:49:23.6902088Z ... optim, 2025-09-07T06:49:23.6902125Z ... T_max=80, 2025-09-07T06:49:23.6902165Z ... eta_min=3e-5, 2025-09-07T06:49:23.6902198Z ... ) 2025-09-07T06:49:23.6902254Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T06:49:23.6902289Z ... optim, 2025-09-07T06:49:23.6902341Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T06:49:23.6902381Z ... milestones=[20], 2025-09-07T06:49:23.6902415Z ... ) 2025-09-07T06:49:23.6902477Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T06:49:23.6902552Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T06:49:23.6902616Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T06:49:23.6902646Z 2025-09-07T06:49:23.6902677Z 2025-09-07T06:49:23.6902764Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6904144Z 2025-09-07T06:49:23.6904184Z warnings.warn(msg) 2025-09-07T06:49:23.6904214Z 2025-09-07T06:49:23.6904285Z --- Parse Warning: 39 / 146 --- 2025-09-07T06:49:23.6904629Z /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=811. 2025-09-07T06:49:23.6904722Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6904838Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T06:49:23.6904871Z 2025-09-07T06:49:23.6904997Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T06:49:23.6905075Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T06:49:23.6905105Z 2025-09-07T06:49:23.6905136Z Args: 2025-09-07T06:49:23.6905191Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:23.6905248Z schedulers (list): List of chained schedulers. 2025-09-07T06:49:23.6905327Z milestones (list): List of integers that reflects milestone points. 2025-09-07T06:49:23.6905388Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T06:49:23.6905418Z 2025-09-07T06:49:23.6905453Z Example: 2025-09-07T06:49:23.6905493Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6905593Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T06:49:23.6905640Z >>> # lr = 0.005 if epoch == 0 2025-09-07T06:49:23.6905681Z >>> # lr = 0.005 if epoch == 1 2025-09-07T06:49:23.6907101Z >>> # lr = 0.005 if epoch == 2 2025-09-07T06:49:23.6907136Z >>> # ... 2025-09-07T06:49:23.6907179Z >>> # lr = 0.05 if epoch == 20 2025-09-07T06:49:23.6907220Z >>> # lr = 0.045 if epoch == 21 2025-09-07T06:49:23.6907260Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T06:49:23.6907332Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T06:49:23.6907391Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T06:49:23.6907433Z >>> scheduler = SequentialLR( 2025-09-07T06:49:23.6907471Z ... optimizer, 2025-09-07T06:49:23.6907547Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T06:49:23.6907591Z ... milestones=[20], 2025-09-07T06:49:23.6907624Z ... ) 2025-09-07T06:49:23.6907685Z >>> for epoch in range(100): 2025-09-07T06:49:23.6907722Z >>> train(...) 2025-09-07T06:49:23.6907759Z >>> validate(...) 2025-09-07T06:49:23.6907800Z >>> scheduler.step() 2025-09-07T06:49:23.6907829Z 2025-09-07T06:49:23.6907898Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T06:49:23.6907929Z 2025-09-07T06:49:23.6908017Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6908047Z 2025-09-07T06:49:23.6909442Z warnings.warn(msg) 2025-09-07T06:49:23.6909473Z 2025-09-07T06:49:23.6909545Z --- Parse Warning: 40 / 146 --- 2025-09-07T06:49:23.6909853Z /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=1236. 2025-09-07T06:49:23.6909945Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6910012Z Reduce learning rate when a metric has stopped improving. 2025-09-07T06:49:23.6910043Z 2025-09-07T06:49:23.6910115Z Models often benefit from reducing the learning rate by a factor 2025-09-07T06:49:23.6910184Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T06:49:23.6910258Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T06:49:23.6910305Z of epochs, the learning rate is reduced. 2025-09-07T06:49:23.6910335Z 2025-09-07T06:49:23.6910366Z Args: 2025-09-07T06:49:23.6910419Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:23.6910483Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T06:49:23.6910548Z be reduced when the quantity monitored has stopped 2025-09-07T06:49:23.6910609Z decreasing; in `max` mode it will be reduced when the 2025-09-07T06:49:23.6910680Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T06:49:23.6910743Z factor (float): Factor by which the learning rate will be 2025-09-07T06:49:23.6910798Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T06:49:23.6910938Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T06:49:23.6910989Z which the learning rate will be reduced. 2025-09-07T06:49:23.6912438Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T06:49:23.6912566Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T06:49:23.6912640Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T06:49:23.6912734Z we have what is considered an intolerable epoch. 2025-09-07T06:49:23.6912829Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T06:49:23.6912894Z the learning rate is reduced at the end of this epoch. 2025-09-07T06:49:23.6913003Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T06:49:23.6913111Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T06:49:23.6913158Z the learning rate is not adjusted. 2025-09-07T06:49:23.6913197Z Default: 10. 2025-09-07T06:49:23.6913265Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T06:49:23.6913326Z to only focus on significant changes. Default: 1e-4. 2025-09-07T06:49:23.6913416Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T06:49:23.6913477Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T06:49:23.6913548Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T06:49:23.6913606Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T06:49:23.6913671Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T06:49:23.6913735Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T06:49:23.6913798Z normal operation after lr has been reduced. Default: 0. 2025-09-07T06:49:23.6913858Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T06:49:23.6913917Z lower bound on the learning rate of all param groups 2025-09-07T06:49:23.6915403Z or each group respectively. Default: 0. 2025-09-07T06:49:23.6915473Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T06:49:23.6915548Z between new and old lr is smaller than eps, the update is 2025-09-07T06:49:23.6915590Z ignored. Default: 1e-8. 2025-09-07T06:49:23.6915624Z 2025-09-07T06:49:23.6915656Z Example: 2025-09-07T06:49:23.6915697Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6915778Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T06:49:23.6915839Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T06:49:23.6915879Z >>> for epoch in range(10): 2025-09-07T06:49:23.6915917Z >>> train(...) 2025-09-07T06:49:23.6915960Z >>> val_loss = validate(...) 2025-09-07T06:49:23.6916019Z >>> # Note that step should be called after validate() 2025-09-07T06:49:23.6916063Z >>> scheduler.step(val_loss) 2025-09-07T06:49:23.6916093Z 2025-09-07T06:49:23.6916165Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T06:49:23.6916196Z 2025-09-07T06:49:23.6916342Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T06:49:23.6916374Z 2025-09-07T06:49:23.6916416Z scheduler.step(val_loss) 2025-09-07T06:49:23.6916447Z ^ 2025-09-07T06:49:23.6916484Z warnings.warn(msg) 2025-09-07T06:49:23.6917868Z 2025-09-07T06:49:23.6917943Z --- Parse Warning: 41 / 146 --- 2025-09-07T06:49:23.6918234Z /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=1433. 2025-09-07T06:49:23.6918327Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6918442Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T06:49:23.6918475Z 2025-09-07T06:49:23.6918623Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T06:49:23.6918716Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T06:49:23.6918799Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T06:49:23.6918839Z or per-cycle basis. 2025-09-07T06:49:23.6918869Z 2025-09-07T06:49:23.6918954Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T06:49:23.6919025Z `step` should be called after a batch has been used for training. 2025-09-07T06:49:23.6919055Z 2025-09-07T06:49:23.6919130Z This class has three built-in policies, as put forth in the paper: 2025-09-07T06:49:23.6919160Z 2025-09-07T06:49:23.6919233Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T06:49:23.6919336Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T06:49:23.6919468Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T06:49:23.6919537Z at each cycle iteration. 2025-09-07T06:49:23.6919567Z 2025-09-07T06:49:23.6921003Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T06:49:23.6921035Z 2025-09-07T06:49:23.6921068Z Args: 2025-09-07T06:49:23.6921122Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:23.6921187Z base_lr (float or list): Initial learning rate which is the 2025-09-07T06:49:23.6921249Z lower boundary in the cycle for each parameter group. 2025-09-07T06:49:23.6921320Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T06:49:23.6921372Z for each parameter group. Functionally, 2025-09-07T06:49:23.6921431Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T06:49:23.6921489Z The lr at any cycle is the sum of base_lr 2025-09-07T06:49:23.6921543Z and some scaling of the amplitude; therefore 2025-09-07T06:49:23.6921601Z max_lr may not actually be reached depending on 2025-09-07T06:49:23.6921640Z scaling function. 2025-09-07T06:49:23.6921704Z step_size_up (int): Number of training iterations in the 2025-09-07T06:49:23.6921754Z increasing half of a cycle. Default: 2000 2025-09-07T06:49:23.6921820Z step_size_down (int): Number of training iterations in the 2025-09-07T06:49:23.6921880Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T06:49:23.6921930Z it is set to step_size_up. Default: None 2025-09-07T06:49:23.6921990Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T06:49:23.6922046Z Values correspond to policies detailed above. 2025-09-07T06:49:23.6922105Z If scale_fn is not None, this argument is ignored. 2025-09-07T06:49:23.6922147Z Default: 'triangular' 2025-09-07T06:49:23.6923548Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T06:49:23.6923594Z gamma**(cycle iterations) 2025-09-07T06:49:23.6923631Z Default: 1.0 2025-09-07T06:49:23.6923702Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T06:49:23.6923749Z argument lambda function, where 2025-09-07T06:49:23.6923797Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T06:49:23.6923845Z If specified, then 'mode' is ignored. 2025-09-07T06:49:23.6923883Z Default: None 2025-09-07T06:49:23.6923935Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T06:49:23.6923985Z Defines whether scale_fn is evaluated on 2025-09-07T06:49:23.6924035Z cycle number or cycle iterations (training 2025-09-07T06:49:23.6924118Z iterations since start of cycle). 2025-09-07T06:49:23.6924159Z Default: 'cycle' 2025-09-07T06:49:23.6924232Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T06:49:23.6924299Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T06:49:23.6924336Z Default: True 2025-09-07T06:49:23.6924476Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T06:49:23.6924549Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T06:49:23.6924610Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T06:49:23.6924665Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T06:49:23.6924703Z Default: 0.8 2025-09-07T06:49:23.6924777Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T06:49:23.6926223Z for each parameter group. Functionally, 2025-09-07T06:49:23.6926299Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T06:49:23.6926389Z The momentum at any cycle is the difference of max_momentum 2025-09-07T06:49:23.6926443Z and some scaling of the amplitude; therefore 2025-09-07T06:49:23.6926506Z base_momentum may not actually be reached depending on 2025-09-07T06:49:23.6926570Z scaling function. Note that momentum is cycled inversely 2025-09-07T06:49:23.6926648Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T06:49:23.6926692Z and learning rate is 'base_lr' 2025-09-07T06:49:23.6926729Z Default: 0.9 2025-09-07T06:49:23.6926808Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T06:49:23.6926886Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T06:49:23.6926969Z batch instead of after each epoch, this number represents the total 2025-09-07T06:49:23.6927049Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T06:49:23.6927118Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T06:49:23.6927157Z Default: -1 2025-09-07T06:49:23.6927187Z 2025-09-07T06:49:23.6927220Z Example: 2025-09-07T06:49:23.6927259Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6927340Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T06:49:23.6927400Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T06:49:23.6927438Z ... optimizer, 2025-09-07T06:49:23.6928827Z ... base_lr=0.01, 2025-09-07T06:49:23.6928866Z ... max_lr=0.1, 2025-09-07T06:49:23.6928908Z ... step_size_up=10, 2025-09-07T06:49:23.6928940Z ... ) 2025-09-07T06:49:23.6929006Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T06:49:23.6929050Z >>> for epoch in range(10): 2025-09-07T06:49:23.6929094Z >>> for batch in data_loader: 2025-09-07T06:49:23.6929134Z >>> train_batch(...) 2025-09-07T06:49:23.6929176Z >>> scheduler.step() 2025-09-07T06:49:23.6929206Z 2025-09-07T06:49:23.6929266Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T06:49:23.6929296Z 2025-09-07T06:49:23.6929403Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T06:49:23.6929465Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T06:49:23.6929496Z 2025-09-07T06:49:23.6929584Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6929614Z 2025-09-07T06:49:23.6929652Z warnings.warn(msg) 2025-09-07T06:49:23.6929682Z 2025-09-07T06:49:23.6929798Z --- Parse Warning: 42 / 146 --- 2025-09-07T06:49:23.6930123Z /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=1725. 2025-09-07T06:49:23.6930216Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6931662Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T06:49:23.6931693Z 2025-09-07T06:49:23.6931762Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T06:49:23.6931847Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T06:49:23.6931901Z of epochs between two warm restarts in SGDR: 2025-09-07T06:49:23.6931951Z 2025-09-07T06:49:23.6931985Z .. math:: 2025-09-07T06:49:23.6932058Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T06:49:23.6932114Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T06:49:23.6932159Z 2025-09-07T06:49:23.6932223Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T06:49:23.6932291Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T06:49:23.6932321Z 2025-09-07T06:49:23.6932362Z It has been proposed in 2025-09-07T06:49:23.6932425Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T06:49:23.6932456Z 2025-09-07T06:49:23.6932487Z Args: 2025-09-07T06:49:23.6932540Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:23.6932600Z T_0 (int): Number of iterations until the first restart. 2025-09-07T06:49:23.6932703Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T06:49:23.6932775Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T06:49:23.6934208Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T06:49:23.6934242Z 2025-09-07T06:49:23.6934306Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T06:49:23.6934353Z https://arxiv.org/abs/1608.03983 2025-09-07T06:49:23.6934439Z 2025-09-07T06:49:23.6934472Z Example: 2025-09-07T06:49:23.6934512Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6934579Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T06:49:23.6934662Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T06:49:23.6934702Z ... optimizer, T_0=20 2025-09-07T06:49:23.6934734Z ... ) 2025-09-07T06:49:23.6934775Z >>> for epoch in range(100): 2025-09-07T06:49:23.6934813Z >>> train(...) 2025-09-07T06:49:23.6934852Z >>> validate(...) 2025-09-07T06:49:23.6934896Z >>> scheduler.step() 2025-09-07T06:49:23.6934928Z 2025-09-07T06:49:23.6935012Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T06:49:23.6935043Z 2025-09-07T06:49:23.6935130Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6935161Z 2025-09-07T06:49:23.6935197Z warnings.warn(msg) 2025-09-07T06:49:23.6935228Z 2025-09-07T06:49:23.6936663Z --- Parse Warning: 43 / 146 --- 2025-09-07T06:49:23.6936961Z /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=1875. 2025-09-07T06:49:23.6937052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6937210Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T06:49:23.6937240Z 2025-09-07T06:49:23.6937347Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T06:49:23.6937449Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T06:49:23.6937497Z lower than the initial learning rate. 2025-09-07T06:49:23.6937577Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T06:49:23.6937652Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T06:49:23.6937681Z 2025-09-07T06:49:23.6937768Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T06:49:23.6937839Z `step` should be called after a batch has been used for training. 2025-09-07T06:49:23.6937869Z 2025-09-07T06:49:23.6937932Z This scheduler is not chainable. 2025-09-07T06:49:23.6937963Z 2025-09-07T06:49:23.6938048Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T06:49:23.6938119Z of two ways (listed in order of precedence): 2025-09-07T06:49:23.6938150Z 2025-09-07T06:49:23.6938207Z #. A value for total_steps is explicitly provided. 2025-09-07T06:49:23.6938274Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T06:49:23.6939672Z (steps_per_epoch) are provided. 2025-09-07T06:49:23.6939736Z In this case, the number of total steps is inferred by 2025-09-07T06:49:23.6939784Z total_steps = epochs * steps_per_epoch 2025-09-07T06:49:23.6939815Z 2025-09-07T06:49:23.6939900Z You must either provide a value for total_steps or provide a value for both 2025-09-07T06:49:23.6939944Z epochs and steps_per_epoch. 2025-09-07T06:49:23.6939974Z 2025-09-07T06:49:23.6940079Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T06:49:23.6940188Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T06:49:23.6940277Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T06:49:23.6940308Z 2025-09-07T06:49:23.6940342Z Args: 2025-09-07T06:49:23.6940393Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T06:49:23.6940465Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T06:49:23.6940508Z for each parameter group. 2025-09-07T06:49:23.6940583Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T06:49:23.6940661Z if a value is not provided here, then it must be inferred by providing 2025-09-07T06:49:23.6940712Z a value for epochs and steps_per_epoch. 2025-09-07T06:49:23.6940750Z Default: None 2025-09-07T06:49:23.6940824Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T06:49:23.6940911Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T06:49:23.6940966Z if a value for total_steps is not provided. 2025-09-07T06:49:23.6942353Z Default: None 2025-09-07T06:49:23.6942436Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T06:49:23.6942516Z used along with epochs in order to infer the total number of steps in the 2025-09-07T06:49:23.6942574Z cycle if a value for total_steps is not provided. 2025-09-07T06:49:23.6942609Z Default: None 2025-09-07T06:49:23.6942691Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T06:49:23.6942737Z increasing the learning rate. 2025-09-07T06:49:23.6942775Z Default: 0.3 2025-09-07T06:49:23.6942825Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T06:49:23.6942949Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T06:49:23.6942989Z linear annealing. 2025-09-07T06:49:23.6943027Z Default: 'cos' 2025-09-07T06:49:23.6943098Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T06:49:23.6943164Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T06:49:23.6943200Z Default: True 2025-09-07T06:49:23.6943277Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T06:49:23.6943349Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T06:49:23.6943409Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T06:49:23.6943465Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T06:49:23.6943523Z Default: 0.85 2025-09-07T06:49:23.6943600Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T06:49:23.6943650Z for each parameter group. Functionally, 2025-09-07T06:49:23.6945180Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T06:49:23.6945229Z Note that momentum is cycled inversely 2025-09-07T06:49:23.6945305Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T06:49:23.6945349Z and learning rate is 'base_lr' 2025-09-07T06:49:23.6945387Z Default: 0.95 2025-09-07T06:49:23.6945454Z div_factor (float): Determines the initial learning rate via 2025-09-07T06:49:23.6945498Z initial_lr = max_lr/div_factor 2025-09-07T06:49:23.6945536Z Default: 25 2025-09-07T06:49:23.6945609Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T06:49:23.6945660Z min_lr = initial_lr/final_div_factor 2025-09-07T06:49:23.6945701Z Default: 1e4 2025-09-07T06:49:23.6945788Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T06:49:23.6945881Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T06:49:23.6945968Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T06:49:23.6946006Z 'pct_start'). 2025-09-07T06:49:23.6946084Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T06:49:23.6946162Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T06:49:23.6946237Z batch instead of after each epoch, this number represents the total 2025-09-07T06:49:23.6946316Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T06:49:23.6946388Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T06:49:23.6946426Z Default: -1 2025-09-07T06:49:23.6947806Z 2025-09-07T06:49:23.6947842Z Example: 2025-09-07T06:49:23.6947881Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6947940Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T06:49:23.6948023Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T06:49:23.6948083Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T06:49:23.6948161Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T06:49:23.6948192Z ... ) 2025-09-07T06:49:23.6948234Z >>> for epoch in range(10): 2025-09-07T06:49:23.6948278Z >>> for batch in data_loader: 2025-09-07T06:49:23.6948320Z >>> train_batch(...) 2025-09-07T06:49:23.6948362Z >>> optimizer.step() 2025-09-07T06:49:23.6948451Z >>> scheduler.step() 2025-09-07T06:49:23.6948481Z 2025-09-07T06:49:23.6948545Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T06:49:23.6948576Z 2025-09-07T06:49:23.6948682Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T06:49:23.6948728Z https://arxiv.org/abs/1708.07120 2025-09-07T06:49:23.6948760Z 2025-09-07T06:49:23.6948847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6948878Z 2025-09-07T06:49:23.6948915Z warnings.warn(msg) 2025-09-07T06:49:23.6950298Z 2025-09-07T06:49:23.6950374Z --- Parse Warning: 44 / 146 --- 2025-09-07T06:49:23.6950696Z /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. 2025-09-07T06:49:23.6950817Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6950914Z 2025-09-07T06:49:23.6950996Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T06:49:23.6951082Z kernel registration will override any existing fake kernel registrations. 2025-09-07T06:49:23.6951112Z 2025-09-07T06:49:23.6951188Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T06:49:23.6951270Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T06:49:23.6951344Z are a record of the input and output tensor metadata. Based on this 2025-09-07T06:49:23.6951425Z information we will match a given input to the recorded profile, and return 2025-09-07T06:49:23.6951504Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T06:49:23.6951559Z doesn't exist then an exception will be thrown. 2025-09-07T06:49:23.6951589Z 2025-09-07T06:49:23.6951673Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T06:49:23.6951755Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T06:49:23.6951840Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T06:49:23.6951929Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T06:49:23.6952014Z possible that data-dependent operations may produce outputs of different 2025-09-07T06:49:23.6952091Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T06:49:23.6953544Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T06:49:23.6953584Z potentially unsafe. 2025-09-07T06:49:23.6953615Z 2025-09-07T06:49:23.6953647Z Args: 2025-09-07T06:49:23.6953726Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T06:49:23.6953804Z name to a set of operator profiles from which we will generate fake 2025-09-07T06:49:23.6953838Z kernels. 2025-09-07T06:49:23.6953871Z 2025-09-07T06:49:23.6953904Z Examples: 2025-09-07T06:49:23.6953934Z 2025-09-07T06:49:23.6954000Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T06:49:23.6954036Z >>> import torch 2025-09-07T06:49:23.6954098Z >>> from torch.export._draft_export import draft_export 2025-09-07T06:49:23.6954129Z >>> 2025-09-07T06:49:23.6954198Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T06:49:23.6954248Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T06:49:23.6954286Z >>> return x + y 2025-09-07T06:49:23.6954317Z >>> 2025-09-07T06:49:23.6954360Z >>> class M(torch.nn.Module): 2025-09-07T06:49:23.6954458Z >>> def forward(self, a, b): 2025-09-07T06:49:23.6954516Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T06:49:23.6954596Z >>> return res 2025-09-07T06:49:23.6955984Z >>> 2025-09-07T06:49:23.6956054Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T06:49:23.6956084Z >>> 2025-09-07T06:49:23.6956190Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T06:49:23.6956237Z >>> decomp = ep.run_decompositions() 2025-09-07T06:49:23.6956268Z 2025-09-07T06:49:23.6956298Z 2025-09-07T06:49:23.6956387Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6956416Z 2025-09-07T06:49:23.6956454Z warnings.warn(msg) 2025-09-07T06:49:23.6956483Z 2025-09-07T06:49:23.6956555Z --- Parse Warning: 45 / 146 --- 2025-09-07T06:49:23.6956886Z /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=397. 2025-09-07T06:49:23.6957001Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.6957086Z Register a FakeTensor implementation for this custom op. 2025-09-07T06:49:23.6957116Z 2025-09-07T06:49:23.6957204Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T06:49:23.6957234Z 2025-09-07T06:49:23.6957313Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T06:49:23.6957392Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T06:49:23.6957450Z Given some input Tensors with certain properties 2025-09-07T06:49:23.6958888Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T06:49:23.6958931Z the output Tensors are. 2025-09-07T06:49:23.6958965Z 2025-09-07T06:49:23.6959045Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T06:49:23.6959075Z 2025-09-07T06:49:23.6959111Z Args: 2025-09-07T06:49:23.6959175Z fn (Callable): The function to register as the FakeTensor 2025-09-07T06:49:23.6959214Z implementation. 2025-09-07T06:49:23.6959244Z 2025-09-07T06:49:23.6959280Z Examples: 2025-09-07T06:49:23.6959318Z >>> import torch 2025-09-07T06:49:23.6959361Z >>> import numpy as np 2025-09-07T06:49:23.6959404Z >>> from torch import Tensor 2025-09-07T06:49:23.6959437Z >>> 2025-09-07T06:49:23.6959505Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T06:49:23.6959573Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T06:49:23.6959643Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T06:49:23.6959695Z >>> return (x @ weight.t()) + bias 2025-09-07T06:49:23.6959727Z >>> 2025-09-07T06:49:23.6959772Z >>> @linear.register_fake 2025-09-07T06:49:23.6959814Z >>> def _(x, weight, bias): 2025-09-07T06:49:23.6959855Z >>> assert x.dim() == 2 2025-09-07T06:49:23.6961243Z >>> assert weight.dim() == 2 2025-09-07T06:49:23.6961289Z >>> assert bias.dim() == 1 2025-09-07T06:49:23.6961340Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T06:49:23.6961393Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T06:49:23.6961442Z >>> assert x.device == weight.device 2025-09-07T06:49:23.6961499Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T06:49:23.6961532Z >>> 2025-09-07T06:49:23.6961573Z >>> x = torch.randn(2, 2) 2025-09-07T06:49:23.6961620Z >>> weight = torch.randn(2, 2) 2025-09-07T06:49:23.6961695Z >>> bias = torch.randn(2) 2025-09-07T06:49:23.6961750Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:23.6961818Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T06:49:23.6961870Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:23.6961958Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T06:49:23.6961991Z >>> 2025-09-07T06:49:23.6962055Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T06:49:23.6962126Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T06:49:23.6962175Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T06:49:23.6962219Z >>> x_np = x.cpu().numpy() 2025-09-07T06:49:23.6962285Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T06:49:23.6962340Z >>> return torch.tensor(res, device=x.device) 2025-09-07T06:49:23.6962386Z >>> 2025-09-07T06:49:23.6963788Z >>> @nonzero.register_fake 2025-09-07T06:49:23.6963829Z >>> def _(x): 2025-09-07T06:49:23.6963888Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T06:49:23.6963949Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T06:49:23.6964010Z >>> # we use the ctx object to construct a new symint that 2025-09-07T06:49:23.6964065Z >>> # represents the data-dependent size. 2025-09-07T06:49:23.6964115Z >>> ctx = torch.library.get_ctx() 2025-09-07T06:49:23.6964163Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T06:49:23.6964209Z >>> shape = [nnz, x.dim()] 2025-09-07T06:49:23.6964269Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T06:49:23.6964315Z >>> return result 2025-09-07T06:49:23.6964347Z >>> 2025-09-07T06:49:23.6964451Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T06:49:23.6964505Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:23.6964565Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T06:49:23.6964616Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T06:49:23.6964668Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T06:49:23.6964699Z 2025-09-07T06:49:23.6964730Z 2025-09-07T06:49:23.6964910Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T06:49:23.6964941Z 2025-09-07T06:49:23.6966320Z _._ = None 2025-09-07T06:49:23.6966353Z ^ 2025-09-07T06:49:23.6966394Z warnings.warn(msg) 2025-09-07T06:49:23.6966423Z 2025-09-07T06:49:23.6966502Z --- Parse Warning: 46 / 146 --- 2025-09-07T06:49:23.6966785Z /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=96. 2025-09-07T06:49:23.6966881Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6966971Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T06:49:23.6967002Z 2025-09-07T06:49:23.6967076Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T06:49:23.6967167Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T06:49:23.6967257Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T06:49:23.6967298Z that is simpler; 2025-09-07T06:49:23.6967452Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T06:49:23.6967533Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T06:49:23.6967610Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T06:49:23.6967689Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T06:49:23.6967730Z a TorchDispatchMode. 2025-09-07T06:49:23.6967760Z 2025-09-07T06:49:23.6967848Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T06:49:23.6967888Z when the implementation 2025-09-07T06:49:23.6967966Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T06:49:23.6969389Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T06:49:23.6969474Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T06:49:23.6969582Z makes the implementation visible to these subsystems, allowing them 2025-09-07T06:49:23.6969627Z to optimize the triton kernel(s). 2025-09-07T06:49:23.6969676Z 2025-09-07T06:49:23.6969747Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T06:49:23.6969826Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T06:49:23.6969897Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T06:49:23.6969927Z 2025-09-07T06:49:23.6969959Z Args: 2025-09-07T06:49:23.6970040Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T06:49:23.6970116Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T06:49:23.6970179Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T06:49:23.6970263Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T06:49:23.6970342Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T06:49:23.6970442Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T06:49:23.6970525Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T06:49:23.6970618Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T06:49:23.6970686Z schema (None | str): A schema string for the operator. If None 2025-09-07T06:49:23.6970759Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T06:49:23.6970830Z annotations. We recommend letting us infer a schema unless you 2025-09-07T06:49:23.6972225Z have a specific reason not to. 2025-09-07T06:49:23.6972286Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T06:49:23.6972319Z 2025-09-07T06:49:23.6972353Z Example:: 2025-09-07T06:49:23.6972389Z 2025-09-07T06:49:23.6972443Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.6972484Z >>> import torch 2025-09-07T06:49:23.6972540Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T06:49:23.6972573Z >>> 2025-09-07T06:49:23.6972611Z >>> import triton 2025-09-07T06:49:23.6972658Z >>> from triton import language as tl 2025-09-07T06:49:23.6972689Z >>> 2025-09-07T06:49:23.6972726Z >>> @triton.jit 2025-09-07T06:49:23.6972763Z >>> def add_kernel( 2025-09-07T06:49:23.6972799Z >>> in_ptr0, 2025-09-07T06:49:23.6972835Z >>> in_ptr1, 2025-09-07T06:49:23.6972869Z >>> out_ptr, 2025-09-07T06:49:23.6972905Z >>> n_elements, 2025-09-07T06:49:23.6972950Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T06:49:23.6972984Z >>> ): 2025-09-07T06:49:23.6973062Z >>> pid = tl.program_id(axis=0) 2025-09-07T06:49:23.6973109Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T06:49:23.6974580Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T06:49:23.6974626Z >>> mask = offsets < n_elements 2025-09-07T06:49:23.6974677Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T06:49:23.6974726Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T06:49:23.6974767Z >>> output = x + y 2025-09-07T06:49:23.6974824Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T06:49:23.6974855Z >>> 2025-09-07T06:49:23.6974908Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T06:49:23.6974974Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T06:49:23.6975021Z >>> output = torch.empty_like(x) 2025-09-07T06:49:23.6975092Z >>> n_elements = output.numel() 2025-09-07T06:49:23.6975127Z >>> 2025-09-07T06:49:23.6975165Z >>> def grid(meta): 2025-09-07T06:49:23.6975250Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T06:49:23.6975281Z >>> 2025-09-07T06:49:23.6975348Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T06:49:23.6975420Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T06:49:23.6975458Z >>> return output 2025-09-07T06:49:23.6975490Z >>> 2025-09-07T06:49:23.6975528Z >>> @torch.compile 2025-09-07T06:49:23.6975565Z >>> def f(x, y): 2025-09-07T06:49:23.6975605Z >>> return add(x, y) 2025-09-07T06:49:23.6976998Z >>> 2025-09-07T06:49:23.6977044Z >>> x = torch.randn(3, device="cuda") 2025-09-07T06:49:23.6977090Z >>> y = torch.randn(3, device="cuda") 2025-09-07T06:49:23.6977124Z >>> 2025-09-07T06:49:23.6977166Z >>> z = f(x, y) 2025-09-07T06:49:23.6977210Z >>> assert torch.allclose(z, x + y) 2025-09-07T06:49:23.6977244Z 2025-09-07T06:49:23.6977274Z 2025-09-07T06:49:23.6977366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6977395Z 2025-09-07T06:49:23.6977432Z warnings.warn(msg) 2025-09-07T06:49:23.6977462Z 2025-09-07T06:49:23.6977533Z --- Parse Warning: 47 / 146 --- 2025-09-07T06:49:23.6977823Z /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=296. 2025-09-07T06:49:23.6977916Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6977985Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T06:49:23.6978031Z non-strict ``torch.export``. 2025-09-07T06:49:23.6978062Z 2025-09-07T06:49:23.6978130Z These technologies perform Dispatcher-based tracing (via 2025-09-07T06:49:23.6978204Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T06:49:23.6978275Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T06:49:23.6979668Z can actually be traced into a graph. 2025-09-07T06:49:23.6979699Z 2025-09-07T06:49:23.6979777Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T06:49:23.6979807Z 2025-09-07T06:49:23.6979841Z Examples: 2025-09-07T06:49:23.6979871Z 2025-09-07T06:49:23.6979910Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.6979947Z >>> import torch 2025-09-07T06:49:23.6979985Z >>> import triton 2025-09-07T06:49:23.6980031Z >>> from triton import language as tl 2025-09-07T06:49:23.6980154Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T06:49:23.6980206Z >>> from torch.library import wrap_triton 2025-09-07T06:49:23.6980240Z >>> 2025-09-07T06:49:23.6980276Z >>> @triton.jit 2025-09-07T06:49:23.6980314Z >>> def add_kernel( 2025-09-07T06:49:23.6980349Z >>> in_ptr0, 2025-09-07T06:49:23.6980384Z >>> in_ptr1, 2025-09-07T06:49:23.6980418Z >>> out_ptr, 2025-09-07T06:49:23.6980454Z >>> n_elements, 2025-09-07T06:49:23.6980499Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T06:49:23.6980531Z >>> ): 2025-09-07T06:49:23.6980575Z >>> pid = tl.program_id(axis=0) 2025-09-07T06:49:23.6981964Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T06:49:23.6982025Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T06:49:23.6982068Z >>> mask = offsets < n_elements 2025-09-07T06:49:23.6982143Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T06:49:23.6982192Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T06:49:23.6982248Z >>> output = x + y 2025-09-07T06:49:23.6982305Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T06:49:23.6982336Z >>> 2025-09-07T06:49:23.6982373Z >>> def add(x, y): 2025-09-07T06:49:23.6982419Z >>> output = torch.empty_like(x) 2025-09-07T06:49:23.6982462Z >>> n_elements = output.numel() 2025-09-07T06:49:23.6982493Z >>> 2025-09-07T06:49:23.6982533Z >>> def grid_fn(meta): 2025-09-07T06:49:23.6982596Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T06:49:23.6982627Z >>> 2025-09-07T06:49:23.6982696Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T06:49:23.6982736Z >>> return output 2025-09-07T06:49:23.6982767Z >>> 2025-09-07T06:49:23.6982816Z >>> x = torch.randn(3, device="cuda") 2025-09-07T06:49:23.6982860Z >>> y = torch.randn(3, device="cuda") 2025-09-07T06:49:23.6982903Z >>> gm = make_fx(add)(x, y) 2025-09-07T06:49:23.6982941Z >>> print(gm.code) 2025-09-07T06:49:23.6984357Z >>> # def forward(self, x_1, y_1): 2025-09-07T06:49:23.6984494Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T06:49:23.6984580Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T06:49:23.6984631Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T06:49:23.6984678Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T06:49:23.6984741Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T06:49:23.6984791Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T06:49:23.6984833Z >>> # }) 2025-09-07T06:49:23.6984875Z >>> # return empty_like 2025-09-07T06:49:23.6984907Z 2025-09-07T06:49:23.6984938Z 2025-09-07T06:49:23.6985026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6985056Z 2025-09-07T06:49:23.6985092Z warnings.warn(msg) 2025-09-07T06:49:23.6985123Z 2025-09-07T06:49:23.6985195Z --- Parse Warning: 48 / 146 --- 2025-09-07T06:49:23.6985536Z /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. 2025-09-07T06:49:23.6985627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6985698Z Return a new dict with new, potentially nested, key value pair 2025-09-07T06:49:23.6985729Z 2025-09-07T06:49:23.6985812Z >>> purchase = { 2025-09-07T06:49:23.6987221Z ... "name": "Alice", 2025-09-07T06:49:23.6987293Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:23.6987342Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:23.6987374Z ... } 2025-09-07T06:49:23.6987449Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T06:49:23.6987493Z {'credit card': '5555-1234-1234-1234', 2025-09-07T06:49:23.6987530Z 'name': 'Alice', 2025-09-07T06:49:23.6987592Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T06:49:23.6987624Z 2025-09-07T06:49:23.6987712Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6987742Z 2025-09-07T06:49:23.6987778Z warnings.warn(msg) 2025-09-07T06:49:23.6987834Z 2025-09-07T06:49:23.6987904Z --- Parse Warning: 49 / 146 --- 2025-09-07T06:49:23.6988247Z /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. 2025-09-07T06:49:23.6988354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6988412Z Update value in a (potentially) nested dictionary 2025-09-07T06:49:23.6988442Z 2025-09-07T06:49:23.6988475Z inputs: 2025-09-07T06:49:23.6988521Z d - dictionary on which to operate 2025-09-07T06:49:23.6988599Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T06:49:23.6988647Z func - function to operate on that value 2025-09-07T06:49:23.6990032Z 2025-09-07T06:49:23.6990107Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T06:49:23.6990197Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T06:49:23.6990235Z original dictionary. 2025-09-07T06:49:23.6990269Z 2025-09-07T06:49:23.6990343Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T06:49:23.6990422Z specified by the keys, with the innermost value set to func(default). 2025-09-07T06:49:23.6990452Z 2025-09-07T06:49:23.6990492Z >>> inc = lambda x: x + 1 2025-09-07T06:49:23.6990537Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T06:49:23.6990569Z {'a': 1} 2025-09-07T06:49:23.6990599Z 2025-09-07T06:49:23.6990636Z >>> transaction = { 2025-09-07T06:49:23.6990674Z ... "name": "Alice", 2025-09-07T06:49:23.6990744Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:23.6990791Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:23.6990857Z ... } 2025-09-07T06:49:23.6990936Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T06:49:23.6990979Z {'credit card': '5555-1234-1234-1234', 2025-09-07T06:49:23.6991016Z 'name': 'Alice', 2025-09-07T06:49:23.6991079Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T06:49:23.6992457Z 2025-09-07T06:49:23.6992505Z >>> # updating a value when k0 is not in d 2025-09-07T06:49:23.6992558Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T06:49:23.6992594Z {1: {2: {3: 'bar'}}} 2025-09-07T06:49:23.6992642Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T06:49:23.6992680Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T06:49:23.6992712Z 2025-09-07T06:49:23.6992800Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6992829Z 2025-09-07T06:49:23.6992866Z warnings.warn(msg) 2025-09-07T06:49:23.6992897Z 2025-09-07T06:49:23.6992970Z --- Parse Warning: 50 / 146 --- 2025-09-07T06:49:23.6993343Z /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. 2025-09-07T06:49:23.6993436Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6993500Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T06:49:23.6993531Z 2025-09-07T06:49:23.6993596Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T06:49:23.6993670Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T06:49:23.6993700Z 2025-09-07T06:49:23.6993773Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T06:49:23.6993822Z structures such as dictionaries and lists. 2025-09-07T06:49:23.6993868Z 2025-09-07T06:49:23.6995327Z >>> transaction = { 2025-09-07T06:49:23.6995368Z ... "name": "Alice", 2025-09-07T06:49:23.6995464Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T06:49:23.6995511Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T06:49:23.6995542Z ... } 2025-09-07T06:49:23.6995597Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T06:49:23.6995630Z 'Apple' 2025-09-07T06:49:23.6995673Z >>> get_in(["name"], transaction) 2025-09-07T06:49:23.6995705Z 'Alice' 2025-09-07T06:49:23.6995757Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T06:49:23.6995815Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T06:49:23.6995866Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T06:49:23.6995918Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T06:49:23.6995952Z 0 2025-09-07T06:49:23.6995996Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T06:49:23.6996045Z Traceback (most recent call last): 2025-09-07T06:49:23.6996078Z ... 2025-09-07T06:49:23.6996113Z KeyError: 'y' 2025-09-07T06:49:23.6996143Z 2025-09-07T06:49:23.6996175Z See Also: 2025-09-07T06:49:23.6996213Z itertoolz.get 2025-09-07T06:49:23.6996251Z operator.getitem 2025-09-07T06:49:23.6997630Z 2025-09-07T06:49:23.6997722Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.6997753Z 2025-09-07T06:49:23.6997789Z warnings.warn(msg) 2025-09-07T06:49:23.6997820Z 2025-09-07T06:49:23.6997892Z --- Parse Warning: 51 / 146 --- 2025-09-07T06:49:23.6998228Z /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. 2025-09-07T06:49:23.6998326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.6998373Z Group a collection by a key function 2025-09-07T06:49:23.6998403Z 2025-09-07T06:49:23.6998467Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T06:49:23.6998514Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T06:49:23.6998573Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T06:49:23.6998604Z 2025-09-07T06:49:23.6998645Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T06:49:23.6998711Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T06:49:23.6998754Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T06:49:23.6998785Z 2025-09-07T06:49:23.6998839Z Non-callable keys imply grouping on a member. 2025-09-07T06:49:23.6998870Z 2025-09-07T06:49:23.6998905Z >>> groupby( 2025-09-07T06:49:23.7000283Z ... "gender", 2025-09-07T06:49:23.7000362Z ... [ 2025-09-07T06:49:23.7000412Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T06:49:23.7000460Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T06:49:23.7000508Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T06:49:23.7000540Z ... ], 2025-09-07T06:49:23.7000580Z ... ) # doctest:+SKIP 2025-09-07T06:49:23.7000623Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T06:49:23.7000667Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T06:49:23.7000711Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T06:49:23.7000742Z 2025-09-07T06:49:23.7000795Z Not to be confused with ``itertools.groupby`` 2025-09-07T06:49:23.7000824Z 2025-09-07T06:49:23.7000857Z See Also: 2025-09-07T06:49:23.7000889Z countby 2025-09-07T06:49:23.7000920Z 2025-09-07T06:49:23.7001026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7001059Z 2025-09-07T06:49:23.7001096Z warnings.warn(msg) 2025-09-07T06:49:23.7001141Z 2025-09-07T06:49:23.7001211Z --- Parse Warning: 52 / 146 --- 2025-09-07T06:49:23.7001507Z /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. 2025-09-07T06:49:23.7002961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7003096Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T06:49:23.7003166Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T06:49:23.7003196Z 2025-09-07T06:49:23.7003246Z It is useful when tracing the code profile. 2025-09-07T06:49:23.7003279Z 2025-09-07T06:49:23.7003310Z Args: 2025-09-07T06:49:23.7003371Z name (str): Label assigned to the block of code. 2025-09-07T06:49:23.7003438Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T06:49:23.7003483Z non-distributed cases. 2025-09-07T06:49:23.7003513Z 2025-09-07T06:49:23.7003546Z Example: 2025-09-07T06:49:23.7003612Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T06:49:23.7003664Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T06:49:23.7003726Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T06:49:23.7003762Z ... y = x**2 2025-09-07T06:49:23.7003824Z ... with torch.autograd.profiler.record_function( 2025-09-07T06:49:23.7003862Z ... "label-z" 2025-09-07T06:49:23.7003903Z ... ): # label the block 2025-09-07T06:49:23.7003940Z ... z = y**3 2025-09-07T06:49:23.7003979Z ... y.backward() 2025-09-07T06:49:23.7004023Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T06:49:23.7005519Z >>> # NOTE: some columns were removed for brevity 2025-09-07T06:49:23.7005596Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T06:49:23.7005674Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7005742Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T06:49:23.7005815Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7005868Z pow 60.77% 47.470us 3 2025-09-07T06:49:23.7005918Z mul 21.73% 25.465us 2 2025-09-07T06:49:23.7005981Z PowBackward0 12.03% 121.891us 1 2025-09-07T06:49:23.7006111Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T06:49:23.7006166Z label-z 2.13% 12.421us 1 2025-09-07T06:49:23.7006236Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T06:49:23.7006306Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7006352Z Self CPU time total: 234.344us 2025-09-07T06:49:23.7006393Z CUDA time total: 0.000us 2025-09-07T06:49:23.7006424Z 2025-09-07T06:49:23.7006454Z 2025-09-07T06:49:23.7006543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7006572Z 2025-09-07T06:49:23.7006609Z warnings.warn(msg) 2025-09-07T06:49:23.7006639Z 2025-09-07T06:49:23.7006712Z --- Parse Warning: 53 / 146 --- 2025-09-07T06:49:23.7008414Z /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. 2025-09-07T06:49:23.7008534Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7008587Z Register a container-like type as pytree node. 2025-09-07T06:49:23.7008618Z 2025-09-07T06:49:23.7008649Z Args: 2025-09-07T06:49:23.7008718Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T06:49:23.7008813Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T06:49:23.7008902Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T06:49:23.7009003Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T06:49:23.7009052Z passed to the ``unflatten_fn``. 2025-09-07T06:49:23.7009153Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T06:49:23.7009247Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T06:49:23.7009306Z The function should return an instance of ``cls``. 2025-09-07T06:49:23.7009401Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T06:49:23.7009462Z qualified name used when serializing the tree spec. 2025-09-07T06:49:23.7009569Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T06:49:23.7009667Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T06:49:23.7009763Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T06:49:23.7009870Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T06:49:23.7009963Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T06:49:23.7010054Z original context. This is used for json deserialization, which is being used in 2025-09-07T06:49:23.7011456Z :mod:`torch.export` right now. 2025-09-07T06:49:23.7011488Z 2025-09-07T06:49:23.7011524Z Example:: 2025-09-07T06:49:23.7011553Z 2025-09-07T06:49:23.7011593Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7011651Z >>> # Registry a Python type with lambda functions 2025-09-07T06:49:23.7011693Z >>> register_pytree_node( 2025-09-07T06:49:23.7011729Z ... set, 2025-09-07T06:49:23.7011779Z ... lambda s: (sorted(s), None, None), 2025-09-07T06:49:23.7011829Z ... lambda children, _: set(children), 2025-09-07T06:49:23.7011862Z ... ) 2025-09-07T06:49:23.7011924Z 2025-09-07T06:49:23.7012012Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7012043Z 2025-09-07T06:49:23.7012080Z warnings.warn(msg) 2025-09-07T06:49:23.7012110Z 2025-09-07T06:49:23.7012181Z --- Parse Warning: 54 / 146 --- 2025-09-07T06:49:23.7012500Z /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=1226. 2025-09-07T06:49:23.7012592Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7012623Z 2025-09-07T06:49:23.7012701Z Context passed to policy function during selective checkpointing. 2025-09-07T06:49:23.7012732Z 2025-09-07T06:49:23.7014163Z This class is used to pass relevant metadata to the policy function during 2025-09-07T06:49:23.7014282Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T06:49:23.7014368Z of the policy function is during recomputation or not. 2025-09-07T06:49:23.7014470Z 2025-09-07T06:49:23.7014502Z Example: 2025-09-07T06:49:23.7014545Z >>> # xdoctest: +SKIP(stub) 2025-09-07T06:49:23.7014577Z >>> 2025-09-07T06:49:23.7014629Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T06:49:23.7014673Z >>> print(ctx.is_recompute) 2025-09-07T06:49:23.7014703Z >>> 2025-09-07T06:49:23.7014804Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T06:49:23.7014834Z >>> 2025-09-07T06:49:23.7014891Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T06:49:23.7014927Z >>> fn, x, y, 2025-09-07T06:49:23.7014967Z >>> use_reentrant=False, 2025-09-07T06:49:23.7015010Z >>> context_fn=context_fn, 2025-09-07T06:49:23.7015042Z >>> ) 2025-09-07T06:49:23.7015075Z 2025-09-07T06:49:23.7015163Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7015195Z 2025-09-07T06:49:23.7015232Z warnings.warn(msg) 2025-09-07T06:49:23.7015262Z 2025-09-07T06:49:23.7016703Z --- Parse Warning: 55 / 146 --- 2025-09-07T06:49:23.7017032Z /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=1366. 2025-09-07T06:49:23.7017124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7017153Z 2025-09-07T06:49:23.7017237Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T06:49:23.7017267Z 2025-09-07T06:49:23.7017347Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T06:49:23.7017415Z operations are recomputed during the backward pass. 2025-09-07T06:49:23.7017447Z 2025-09-07T06:49:23.7017480Z Args: 2025-09-07T06:49:23.7017527Z policy_fn_or_list (Callable or List): 2025-09-07T06:49:23.7017591Z - If a policy function is provided, it should accept a 2025-09-07T06:49:23.7017676Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T06:49:23.7017751Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T06:49:23.7017835Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T06:49:23.7017908Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T06:49:23.7017976Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T06:49:23.7018053Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T06:49:23.7018091Z operations. 2025-09-07T06:49:23.7018211Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T06:49:23.7018287Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T06:49:23.7019709Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T06:49:23.7019746Z is disabled. 2025-09-07T06:49:23.7019779Z Returns: 2025-09-07T06:49:23.7019823Z A tuple of two context managers. 2025-09-07T06:49:23.7019853Z 2025-09-07T06:49:23.7019886Z Example: 2025-09-07T06:49:23.7019929Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T06:49:23.7019967Z >>> import functools 2025-09-07T06:49:23.7019998Z >>> 2025-09-07T06:49:23.7020048Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T06:49:23.7020095Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T06:49:23.7020127Z >>> 2025-09-07T06:49:23.7020189Z >>> ops_to_save = [ 2025-09-07T06:49:23.7020239Z >>> torch.ops.aten.mm.default, 2025-09-07T06:49:23.7020270Z >>> ] 2025-09-07T06:49:23.7020322Z >>> 2025-09-07T06:49:23.7020374Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T06:49:23.7020415Z >>> if op in ops_to_save: 2025-09-07T06:49:23.7020464Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T06:49:23.7020498Z >>> else: 2025-09-07T06:49:23.7020554Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T06:49:23.7020585Z >>> 2025-09-07T06:49:23.7022031Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T06:49:23.7022065Z >>> 2025-09-07T06:49:23.7022103Z >>> # or equivalently 2025-09-07T06:49:23.7022201Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T06:49:23.7022232Z >>> 2025-09-07T06:49:23.7022272Z >>> def fn(x, y): 2025-09-07T06:49:23.7022347Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T06:49:23.7022379Z >>> 2025-09-07T06:49:23.7022437Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T06:49:23.7022472Z >>> fn, x, y, 2025-09-07T06:49:23.7022512Z >>> use_reentrant=False, 2025-09-07T06:49:23.7022552Z >>> context_fn=context_fn, 2025-09-07T06:49:23.7022583Z >>> ) 2025-09-07T06:49:23.7022613Z 2025-09-07T06:49:23.7022701Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7022730Z 2025-09-07T06:49:23.7022767Z warnings.warn(msg) 2025-09-07T06:49:23.7022796Z 2025-09-07T06:49:23.7022868Z --- Parse Warning: 56 / 146 --- 2025-09-07T06:49:23.7023189Z /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. 2025-09-07T06:49:23.7023283Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7023314Z 2025-09-07T06:49:23.7024826Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T06:49:23.7024859Z 2025-09-07T06:49:23.7024967Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T06:49:23.7025054Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T06:49:23.7025135Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T06:49:23.7025219Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T06:49:23.7025305Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T06:49:23.7025335Z 2025-09-07T06:49:23.7025429Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T06:49:23.7025548Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T06:49:23.7025634Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T06:49:23.7025715Z model for inference deployment it is better to switch to using it in this 2025-09-07T06:49:23.7025749Z benchmark. 2025-09-07T06:49:23.7025779Z 2025-09-07T06:49:23.7025813Z Example:: 2025-09-07T06:49:23.7025843Z 2025-09-07T06:49:23.7025891Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7025945Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T06:49:23.7025994Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T06:49:23.7026057Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T06:49:23.7026100Z >>> for input in inputs: 2025-09-07T06:49:23.7027540Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T06:49:23.7027625Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T06:49:23.7027697Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T06:49:23.7027758Z >>> stats = bench.benchmark( 2025-09-07T06:49:23.7027799Z ... num_calling_threads=4, 2025-09-07T06:49:23.7027840Z ... num_warmup_iters = 100, 2025-09-07T06:49:23.7027879Z ... num_iters = 1000, 2025-09-07T06:49:23.7027910Z ... ) 2025-09-07T06:49:23.7027981Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T06:49:23.7028050Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T06:49:23.7028080Z 2025-09-07T06:49:23.7028167Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7028198Z 2025-09-07T06:49:23.7028234Z warnings.warn(msg) 2025-09-07T06:49:23.7028265Z 2025-09-07T06:49:23.7028337Z --- Parse Warning: 57 / 146 --- 2025-09-07T06:49:23.7028647Z /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=1158. 2025-09-07T06:49:23.7028739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7028770Z 2025-09-07T06:49:23.7028824Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T06:49:23.7028854Z 2025-09-07T06:49:23.7028938Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:23.7030366Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T06:49:23.7030398Z 2025-09-07T06:49:23.7030474Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:23.7030530Z constructor. Full list arguments can be found at 2025-09-07T06:49:23.7030654Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T06:49:23.7030685Z 2025-09-07T06:49:23.7030719Z .. warning:: 2025-09-07T06:49:23.7030804Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:23.7030941Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:23.7031015Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:23.7031095Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:23.7031172Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:23.7031246Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:23.7031297Z register the ops through the dispatcher. 2025-09-07T06:49:23.7031326Z 2025-09-07T06:49:23.7031405Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:23.7031515Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:23.7031591Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:23.7031668Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:23.7031743Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:23.7031818Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:23.7033222Z CPython version of choice. 2025-09-07T06:49:23.7033254Z 2025-09-07T06:49:23.7033287Z Example: 2025-09-07T06:49:23.7033327Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7033387Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:23.7033431Z >>> from setuptools import setup 2025-09-07T06:49:23.7033509Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T06:49:23.7033563Z >>> setup( 2025-09-07T06:49:23.7033606Z ... name='extension', 2025-09-07T06:49:23.7033642Z ... ext_modules=[ 2025-09-07T06:49:23.7033697Z ... CppExtension( 2025-09-07T06:49:23.7033739Z ... name='extension', 2025-09-07T06:49:23.7033787Z ... sources=['extension.cpp'], 2025-09-07T06:49:23.7033836Z ... extra_compile_args=['-g'], 2025-09-07T06:49:23.7033895Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T06:49:23.7033927Z ... ], 2025-09-07T06:49:23.7033963Z ... cmdclass={ 2025-09-07T06:49:23.7034009Z ... 'build_ext': BuildExtension 2025-09-07T06:49:23.7034040Z ... }) 2025-09-07T06:49:23.7034070Z 2025-09-07T06:49:23.7034158Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7034188Z 2025-09-07T06:49:23.7035631Z warnings.warn(msg) 2025-09-07T06:49:23.7035667Z 2025-09-07T06:49:23.7035744Z --- Parse Warning: 58 / 146 --- 2025-09-07T06:49:23.7036049Z /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=1228. 2025-09-07T06:49:23.7036143Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7036173Z 2025-09-07T06:49:23.7036234Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T06:49:23.7036265Z 2025-09-07T06:49:23.7036349Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:23.7036422Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T06:49:23.7036503Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T06:49:23.7036535Z library. 2025-09-07T06:49:23.7036566Z 2025-09-07T06:49:23.7036640Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:23.7036699Z constructor. Full list arguments can be found at 2025-09-07T06:49:23.7036815Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T06:49:23.7036846Z 2025-09-07T06:49:23.7036879Z .. warning:: 2025-09-07T06:49:23.7036961Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:23.7037033Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:23.7037104Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:23.7037183Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:23.7038616Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:23.7038691Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:23.7038742Z register the ops through the dispatcher. 2025-09-07T06:49:23.7038823Z 2025-09-07T06:49:23.7038902Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:23.7038976Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:23.7039052Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:23.7039131Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:23.7039207Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:23.7039283Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:23.7039326Z CPython version of choice. 2025-09-07T06:49:23.7039355Z 2025-09-07T06:49:23.7039388Z Example: 2025-09-07T06:49:23.7039426Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7039487Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:23.7039556Z >>> from setuptools import setup 2025-09-07T06:49:23.7039636Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T06:49:23.7039689Z >>> setup( 2025-09-07T06:49:23.7039729Z ... name='cuda_extension', 2025-09-07T06:49:23.7039767Z ... ext_modules=[ 2025-09-07T06:49:23.7039806Z ... CUDAExtension( 2025-09-07T06:49:23.7041211Z ... name='cuda_extension', 2025-09-07T06:49:23.7041274Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:23.7041327Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T06:49:23.7041375Z ... 'nvcc': ['-O2']}, 2025-09-07T06:49:23.7041433Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T06:49:23.7041464Z ... ], 2025-09-07T06:49:23.7041502Z ... cmdclass={ 2025-09-07T06:49:23.7041548Z ... 'build_ext': BuildExtension 2025-09-07T06:49:23.7041583Z ... }) 2025-09-07T06:49:23.7041613Z 2025-09-07T06:49:23.7041652Z Compute capabilities: 2025-09-07T06:49:23.7041685Z 2025-09-07T06:49:23.7041791Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T06:49:23.7041894Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T06:49:23.7041998Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T06:49:23.7042103Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T06:49:23.7042202Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T06:49:23.7042250Z support (see below for details on PTX). 2025-09-07T06:49:23.7042280Z 2025-09-07T06:49:23.7042387Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T06:49:23.7042439Z CCs you want the extension to support: 2025-09-07T06:49:23.7042468Z 2025-09-07T06:49:23.7043889Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T06:49:23.7043975Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T06:49:23.7044006Z 2025-09-07T06:49:23.7044121Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T06:49:23.7044229Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T06:49:23.7044336Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T06:49:23.7044487Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T06:49:23.7044601Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T06:49:23.7044747Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T06:49:23.7044860Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T06:49:23.7044969Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T06:49:23.7045008Z "8.0 8.6" would be better. 2025-09-07T06:49:23.7045037Z 2025-09-07T06:49:23.7045141Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T06:49:23.7045241Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T06:49:23.7045271Z 2025-09-07T06:49:23.7045387Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T06:49:23.7045464Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T06:49:23.7045512Z 2025-09-07T06:49:23.7045548Z Example use: 2025-09-07T06:49:23.7045587Z #include 2025-09-07T06:49:23.7047041Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T06:49:23.7047072Z 2025-09-07T06:49:23.7047106Z Instead of: 2025-09-07T06:49:23.7047147Z #include 2025-09-07T06:49:23.7047211Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T06:49:23.7047240Z 2025-09-07T06:49:23.7047338Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T06:49:23.7047513Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T06:49:23.7047544Z 2025-09-07T06:49:23.7047585Z Relocatable device code linking: 2025-09-07T06:49:23.7047615Z 2025-09-07T06:49:23.7047713Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T06:49:23.7047807Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T06:49:23.7047933Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T06:49:23.7048049Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T06:49:23.7048159Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T06:49:23.7048228Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T06:49:23.7048291Z Note that it needs to be used at both steps to be useful. 2025-09-07T06:49:23.7048322Z 2025-09-07T06:49:23.7048446Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T06:49:23.7048512Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T06:49:23.7048602Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T06:49:23.7050033Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T06:49:23.7050066Z 2025-09-07T06:49:23.7050142Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T06:49:23.7050172Z 2025-09-07T06:49:23.7050205Z Example: 2025-09-07T06:49:23.7050245Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7050303Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:23.7050341Z >>> CUDAExtension( 2025-09-07T06:49:23.7050383Z ... name='cuda_extension', 2025-09-07T06:49:23.7050446Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:23.7050482Z ... dlink=True, 2025-09-07T06:49:23.7050530Z ... dlink_libraries=["dlink_lib"], 2025-09-07T06:49:23.7050580Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T06:49:23.7050630Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T06:49:23.7050701Z 2025-09-07T06:49:23.7050793Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7050824Z 2025-09-07T06:49:23.7050861Z warnings.warn(msg) 2025-09-07T06:49:23.7050891Z 2025-09-07T06:49:23.7050965Z --- Parse Warning: 59 / 146 --- 2025-09-07T06:49:23.7051263Z /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=1420. 2025-09-07T06:49:23.7052713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7052744Z 2025-09-07T06:49:23.7052808Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T06:49:23.7052838Z 2025-09-07T06:49:23.7052924Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T06:49:23.7053021Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T06:49:23.7053056Z extension. 2025-09-07T06:49:23.7053100Z 2025-09-07T06:49:23.7053174Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T06:49:23.7053209Z constructor. 2025-09-07T06:49:23.7053238Z 2025-09-07T06:49:23.7053272Z .. warning:: 2025-09-07T06:49:23.7053353Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T06:49:23.7053429Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T06:49:23.7053500Z the user's responsibility in their library to not use APIs from 2025-09-07T06:49:23.7053580Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T06:49:23.7053655Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T06:49:23.7053730Z example, to give access to custom ops from python, the library should 2025-09-07T06:49:23.7053786Z register the ops through the dispatcher. 2025-09-07T06:49:23.7053817Z 2025-09-07T06:49:23.7053895Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T06:49:23.7053968Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T06:49:23.7055451Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T06:49:23.7055531Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T06:49:23.7055605Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T06:49:23.7055682Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T06:49:23.7055724Z CPython version of choice. 2025-09-07T06:49:23.7055755Z 2025-09-07T06:49:23.7055787Z Example: 2025-09-07T06:49:23.7055826Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7055888Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:23.7055972Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T06:49:23.7056006Z >>> setup( 2025-09-07T06:49:23.7056047Z ... name='xpu_extension', 2025-09-07T06:49:23.7056084Z ... ext_modules=[ 2025-09-07T06:49:23.7056122Z ... SyclExtension( 2025-09-07T06:49:23.7056169Z ... name='xpu_extension', 2025-09-07T06:49:23.7056229Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T06:49:23.7056297Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T06:49:23.7056329Z ... ], 2025-09-07T06:49:23.7056366Z ... cmdclass={ 2025-09-07T06:49:23.7056411Z ... 'build_ext': BuildExtension 2025-09-07T06:49:23.7056442Z ... }) 2025-09-07T06:49:23.7056472Z 2025-09-07T06:49:23.7057927Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T06:49:23.7058065Z building process of the extension. If down the road a new card is installed the 2025-09-07T06:49:23.7058158Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T06:49:23.7058262Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T06:49:23.7058297Z to support: 2025-09-07T06:49:23.7058327Z 2025-09-07T06:49:23.7058399Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T06:49:23.7058429Z 2025-09-07T06:49:23.7058531Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T06:49:23.7058632Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T06:49:23.7058663Z 2025-09-07T06:49:23.7058716Z Note: Ninja is required to build SyclExtension. 2025-09-07T06:49:23.7058765Z 2025-09-07T06:49:23.7058855Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7058884Z 2025-09-07T06:49:23.7058921Z warnings.warn(msg) 2025-09-07T06:49:23.7058972Z 2025-09-07T06:49:23.7059044Z --- Parse Warning: 60 / 146 --- 2025-09-07T06:49:23.7059333Z /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=1597. 2025-09-07T06:49:23.7059426Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7059455Z 2025-09-07T06:49:23.7060875Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T06:49:23.7060907Z 2025-09-07T06:49:23.7060982Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T06:49:23.7061055Z compile the given sources into a dynamic library. This library is 2025-09-07T06:49:23.7061143Z subsequently loaded into the current Python process as a module and 2025-09-07T06:49:23.7061191Z returned from this function, ready for use. 2025-09-07T06:49:23.7061226Z 2025-09-07T06:49:23.7061300Z By default, the directory to which the build file is emitted and the 2025-09-07T06:49:23.7061383Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T06:49:23.7061455Z ```` is the temporary folder on the current platform and ```` 2025-09-07T06:49:23.7061529Z the name of the extension. This location can be overridden in two ways. 2025-09-07T06:49:23.7061602Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T06:49:23.7061680Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T06:49:23.7061756Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T06:49:23.7061837Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T06:49:23.7061900Z the library will be compiled into that folder directly. 2025-09-07T06:49:23.7061931Z 2025-09-07T06:49:23.7062006Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T06:49:23.7062091Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T06:49:23.7062169Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T06:49:23.7062246Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T06:49:23.7062321Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T06:49:23.7063727Z ``extra_cflags`` to pass further include directories. 2025-09-07T06:49:23.7063759Z 2025-09-07T06:49:23.7063843Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T06:49:23.7063910Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T06:49:23.7064042Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T06:49:23.7064117Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T06:49:23.7064177Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T06:49:23.7064249Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T06:49:23.7064330Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T06:49:23.7064447Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T06:49:23.7064483Z safest option. 2025-09-07T06:49:23.7064513Z 2025-09-07T06:49:23.7064593Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T06:49:23.7064664Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T06:49:23.7064740Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T06:49:23.7064841Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T06:49:23.7064913Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T06:49:23.7065006Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T06:49:23.7065039Z variable. 2025-09-07T06:49:23.7065070Z 2025-09-07T06:49:23.7065101Z Args: 2025-09-07T06:49:23.7066548Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T06:49:23.7066593Z name of the pybind11 module! 2025-09-07T06:49:23.7066666Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T06:49:23.7066745Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T06:49:23.7066822Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T06:49:23.7066864Z when building CUDA sources. 2025-09-07T06:49:23.7066939Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T06:49:23.7066993Z compiler when building SYCL sources. 2025-09-07T06:49:23.7067068Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T06:49:23.7067146Z extra_include_paths: optional list of include directories to forward 2025-09-07T06:49:23.7067183Z to the build. 2025-09-07T06:49:23.7067250Z build_directory: optional path to use as build workspace. 2025-09-07T06:49:23.7067317Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T06:49:23.7067394Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T06:49:23.7067456Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:23.7067528Z automatically determined based on the existence of ``.cu`` or 2025-09-07T06:49:23.7067589Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T06:49:23.7067634Z and libraries to be included. 2025-09-07T06:49:23.7067711Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T06:49:23.7067772Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:23.7067846Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T06:49:23.7067907Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T06:49:23.7069299Z libraries to be included. 2025-09-07T06:49:23.7069374Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T06:49:23.7069445Z library as a Python module. If ``False``, behavior depends on 2025-09-07T06:49:23.7069485Z ``is_standalone``. 2025-09-07T06:49:23.7069557Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T06:49:23.7069625Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T06:49:23.7069667Z standalone executable. 2025-09-07T06:49:23.7069698Z 2025-09-07T06:49:23.7069773Z Returns: 2025-09-07T06:49:23.7069820Z If ``is_python_module`` is ``True``: 2025-09-07T06:49:23.7069886Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T06:49:23.7069917Z 2025-09-07T06:49:23.7069990Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T06:49:23.7070065Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T06:49:23.7070102Z a side effect.) 2025-09-07T06:49:23.7070132Z 2025-09-07T06:49:23.7070175Z If ``is_standalone`` is ``True``. 2025-09-07T06:49:23.7070246Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T06:49:23.7070309Z added to the PATH environment variable as a side effect.) 2025-09-07T06:49:23.7070339Z 2025-09-07T06:49:23.7070371Z Example: 2025-09-07T06:49:23.7071835Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7071898Z >>> from torch.utils.cpp_extension import load 2025-09-07T06:49:23.7071935Z >>> module = load( 2025-09-07T06:49:23.7071994Z ... name='extension', 2025-09-07T06:49:23.7072056Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T06:49:23.7072096Z ... extra_cflags=['-O2'], 2025-09-07T06:49:23.7072133Z ... verbose=True) 2025-09-07T06:49:23.7072164Z 2025-09-07T06:49:23.7072252Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7072282Z 2025-09-07T06:49:23.7072319Z warnings.warn(msg) 2025-09-07T06:49:23.7072350Z 2025-09-07T06:49:23.7072422Z --- Parse Warning: 61 / 146 --- 2025-09-07T06:49:23.7072714Z /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=1885. 2025-09-07T06:49:23.7072812Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7072842Z 2025-09-07T06:49:23.7072920Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T06:49:23.7072950Z 2025-09-07T06:49:23.7073030Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T06:49:23.7073111Z strings rather than filenames. These strings are stored to files in the 2025-09-07T06:49:23.7073186Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T06:49:23.7073227Z identical to :func:`load`. 2025-09-07T06:49:23.7074668Z 2025-09-07T06:49:23.7074703Z See `the 2025-09-07T06:49:23.7074820Z tests `_ 2025-09-07T06:49:23.7074870Z for good examples of using this function. 2025-09-07T06:49:23.7074899Z 2025-09-07T06:49:23.7074983Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T06:49:23.7075073Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T06:49:23.7075163Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T06:49:23.7075232Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T06:49:23.7075271Z `` 2025-09-07T06:49:23.7075300Z 2025-09-07T06:49:23.7075379Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T06:49:23.7075463Z automatically generated for each function specified. ``functions`` can 2025-09-07T06:49:23.7075539Z either be a list of function names, or a dictionary mapping from function 2025-09-07T06:49:23.7075617Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T06:49:23.7075652Z as its docstring. 2025-09-07T06:49:23.7075683Z 2025-09-07T06:49:23.7075759Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T06:49:23.7075873Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T06:49:23.7075948Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T06:49:23.7076027Z separately, but ultimately linked into a single library. Note that no 2025-09-07T06:49:23.7076106Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T06:49:23.7077548Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T06:49:23.7077623Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T06:49:23.7077667Z include its name in ``functions``). 2025-09-07T06:49:23.7077697Z 2025-09-07T06:49:23.7077776Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T06:49:23.7077853Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T06:49:23.7077950Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T06:49:23.7078028Z linked into a single library. Note that no bindings are generated for 2025-09-07T06:49:23.7078121Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T06:49:23.7078194Z create a C++ function that calls it, and either declare or define this 2025-09-07T06:49:23.7078262Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T06:49:23.7078298Z in ``functions``). 2025-09-07T06:49:23.7078329Z 2025-09-07T06:49:23.7078358Z 2025-09-07T06:49:23.7078388Z 2025-09-07T06:49:23.7078456Z See :func:`load` for a description of arguments omitted below. 2025-09-07T06:49:23.7078486Z 2025-09-07T06:49:23.7078518Z Args: 2025-09-07T06:49:23.7078595Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T06:49:23.7078672Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T06:49:23.7078753Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T06:49:23.7080174Z functions: A list of function names for which to generate function 2025-09-07T06:49:23.7080253Z bindings. If a dictionary is given, it should map function names to 2025-09-07T06:49:23.7080319Z docstrings (which are otherwise just the function names). 2025-09-07T06:49:23.7080396Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T06:49:23.7080456Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:23.7080527Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T06:49:23.7080587Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T06:49:23.7080630Z and libraries to be included. 2025-09-07T06:49:23.7080705Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T06:49:23.7080764Z the build. If set to ``None`` (default), this value is 2025-09-07T06:49:23.7080837Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T06:49:23.7080894Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T06:49:23.7080938Z and libraries to be included. 2025-09-07T06:49:23.7081010Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T06:49:23.7081082Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T06:49:23.7081159Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T06:49:23.7081230Z function. This redirection might cause issues in obscure cases 2025-09-07T06:49:23.7081296Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T06:49:23.7081334Z causes issues. 2025-09-07T06:49:23.7081424Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T06:49:23.7081541Z ``#include `` and ``#include `` lines. 2025-09-07T06:49:23.7081602Z Use this option to improve cold start times when you 2025-09-07T06:49:23.7083071Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T06:49:23.7083103Z 2025-09-07T06:49:23.7083136Z Example: 2025-09-07T06:49:23.7083194Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T06:49:23.7083255Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T06:49:23.7083291Z >>> source = """ 2025-09-07T06:49:23.7083349Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T06:49:23.7083390Z return x.sin() + y.sin(); 2025-09-07T06:49:23.7083422Z } 2025-09-07T06:49:23.7083453Z """ 2025-09-07T06:49:23.7083507Z >>> module = load_inline(name='inline_extension', 2025-09-07T06:49:23.7083577Z ... cpp_sources=[source], 2025-09-07T06:49:23.7083627Z ... functions=['sin_add']) 2025-09-07T06:49:23.7083658Z 2025-09-07T06:49:23.7083706Z .. note:: 2025-09-07T06:49:23.7083792Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T06:49:23.7083872Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T06:49:23.7083950Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T06:49:23.7084035Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T06:49:23.7084122Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T06:49:23.7084205Z are not included when you install torch and must be additionally installed. 2025-09-07T06:49:23.7085649Z 2025-09-07T06:49:23.7085745Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T06:49:23.7085828Z the extension. This may use up too many resources on some systems. One 2025-09-07T06:49:23.7085905Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T06:49:23.7085955Z variable to a non-negative number. 2025-09-07T06:49:23.7085985Z 2025-09-07T06:49:23.7086072Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7086103Z 2025-09-07T06:49:23.7086140Z warnings.warn(msg) 2025-09-07T06:49:23.7086170Z 2025-09-07T06:49:23.7086245Z --- Parse Warning: 62 / 146 --- 2025-09-07T06:49:23.7086557Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T06:49:23.7086649Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7086741Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T06:49:23.7086772Z 2025-09-07T06:49:23.7086806Z Args: 2025-09-07T06:49:23.7086892Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T06:49:23.7086946Z num_samples (int): number of samples to draw 2025-09-07T06:49:23.7087021Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T06:49:23.7087093Z If not, they are drawn without replacement, which means that when a 2025-09-07T06:49:23.7087172Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T06:49:23.7087231Z generator (Generator): Generator used in sampling. 2025-09-07T06:49:23.7088616Z 2025-09-07T06:49:23.7088651Z Example: 2025-09-07T06:49:23.7088708Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.7088743Z >>> list( 2025-09-07T06:49:23.7088841Z ... WeightedRandomSampler( 2025-09-07T06:49:23.7088898Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T06:49:23.7088933Z ... ) 2025-09-07T06:49:23.7088965Z ... ) 2025-09-07T06:49:23.7089000Z [4, 4, 1, 4, 5] 2025-09-07T06:49:23.7089034Z >>> list( 2025-09-07T06:49:23.7089077Z ... WeightedRandomSampler( 2025-09-07T06:49:23.7089131Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T06:49:23.7089163Z ... ) 2025-09-07T06:49:23.7089194Z ... ) 2025-09-07T06:49:23.7089228Z [0, 1, 4, 3, 2] 2025-09-07T06:49:23.7089260Z 2025-09-07T06:49:23.7089347Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7089378Z 2025-09-07T06:49:23.7089414Z warnings.warn(msg) 2025-09-07T06:49:23.7089463Z 2025-09-07T06:49:23.7089535Z --- Parse Warning: 63 / 146 --- 2025-09-07T06:49:23.7091185Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T06:49:23.7091301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7091367Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T06:49:23.7091397Z 2025-09-07T06:49:23.7091429Z Args: 2025-09-07T06:49:23.7091510Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T06:49:23.7091559Z batch_size (int): Size of mini-batch. 2025-09-07T06:49:23.7091632Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T06:49:23.7091684Z its size would be less than ``batch_size`` 2025-09-07T06:49:23.7091717Z 2025-09-07T06:49:23.7091750Z Example: 2025-09-07T06:49:23.7091787Z >>> list( 2025-09-07T06:49:23.7091827Z ... BatchSampler( 2025-09-07T06:49:23.7091901Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T06:49:23.7091933Z ... ) 2025-09-07T06:49:23.7091965Z ... ) 2025-09-07T06:49:23.7092005Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T06:49:23.7092038Z >>> list( 2025-09-07T06:49:23.7092128Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T06:49:23.7092161Z ... ) 2025-09-07T06:49:23.7092200Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T06:49:23.7092231Z 2025-09-07T06:49:23.7093665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7093698Z 2025-09-07T06:49:23.7093735Z warnings.warn(msg) 2025-09-07T06:49:23.7093767Z 2025-09-07T06:49:23.7093839Z --- Parse Warning: 64 / 146 --- 2025-09-07T06:49:23.7094159Z /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. 2025-09-07T06:49:23.7094251Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7094326Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T06:49:23.7094356Z 2025-09-07T06:49:23.7094456Z It is especially useful in conjunction with 2025-09-07T06:49:23.7094546Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T06:49:23.7094640Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T06:49:23.7094722Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T06:49:23.7094771Z original dataset that is exclusive to it. 2025-09-07T06:49:23.7094849Z 2025-09-07T06:49:23.7094883Z .. note:: 2025-09-07T06:49:23.7094966Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T06:49:23.7095021Z returns the same elements in the same order. 2025-09-07T06:49:23.7095051Z 2025-09-07T06:49:23.7095083Z Args: 2025-09-07T06:49:23.7095130Z dataset: Dataset used for sampling. 2025-09-07T06:49:23.7095207Z num_replicas (int, optional): Number of processes participating in 2025-09-07T06:49:23.7096655Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T06:49:23.7096701Z current distributed group. 2025-09-07T06:49:23.7096787Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T06:49:23.7096860Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T06:49:23.7096924Z group. 2025-09-07T06:49:23.7097012Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T06:49:23.7097067Z indices. 2025-09-07T06:49:23.7097138Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T06:49:23.7097209Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T06:49:23.7097270Z processes in the distributed group. Default: ``0``. 2025-09-07T06:49:23.7097347Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T06:49:23.7097416Z tail of the data to make it evenly divisible across the number of 2025-09-07T06:49:23.7097487Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T06:49:23.7097558Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T06:49:23.7097591Z 2025-09-07T06:49:23.7097626Z .. warning:: 2025-09-07T06:49:23.7097701Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T06:49:23.7097791Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T06:49:23.7097880Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T06:49:23.7097929Z the same ordering will be always used. 2025-09-07T06:49:23.7097959Z 2025-09-07T06:49:23.7099335Z Example:: 2025-09-07T06:49:23.7099366Z 2025-09-07T06:49:23.7099408Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7099486Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T06:49:23.7099552Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T06:49:23.7099600Z ... sampler=sampler) 2025-09-07T06:49:23.7099652Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T06:49:23.7099696Z ... if is_distributed: 2025-09-07T06:49:23.7099748Z ... sampler.set_epoch(epoch) 2025-09-07T06:49:23.7099787Z ... train(loader) 2025-09-07T06:49:23.7099819Z 2025-09-07T06:49:23.7099906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7099937Z 2025-09-07T06:49:23.7099973Z warnings.warn(msg) 2025-09-07T06:49:23.7100003Z 2025-09-07T06:49:23.7100076Z --- Parse Warning: 65 / 146 --- 2025-09-07T06:49:23.7100397Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T06:49:23.7100486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7100517Z 2025-09-07T06:49:23.7100560Z Iterable-style DataPipe. 2025-09-07T06:49:23.7100591Z 2025-09-07T06:49:23.7100716Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T06:49:23.7102155Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T06:49:23.7102283Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T06:49:23.7102388Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T06:49:23.7102417Z 2025-09-07T06:49:23.7102499Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T06:49:23.7102618Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T06:49:23.7102747Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T06:49:23.7102844Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T06:49:23.7102940Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T06:49:23.7102985Z 2025-09-07T06:49:23.7103016Z Note: 2025-09-07T06:49:23.7103093Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T06:49:23.7103211Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T06:49:23.7103337Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T06:49:23.7103429Z The code example below presents details on how this constraint looks in practice. 2025-09-07T06:49:23.7103555Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T06:49:23.7103585Z 2025-09-07T06:49:23.7103685Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T06:49:23.7103812Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T06:49:23.7103922Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T06:49:23.7103963Z operations in succession. 2025-09-07T06:49:23.7105415Z 2025-09-07T06:49:23.7105470Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T06:49:23.7105524Z https://github.com/pytorch/data/issues/45 2025-09-07T06:49:23.7105554Z 2025-09-07T06:49:23.7105586Z Note: 2025-09-07T06:49:23.7105668Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T06:49:23.7105765Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T06:49:23.7105843Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T06:49:23.7105927Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T06:49:23.7106014Z each copy independently to avoid having duplicate data returned from the 2025-09-07T06:49:23.7106101Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T06:49:23.7106189Z process, returns information about the worker. It can be used in either the 2025-09-07T06:49:23.7106276Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T06:49:23.7106345Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T06:49:23.7106376Z 2025-09-07T06:49:23.7106409Z Examples: 2025-09-07T06:49:23.7106445Z General Usage: 2025-09-07T06:49:23.7106484Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7106559Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T06:49:23.7106606Z >>> dp = IterableWrapper(range(10)) 2025-09-07T06:49:23.7106677Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T06:49:23.7106762Z >>> map_dp_2 = dp.map( 2025-09-07T06:49:23.7108172Z ... lambda x: x + 1 2025-09-07T06:49:23.7108227Z ... ) # Using functional form (recommended) 2025-09-07T06:49:23.7108267Z >>> list(map_dp_1) 2025-09-07T06:49:23.7108307Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T06:49:23.7108345Z >>> list(map_dp_2) 2025-09-07T06:49:23.7108383Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T06:49:23.7108443Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T06:49:23.7108483Z >>> list(filter_dp) 2025-09-07T06:49:23.7108519Z [2, 4, 6, 8, 10] 2025-09-07T06:49:23.7108565Z Single Iterator Constraint Example: 2025-09-07T06:49:23.7108637Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T06:49:23.7108688Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T06:49:23.7108754Z >>> it1 = iter(source_dp) 2025-09-07T06:49:23.7108796Z >>> list(it1) 2025-09-07T06:49:23.7108834Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T06:49:23.7108900Z >>> it1 = iter(source_dp) 2025-09-07T06:49:23.7108936Z >>> it2 = iter( 2025-09-07T06:49:23.7108974Z ... source_dp 2025-09-07T06:49:23.7109034Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T06:49:23.7109070Z >>> next(it2) 2025-09-07T06:49:23.7109101Z 0 2025-09-07T06:49:23.7110522Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T06:49:23.7110554Z 2025-09-07T06:49:23.7110643Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7110672Z 2025-09-07T06:49:23.7110712Z warnings.warn(msg) 2025-09-07T06:49:23.7110741Z 2025-09-07T06:49:23.7110815Z --- Parse Warning: 66 / 146 --- 2025-09-07T06:49:23.7111197Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T06:49:23.7111291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7111322Z 2025-09-07T06:49:23.7111463Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T06:49:23.7111494Z 2025-09-07T06:49:23.7111536Z (functional name: ``groupby``). 2025-09-07T06:49:23.7111567Z 2025-09-07T06:49:23.7111699Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T06:49:23.7111803Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T06:49:23.7111911Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T06:49:23.7112023Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T06:49:23.7112054Z 2025-09-07T06:49:23.7112182Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T06:49:23.7112292Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T06:49:23.7112323Z 2025-09-07T06:49:23.7113703Z Args: 2025-09-07T06:49:23.7113757Z datapipe: Iterable datapipe to be grouped 2025-09-07T06:49:23.7113858Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T06:49:23.7113941Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T06:49:23.7114006Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T06:49:23.7114100Z buffer_size: The size of buffer for ungrouped data 2025-09-07T06:49:23.7114199Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T06:49:23.7114315Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T06:49:23.7114502Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T06:49:23.7114542Z when the buffer is full 2025-09-07T06:49:23.7114572Z 2025-09-07T06:49:23.7114605Z Example: 2025-09-07T06:49:23.7114639Z >>> import os 2025-09-07T06:49:23.7114678Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7114745Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:23.7114784Z >>> def group_fn(file): 2025-09-07T06:49:23.7114841Z ... return os.path.basename(file).split(".")[0] 2025-09-07T06:49:23.7114913Z >>> source_dp = IterableWrapper( 2025-09-07T06:49:23.7114980Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T06:49:23.7115029Z ... ) 2025-09-07T06:49:23.7115084Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T06:49:23.7116478Z >>> list(dp0) 2025-09-07T06:49:23.7116544Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T06:49:23.7116613Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T06:49:23.7116681Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T06:49:23.7116715Z >>> list(dp1) 2025-09-07T06:49:23.7116777Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T06:49:23.7116897Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T06:49:23.7116939Z >>> dp2 = source_dp.groupby( 2025-09-07T06:49:23.7116983Z ... group_key_fn=group_fn, 2025-09-07T06:49:23.7117026Z ... buffer_size=3, 2025-09-07T06:49:23.7117063Z ... group_size=3, 2025-09-07T06:49:23.7117107Z ... guaranteed_group_size=2, 2025-09-07T06:49:23.7117138Z ... ) 2025-09-07T06:49:23.7117172Z >>> list(dp2) 2025-09-07T06:49:23.7117229Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T06:49:23.7117259Z 2025-09-07T06:49:23.7117347Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7117377Z 2025-09-07T06:49:23.7117414Z warnings.warn(msg) 2025-09-07T06:49:23.7117444Z 2025-09-07T06:49:23.7117516Z --- Parse Warning: 67 / 146 --- 2025-09-07T06:49:23.7117862Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T06:49:23.7119298Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7119333Z 2025-09-07T06:49:23.7119462Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T06:49:23.7119492Z 2025-09-07T06:49:23.7119523Z Args: 2025-09-07T06:49:23.7119586Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T06:49:23.7119651Z mode: An optional string that specifies the mode in which 2025-09-07T06:49:23.7119729Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T06:49:23.7119790Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T06:49:23.7119861Z encoding: An optional string that specifies the encoding of the 2025-09-07T06:49:23.7119954Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T06:49:23.7120003Z length: Nominal length of the datapipe 2025-09-07T06:49:23.7120078Z 2025-09-07T06:49:23.7120110Z Note: 2025-09-07T06:49:23.7120206Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T06:49:23.7120247Z to close them explicitly. 2025-09-07T06:49:23.7120278Z 2025-09-07T06:49:23.7120310Z Example: 2025-09-07T06:49:23.7120349Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7120399Z >>> from torchdata.datapipes.iter import ( 2025-09-07T06:49:23.7120437Z ... FileLister, 2025-09-07T06:49:23.7120473Z ... FileOpener, 2025-09-07T06:49:23.7121853Z ... StreamReader, 2025-09-07T06:49:23.7121885Z ... ) 2025-09-07T06:49:23.7121966Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T06:49:23.7122005Z >>> dp = FileOpener(dp) 2025-09-07T06:49:23.7122045Z >>> dp = StreamReader(dp) 2025-09-07T06:49:23.7122098Z >>> list(dp) 2025-09-07T06:49:23.7122141Z [('./abc.txt', 'abc')] 2025-09-07T06:49:23.7122171Z 2025-09-07T06:49:23.7122259Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7122305Z 2025-09-07T06:49:23.7122341Z warnings.warn(msg) 2025-09-07T06:49:23.7122372Z 2025-09-07T06:49:23.7122444Z --- Parse Warning: 68 / 146 --- 2025-09-07T06:49:23.7122793Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T06:49:23.7122884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7122915Z 2025-09-07T06:49:23.7123066Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T06:49:23.7123098Z 2025-09-07T06:49:23.7123171Z A list of the child DataPipes is returned from this operation. 2025-09-07T06:49:23.7123201Z 2025-09-07T06:49:23.7123233Z Args: 2025-09-07T06:49:23.7124690Z datapipe: Iterable DataPipe being filtered 2025-09-07T06:49:23.7124764Z num_instances: number of instances of the DataPipe to create 2025-09-07T06:49:23.7124894Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T06:49:23.7125006Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T06:49:23.7125118Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T06:49:23.7125185Z DataPipes while waiting for their values to be yielded. 2025-09-07T06:49:23.7125250Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T06:49:23.7125282Z 2025-09-07T06:49:23.7125315Z Examples: 2025-09-07T06:49:23.7125368Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:23.7125436Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:23.7125474Z >>> def odd_or_even(n): 2025-09-07T06:49:23.7125512Z ... return n % 2 2025-09-07T06:49:23.7125559Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T06:49:23.7125635Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T06:49:23.7125670Z >>> list(dp1) 2025-09-07T06:49:23.7125701Z [0, 2, 4] 2025-09-07T06:49:23.7125734Z >>> list(dp2) 2025-09-07T06:49:23.7125765Z [1, 3] 2025-09-07T06:49:23.7125849Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T06:49:23.7125890Z >>> def odd_or_even_no_zero(n): 2025-09-07T06:49:23.7125937Z ... return n % 2 if n != 0 else None 2025-09-07T06:49:23.7127327Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T06:49:23.7127458Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T06:49:23.7127493Z ... ) 2025-09-07T06:49:23.7127527Z >>> list(dp1) 2025-09-07T06:49:23.7127558Z [2, 4] 2025-09-07T06:49:23.7127592Z >>> list(dp2) 2025-09-07T06:49:23.7127623Z [1, 3] 2025-09-07T06:49:23.7127653Z 2025-09-07T06:49:23.7127741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7127771Z 2025-09-07T06:49:23.7127807Z warnings.warn(msg) 2025-09-07T06:49:23.7127837Z 2025-09-07T06:49:23.7127910Z --- Parse Warning: 69 / 146 --- 2025-09-07T06:49:23.7128258Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T06:49:23.7128370Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7128400Z 2025-09-07T06:49:23.7128526Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T06:49:23.7128556Z 2025-09-07T06:49:23.7128674Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T06:49:23.7128743Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T06:49:23.7128773Z 2025-09-07T06:49:23.7130167Z Args: 2025-09-07T06:49:23.7130308Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T06:49:23.7130338Z 2025-09-07T06:49:23.7130371Z Example: 2025-09-07T06:49:23.7130419Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:23.7130485Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:23.7130527Z >>> dp1, dp2, dp3 = ( 2025-09-07T06:49:23.7130576Z ... IterableWrapper(range(3)), 2025-09-07T06:49:23.7130625Z ... IterableWrapper(range(10, 15)), 2025-09-07T06:49:23.7130670Z ... IterableWrapper(range(20, 25)), 2025-09-07T06:49:23.7130701Z ... ) 2025-09-07T06:49:23.7130744Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T06:49:23.7130782Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T06:49:23.7130813Z 2025-09-07T06:49:23.7130901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7130931Z 2025-09-07T06:49:23.7130967Z warnings.warn(msg) 2025-09-07T06:49:23.7130996Z 2025-09-07T06:49:23.7131067Z --- Parse Warning: 70 / 146 --- 2025-09-07T06:49:23.7131403Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T06:49:23.7131498Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7131528Z 2025-09-07T06:49:23.7132982Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T06:49:23.7133015Z 2025-09-07T06:49:23.7133096Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T06:49:23.7133126Z 2025-09-07T06:49:23.7133158Z Args: 2025-09-07T06:49:23.7133215Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T06:49:23.7133246Z 2025-09-07T06:49:23.7133278Z Example: 2025-09-07T06:49:23.7133325Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T06:49:23.7133392Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T06:49:23.7133431Z >>> dp1, dp2, dp3 = ( 2025-09-07T06:49:23.7133474Z ... IterableWrapper(range(5)), 2025-09-07T06:49:23.7133555Z ... IterableWrapper(range(10, 15)), 2025-09-07T06:49:23.7133600Z ... IterableWrapper(range(20, 25)), 2025-09-07T06:49:23.7133633Z ... ) 2025-09-07T06:49:23.7133673Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T06:49:23.7133727Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T06:49:23.7133756Z 2025-09-07T06:49:23.7133844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7133874Z 2025-09-07T06:49:23.7133910Z warnings.warn(msg) 2025-09-07T06:49:23.7133940Z 2025-09-07T06:49:23.7135412Z --- Parse Warning: 71 / 146 --- 2025-09-07T06:49:23.7135712Z /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. 2025-09-07T06:49:23.7135840Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7135903Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T06:49:23.7135962Z 2025-09-07T06:49:23.7136090Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T06:49:23.7136119Z 2025-09-07T06:49:23.7136154Z .. math:: 2025-09-07T06:49:23.7136183Z 2025-09-07T06:49:23.7136310Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T06:49:23.7136339Z 2025-09-07T06:49:23.7136463Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T06:49:23.7136536Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T06:49:23.7136566Z 2025-09-07T06:49:23.7136637Z In addition, they are only considered close if they have the same 2025-09-07T06:49:23.7136668Z 2025-09-07T06:49:23.7136746Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T06:49:23.7136800Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T06:49:23.7136855Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T06:49:23.7136903Z - stride (if ``check_stride`` is ``True``). 2025-09-07T06:49:23.7136933Z 2025-09-07T06:49:23.7138393Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T06:49:23.7138425Z 2025-09-07T06:49:23.7138551Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T06:49:23.7138679Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T06:49:23.7138762Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T06:49:23.7138900Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T06:49:23.7138933Z 2025-09-07T06:49:23.7139033Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T06:49:23.7139154Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T06:49:23.7139192Z definition above. 2025-09-07T06:49:23.7139223Z 2025-09-07T06:49:23.7139331Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T06:49:23.7139462Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T06:49:23.7139586Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T06:49:23.7139758Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T06:49:23.7139839Z their elements are considered close according to the above definition. 2025-09-07T06:49:23.7139872Z 2025-09-07T06:49:23.7139905Z .. note:: 2025-09-07T06:49:23.7139934Z 2025-09-07T06:49:23.7140048Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T06:49:23.7140159Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T06:49:23.7140257Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T06:49:23.7141643Z 2025-09-07T06:49:23.7141676Z Args: 2025-09-07T06:49:23.7141719Z actual (Any): Actual input. 2025-09-07T06:49:23.7141766Z expected (Any): Expected input. 2025-09-07T06:49:23.7141910Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T06:49:23.7141973Z are allowed. Otherwise type equality is required. 2025-09-07T06:49:23.7142116Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T06:49:23.7142210Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T06:49:23.7142328Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T06:49:23.7142418Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T06:49:23.7142506Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T06:49:23.7142604Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T06:49:23.7142698Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T06:49:23.7142781Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T06:49:23.7142898Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T06:49:23.7143017Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T06:49:23.7143078Z :func:`torch.promote_types`) before being compared. 2025-09-07T06:49:23.7143198Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T06:49:23.7143310Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T06:49:23.7143347Z compared. 2025-09-07T06:49:23.7143471Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T06:49:23.7144991Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T06:49:23.7145119Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T06:49:23.7145166Z should return the new message. 2025-09-07T06:49:23.7145196Z 2025-09-07T06:49:23.7145228Z Raises: 2025-09-07T06:49:23.7145313Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T06:49:23.7145377Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T06:49:23.7145490Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T06:49:23.7145611Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T06:49:23.7145699Z different types. 2025-09-07T06:49:23.7145824Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T06:49:23.7145947Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T06:49:23.7146056Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T06:49:23.7146158Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T06:49:23.7146207Z :attr:`~torch.Tensor.layout`. 2025-09-07T06:49:23.7146283Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T06:49:23.7146412Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T06:49:23.7146529Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T06:49:23.7146576Z :attr:`~torch.Tensor.device`. 2025-09-07T06:49:23.7146705Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T06:49:23.7146824Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T06:49:23.7148296Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T06:49:23.7148328Z 2025-09-07T06:49:23.7148453Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T06:49:23.7148511Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T06:49:23.7148542Z 2025-09-07T06:49:23.7148598Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7148656Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T06:49:23.7148700Z +===========================+============+==========+ 2025-09-07T06:49:23.7148754Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T06:49:23.7148803Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7148854Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T06:49:23.7148901Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7148951Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7148999Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7149050Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T06:49:23.7149099Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7149150Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T06:49:23.7149203Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7149253Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7149301Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7149351Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T06:49:23.7149398Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7150784Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7150869Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7150918Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7150966Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7151014Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7151061Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7151112Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7151196Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7151248Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T06:49:23.7151295Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7151341Z | other | ``0.0`` | ``0.0`` | 2025-09-07T06:49:23.7151387Z +---------------------------+------------+----------+ 2025-09-07T06:49:23.7151417Z 2025-09-07T06:49:23.7151451Z .. note:: 2025-09-07T06:49:23.7151482Z 2025-09-07T06:49:23.7151610Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T06:49:23.7151729Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T06:49:23.7151823Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T06:49:23.7151870Z 2025-09-07T06:49:23.7151910Z >>> import functools 2025-09-07T06:49:23.7152004Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T06:49:23.7152063Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T06:49:23.7153508Z Traceback (most recent call last): 2025-09-07T06:49:23.7153540Z ... 2025-09-07T06:49:23.7153589Z AssertionError: Scalars are not equal! 2025-09-07T06:49:23.7153624Z 2025-09-07T06:49:23.7153669Z Expected 1e-10 but got 1e-09. 2025-09-07T06:49:23.7153718Z Absolute difference: 9.000000000000001e-10 2025-09-07T06:49:23.7153761Z Relative difference: 9.0 2025-09-07T06:49:23.7153790Z 2025-09-07T06:49:23.7153824Z Examples: 2025-09-07T06:49:23.7153868Z >>> # tensor to tensor comparison 2025-09-07T06:49:23.7153921Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T06:49:23.7153980Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T06:49:23.7154036Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7154069Z 2025-09-07T06:49:23.7154113Z >>> # scalar to scalar comparison 2025-09-07T06:49:23.7154151Z >>> import math 2025-09-07T06:49:23.7154192Z >>> expected = math.sqrt(2.0) 2025-09-07T06:49:23.7154235Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T06:49:23.7154289Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7154320Z 2025-09-07T06:49:23.7154370Z >>> # numpy array to numpy array comparison 2025-09-07T06:49:23.7155809Z >>> import numpy as np 2025-09-07T06:49:23.7155859Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T06:49:23.7155911Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T06:49:23.7155964Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7155997Z 2025-09-07T06:49:23.7156050Z >>> # sequence to sequence comparison 2025-09-07T06:49:23.7156092Z >>> import numpy as np 2025-09-07T06:49:23.7156185Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T06:49:23.7156236Z >>> # length and their elements have to match. 2025-09-07T06:49:23.7156297Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T06:49:23.7156340Z >>> actual = tuple(expected) 2025-09-07T06:49:23.7156393Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7156424Z 2025-09-07T06:49:23.7156470Z >>> # mapping to mapping comparison 2025-09-07T06:49:23.7156517Z >>> from collections import OrderedDict 2025-09-07T06:49:23.7156556Z >>> import numpy as np 2025-09-07T06:49:23.7156597Z >>> foo = torch.tensor(1.0) 2025-09-07T06:49:23.7156635Z >>> bar = 2.0 2025-09-07T06:49:23.7156727Z >>> baz = np.array(3.0) 2025-09-07T06:49:23.7156823Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T06:49:23.7156898Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T06:49:23.7156971Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T06:49:23.7158376Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T06:49:23.7158432Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7158462Z 2025-09-07T06:49:23.7158512Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T06:49:23.7158556Z >>> actual = expected.clone() 2025-09-07T06:49:23.7158620Z >>> # By default, directly related instances can be compared 2025-09-07T06:49:23.7158698Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T06:49:23.7158797Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T06:49:23.7158841Z >>> torch.testing.assert_close( 2025-09-07T06:49:23.7158933Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T06:49:23.7158965Z ... ) 2025-09-07T06:49:23.7159011Z Traceback (most recent call last): 2025-09-07T06:49:23.7159042Z ... 2025-09-07T06:49:23.7159117Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T06:49:23.7159196Z and . 2025-09-07T06:49:23.7159277Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T06:49:23.7159340Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T06:49:23.7159386Z Traceback (most recent call last): 2025-09-07T06:49:23.7159417Z ... 2025-09-07T06:49:23.7159525Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T06:49:23.7159567Z and . 2025-09-07T06:49:23.7161006Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T06:49:23.7161055Z >>> # their type if check_dtype=False. 2025-09-07T06:49:23.7161119Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T06:49:23.7161149Z 2025-09-07T06:49:23.7161191Z >>> # NaN != NaN by default. 2025-09-07T06:49:23.7161241Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T06:49:23.7161284Z >>> actual = expected.clone() 2025-09-07T06:49:23.7161339Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T06:49:23.7161383Z Traceback (most recent call last): 2025-09-07T06:49:23.7161415Z ... 2025-09-07T06:49:23.7161462Z AssertionError: Scalars are not close! 2025-09-07T06:49:23.7161500Z 2025-09-07T06:49:23.7161546Z Expected nan but got nan. 2025-09-07T06:49:23.7161602Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T06:49:23.7161658Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T06:49:23.7161730Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T06:49:23.7161760Z 2025-09-07T06:49:23.7161809Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T06:49:23.7161855Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T06:49:23.7161911Z >>> # The default error message can be overwritten. 2025-09-07T06:49:23.7161954Z >>> torch.testing.assert_close( 2025-09-07T06:49:23.7162022Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T06:49:23.7163389Z ... ) 2025-09-07T06:49:23.7163437Z Traceback (most recent call last): 2025-09-07T06:49:23.7163469Z ... 2025-09-07T06:49:23.7163560Z AssertionError: Argh, the tensors are not close! 2025-09-07T06:49:23.7163640Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T06:49:23.7163683Z >>> # extra information 2025-09-07T06:49:23.7163727Z >>> torch.testing.assert_close( 2025-09-07T06:49:23.7163800Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T06:49:23.7163832Z ... ) 2025-09-07T06:49:23.7163877Z Traceback (most recent call last): 2025-09-07T06:49:23.7163908Z ... 2025-09-07T06:49:23.7163948Z AssertionError: Header 2025-09-07T06:49:23.7163984Z 2025-09-07T06:49:23.7164025Z Tensor-likes are not close! 2025-09-07T06:49:23.7164061Z 2025-09-07T06:49:23.7164104Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T06:49:23.7164205Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T06:49:23.7164288Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T06:49:23.7164337Z 2025-09-07T06:49:23.7164370Z Footer 2025-09-07T06:49:23.7164442Z 2025-09-07T06:49:23.7164529Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7165920Z 2025-09-07T06:49:23.7165959Z warnings.warn(msg) 2025-09-07T06:49:23.7165990Z 2025-09-07T06:49:23.7166066Z --- Parse Warning: 72 / 146 --- 2025-09-07T06:49:23.7166410Z /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. 2025-09-07T06:49:23.7166503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7166536Z 2025-09-07T06:49:23.7166618Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T06:49:23.7166692Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T06:49:23.7166777Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T06:49:23.7166839Z its samples are on simplex, and are reparametrizable. 2025-09-07T06:49:23.7166869Z 2025-09-07T06:49:23.7166904Z Example:: 2025-09-07T06:49:23.7166933Z 2025-09-07T06:49:23.7166986Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.7167045Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T06:49:23.7167094Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T06:49:23.7167131Z >>> m.sample() 2025-09-07T06:49:23.7167179Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T06:49:23.7167210Z 2025-09-07T06:49:23.7167242Z Args: 2025-09-07T06:49:23.7168648Z temperature (Tensor): relaxation temperature 2025-09-07T06:49:23.7168697Z probs (Tensor): event probabilities 2025-09-07T06:49:23.7168769Z logits (Tensor): unnormalized log probability for each event 2025-09-07T06:49:23.7168799Z 2025-09-07T06:49:23.7168888Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7168918Z 2025-09-07T06:49:23.7168957Z warnings.warn(msg) 2025-09-07T06:49:23.7168986Z 2025-09-07T06:49:23.7169059Z --- Parse Warning: 73 / 146 --- 2025-09-07T06:49:23.7169388Z /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. 2025-09-07T06:49:23.7169480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7169511Z 2025-09-07T06:49:23.7169635Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T06:49:23.7169721Z distribution where all component are from different parameterizations of 2025-09-07T06:49:23.7169798Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T06:49:23.7169871Z "selecting distribution" (over `k` component) and a component 2025-09-07T06:49:23.7169946Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T06:49:23.7170006Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T06:49:23.7170036Z 2025-09-07T06:49:23.7170072Z Examples:: 2025-09-07T06:49:23.7170102Z 2025-09-07T06:49:23.7170149Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7171565Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T06:49:23.7171612Z >>> # weighted normal distributions 2025-09-07T06:49:23.7171658Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T06:49:23.7171744Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T06:49:23.7171790Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:23.7171839Z 2025-09-07T06:49:23.7171909Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T06:49:23.7171960Z >>> # weighted bivariate normal distributions 2025-09-07T06:49:23.7172004Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T06:49:23.7172048Z >>> comp = D.Independent(D.Normal( 2025-09-07T06:49:23.7172100Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T06:49:23.7172146Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:23.7172176Z 2025-09-07T06:49:23.7172244Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T06:49:23.7172316Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T06:49:23.7172363Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T06:49:23.7172411Z >>> comp = D.Independent(D.Normal( 2025-09-07T06:49:23.7172465Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T06:49:23.7172511Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T06:49:23.7172541Z 2025-09-07T06:49:23.7172573Z Args: 2025-09-07T06:49:23.7173995Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T06:49:23.7174066Z instance. Manages the probability of selecting component. 2025-09-07T06:49:23.7174130Z The number of categories must match the rightmost batch 2025-09-07T06:49:23.7174198Z dimension of the `component_distribution`. Must have either 2025-09-07T06:49:23.7174253Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T06:49:23.7174308Z `component_distribution.batch_shape[:-1]` 2025-09-07T06:49:23.7174465Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T06:49:23.7174533Z instance. Right-most batch dimension indexes component. 2025-09-07T06:49:23.7174569Z 2025-09-07T06:49:23.7174658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7174690Z 2025-09-07T06:49:23.7174728Z warnings.warn(msg) 2025-09-07T06:49:23.7174758Z 2025-09-07T06:49:23.7174831Z --- Parse Warning: 74 / 146 --- 2025-09-07T06:49:23.7175152Z /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. 2025-09-07T06:49:23.7175245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7175275Z 2025-09-07T06:49:23.7175342Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T06:49:23.7175411Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T06:49:23.7175533Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T06:49:23.7175603Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T06:49:23.7175634Z 2025-09-07T06:49:23.7177032Z Example:: 2025-09-07T06:49:23.7177064Z 2025-09-07T06:49:23.7177118Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.7177169Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T06:49:23.7177218Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T06:49:23.7177254Z >>> m.sample() 2025-09-07T06:49:23.7177301Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T06:49:23.7177332Z 2025-09-07T06:49:23.7177363Z Args: 2025-09-07T06:49:23.7177416Z temperature (Tensor): relaxation temperature 2025-09-07T06:49:23.7177482Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T06:49:23.7177542Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T06:49:23.7177596Z 2025-09-07T06:49:23.7177689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7177739Z 2025-09-07T06:49:23.7177776Z warnings.warn(msg) 2025-09-07T06:49:23.7177806Z 2025-09-07T06:49:23.7177876Z --- Parse Warning: 75 / 146 --- 2025-09-07T06:49:23.7178232Z /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. 2025-09-07T06:49:23.7178322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7178353Z 2025-09-07T06:49:23.7178458Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T06:49:23.7179884Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T06:49:23.7179917Z 2025-09-07T06:49:23.7179989Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T06:49:23.7180019Z 2025-09-07T06:49:23.7180057Z Example: 2025-09-07T06:49:23.7180113Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T06:49:23.7180167Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.7180214Z >>> m = LowRankMultivariateNormal( 2025-09-07T06:49:23.7180281Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T06:49:23.7180312Z ... ) 2025-09-07T06:49:23.7180416Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T06:49:23.7180457Z tensor([-0.2102, -0.5429]) 2025-09-07T06:49:23.7180486Z 2025-09-07T06:49:23.7180518Z Args: 2025-09-07T06:49:23.7180603Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T06:49:23.7180699Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T06:49:23.7180747Z `batch_shape + event_shape + (rank,)` 2025-09-07T06:49:23.7180838Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T06:49:23.7180880Z `batch_shape + event_shape` 2025-09-07T06:49:23.7180911Z 2025-09-07T06:49:23.7180942Z Note: 2025-09-07T06:49:23.7182387Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T06:49:23.7182476Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T06:49:23.7182554Z `_ and 2025-09-07T06:49:23.7182658Z `matrix determinant lemma `_. 2025-09-07T06:49:23.7182747Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T06:49:23.7182797Z the small size "capacitance" matrix:: 2025-09-07T06:49:23.7182865Z 2025-09-07T06:49:23.7182933Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T06:49:23.7182965Z 2025-09-07T06:49:23.7183052Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7183082Z 2025-09-07T06:49:23.7183119Z warnings.warn(msg) 2025-09-07T06:49:23.7183149Z 2025-09-07T06:49:23.7183220Z --- Parse Warning: 76 / 146 --- 2025-09-07T06:49:23.7183526Z /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. 2025-09-07T06:49:23.7183617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7183647Z 2025-09-07T06:49:23.7183724Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T06:49:23.7183773Z 2025-09-07T06:49:23.7183831Z The test is performed with ``actual == desired``. 2025-09-07T06:49:23.7183877Z 2025-09-07T06:49:23.7183912Z Parameters 2025-09-07T06:49:23.7185361Z ---------- 2025-09-07T06:49:23.7185400Z test_string : str 2025-09-07T06:49:23.7185448Z The message supplied to AssertionError. 2025-09-07T06:49:23.7185483Z actual : object 2025-09-07T06:49:23.7185542Z The object to test for equality against `desired`. 2025-09-07T06:49:23.7185578Z desired : object 2025-09-07T06:49:23.7185618Z The expected result. 2025-09-07T06:49:23.7185648Z 2025-09-07T06:49:23.7185680Z Examples 2025-09-07T06:49:23.7185713Z -------- 2025-09-07T06:49:23.7185757Z >>> np.testing.print_assert_equal( 2025-09-07T06:49:23.7185804Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T06:49:23.7185842Z ... ) # doctest: +SKIP 2025-09-07T06:49:23.7185885Z >>> np.testing.print_assert_equal( 2025-09-07T06:49:23.7185934Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T06:49:23.7185970Z ... ) # doctest: +SKIP 2025-09-07T06:49:23.7186015Z Traceback (most recent call last): 2025-09-07T06:49:23.7186047Z ... 2025-09-07T06:49:23.7186096Z AssertionError: Test XYZ of func xyz failed 2025-09-07T06:49:23.7186127Z ACTUAL: 2025-09-07T06:49:23.7186159Z [0, 1] 2025-09-07T06:49:23.7186191Z DESIRED: 2025-09-07T06:49:23.7187571Z [0, 2] 2025-09-07T06:49:23.7187601Z 2025-09-07T06:49:23.7187632Z 2025-09-07T06:49:23.7187723Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7187753Z 2025-09-07T06:49:23.7187790Z warnings.warn(msg) 2025-09-07T06:49:23.7187820Z 2025-09-07T06:49:23.7187891Z --- Parse Warning: 77 / 146 --- 2025-09-07T06:49:23.7188200Z /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. 2025-09-07T06:49:23.7188292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7188325Z 2025-09-07T06:49:23.7188400Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T06:49:23.7188434Z precision. 2025-09-07T06:49:23.7188463Z 2025-09-07T06:49:23.7188527Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:23.7188596Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:23.7188664Z instead of this function for more consistent floating point 2025-09-07T06:49:23.7188702Z comparisons. 2025-09-07T06:49:23.7188732Z 2025-09-07T06:49:23.7188812Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T06:49:23.7188841Z 2025-09-07T06:49:23.7190243Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T06:49:23.7190276Z 2025-09-07T06:49:23.7190408Z That is a looser test than originally documented, but agrees with what the 2025-09-07T06:49:23.7190492Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T06:49:23.7190574Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T06:49:23.7190621Z delegates to assert_array_almost_equal 2025-09-07T06:49:23.7190651Z 2025-09-07T06:49:23.7190685Z Parameters 2025-09-07T06:49:23.7190719Z ---------- 2025-09-07T06:49:23.7190755Z actual : array_like 2025-09-07T06:49:23.7190795Z The object to check. 2025-09-07T06:49:23.7190883Z desired : array_like 2025-09-07T06:49:23.7190921Z The expected object. 2025-09-07T06:49:23.7190960Z decimal : int, optional 2025-09-07T06:49:23.7191004Z Desired precision, default is 7. 2025-09-07T06:49:23.7191042Z err_msg : str, optional 2025-09-07T06:49:23.7191122Z The error message to be printed in case of failure. 2025-09-07T06:49:23.7191164Z verbose : bool, optional 2025-09-07T06:49:23.7191237Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:23.7191288Z 2025-09-07T06:49:23.7191319Z Raises 2025-09-07T06:49:23.7191351Z ------ 2025-09-07T06:49:23.7192759Z AssertionError 2025-09-07T06:49:23.7192838Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:23.7192868Z 2025-09-07T06:49:23.7192900Z See Also 2025-09-07T06:49:23.7192932Z -------- 2025-09-07T06:49:23.7193019Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:23.7193070Z relative and/or absolute precision. 2025-09-07T06:49:23.7193146Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:23.7193176Z 2025-09-07T06:49:23.7193208Z Examples 2025-09-07T06:49:23.7193239Z -------- 2025-09-07T06:49:23.7193304Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T06:49:23.7193361Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T06:49:23.7193427Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T06:49:23.7193471Z Traceback (most recent call last): 2025-09-07T06:49:23.7193501Z ... 2025-09-07T06:49:23.7193537Z AssertionError: 2025-09-07T06:49:23.7193586Z Arrays are not almost equal to 10 decimals 2025-09-07T06:49:23.7193623Z ACTUAL: 2.3333333333333 2025-09-07T06:49:23.7193658Z DESIRED: 2.33333334 2025-09-07T06:49:23.7193688Z 2025-09-07T06:49:23.7193726Z >>> assert_almost_equal( 2025-09-07T06:49:23.7195229Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T06:49:23.7195262Z ... ) 2025-09-07T06:49:23.7195305Z Traceback (most recent call last): 2025-09-07T06:49:23.7195336Z ... 2025-09-07T06:49:23.7195372Z AssertionError: 2025-09-07T06:49:23.7195421Z Arrays are not almost equal to 9 decimals 2025-09-07T06:49:23.7195460Z 2025-09-07T06:49:23.7195502Z Mismatched elements: 1 / 2 (50%) 2025-09-07T06:49:23.7195557Z Max absolute difference: 6.666699636781459e-09 2025-09-07T06:49:23.7195608Z Max relative difference: 2.8571569790287484e-09 2025-09-07T06:49:23.7195662Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T06:49:23.7195715Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T06:49:23.7195745Z 2025-09-07T06:49:23.7195775Z 2025-09-07T06:49:23.7195863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7195893Z 2025-09-07T06:49:23.7195929Z warnings.warn(msg) 2025-09-07T06:49:23.7195959Z 2025-09-07T06:49:23.7196030Z --- Parse Warning: 78 / 146 --- 2025-09-07T06:49:23.7196382Z /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. 2025-09-07T06:49:23.7196473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7197868Z 2025-09-07T06:49:23.7197950Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T06:49:23.7197982Z digits. 2025-09-07T06:49:23.7198011Z 2025-09-07T06:49:23.7198076Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:23.7198142Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:23.7198209Z instead of this function for more consistent floating point 2025-09-07T06:49:23.7198247Z comparisons. 2025-09-07T06:49:23.7198277Z 2025-09-07T06:49:23.7198345Z Given two numbers, check that they are approximately equal. 2025-09-07T06:49:23.7198424Z Approximately equal is defined as the number of significant digits 2025-09-07T06:49:23.7198481Z that agree. 2025-09-07T06:49:23.7198511Z 2025-09-07T06:49:23.7198549Z Parameters 2025-09-07T06:49:23.7198582Z ---------- 2025-09-07T06:49:23.7198635Z actual : scalar 2025-09-07T06:49:23.7198673Z The object to check. 2025-09-07T06:49:23.7198708Z desired : scalar 2025-09-07T06:49:23.7198747Z The expected object. 2025-09-07T06:49:23.7198789Z significant : int, optional 2025-09-07T06:49:23.7198833Z Desired precision, default is 7. 2025-09-07T06:49:23.7198871Z err_msg : str, optional 2025-09-07T06:49:23.7200282Z The error message to be printed in case of failure. 2025-09-07T06:49:23.7200322Z verbose : bool, optional 2025-09-07T06:49:23.7200395Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:23.7200426Z 2025-09-07T06:49:23.7200457Z Raises 2025-09-07T06:49:23.7200489Z ------ 2025-09-07T06:49:23.7200524Z AssertionError 2025-09-07T06:49:23.7200597Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:23.7200633Z 2025-09-07T06:49:23.7200665Z See Also 2025-09-07T06:49:23.7200697Z -------- 2025-09-07T06:49:23.7200781Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:23.7200831Z relative and/or absolute precision. 2025-09-07T06:49:23.7200904Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:23.7200935Z 2025-09-07T06:49:23.7200966Z Examples 2025-09-07T06:49:23.7200998Z -------- 2025-09-07T06:49:23.7201042Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:23.7201086Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T06:49:23.7201123Z ... ) # doctest: +SKIP 2025-09-07T06:49:23.7201167Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:23.7201202Z ... 0.12345670e-20, 2025-09-07T06:49:23.7202590Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T06:49:23.7202632Z ... significant=8, 2025-09-07T06:49:23.7202664Z ... ) 2025-09-07T06:49:23.7202710Z >>> np.testing.assert_approx_equal( 2025-09-07T06:49:23.7202745Z ... 0.12345670e-20, 2025-09-07T06:49:23.7202791Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T06:49:23.7202827Z ... significant=8, 2025-09-07T06:49:23.7202858Z ... ) 2025-09-07T06:49:23.7202901Z Traceback (most recent call last): 2025-09-07T06:49:23.7202932Z ... 2025-09-07T06:49:23.7202966Z AssertionError: 2025-09-07T06:49:23.7203017Z Items are not equal to 8 significant digits: 2025-09-07T06:49:23.7203054Z ACTUAL: 1.234567e-21 2025-09-07T06:49:23.7203092Z DESIRED: 1.2345672e-21 2025-09-07T06:49:23.7203122Z 2025-09-07T06:49:23.7203182Z the evaluated condition that raises the exception is 2025-09-07T06:49:23.7203212Z 2025-09-07T06:49:23.7203284Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T06:49:23.7203316Z True 2025-09-07T06:49:23.7203346Z 2025-09-07T06:49:23.7203376Z 2025-09-07T06:49:23.7204917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7204953Z 2025-09-07T06:49:23.7204991Z warnings.warn(msg) 2025-09-07T06:49:23.7205022Z 2025-09-07T06:49:23.7205094Z --- Parse Warning: 79 / 146 --- 2025-09-07T06:49:23.7205397Z /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. 2025-09-07T06:49:23.7205487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7205518Z 2025-09-07T06:49:23.7205591Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T06:49:23.7205621Z 2025-09-07T06:49:23.7205695Z Given two array_like objects, check that the shape is equal and all 2025-09-07T06:49:23.7205794Z elements of these objects are equal (but see the Notes for the special 2025-09-07T06:49:23.7205869Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T06:49:23.7205971Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T06:49:23.7206047Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T06:49:23.7206090Z NaNs in the same positions. 2025-09-07T06:49:23.7206119Z 2025-09-07T06:49:23.7206199Z The usual caution for verifying equality with floating point numbers is 2025-09-07T06:49:23.7206230Z advised. 2025-09-07T06:49:23.7206260Z 2025-09-07T06:49:23.7206293Z Parameters 2025-09-07T06:49:23.7206325Z ---------- 2025-09-07T06:49:23.7207719Z x : array_like 2025-09-07T06:49:23.7207764Z The actual object to check. 2025-09-07T06:49:23.7207798Z y : array_like 2025-09-07T06:49:23.7207843Z The desired, expected object. 2025-09-07T06:49:23.7207886Z err_msg : str, optional 2025-09-07T06:49:23.7207949Z The error message to be printed in case of failure. 2025-09-07T06:49:23.7207988Z verbose : bool, optional 2025-09-07T06:49:23.7208061Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:23.7208101Z strict : bool, optional 2025-09-07T06:49:23.7208172Z If True, raise an AssertionError when either the shape or the data 2025-09-07T06:49:23.7208239Z type of the array_like objects does not match. The special 2025-09-07T06:49:23.7208311Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T06:49:23.7208341Z 2025-09-07T06:49:23.7208372Z Raises 2025-09-07T06:49:23.7208405Z ------ 2025-09-07T06:49:23.7208438Z AssertionError 2025-09-07T06:49:23.7208491Z If actual and desired objects are not equal. 2025-09-07T06:49:23.7208521Z 2025-09-07T06:49:23.7208552Z See Also 2025-09-07T06:49:23.7208584Z -------- 2025-09-07T06:49:23.7208669Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:23.7208724Z relative and/or absolute precision. 2025-09-07T06:49:23.7210144Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:23.7210176Z 2025-09-07T06:49:23.7210207Z Notes 2025-09-07T06:49:23.7210240Z ----- 2025-09-07T06:49:23.7210308Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T06:49:23.7210388Z function checks that each element of the array_like object is equal to 2025-09-07T06:49:23.7210467Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T06:49:23.7210498Z 2025-09-07T06:49:23.7210530Z Examples 2025-09-07T06:49:23.7210562Z -------- 2025-09-07T06:49:23.7210613Z The first assert does not raise an exception: 2025-09-07T06:49:23.7210644Z 2025-09-07T06:49:23.7210686Z >>> np.testing.assert_array_equal( 2025-09-07T06:49:23.7210746Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T06:49:23.7210777Z ... ) 2025-09-07T06:49:23.7210843Z 2025-09-07T06:49:23.7210923Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T06:49:23.7210966Z functions for these cases instead: 2025-09-07T06:49:23.7210997Z 2025-09-07T06:49:23.7211038Z >>> np.testing.assert_allclose( 2025-09-07T06:49:23.7211113Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T06:49:23.7212504Z ... ) 2025-09-07T06:49:23.7212537Z 2025-09-07T06:49:23.7212612Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T06:49:23.7212691Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T06:49:23.7212721Z 2025-09-07T06:49:23.7212764Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T06:49:23.7212810Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T06:49:23.7212840Z 2025-09-07T06:49:23.7212934Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T06:49:23.7212971Z array: 2025-09-07T06:49:23.7213001Z 2025-09-07T06:49:23.7213077Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T06:49:23.7213119Z Traceback (most recent call last): 2025-09-07T06:49:23.7213151Z ... 2025-09-07T06:49:23.7213185Z AssertionError: 2025-09-07T06:49:23.7213223Z Arrays are not equal 2025-09-07T06:49:23.7213256Z 2025-09-07T06:49:23.7213297Z (shapes (2, 5), () mismatch) 2025-09-07T06:49:23.7213339Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T06:49:23.7213375Z [3, 3, 3, 3, 3]]) 2025-09-07T06:49:23.7213413Z y: torch.ndarray(3) 2025-09-07T06:49:23.7213443Z 2025-09-07T06:49:23.7214924Z The `strict` parameter also ensures that the array data types match: 2025-09-07T06:49:23.7214956Z 2025-09-07T06:49:23.7214996Z >>> x = np.array([2, 2, 2]) 2025-09-07T06:49:23.7215052Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T06:49:23.7215115Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T06:49:23.7215157Z Traceback (most recent call last): 2025-09-07T06:49:23.7215190Z ... 2025-09-07T06:49:23.7215223Z AssertionError: 2025-09-07T06:49:23.7215261Z Arrays are not equal 2025-09-07T06:49:23.7215293Z 2025-09-07T06:49:23.7215351Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T06:49:23.7215391Z x: torch.ndarray([2, 2, 2]) 2025-09-07T06:49:23.7215431Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T06:49:23.7215461Z 2025-09-07T06:49:23.7215550Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7215580Z 2025-09-07T06:49:23.7215617Z warnings.warn(msg) 2025-09-07T06:49:23.7215647Z 2025-09-07T06:49:23.7215719Z --- Parse Warning: 80 / 146 --- 2025-09-07T06:49:23.7216037Z /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. 2025-09-07T06:49:23.7216130Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7216161Z 2025-09-07T06:49:23.7217588Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T06:49:23.7217624Z precision. 2025-09-07T06:49:23.7217654Z 2025-09-07T06:49:23.7217719Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T06:49:23.7217787Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T06:49:23.7217855Z instead of this function for more consistent floating point 2025-09-07T06:49:23.7217892Z comparisons. 2025-09-07T06:49:23.7217922Z 2025-09-07T06:49:23.7218006Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T06:49:23.7218045Z ``desired`` satisfy. 2025-09-07T06:49:23.7218074Z 2025-09-07T06:49:23.7218173Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T06:49:23.7218206Z 2025-09-07T06:49:23.7218285Z That is a looser test than originally documented, but agrees with what the 2025-09-07T06:49:23.7218369Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T06:49:23.7218448Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T06:49:23.7218525Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T06:49:23.7218573Z objects have NaNs in the same positions. 2025-09-07T06:49:23.7218604Z 2025-09-07T06:49:23.7218636Z Parameters 2025-09-07T06:49:23.7218669Z ---------- 2025-09-07T06:49:23.7218701Z x : array_like 2025-09-07T06:49:23.7220094Z The actual object to check. 2025-09-07T06:49:23.7220129Z y : array_like 2025-09-07T06:49:23.7220172Z The desired, expected object. 2025-09-07T06:49:23.7220235Z decimal : int, optional 2025-09-07T06:49:23.7220283Z Desired precision, default is 6. 2025-09-07T06:49:23.7220321Z err_msg : str, optional 2025-09-07T06:49:23.7220402Z The error message to be printed in case of failure. 2025-09-07T06:49:23.7220441Z verbose : bool, optional 2025-09-07T06:49:23.7220514Z If True, the conflicting values are appended to the error message. 2025-09-07T06:49:23.7220544Z 2025-09-07T06:49:23.7220575Z Raises 2025-09-07T06:49:23.7220607Z ------ 2025-09-07T06:49:23.7220641Z AssertionError 2025-09-07T06:49:23.7220712Z If actual and desired are not equal up to specified precision. 2025-09-07T06:49:23.7220742Z 2025-09-07T06:49:23.7220774Z See Also 2025-09-07T06:49:23.7220805Z -------- 2025-09-07T06:49:23.7220888Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T06:49:23.7220936Z relative and/or absolute precision. 2025-09-07T06:49:23.7221016Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T06:49:23.7221046Z 2025-09-07T06:49:23.7222429Z Examples 2025-09-07T06:49:23.7222464Z -------- 2025-09-07T06:49:23.7222515Z the first assert does not raise an exception 2025-09-07T06:49:23.7222545Z 2025-09-07T06:49:23.7222635Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T06:49:23.7222665Z 2025-09-07T06:49:23.7222712Z >>> np.testing.assert_array_almost_equal( 2025-09-07T06:49:23.7222772Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T06:49:23.7222803Z ... ) 2025-09-07T06:49:23.7222846Z Traceback (most recent call last): 2025-09-07T06:49:23.7222876Z ... 2025-09-07T06:49:23.7222911Z AssertionError: 2025-09-07T06:49:23.7222957Z Arrays are not almost equal to 5 decimals 2025-09-07T06:49:23.7222990Z 2025-09-07T06:49:23.7223031Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T06:49:23.7223088Z Max absolute difference: 5.999999999994898e-05 2025-09-07T06:49:23.7223138Z Max relative difference: 2.5713661239633743e-05 2025-09-07T06:49:23.7223203Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T06:49:23.7223264Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T06:49:23.7223295Z 2025-09-07T06:49:23.7223340Z >>> np.testing.assert_array_almost_equal( 2025-09-07T06:49:23.7223393Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T06:49:23.7224822Z ... ) 2025-09-07T06:49:23.7224869Z Traceback (most recent call last): 2025-09-07T06:49:23.7224900Z ... 2025-09-07T06:49:23.7224935Z AssertionError: 2025-09-07T06:49:23.7224980Z Arrays are not almost equal to 5 decimals 2025-09-07T06:49:23.7225013Z 2025-09-07T06:49:23.7225055Z x and y nan location mismatch: 2025-09-07T06:49:23.7225114Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T06:49:23.7225223Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T06:49:23.7225254Z 2025-09-07T06:49:23.7225284Z 2025-09-07T06:49:23.7225373Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7225403Z 2025-09-07T06:49:23.7225440Z warnings.warn(msg) 2025-09-07T06:49:23.7225470Z 2025-09-07T06:49:23.7225542Z --- Parse Warning: 81 / 146 --- 2025-09-07T06:49:23.7225857Z /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. 2025-09-07T06:49:23.7225947Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7226023Z Context manager that resets warning registry for catching warnings 2025-09-07T06:49:23.7226052Z 2025-09-07T06:49:23.7226163Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T06:49:23.7226241Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T06:49:23.7227708Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T06:49:23.7227796Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T06:49:23.7227850Z as a keyword argument to its constructor and: 2025-09-07T06:49:23.7227880Z 2025-09-07T06:49:23.7227961Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T06:49:23.7227994Z on entry; 2025-09-07T06:49:23.7228063Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T06:49:23.7228093Z 2025-09-07T06:49:23.7228171Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T06:49:23.7228237Z manager without disturbing the state of warnings outside. 2025-09-07T06:49:23.7228271Z 2025-09-07T06:49:23.7228357Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T06:49:23.7228396Z keyword-only. 2025-09-07T06:49:23.7228426Z 2025-09-07T06:49:23.7228460Z Parameters 2025-09-07T06:49:23.7228495Z ---------- 2025-09-07T06:49:23.7228535Z record : bool, optional 2025-09-07T06:49:23.7228604Z Specifies whether warnings should be captured by a custom 2025-09-07T06:49:23.7228687Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T06:49:23.7228760Z returned by the context manager. Otherwise None is returned by the 2025-09-07T06:49:23.7228839Z context manager. The objects appended to the list are arguments whose 2025-09-07T06:49:23.7230248Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T06:49:23.7230292Z modules : sequence, optional 2025-09-07T06:49:23.7230373Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T06:49:23.7230447Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T06:49:23.7230493Z filter by one of these modules. 2025-09-07T06:49:23.7230523Z 2025-09-07T06:49:23.7230557Z Examples 2025-09-07T06:49:23.7230590Z -------- 2025-09-07T06:49:23.7230628Z >>> import warnings 2025-09-07T06:49:23.7230696Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T06:49:23.7230743Z ... modules=[np.core.fromnumeric] 2025-09-07T06:49:23.7230774Z ... ): 2025-09-07T06:49:23.7230863Z ... warnings.simplefilter("always") 2025-09-07T06:49:23.7230943Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T06:49:23.7231010Z ... # do something that raises a warning but ignore those in 2025-09-07T06:49:23.7231052Z ... # np.core.fromnumeric 2025-09-07T06:49:23.7231084Z 2025-09-07T06:49:23.7231207Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7231237Z 2025-09-07T06:49:23.7231277Z warnings.warn(msg) 2025-09-07T06:49:23.7231306Z 2025-09-07T06:49:23.7231377Z --- Parse Warning: 82 / 146 --- 2025-09-07T06:49:23.7233054Z /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=701. 2025-09-07T06:49:23.7233144Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T06:49:23.7233174Z 2025-09-07T06:49:23.7233269Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T06:49:23.7233362Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T06:49:23.7233424Z ``mesh_dim_names`` 2025-09-07T06:49:23.7233454Z 2025-09-07T06:49:23.7233492Z Args: 2025-09-07T06:49:23.7233575Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T06:49:23.7233662Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T06:49:23.7233695Z Returns: 2025-09-07T06:49:23.7233737Z A :class:`DeviceMesh` object 2025-09-07T06:49:23.7233766Z 2025-09-07T06:49:23.7233867Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T06:49:23.7233908Z In the first example: 2025-09-07T06:49:23.7234002Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T06:49:23.7234089Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T06:49:23.7234168Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T06:49:23.7234249Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T06:49:23.7234325Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T06:49:23.7234458Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T06:49:23.7235843Z 2025-09-07T06:49:23.7235884Z In the second example: 2025-09-07T06:49:23.7235978Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T06:49:23.7236067Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T06:49:23.7236155Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T06:49:23.7236241Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T06:49:23.7236272Z 2025-09-07T06:49:23.7236309Z Example:: 2025-09-07T06:49:23.7236340Z 2025-09-07T06:49:23.7236388Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:23.7236454Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T06:49:23.7236487Z >>> 2025-09-07T06:49:23.7236560Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T06:49:23.7236617Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T06:49:23.7236708Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T06:49:23.7236752Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T06:49:23.7236791Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T06:49:23.7236823Z >>> 2025-09-07T06:49:23.7236860Z >>> # Initialize a 3D mesh. 2025-09-07T06:49:23.7236959Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T06:49:23.7237063Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T06:49:23.7237154Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T06:49:23.7238549Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T06:49:23.7238582Z 2025-09-07T06:49:23.7238813Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-09-07T06:49:23.7238844Z 2025-09-07T06:49:23.7238933Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T06:49:23.7238982Z ^ 2025-09-07T06:49:23.7239020Z warnings.warn(msg) 2025-09-07T06:49:23.7239050Z 2025-09-07T06:49:23.7239122Z --- Parse Warning: 83 / 146 --- 2025-09-07T06:49:23.7239444Z /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=2705. 2025-09-07T06:49:23.7239580Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7239611Z 2025-09-07T06:49:23.7239697Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T06:49:23.7239727Z 2025-09-07T06:49:23.7239814Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T06:49:23.7239887Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T06:49:23.7239917Z 2025-09-07T06:49:23.7239949Z Args: 2025-09-07T06:49:23.7240031Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T06:49:23.7240112Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T06:49:23.7240187Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T06:49:23.7241581Z remote end. 2025-09-07T06:49:23.7241613Z 2025-09-07T06:49:23.7241645Z Returns: 2025-09-07T06:49:23.7241734Z A list of distributed request objects returned by calling the corresponding 2025-09-07T06:49:23.7241772Z op in the op_list. 2025-09-07T06:49:23.7241802Z 2025-09-07T06:49:23.7241835Z Examples: 2025-09-07T06:49:23.7241879Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:23.7241951Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T06:49:23.7242010Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T06:49:23.7242088Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T06:49:23.7242130Z >>> recv_op = dist.P2POp( 2025-09-07T06:49:23.7242200Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T06:49:23.7242232Z ... ) 2025-09-07T06:49:23.7242286Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T06:49:23.7242328Z >>> for req in reqs: 2025-09-07T06:49:23.7242365Z >>> req.wait() 2025-09-07T06:49:23.7242402Z >>> recv_tensor 2025-09-07T06:49:23.7242439Z tensor([2, 3]) # Rank 0 2025-09-07T06:49:23.7242475Z tensor([0, 1]) # Rank 1 2025-09-07T06:49:23.7242505Z 2025-09-07T06:49:23.7242590Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T06:49:23.7244006Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T06:49:23.7244051Z lead to unexpected hang issues. 2025-09-07T06:49:23.7244081Z 2025-09-07T06:49:23.7244156Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T06:49:23.7244239Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T06:49:23.7244318Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T06:49:23.7244521Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T06:49:23.7244597Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T06:49:23.7244631Z 2025-09-07T06:49:23.7244716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7244747Z 2025-09-07T06:49:23.7244784Z warnings.warn(msg) 2025-09-07T06:49:23.7244814Z 2025-09-07T06:49:23.7244886Z --- Parse Warning: 84 / 146 --- 2025-09-07T06:49:23.7245195Z /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=2837. 2025-09-07T06:49:23.7245286Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7245317Z 2025-09-07T06:49:23.7245432Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T06:49:23.7245463Z 2025-09-07T06:49:23.7245541Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T06:49:23.7245590Z 2025-09-07T06:49:23.7245633Z Complex tensors are supported. 2025-09-07T06:49:23.7247029Z 2025-09-07T06:49:23.7247062Z Args: 2025-09-07T06:49:23.7247137Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T06:49:23.7247178Z operates in-place. 2025-09-07T06:49:23.7247225Z op (optional): One of the values from 2025-09-07T06:49:23.7247275Z ``torch.distributed.ReduceOp`` 2025-09-07T06:49:23.7247349Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T06:49:23.7247429Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7247479Z the default process group will be used. 2025-09-07T06:49:23.7247553Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T06:49:23.7247587Z 2025-09-07T06:49:23.7247619Z Returns: 2025-09-07T06:49:23.7247674Z Async work handle, if async_op is set to True. 2025-09-07T06:49:23.7247732Z None, if not async_op or if not part of the group 2025-09-07T06:49:23.7247762Z 2025-09-07T06:49:23.7247794Z Examples: 2025-09-07T06:49:23.7247837Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T06:49:23.7247890Z >>> # All tensors below are of torch.int64 type. 2025-09-07T06:49:23.7247936Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7247984Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T06:49:23.7248064Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T06:49:23.7249439Z >>> tensor 2025-09-07T06:49:23.7249486Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7249530Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7249589Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T06:49:23.7249623Z >>> tensor 2025-09-07T06:49:23.7249667Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7249710Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7249740Z 2025-09-07T06:49:23.7249792Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T06:49:23.7249838Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7249879Z >>> tensor = torch.tensor( 2025-09-07T06:49:23.7249937Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T06:49:23.7249975Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T06:49:23.7250008Z >>> tensor 2025-09-07T06:49:23.7250062Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7250115Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7250166Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T06:49:23.7250199Z >>> tensor 2025-09-07T06:49:23.7250283Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7250334Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7250364Z 2025-09-07T06:49:23.7250394Z 2025-09-07T06:49:23.7251840Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7251873Z 2025-09-07T06:49:23.7251910Z warnings.warn(msg) 2025-09-07T06:49:23.7251940Z 2025-09-07T06:49:23.7252014Z --- Parse Warning: 85 / 146 --- 2025-09-07T06:49:23.7252330Z /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=3201. 2025-09-07T06:49:23.7252422Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7252471Z 2025-09-07T06:49:23.7252557Z Gathers picklable objects from the whole group in a single process. 2025-09-07T06:49:23.7252588Z 2025-09-07T06:49:23.7252670Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T06:49:23.7252746Z object must be picklable in order to be gathered. 2025-09-07T06:49:23.7252777Z 2025-09-07T06:49:23.7252807Z Args: 2025-09-07T06:49:23.7252857Z obj (Any): Input object. Must be picklable. 2025-09-07T06:49:23.7252935Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T06:49:23.7253005Z should be correctly sized as the size of the group for this 2025-09-07T06:49:23.7253081Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T06:49:23.7253124Z ranks. (default is ``None``) 2025-09-07T06:49:23.7253234Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T06:49:23.7253309Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T06:49:23.7253389Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7254881Z the default process group will be used. Default is ``None``. 2025-09-07T06:49:23.7255002Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T06:49:23.7255033Z 2025-09-07T06:49:23.7255065Z Returns: 2025-09-07T06:49:23.7255132Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T06:49:23.7255173Z output of the collective. 2025-09-07T06:49:23.7255204Z 2025-09-07T06:49:23.7255280Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T06:49:23.7255357Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T06:49:23.7255389Z call. 2025-09-07T06:49:23.7255421Z 2025-09-07T06:49:23.7255504Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T06:49:23.7255579Z of objects must be moved to the GPU device before communication takes 2025-09-07T06:49:23.7255637Z place. In this case, the device used is given by 2025-09-07T06:49:23.7255714Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T06:49:23.7255788Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T06:49:23.7255831Z ``torch.cuda.set_device()``. 2025-09-07T06:49:23.7255860Z 2025-09-07T06:49:23.7255894Z .. warning:: 2025-09-07T06:49:23.7255976Z Object collectives have a number of serious performance and scalability 2025-09-07T06:49:23.7256042Z limitations. See :ref:`object_collectives` for details. 2025-09-07T06:49:23.7257429Z 2025-09-07T06:49:23.7257464Z .. warning:: 2025-09-07T06:49:23.7257539Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T06:49:23.7257668Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T06:49:23.7257745Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T06:49:23.7257789Z function with data you trust. 2025-09-07T06:49:23.7257820Z 2025-09-07T06:49:23.7257852Z .. warning:: 2025-09-07T06:49:23.7257928Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T06:49:23.7258006Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T06:49:23.7258069Z pickled. Please consider using :func:`gather` instead. 2025-09-07T06:49:23.7258099Z 2025-09-07T06:49:23.7258133Z Example:: 2025-09-07T06:49:23.7258185Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T06:49:23.7258252Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T06:49:23.7258298Z >>> import torch.distributed as dist 2025-09-07T06:49:23.7258359Z >>> # Assumes world_size of 3. 2025-09-07T06:49:23.7258427Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T06:49:23.7258494Z >>> output = [None for _ in gather_objects] 2025-09-07T06:49:23.7258534Z >>> dist.gather_object( 2025-09-07T06:49:23.7258581Z ... gather_objects[dist.get_rank()], 2025-09-07T06:49:23.7258632Z ... output if dist.get_rank() == 0 else None, 2025-09-07T06:49:23.7260023Z ... dst=0 2025-09-07T06:49:23.7260056Z ... ) 2025-09-07T06:49:23.7260092Z >>> # On rank 0 2025-09-07T06:49:23.7260125Z >>> output 2025-09-07T06:49:23.7260161Z ['foo', 12, {1: 2}] 2025-09-07T06:49:23.7260192Z 2025-09-07T06:49:23.7260282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7260313Z 2025-09-07T06:49:23.7260349Z warnings.warn(msg) 2025-09-07T06:49:23.7260379Z 2025-09-07T06:49:23.7260456Z --- Parse Warning: 86 / 146 --- 2025-09-07T06:49:23.7260769Z /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=3849. 2025-09-07T06:49:23.7260862Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7260892Z 2025-09-07T06:49:23.7260945Z Gathers tensors from the whole group in a list. 2025-09-07T06:49:23.7260975Z 2025-09-07T06:49:23.7261028Z Complex and uneven sized tensors are supported. 2025-09-07T06:49:23.7261058Z 2025-09-07T06:49:23.7261088Z Args: 2025-09-07T06:49:23.7261156Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T06:49:23.7261231Z correctly-sized tensors to be used for output of the collective. 2025-09-07T06:49:23.7262622Z Uneven sized tensors are supported. 2025-09-07T06:49:23.7262702Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T06:49:23.7262783Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7262833Z the default process group will be used. 2025-09-07T06:49:23.7262904Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T06:49:23.7262934Z 2025-09-07T06:49:23.7262965Z Returns: 2025-09-07T06:49:23.7263019Z Async work handle, if async_op is set to True. 2025-09-07T06:49:23.7263074Z None, if not async_op or if not part of the group 2025-09-07T06:49:23.7263105Z 2025-09-07T06:49:23.7263137Z Examples: 2025-09-07T06:49:23.7263189Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T06:49:23.7263239Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T06:49:23.7263285Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7263330Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T06:49:23.7263369Z >>> tensor_list = [ 2025-09-07T06:49:23.7263479Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T06:49:23.7263512Z ... ] 2025-09-07T06:49:23.7263547Z >>> tensor_list 2025-09-07T06:49:23.7263621Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T06:49:23.7263689Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T06:49:23.7263770Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T06:49:23.7265200Z >>> tensor 2025-09-07T06:49:23.7265249Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7265292Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7265337Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T06:49:23.7265371Z >>> tensor_list 2025-09-07T06:49:23.7265442Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T06:49:23.7265544Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T06:49:23.7265593Z 2025-09-07T06:49:23.7265647Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T06:49:23.7265691Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7265729Z >>> tensor_list = [ 2025-09-07T06:49:23.7265803Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T06:49:23.7265834Z ... ] 2025-09-07T06:49:23.7265869Z >>> tensor_list 2025-09-07T06:49:23.7265957Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T06:49:23.7266039Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T06:49:23.7266081Z >>> tensor = torch.tensor( 2025-09-07T06:49:23.7266137Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T06:49:23.7266176Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T06:49:23.7266213Z >>> tensor 2025-09-07T06:49:23.7266266Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T06:49:23.7266317Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T06:49:23.7267725Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T06:49:23.7267762Z >>> tensor_list 2025-09-07T06:49:23.7267846Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T06:49:23.7267925Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T06:49:23.7267955Z 2025-09-07T06:49:23.7267985Z 2025-09-07T06:49:23.7268076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7268106Z 2025-09-07T06:49:23.7268143Z warnings.warn(msg) 2025-09-07T06:49:23.7268175Z 2025-09-07T06:49:23.7268248Z --- Parse Warning: 87 / 146 --- 2025-09-07T06:49:23.7268566Z /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=4555. 2025-09-07T06:49:23.7268661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7268690Z 2025-09-07T06:49:23.7268777Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T06:49:23.7268807Z 2025-09-07T06:49:23.7268896Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T06:49:23.7268944Z and returned as a single output tensor. 2025-09-07T06:49:23.7268973Z 2025-09-07T06:49:23.7269016Z Complex tensors are supported. 2025-09-07T06:49:23.7269046Z 2025-09-07T06:49:23.7270420Z Args: 2025-09-07T06:49:23.7270489Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T06:49:23.7270579Z input (Tensor): Input tensor to scatter. 2025-09-07T06:49:23.7270659Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T06:49:23.7270737Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T06:49:23.7270779Z equally by ``world_size``. 2025-09-07T06:49:23.7270900Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T06:49:23.7270971Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T06:49:23.7271013Z equally by ``world_size``. 2025-09-07T06:49:23.7271092Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7271142Z the default process group will be used. 2025-09-07T06:49:23.7271213Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T06:49:23.7271261Z 2025-09-07T06:49:23.7271292Z Returns: 2025-09-07T06:49:23.7271350Z Async work handle, if async_op is set to True. 2025-09-07T06:49:23.7271408Z None, if not async_op or if not part of the group. 2025-09-07T06:49:23.7271454Z 2025-09-07T06:49:23.7271488Z .. warning:: 2025-09-07T06:49:23.7271556Z `all_to_all_single` is experimental and subject to change. 2025-09-07T06:49:23.7271587Z 2025-09-07T06:49:23.7271619Z Examples: 2025-09-07T06:49:23.7271666Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T06:49:23.7273073Z >>> input = torch.arange(4) + rank * 4 2025-09-07T06:49:23.7273109Z >>> input 2025-09-07T06:49:23.7273151Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T06:49:23.7273191Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T06:49:23.7273230Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T06:49:23.7273268Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T06:49:23.7273324Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T06:49:23.7273380Z >>> dist.all_to_all_single(output, input) 2025-09-07T06:49:23.7273413Z >>> output 2025-09-07T06:49:23.7273451Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T06:49:23.7273491Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T06:49:23.7273528Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T06:49:23.7273564Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T06:49:23.7273594Z 2025-09-07T06:49:23.7273656Z >>> # Essentially, it is similar to following operation: 2025-09-07T06:49:23.7273710Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T06:49:23.7273762Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T06:49:23.7273804Z >>> for i in range(world_size): 2025-09-07T06:49:23.7273891Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T06:49:23.7273921Z 2025-09-07T06:49:23.7273969Z >>> # Another example with uneven split 2025-09-07T06:49:23.7274003Z >>> input 2025-09-07T06:49:23.7275487Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T06:49:23.7275550Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T06:49:23.7275606Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T06:49:23.7275662Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T06:49:23.7275701Z >>> input_splits 2025-09-07T06:49:23.7275749Z [2, 2, 1, 1] # Rank 0 2025-09-07T06:49:23.7275795Z [3, 2, 2, 2] # Rank 1 2025-09-07T06:49:23.7275839Z [2, 1, 1, 1] # Rank 2 2025-09-07T06:49:23.7275884Z [2, 2, 2, 1] # Rank 3 2025-09-07T06:49:23.7275923Z >>> output_splits 2025-09-07T06:49:23.7276011Z [2, 3, 2, 2] # Rank 0 2025-09-07T06:49:23.7276056Z [2, 2, 1, 2] # Rank 1 2025-09-07T06:49:23.7276102Z [1, 2, 1, 2] # Rank 2 2025-09-07T06:49:23.7276146Z [1, 2, 1, 1] # Rank 3 2025-09-07T06:49:23.7276182Z >>> output = ... 2025-09-07T06:49:23.7276256Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T06:49:23.7276289Z >>> output 2025-09-07T06:49:23.7276344Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T06:49:23.7276400Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T06:49:23.7276455Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T06:49:23.7276530Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T06:49:23.7277916Z 2025-09-07T06:49:23.7277948Z 2025-09-07T06:49:23.7278032Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T06:49:23.7278073Z >>> input = torch.tensor( 2025-09-07T06:49:23.7278129Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T06:49:23.7278168Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T06:49:23.7278200Z >>> input 2025-09-07T06:49:23.7278266Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T06:49:23.7278326Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T06:49:23.7278390Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T06:49:23.7278453Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T06:49:23.7278505Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T06:49:23.7278561Z >>> dist.all_to_all_single(output, input) 2025-09-07T06:49:23.7278593Z >>> output 2025-09-07T06:49:23.7278655Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T06:49:23.7278716Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T06:49:23.7278777Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T06:49:23.7278836Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T06:49:23.7278867Z 2025-09-07T06:49:23.7278953Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7278984Z 2025-09-07T06:49:23.7279020Z warnings.warn(msg) 2025-09-07T06:49:23.7280397Z 2025-09-07T06:49:23.7280471Z --- Parse Warning: 88 / 146 --- 2025-09-07T06:49:23.7280782Z /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=4697. 2025-09-07T06:49:23.7280876Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7280907Z 2025-09-07T06:49:23.7281033Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T06:49:23.7281064Z 2025-09-07T06:49:23.7281106Z Complex tensors are supported. 2025-09-07T06:49:23.7281137Z 2025-09-07T06:49:23.7281168Z Args: 2025-09-07T06:49:23.7281247Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T06:49:23.7281281Z per rank. 2025-09-07T06:49:23.7281361Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T06:49:23.7281440Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7281491Z the default process group will be used. 2025-09-07T06:49:23.7281605Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T06:49:23.7281636Z 2025-09-07T06:49:23.7281669Z Returns: 2025-09-07T06:49:23.7281722Z Async work handle, if async_op is set to True. 2025-09-07T06:49:23.7281779Z None, if not async_op or if not part of the group. 2025-09-07T06:49:23.7281809Z 2025-09-07T06:49:23.7283191Z .. warning:: 2025-09-07T06:49:23.7283253Z `all_to_all` is experimental and subject to change. 2025-09-07T06:49:23.7283284Z 2025-09-07T06:49:23.7283316Z Examples: 2025-09-07T06:49:23.7283363Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T06:49:23.7283409Z >>> input = torch.arange(4) + rank * 4 2025-09-07T06:49:23.7283453Z >>> input = list(input.chunk(4)) 2025-09-07T06:49:23.7283485Z >>> input 2025-09-07T06:49:23.7283549Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T06:49:23.7283634Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T06:49:23.7283693Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T06:49:23.7283769Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T06:49:23.7283837Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T06:49:23.7283881Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:23.7283915Z >>> output 2025-09-07T06:49:23.7283971Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T06:49:23.7284029Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T06:49:23.7284088Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T06:49:23.7284145Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T06:49:23.7284175Z 2025-09-07T06:49:23.7284236Z >>> # Essentially, it is similar to following operation: 2025-09-07T06:49:23.7284281Z >>> scatter_list = input 2025-09-07T06:49:23.7285742Z >>> gather_list = output 2025-09-07T06:49:23.7285788Z >>> for i in range(world_size): 2025-09-07T06:49:23.7285870Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T06:49:23.7285901Z 2025-09-07T06:49:23.7285932Z >>> input 2025-09-07T06:49:23.7285990Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T06:49:23.7286047Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T06:49:23.7286103Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T06:49:23.7286158Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T06:49:23.7286195Z >>> input_splits 2025-09-07T06:49:23.7286239Z [2, 2, 1, 1] # Rank 0 2025-09-07T06:49:23.7286287Z [3, 2, 2, 2] # Rank 1 2025-09-07T06:49:23.7286333Z [2, 1, 1, 1] # Rank 2 2025-09-07T06:49:23.7286378Z [2, 2, 2, 1] # Rank 3 2025-09-07T06:49:23.7286414Z >>> output_splits 2025-09-07T06:49:23.7286459Z [2, 3, 2, 2] # Rank 0 2025-09-07T06:49:23.7286502Z [2, 2, 1, 2] # Rank 1 2025-09-07T06:49:23.7286546Z [1, 2, 1, 2] # Rank 2 2025-09-07T06:49:23.7286590Z [1, 2, 1, 1] # Rank 3 2025-09-07T06:49:23.7286639Z >>> input = list(input.split(input_splits)) 2025-09-07T06:49:23.7286671Z >>> input 2025-09-07T06:49:23.7288139Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T06:49:23.7288226Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T06:49:23.7288298Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T06:49:23.7288367Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T06:49:23.7288404Z >>> output = ... 2025-09-07T06:49:23.7288449Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:23.7288482Z >>> output 2025-09-07T06:49:23.7288551Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T06:49:23.7288620Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T06:49:23.7288689Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T06:49:23.7288779Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T06:49:23.7288809Z 2025-09-07T06:49:23.7288890Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T06:49:23.7288930Z >>> input = torch.tensor( 2025-09-07T06:49:23.7288985Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T06:49:23.7289022Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T06:49:23.7289065Z >>> input = list(input.chunk(4)) 2025-09-07T06:49:23.7289097Z >>> input 2025-09-07T06:49:23.7289172Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T06:49:23.7289244Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T06:49:23.7289319Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T06:49:23.7289398Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T06:49:23.7290834Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T06:49:23.7290882Z >>> dist.all_to_all(output, input) 2025-09-07T06:49:23.7290915Z >>> output 2025-09-07T06:49:23.7290989Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T06:49:23.7291060Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T06:49:23.7291132Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T06:49:23.7291202Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T06:49:23.7291233Z 2025-09-07T06:49:23.7291262Z 2025-09-07T06:49:23.7291352Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7291382Z 2025-09-07T06:49:23.7291419Z warnings.warn(msg) 2025-09-07T06:49:23.7291450Z 2025-09-07T06:49:23.7291529Z --- Parse Warning: 89 / 146 --- 2025-09-07T06:49:23.7291817Z /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. 2025-09-07T06:49:23.7291910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7291940Z 2025-09-07T06:49:23.7291986Z Module ``torch.distributed.launch``. 2025-09-07T06:49:23.7292016Z 2025-09-07T06:49:23.7292104Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T06:49:23.7292162Z training processes on each of the training nodes. 2025-09-07T06:49:23.7293551Z 2025-09-07T06:49:23.7293587Z .. warning:: 2025-09-07T06:49:23.7293617Z 2025-09-07T06:49:23.7293708Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T06:49:23.7293740Z 2025-09-07T06:49:23.7293860Z The utility can be used for single-node distributed training, in which one or 2025-09-07T06:49:23.7293946Z more processes per node will be spawned. The utility can be used for either 2025-09-07T06:49:23.7294025Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T06:49:23.7294110Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T06:49:23.7294193Z well-improved single-node training performance. It can also be used in 2025-09-07T06:49:23.7294287Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T06:49:23.7294368Z for well-improved multi-node distributed training performance as well. 2025-09-07T06:49:23.7294563Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T06:49:23.7294653Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T06:49:23.7294723Z aggregated communication bandwidth. 2025-09-07T06:49:23.7294760Z 2025-09-07T06:49:23.7294840Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T06:49:23.7294943Z training, this utility will launch the given number of processes per node 2025-09-07T06:49:23.7295024Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T06:49:23.7295102Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T06:49:23.7295175Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T06:49:23.7295218Z GPU (nproc_per_node - 1)*. 2025-09-07T06:49:23.7296634Z 2025-09-07T06:49:23.7296678Z **How to use this module:** 2025-09-07T06:49:23.7296708Z 2025-09-07T06:49:23.7296769Z 1. Single-Node multi-process distributed training 2025-09-07T06:49:23.7296798Z 2025-09-07T06:49:23.7296832Z :: 2025-09-07T06:49:23.7296864Z 2025-09-07T06:49:23.7296956Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:23.7297030Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T06:49:23.7297082Z arguments of your training script) 2025-09-07T06:49:23.7297112Z 2025-09-07T06:49:23.7297189Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T06:49:23.7297219Z 2025-09-07T06:49:23.7297249Z 2025-09-07T06:49:23.7297306Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T06:49:23.7297335Z 2025-09-07T06:49:23.7297367Z :: 2025-09-07T06:49:23.7297396Z 2025-09-07T06:49:23.7297480Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:23.7297542Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T06:49:23.7297620Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T06:49:23.7297678Z and all other arguments of your training script) 2025-09-07T06:49:23.7299058Z 2025-09-07T06:49:23.7299092Z Node 2: 2025-09-07T06:49:23.7299125Z 2025-09-07T06:49:23.7299155Z :: 2025-09-07T06:49:23.7299185Z 2025-09-07T06:49:23.7299267Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T06:49:23.7299326Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T06:49:23.7299399Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T06:49:23.7299456Z and all other arguments of your training script) 2025-09-07T06:49:23.7299486Z 2025-09-07T06:49:23.7299549Z 3. To look up what optional arguments this module offers: 2025-09-07T06:49:23.7299578Z 2025-09-07T06:49:23.7299608Z :: 2025-09-07T06:49:23.7299638Z 2025-09-07T06:49:23.7299691Z python -m torch.distributed.launch --help 2025-09-07T06:49:23.7299723Z 2025-09-07T06:49:23.7299752Z 2025-09-07T06:49:23.7299791Z **Important Notices:** 2025-09-07T06:49:23.7299866Z 2025-09-07T06:49:23.7299938Z 1. This utility and multi-process distributed (single-node or 2025-09-07T06:49:23.7300025Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T06:49:23.7301457Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T06:49:23.7301498Z use for GPU training. 2025-09-07T06:49:23.7301529Z 2025-09-07T06:49:23.7301605Z 2. In your training program, you must parse the command-line argument: 2025-09-07T06:49:23.7301686Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T06:49:23.7301765Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T06:49:23.7301837Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T06:49:23.7301867Z 2025-09-07T06:49:23.7301911Z Parsing the local_rank argument 2025-09-07T06:49:23.7301959Z 2025-09-07T06:49:23.7301990Z :: 2025-09-07T06:49:23.7302026Z 2025-09-07T06:49:23.7302066Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7302126Z >>> import argparse 2025-09-07T06:49:23.7302177Z >>> parser = argparse.ArgumentParser() 2025-09-07T06:49:23.7302251Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T06:49:23.7302294Z >>> args = parser.parse_args() 2025-09-07T06:49:23.7302324Z 2025-09-07T06:49:23.7302372Z Set your device to local rank using either 2025-09-07T06:49:23.7302403Z 2025-09-07T06:49:23.7302433Z :: 2025-09-07T06:49:23.7302462Z 2025-09-07T06:49:23.7303889Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T06:49:23.7303921Z 2025-09-07T06:49:23.7303951Z or 2025-09-07T06:49:23.7303981Z 2025-09-07T06:49:23.7304012Z :: 2025-09-07T06:49:23.7304041Z 2025-09-07T06:49:23.7304095Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T06:49:23.7304142Z >>> # your code to run 2025-09-07T06:49:23.7304175Z >>> ... 2025-09-07T06:49:23.7304204Z 2025-09-07T06:49:23.7304247Z .. versionchanged:: 2.0.0 2025-09-07T06:49:23.7304277Z 2025-09-07T06:49:23.7304366Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T06:49:23.7304532Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T06:49:23.7304588Z previously used underscored ``--local_rank``. 2025-09-07T06:49:23.7304618Z 2025-09-07T06:49:23.7304705Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T06:49:23.7304797Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T06:49:23.7304875Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T06:49:23.7304964Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T06:49:23.7305054Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T06:49:23.7305112Z including ``"--local-rank"`` should be sufficient. 2025-09-07T06:49:23.7306505Z 2025-09-07T06:49:23.7306590Z 3. In your training program, you are supposed to call the following function 2025-09-07T06:49:23.7306673Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T06:49:23.7306753Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T06:49:23.7306823Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T06:49:23.7306854Z 2025-09-07T06:49:23.7306886Z :: 2025-09-07T06:49:23.7306915Z 2025-09-07T06:49:23.7306989Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T06:49:23.7307044Z >>> init_method='env://') 2025-09-07T06:49:23.7307075Z 2025-09-07T06:49:23.7307206Z 4. In your training program, you can either use regular distributed functions 2025-09-07T06:49:23.7307292Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T06:49:23.7307371Z training program uses GPUs for training and you would like to use 2025-09-07T06:49:23.7307443Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T06:49:23.7307486Z here is how to configure it. 2025-09-07T06:49:23.7307516Z 2025-09-07T06:49:23.7307548Z :: 2025-09-07T06:49:23.7307577Z 2025-09-07T06:49:23.7307651Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T06:49:23.7307705Z >>> device_ids=[args.local_rank], 2025-09-07T06:49:23.7309110Z >>> output_device=args.local_rank) 2025-09-07T06:49:23.7309141Z 2025-09-07T06:49:23.7309253Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T06:49:23.7309340Z that your code will be operating on. This is generally the local rank of the 2025-09-07T06:49:23.7309442Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T06:49:23.7309519Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T06:49:23.7309552Z utility 2025-09-07T06:49:23.7309582Z 2025-09-07T06:49:23.7309668Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T06:49:23.7309743Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T06:49:23.7309821Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T06:49:23.7309891Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T06:49:23.7309957Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T06:49:23.7309988Z 2025-09-07T06:49:23.7310022Z .. warning:: 2025-09-07T06:49:23.7310057Z 2025-09-07T06:49:23.7310127Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T06:49:23.7310198Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T06:49:23.7310245Z write to a networked filesystem. See 2025-09-07T06:49:23.7310327Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T06:49:23.7310389Z how things can go wrong if you don't do this correctly. 2025-09-07T06:49:23.7310420Z 2025-09-07T06:49:23.7311873Z 2025-09-07T06:49:23.7311906Z 2025-09-07T06:49:23.7311936Z 2025-09-07T06:49:23.7312026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7312055Z 2025-09-07T06:49:23.7312093Z warnings.warn(msg) 2025-09-07T06:49:23.7312123Z 2025-09-07T06:49:23.7312201Z --- Parse Warning: 90 / 146 --- 2025-09-07T06:49:23.7312531Z /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. 2025-09-07T06:49:23.7312626Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7312655Z 2025-09-07T06:49:23.7312736Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T06:49:23.7312800Z reduces losses of multiple microbatches into one value. 2025-09-07T06:49:23.7312831Z 2025-09-07T06:49:23.7312863Z Example: 2025-09-07T06:49:23.7312901Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7312945Z >>> sum_reducer = _CustomReducer( 2025-09-07T06:49:23.7312984Z >>> torch.tensor(0.0), 2025-09-07T06:49:23.7313022Z >>> lambda a, b: a + b 2025-09-07T06:49:23.7313053Z >>> ) 2025-09-07T06:49:23.7313083Z 2025-09-07T06:49:23.7313170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7314663Z 2025-09-07T06:49:23.7314703Z warnings.warn(msg) 2025-09-07T06:49:23.7314737Z 2025-09-07T06:49:23.7314810Z --- Parse Warning: 91 / 146 --- 2025-09-07T06:49:23.7315160Z /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. 2025-09-07T06:49:23.7315251Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7315281Z 2025-09-07T06:49:23.7315356Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T06:49:23.7315424Z function can be called multiple times to incrementally add 2025-09-07T06:49:23.7315470Z device placement configurations. 2025-09-07T06:49:23.7315519Z 2025-09-07T06:49:23.7315550Z Args: 2025-09-07T06:49:23.7315591Z to (str): Callee name. 2025-09-07T06:49:23.7315667Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T06:49:23.7315753Z mappings from this worker to the callee. This map must be 2025-09-07T06:49:23.7315788Z invertible. 2025-09-07T06:49:23.7315818Z 2025-09-07T06:49:23.7315851Z Example: 2025-09-07T06:49:23.7315898Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7315935Z >>> # both workers 2025-09-07T06:49:23.7315971Z >>> def add(x, y): 2025-09-07T06:49:23.7317404Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T06:49:23.7317450Z >>> return x + y, (x + y).to(2) 2025-09-07T06:49:23.7317482Z >>> 2025-09-07T06:49:23.7317518Z >>> # on worker 0 2025-09-07T06:49:23.7317573Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T06:49:23.7317614Z >>> num_worker_threads=8, 2025-09-07T06:49:23.7317663Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T06:49:23.7317719Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T06:49:23.7317754Z >>> ) 2025-09-07T06:49:23.7317805Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T06:49:23.7317856Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T06:49:23.7317887Z >>> 2025-09-07T06:49:23.7317924Z >>> rpc.init_rpc( 2025-09-07T06:49:23.7317959Z >>> "worker0", 2025-09-07T06:49:23.7317994Z >>> rank=0, 2025-09-07T06:49:23.7318031Z >>> world_size=2, 2025-09-07T06:49:23.7318082Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T06:49:23.7318126Z >>> rpc_backend_options=options 2025-09-07T06:49:23.7318157Z >>> ) 2025-09-07T06:49:23.7318188Z >>> 2025-09-07T06:49:23.7318225Z >>> x = torch.ones(2) 2025-09-07T06:49:23.7318290Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T06:49:23.7319714Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T06:49:23.7319785Z >>> # sending the return value back, it will follow the invert of 2025-09-07T06:49:23.7319852Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T06:49:23.7319891Z >>> # cuda:1 on worker0 2025-09-07T06:49:23.7319948Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T06:49:23.7320003Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T06:49:23.7320034Z 2025-09-07T06:49:23.7320123Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7320152Z 2025-09-07T06:49:23.7320190Z warnings.warn(msg) 2025-09-07T06:49:23.7320219Z 2025-09-07T06:49:23.7320291Z --- Parse Warning: 92 / 146 --- 2025-09-07T06:49:23.7320629Z /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. 2025-09-07T06:49:23.7320724Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7320755Z 2025-09-07T06:49:23.7320839Z A decorator for a function indicating that the return value of the function 2025-09-07T06:49:23.7320913Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T06:49:23.7320999Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T06:49:23.7321081Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T06:49:23.7321164Z function and installs subsequent processing steps as a callback to that 2025-09-07T06:49:23.7321245Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T06:49:23.7322670Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T06:49:23.7322763Z value back as the RPC response. That also means the returned 2025-09-07T06:49:23.7322843Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T06:49:23.7322937Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T06:49:23.7323009Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T06:49:23.7323088Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T06:49:23.7323119Z 2025-09-07T06:49:23.7323195Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T06:49:23.7323277Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T06:49:23.7323352Z attributes installed by this decorator, it knows that this function 2025-09-07T06:49:23.7323420Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T06:49:23.7323496Z However, this does not mean this decorator has to be outmost one when 2025-09-07T06:49:23.7323580Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T06:49:23.7323656Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T06:49:23.7323731Z inner decorator to allow the target function be recognized as a static 2025-09-07T06:49:23.7323810Z or class function. This target function can still execute asynchronously 2025-09-07T06:49:23.7323887Z because, when accessed, the static or class method preserves attributes 2025-09-07T06:49:23.7323945Z installed by ``@rpc.functions.async_execution``. 2025-09-07T06:49:23.7323975Z 2025-09-07T06:49:23.7324006Z 2025-09-07T06:49:23.7324040Z Example:: 2025-09-07T06:49:23.7324111Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T06:49:23.7325579Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T06:49:23.7325666Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T06:49:23.7325738Z constructor. The example below shows directly using the 2025-09-07T06:49:23.7325788Z :class:`~torch.futures.Future` returned by 2025-09-07T06:49:23.7325838Z :meth:`~torch.futures.Future.then`. 2025-09-07T06:49:23.7325869Z 2025-09-07T06:49:23.7325917Z >>> from torch.distributed import rpc 2025-09-07T06:49:23.7325949Z >>> 2025-09-07T06:49:23.7325994Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:23.7326027Z >>> 2025-09-07T06:49:23.7326064Z >>> # On all workers 2025-09-07T06:49:23.7326110Z >>> @rpc.functions.async_execution 2025-09-07T06:49:23.7326155Z >>> def async_add_chained(to, x, y, z): 2025-09-07T06:49:23.7326224Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T06:49:23.7326294Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T06:49:23.7326364Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T06:49:23.7326469Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T06:49:23.7326538Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T06:49:23.7326604Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T06:49:23.7326668Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T06:49:23.7326731Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:23.7328134Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:23.7328167Z >>> ) 2025-09-07T06:49:23.7328198Z >>> 2025-09-07T06:49:23.7328234Z >>> # On worker0 2025-09-07T06:49:23.7328274Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7328315Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:23.7328350Z >>> "worker1", 2025-09-07T06:49:23.7328423Z >>> async_add_chained, 2025-09-07T06:49:23.7328478Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T06:49:23.7328510Z >>> ) 2025-09-07T06:49:23.7328575Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T06:49:23.7328605Z 2025-09-07T06:49:23.7328683Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T06:49:23.7328718Z outmost one. 2025-09-07T06:49:23.7328748Z 2025-09-07T06:49:23.7328790Z >>> from torch import Tensor 2025-09-07T06:49:23.7328835Z >>> from torch.futures import Future 2025-09-07T06:49:23.7328880Z >>> from torch.distributed import rpc 2025-09-07T06:49:23.7328912Z >>> 2025-09-07T06:49:23.7328956Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:23.7328987Z >>> 2025-09-07T06:49:23.7329024Z >>> # On all workers 2025-09-07T06:49:23.7330414Z >>> @torch.jit.script 2025-09-07T06:49:23.7330475Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T06:49:23.7330515Z >>> return x + y 2025-09-07T06:49:23.7330551Z >>> 2025-09-07T06:49:23.7330597Z >>> @rpc.functions.async_execution 2025-09-07T06:49:23.7330637Z >>> @torch.jit.script 2025-09-07T06:49:23.7330707Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T06:49:23.7330762Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T06:49:23.7330793Z >>> 2025-09-07T06:49:23.7330828Z >>> # On worker0 2025-09-07T06:49:23.7330868Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:23.7330903Z >>> "worker1", 2025-09-07T06:49:23.7330939Z >>> async_add, 2025-09-07T06:49:23.7330984Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T06:49:23.7331015Z >>> ) 2025-09-07T06:49:23.7331062Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T06:49:23.7331091Z 2025-09-07T06:49:23.7331167Z When combined with static or class method, this decorator must be the 2025-09-07T06:49:23.7331201Z inner one. 2025-09-07T06:49:23.7331234Z 2025-09-07T06:49:23.7331279Z >>> from torch.distributed import rpc 2025-09-07T06:49:23.7331311Z >>> 2025-09-07T06:49:23.7332696Z >>> # omitting setup and shutdown RPC 2025-09-07T06:49:23.7332729Z >>> 2025-09-07T06:49:23.7332765Z >>> # On all workers 2025-09-07T06:49:23.7332810Z >>> class AsyncExecutionClass: 2025-09-07T06:49:23.7332840Z >>> 2025-09-07T06:49:23.7332877Z >>> @staticmethod 2025-09-07T06:49:23.7332923Z >>> @rpc.functions.async_execution 2025-09-07T06:49:23.7332969Z >>> def static_async_add(to, x, y, z): 2025-09-07T06:49:23.7333032Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:23.7333076Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:23.7333109Z >>> ) 2025-09-07T06:49:23.7333139Z >>> 2025-09-07T06:49:23.7333178Z >>> @classmethod 2025-09-07T06:49:23.7333257Z >>> @rpc.functions.async_execution 2025-09-07T06:49:23.7333308Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T06:49:23.7333359Z >>> ret_fut = torch.futures.Future() 2025-09-07T06:49:23.7333417Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:23.7333475Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T06:49:23.7333509Z >>> ) 2025-09-07T06:49:23.7333547Z >>> return ret_fut 2025-09-07T06:49:23.7333579Z >>> 2025-09-07T06:49:23.7333623Z >>> @rpc.functions.async_execution 2025-09-07T06:49:23.7335072Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T06:49:23.7335135Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T06:49:23.7335179Z >>> lambda fut: fut.wait() + z 2025-09-07T06:49:23.7335213Z >>> ) 2025-09-07T06:49:23.7335272Z >>> 2025-09-07T06:49:23.7335312Z >>> # On worker0 2025-09-07T06:49:23.7335352Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:23.7335407Z >>> "worker1", 2025-09-07T06:49:23.7335460Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T06:49:23.7335507Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:23.7335538Z >>> ) 2025-09-07T06:49:23.7335585Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:23.7335615Z >>> 2025-09-07T06:49:23.7335654Z >>> ret = rpc.rpc_sync( 2025-09-07T06:49:23.7335689Z >>> "worker1", 2025-09-07T06:49:23.7335741Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T06:49:23.7335786Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:23.7335817Z >>> ) 2025-09-07T06:49:23.7335863Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:23.7335893Z 2025-09-07T06:49:23.7335955Z This decorator also works with RRef helpers, i.e., . 2025-09-07T06:49:23.7337381Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T06:49:23.7337446Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T06:49:23.7337502Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T06:49:23.7337531Z 2025-09-07T06:49:23.7337579Z >>> from torch.distributed import rpc 2025-09-07T06:49:23.7337609Z >>> 2025-09-07T06:49:23.7337662Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T06:49:23.7337722Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:23.7337797Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T06:49:23.7337842Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:23.7337872Z >>> 2025-09-07T06:49:23.7337930Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:23.7338013Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T06:49:23.7338062Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:23.7338092Z >>> 2025-09-07T06:49:23.7338151Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T06:49:23.7338232Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T06:49:23.7338277Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T06:49:23.7338307Z 2025-09-07T06:49:23.7338396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7338426Z 2025-09-07T06:49:23.7338463Z warnings.warn(msg) 2025-09-07T06:49:23.7339840Z 2025-09-07T06:49:23.7339918Z --- Parse Warning: 93 / 146 --- 2025-09-07T06:49:23.7340286Z /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. 2025-09-07T06:49:23.7340425Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7340458Z 2025-09-07T06:49:23.7340533Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T06:49:23.7340630Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T06:49:23.7340661Z 2025-09-07T06:49:23.7340757Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T06:49:23.7340838Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T06:49:23.7340919Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T06:49:23.7340968Z only report runtime of PyTorch functions. 2025-09-07T06:49:23.7341063Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T06:49:23.7341110Z 2025-09-07T06:49:23.7341142Z Args: 2025-09-07T06:49:23.7341242Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T06:49:23.7341296Z Default: ``True``. 2025-09-07T06:49:23.7341326Z 2025-09-07T06:49:23.7341428Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T06:49:23.7341500Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T06:49:23.7341538Z Default: ``False`` 2025-09-07T06:49:23.7342928Z 2025-09-07T06:49:23.7343014Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T06:49:23.7343096Z about input dimensions will be collected. This allows one to see which 2025-09-07T06:49:23.7343174Z dimensions have been used under the hood and further group by them 2025-09-07T06:49:23.7343249Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T06:49:23.7343337Z shape recording might skew your profiling data. It is recommended to 2025-09-07T06:49:23.7343420Z use separate runs with and without shape recording to validate the timing. 2025-09-07T06:49:23.7343502Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T06:49:23.7343576Z of nested function calls). But for higher level functions the total 2025-09-07T06:49:23.7343649Z self cpu time might be artificially increased because of the shape 2025-09-07T06:49:23.7343684Z collection. 2025-09-07T06:49:23.7343715Z 2025-09-07T06:49:23.7343809Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T06:49:23.7343840Z 2025-09-07T06:49:23.7343873Z .. warning:: 2025-09-07T06:49:23.7343946Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T06:49:23.7343976Z 2025-09-07T06:49:23.7344009Z .. warning:: 2025-09-07T06:49:23.7344113Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T06:49:23.7344186Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T06:49:23.7344270Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T06:49:23.7344333Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T06:49:23.7345763Z 2025-09-07T06:49:23.7345801Z Example: 2025-09-07T06:49:23.7345840Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7345877Z >>> # On worker 0: 2025-09-07T06:49:23.7345913Z >>> import torch 2025-09-07T06:49:23.7345963Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7346018Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:23.7346066Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T06:49:23.7346112Z >>> outer_profile_rref = rpc.remote( 2025-09-07T06:49:23.7346175Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T06:49:23.7346255Z ... ) 2025-09-07T06:49:23.7346306Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T06:49:23.7346366Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T06:49:23.7346411Z >>> inner_profile_rref = rpc.remote( 2025-09-07T06:49:23.7346471Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T06:49:23.7346502Z ... ) 2025-09-07T06:49:23.7346552Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T06:49:23.7346609Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T06:49:23.7346676Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T06:49:23.7346740Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T06:49:23.7346800Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T06:49:23.7346892Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7348393Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T06:49:23.7348502Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7348576Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T06:49:23.7348646Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T06:49:23.7348728Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7348772Z Self CPU time total: 89.667us 2025-09-07T06:49:23.7348833Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T06:49:23.7348913Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7349024Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T06:49:23.7349105Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7349170Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T06:49:23.7349236Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T06:49:23.7349300Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T06:49:23.7349382Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T06:49:23.7349426Z Self CPU time total: 213.926us 2025-09-07T06:49:23.7349466Z >>> rpc.shutdown() 2025-09-07T06:49:23.7349496Z 2025-09-07T06:49:23.7349534Z >>> # On worker 1: 2025-09-07T06:49:23.7349586Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7349645Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:23.7349708Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T06:49:23.7351147Z >>> rpc.shutdown() 2025-09-07T06:49:23.7351180Z 2025-09-07T06:49:23.7351271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7351302Z 2025-09-07T06:49:23.7351338Z warnings.warn(msg) 2025-09-07T06:49:23.7351368Z 2025-09-07T06:49:23.7351441Z --- Parse Warning: 94 / 146 --- 2025-09-07T06:49:23.7351798Z /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. 2025-09-07T06:49:23.7351932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7351964Z 2025-09-07T06:49:23.7352101Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T06:49:23.7352132Z 2025-09-07T06:49:23.7352220Z The sharing is done as described by `ZeRO `_. 2025-09-07T06:49:23.7352250Z 2025-09-07T06:49:23.7352306Z The local optimizer instance in each rank is only 2025-09-07T06:49:23.7352392Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T06:49:23.7352466Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T06:49:23.7352551Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T06:49:23.7352620Z all other peers to keep all model replicas in the same state. 2025-09-07T06:49:23.7352709Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T06:49:23.7352801Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T06:49:23.7352855Z memory consumption. 2025-09-07T06:49:23.7354260Z 2025-09-07T06:49:23.7354349Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T06:49:23.7354479Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T06:49:23.7354561Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T06:49:23.7354612Z the parameter registration or usage order. 2025-09-07T06:49:23.7354642Z 2025-09-07T06:49:23.7354676Z Arguments: 2025-09-07T06:49:23.7354747Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T06:49:23.7354816Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T06:49:23.7354855Z across ranks. 2025-09-07T06:49:23.7354885Z 2025-09-07T06:49:23.7354923Z Keyword Args: 2025-09-07T06:49:23.7355003Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T06:49:23.7355040Z optimizer. 2025-09-07T06:49:23.7355117Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T06:49:23.7355186Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T06:49:23.7355244Z :meth:`torch.distributed.init_process_group`). 2025-09-07T06:49:23.7355325Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T06:49:23.7355400Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T06:49:23.7355471Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T06:49:23.7355541Z each individual parameter is communicated separately, and each 2025-09-07T06:49:23.7356958Z ``params.data`` stays intact (default: ``False``). 2025-09-07T06:49:23.7357042Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T06:49:23.7357114Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T06:49:23.7357192Z synchronization; this requires (1) either a functional optimizer 2025-09-07T06:49:23.7357258Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T06:49:23.7357321Z equivalent and (2) registering a DDP communication hook 2025-09-07T06:49:23.7357393Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T06:49:23.7357455Z parameters are packed into buckets matching those in 2025-09-07T06:49:23.7357515Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T06:49:23.7357570Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T06:49:23.7357638Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T06:49:23.7357677Z (per normal). 2025-09-07T06:49:23.7357763Z (default: ``False``) 2025-09-07T06:49:23.7357838Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T06:49:23.7357875Z optimizer. 2025-09-07T06:49:23.7357905Z 2025-09-07T06:49:23.7357940Z Example:: 2025-09-07T06:49:23.7357969Z 2025-09-07T06:49:23.7358008Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7358047Z >>> import torch.nn as nn 2025-09-07T06:49:23.7358122Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T06:49:23.7358194Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T06:49:23.7359634Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T06:49:23.7359683Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T06:49:23.7359731Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T06:49:23.7359796Z >>> ddp.parameters(), 2025-09-07T06:49:23.7359851Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T06:49:23.7359885Z >>> lr=0.01 2025-09-07T06:49:23.7359934Z >>> ) 2025-09-07T06:49:23.7359978Z >>> ddp(inputs).sum().backward() 2025-09-07T06:49:23.7360014Z >>> opt.step() 2025-09-07T06:49:23.7360045Z 2025-09-07T06:49:23.7360079Z .. warning:: 2025-09-07T06:49:23.7360155Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T06:49:23.7360213Z passed-in parameters are the same dense type. 2025-09-07T06:49:23.7360244Z 2025-09-07T06:49:23.7360276Z .. warning:: 2025-09-07T06:49:23.7360353Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T06:49:23.7360427Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T06:49:23.7360509Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T06:49:23.7360586Z two or three training iterations do not perform parameter updates in 2025-09-07T06:49:23.7360662Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T06:49:23.7360730Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T06:49:23.7362160Z information about the gradient bucketing strategy used by 2025-09-07T06:49:23.7362238Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T06:49:23.7362310Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T06:49:23.7362384Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T06:49:23.7362428Z is to prepend dummy inputs. 2025-09-07T06:49:23.7362459Z 2025-09-07T06:49:23.7362547Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T06:49:23.7362578Z 2025-09-07T06:49:23.7362667Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7362699Z 2025-09-07T06:49:23.7362738Z warnings.warn(msg) 2025-09-07T06:49:23.7362767Z 2025-09-07T06:49:23.7362841Z --- Parse Warning: 95 / 146 --- 2025-09-07T06:49:23.7363195Z /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. 2025-09-07T06:49:23.7363287Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7363319Z 2025-09-07T06:49:23.7363453Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T06:49:23.7363511Z This optimizer runs local optimizer at every step. 2025-09-07T06:49:23.7363628Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T06:49:23.7363660Z 2025-09-07T06:49:23.7363692Z Args: 2025-09-07T06:49:23.7363767Z optim: The local optimizer. 2025-09-07T06:49:23.7365254Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T06:49:23.7365289Z 2025-09-07T06:49:23.7365324Z Example:: 2025-09-07T06:49:23.7365354Z 2025-09-07T06:49:23.7365405Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7365442Z >>> import torch 2025-09-07T06:49:23.7365488Z >>> import torch.distributed as dist 2025-09-07T06:49:23.7365587Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T06:49:23.7365627Z >>> import torch.nn as nn 2025-09-07T06:49:23.7365700Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T06:49:23.7365796Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T06:49:23.7365837Z >>> PostLocalSGDState, 2025-09-07T06:49:23.7365905Z >>> post_localSGD_hook, 2025-09-07T06:49:23.7365937Z >>> ) 2025-09-07T06:49:23.7365974Z >>> 2025-09-07T06:49:23.7366035Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:23.7366111Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:23.7366142Z >>> ) 2025-09-07T06:49:23.7366174Z >>> 2025-09-07T06:49:23.7366229Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:23.7366333Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T06:49:23.7366395Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:23.7367799Z >>> 2025-09-07T06:49:23.7367872Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T06:49:23.7367963Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T06:49:23.7368026Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:23.7368109Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T06:49:23.7368155Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T06:49:23.7368200Z >>> optim=local_optim, 2025-09-07T06:49:23.7368289Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T06:49:23.7368321Z >>> ) 2025-09-07T06:49:23.7368351Z >>> 2025-09-07T06:49:23.7368432Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T06:49:23.7368534Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T06:49:23.7368663Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T06:49:23.7368704Z >>> for step in range(0, 200): 2025-09-07T06:49:23.7368744Z >>> opt.zero_grad() 2025-09-07T06:49:23.7368792Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:23.7368833Z >>> loss.backward() 2025-09-07T06:49:23.7368870Z >>> opt.step() 2025-09-07T06:49:23.7368902Z 2025-09-07T06:49:23.7368991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7369021Z 2025-09-07T06:49:23.7370400Z warnings.warn(msg) 2025-09-07T06:49:23.7370432Z 2025-09-07T06:49:23.7370507Z --- Parse Warning: 96 / 146 --- 2025-09-07T06:49:23.7370835Z /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. 2025-09-07T06:49:23.7370928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7370957Z 2025-09-07T06:49:23.7371042Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T06:49:23.7371171Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T06:49:23.7371203Z 2025-09-07T06:49:23.7371286Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T06:49:23.7371345Z to retrieve the gradients for specific parameters. 2025-09-07T06:49:23.7371375Z 2025-09-07T06:49:23.7371412Z Concurrent calls to 2025-09-07T06:49:23.7371490Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T06:49:23.7371543Z either from the same or different clients, will 2025-09-07T06:49:23.7371622Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T06:49:23.7371694Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T06:49:23.7371779Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T06:49:23.7371855Z at a time. This means that the gradients being applied may not correspond 2025-09-07T06:49:23.7371950Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T06:49:23.7371995Z guaranteed ordering across workers. 2025-09-07T06:49:23.7372041Z 2025-09-07T06:49:23.7373490Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T06:49:23.7373571Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T06:49:23.7373658Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T06:49:23.7373742Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T06:49:23.7373833Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T06:49:23.7373876Z for your own custom optimizers. 2025-09-07T06:49:23.7373907Z 2025-09-07T06:49:23.7373939Z Args: 2025-09-07T06:49:23.7374012Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T06:49:23.7374056Z instantiate on each worker. 2025-09-07T06:49:23.7374136Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T06:49:23.7374175Z to optimize. 2025-09-07T06:49:23.7374253Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T06:49:23.7374331Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T06:49:23.7374361Z 2025-09-07T06:49:23.7374438Z Example:: 2025-09-07T06:49:23.7374485Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7374548Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T06:49:23.7374597Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7374638Z >>> from torch import optim 2025-09-07T06:49:23.7374707Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T06:49:23.7376089Z >>> 2025-09-07T06:49:23.7376144Z >>> with dist_autograd.context() as context_id: 2025-09-07T06:49:23.7376189Z >>> # Forward pass. 2025-09-07T06:49:23.7376267Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T06:49:23.7376338Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T06:49:23.7376388Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T06:49:23.7376419Z >>> 2025-09-07T06:49:23.7376457Z >>> # Backward pass. 2025-09-07T06:49:23.7376515Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T06:49:23.7376546Z >>> 2025-09-07T06:49:23.7376583Z >>> # Optimizer. 2025-09-07T06:49:23.7376633Z >>> dist_optim = DistributedOptimizer( 2025-09-07T06:49:23.7376671Z >>> optim.SGD, 2025-09-07T06:49:23.7376710Z >>> [rref1, rref2], 2025-09-07T06:49:23.7376745Z >>> lr=0.05, 2025-09-07T06:49:23.7376777Z >>> ) 2025-09-07T06:49:23.7376821Z >>> dist_optim.step(context_id) 2025-09-07T06:49:23.7376853Z 2025-09-07T06:49:23.7376973Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T06:49:23.7377004Z 2025-09-07T06:49:23.7377094Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7377124Z 2025-09-07T06:49:23.7378520Z warnings.warn(msg) 2025-09-07T06:49:23.7378553Z 2025-09-07T06:49:23.7378626Z --- Parse Warning: 97 / 146 --- 2025-09-07T06:49:23.7378981Z /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. 2025-09-07T06:49:23.7379072Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7379103Z 2025-09-07T06:49:23.7379205Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T06:49:23.7379324Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T06:49:23.7379354Z 2025-09-07T06:49:23.7379416Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T06:49:23.7379466Z 2025-09-07T06:49:23.7379500Z .. warning:: 2025-09-07T06:49:23.7379564Z Current implementation only supports loading Tensors. 2025-09-07T06:49:23.7379595Z 2025-09-07T06:49:23.7379639Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7379678Z >>> sd = {"mode": model} 2025-09-07T06:49:23.7379712Z >>> dcp.load( 2025-09-07T06:49:23.7379745Z >>> sd, 2025-09-07T06:49:23.7379805Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T06:49:23.7379852Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T06:49:23.7379898Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T06:49:23.7379928Z >>> ) 2025-09-07T06:49:23.7381319Z 2025-09-07T06:49:23.7381408Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7381442Z 2025-09-07T06:49:23.7381484Z warnings.warn(msg) 2025-09-07T06:49:23.7381514Z 2025-09-07T06:49:23.7381587Z --- Parse Warning: 98 / 146 --- 2025-09-07T06:49:23.7381934Z /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. 2025-09-07T06:49:23.7382024Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7382055Z 2025-09-07T06:49:23.7382179Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T06:49:23.7382292Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T06:49:23.7382336Z metadata file, like Torch Save files. 2025-09-07T06:49:23.7382367Z 2025-09-07T06:49:23.7382436Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T06:49:23.7382466Z 2025-09-07T06:49:23.7382501Z .. warning:: 2025-09-07T06:49:23.7382563Z Current implementation only supports loading Tensors. 2025-09-07T06:49:23.7382594Z 2025-09-07T06:49:23.7382637Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7382677Z >>> sd = {"mode": model} 2025-09-07T06:49:23.7382710Z >>> dcp.load( 2025-09-07T06:49:23.7384088Z >>> sd, 2025-09-07T06:49:23.7384148Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T06:49:23.7384197Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T06:49:23.7384243Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T06:49:23.7384275Z >>> ) 2025-09-07T06:49:23.7384304Z 2025-09-07T06:49:23.7384524Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7384555Z 2025-09-07T06:49:23.7384594Z warnings.warn(msg) 2025-09-07T06:49:23.7384624Z 2025-09-07T06:49:23.7384746Z --- Parse Warning: 99 / 146 --- 2025-09-07T06:49:23.7385063Z /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=97. 2025-09-07T06:49:23.7385157Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7385186Z 2025-09-07T06:49:23.7385231Z Save a distributed model in SPMD style. 2025-09-07T06:49:23.7385260Z 2025-09-07T06:49:23.7385330Z This function is different from ``torch.save()`` as it handles 2025-09-07T06:49:23.7385422Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T06:49:23.7385451Z 2025-09-07T06:49:23.7385544Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T06:49:23.7385623Z save will call ``state_dict`` before serialization. 2025-09-07T06:49:23.7385654Z 2025-09-07T06:49:23.7387074Z .. warning:: 2025-09-07T06:49:23.7387183Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T06:49:23.7387223Z for saved state_dicts. 2025-09-07T06:49:23.7387253Z 2025-09-07T06:49:23.7387286Z .. warning:: 2025-09-07T06:49:23.7387362Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T06:49:23.7387436Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T06:49:23.7387466Z 2025-09-07T06:49:23.7387498Z .. note:: 2025-09-07T06:49:23.7387590Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T06:49:23.7387680Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T06:49:23.7387723Z group needs to be passed in. 2025-09-07T06:49:23.7387755Z 2025-09-07T06:49:23.7387787Z .. note:: 2025-09-07T06:49:23.7387884Z If no process group is available, this function assumes the intention is to save the 2025-09-07T06:49:23.7387933Z state_dict in the local process. 2025-09-07T06:49:23.7387963Z 2025-09-07T06:49:23.7387994Z .. note: 2025-09-07T06:49:23.7388049Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T06:49:23.7388078Z 2025-09-07T06:49:23.7388108Z 2025-09-07T06:49:23.7388140Z Args: 2025-09-07T06:49:23.7389561Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T06:49:23.7389617Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:23.7389693Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:23.7389765Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:23.7389830Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:23.7389872Z (Default: ``None``) 2025-09-07T06:49:23.7389928Z storage_writer (Optional[StorageWriter]): 2025-09-07T06:49:23.7390004Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T06:49:23.7390079Z specified, DCP will automatically infer the writer based on the 2025-09-07T06:49:23.7390149Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:23.7390193Z be raised. (Default: ``None``) 2025-09-07T06:49:23.7390237Z planner (Optional[SavePlanner]): 2025-09-07T06:49:23.7390308Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T06:49:23.7390358Z planner will be used. (Default: ``None``) 2025-09-07T06:49:23.7390408Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:23.7390474Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:23.7390513Z (Default: ``None``) 2025-09-07T06:49:23.7390549Z no_dist (bool): 2025-09-07T06:49:23.7390646Z If ``True``, this function will assume the intent is to load 2025-09-07T06:49:23.7390694Z a checkpoint on a single rank/process. 2025-09-07T06:49:23.7390735Z (Default: ``False``) 2025-09-07T06:49:23.7392223Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T06:49:23.7392292Z a checkpoint without using cross-rank synchronization. 2025-09-07T06:49:23.7392329Z (Default: ``True``) 2025-09-07T06:49:23.7392406Z This configuration is experimental and should be used with caution. 2025-09-07T06:49:23.7392502Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T06:49:23.7392533Z 2025-09-07T06:49:23.7392565Z Returns: 2025-09-07T06:49:23.7392626Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T06:49:23.7392657Z 2025-09-07T06:49:23.7392712Z Example: 2025-09-07T06:49:23.7392753Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7392798Z >>> my_model = MyModule() 2025-09-07T06:49:23.7392828Z 2025-09-07T06:49:23.7392888Z >>> state_dict = {"model": my_model} 2025-09-07T06:49:23.7392919Z 2025-09-07T06:49:23.7393002Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T06:49:23.7393042Z ... "/checkpoint/1" 2025-09-07T06:49:23.7393073Z ... ) 2025-09-07T06:49:23.7393126Z >>> torch.distributed.checkpoint.save( 2025-09-07T06:49:23.7393167Z >>> state_dict=state_dict, 2025-09-07T06:49:23.7393213Z >>> storage_writer=fs_storage_writer, 2025-09-07T06:49:23.7393245Z >>> ) 2025-09-07T06:49:23.7394682Z 2025-09-07T06:49:23.7394717Z .. note:: 2025-09-07T06:49:23.7394793Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T06:49:23.7394870Z For NCCL-based process groups, internal tensor representations of 2025-09-07T06:49:23.7394958Z objects must be moved to the GPU device before communication takes place. 2025-09-07T06:49:23.7395039Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T06:49:23.7395114Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T06:49:23.7395186Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T06:49:23.7395216Z 2025-09-07T06:49:23.7395303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7395333Z 2025-09-07T06:49:23.7395371Z warnings.warn(msg) 2025-09-07T06:49:23.7395400Z 2025-09-07T06:49:23.7395475Z --- Parse Warning: 100 / 146 --- 2025-09-07T06:49:23.7395802Z /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=230. 2025-09-07T06:49:23.7395896Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7395990Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T06:49:23.7396093Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T06:49:23.7396123Z 2025-09-07T06:49:23.7396159Z .. warning:: 2025-09-07T06:49:23.7396219Z This feature is experimental and subject to change. 2025-09-07T06:49:23.7397632Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T06:49:23.7397663Z 2025-09-07T06:49:23.7397696Z Args: 2025-09-07T06:49:23.7397755Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T06:49:23.7397812Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:23.7397887Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:23.7398014Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:23.7398077Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:23.7398120Z (Default: ``None``) 2025-09-07T06:49:23.7398172Z storage_writer (Optional[StorageWriter]): 2025-09-07T06:49:23.7398246Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T06:49:23.7398331Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T06:49:23.7398403Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:23.7398448Z be raised. (Default: ``None``) 2025-09-07T06:49:23.7398494Z planner (Optional[SavePlanner]): 2025-09-07T06:49:23.7398564Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T06:49:23.7398644Z planner will be used. (Default: ``None``) 2025-09-07T06:49:23.7398697Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:23.7398763Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:23.7398829Z (Default: ``None``) 2025-09-07T06:49:23.7398889Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T06:49:23.7398953Z whether to do checkpoint in separate thread or process 2025-09-07T06:49:23.7400367Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T06:49:23.7400412Z async_stager (AsyncStager): 2025-09-07T06:49:23.7400501Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T06:49:23.7400578Z and async_stager is provided, async_stager will be used for staging 2025-09-07T06:49:23.7402149Z no_dist (bool): 2025-09-07T06:49:23.7402215Z If ``True``, this function will assume the intent is to save 2025-09-07T06:49:23.7402272Z a checkpoint on a single rank/process. 2025-09-07T06:49:23.7402313Z (Default: ``False``) 2025-09-07T06:49:23.7402426Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T06:49:23.7402508Z This configuration is experimental and should be used with caution. 2025-09-07T06:49:23.7402602Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T06:49:23.7402633Z 2025-09-07T06:49:23.7402665Z Returns: 2025-09-07T06:49:23.7402740Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T06:49:23.7402769Z 2025-09-07T06:49:23.7402803Z Example: 2025-09-07T06:49:23.7402842Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7402898Z >>> my_model = MyModule() 2025-09-07T06:49:23.7402929Z 2025-09-07T06:49:23.7402976Z >>> state_dict = {"model": my_model} 2025-09-07T06:49:23.7404820Z 2025-09-07T06:49:23.7404913Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T06:49:23.7404956Z ... "/checkpoint/1" 2025-09-07T06:49:23.7404988Z ... ) 2025-09-07T06:49:23.7405068Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T06:49:23.7405111Z >>> state_dict=state_dict, 2025-09-07T06:49:23.7405160Z >>> storage_writer=fs_storage_writer, 2025-09-07T06:49:23.7405192Z >>> ) 2025-09-07T06:49:23.7405223Z >>> 2025-09-07T06:49:23.7405262Z >>> # ... do some work ... 2025-09-07T06:49:23.7405294Z >>> 2025-09-07T06:49:23.7405337Z >>> checkpoint_future.result() 2025-09-07T06:49:23.7405368Z 2025-09-07T06:49:23.7405434Z 2025-09-07T06:49:23.7405523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7405557Z 2025-09-07T06:49:23.7405594Z warnings.warn(msg) 2025-09-07T06:49:23.7405670Z 2025-09-07T06:49:23.7405756Z --- Parse Warning: 101 / 146 --- 2025-09-07T06:49:23.7406077Z /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. 2025-09-07T06:49:23.7406169Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7407763Z 2025-09-07T06:49:23.7407867Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T06:49:23.7407898Z 2025-09-07T06:49:23.7408007Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T06:49:23.7408038Z 2025-09-07T06:49:23.7408137Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T06:49:23.7408211Z will be visible to the whole process. 2025-09-07T06:49:23.7408243Z 2025-09-07T06:49:23.7408339Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T06:49:23.7408387Z 2025-09-07T06:49:23.7408435Z 1) set_up_planner - called on all ranks. 2025-09-07T06:49:23.7408484Z Signals the start of a checkpoint save. 2025-09-07T06:49:23.7408514Z 2025-09-07T06:49:23.7408561Z 2) create_local_plan - called on all ranks. 2025-09-07T06:49:23.7408663Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T06:49:23.7408692Z 2025-09-07T06:49:23.7408761Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T06:49:23.7408833Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T06:49:23.7408862Z 2025-09-07T06:49:23.7408980Z 4) finish_plan - called on all ranks. 2025-09-07T06:49:23.7409060Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T06:49:23.7409094Z 2025-09-07T06:49:23.7410555Z 5) resolve_data - called multiple times on each rank 2025-09-07T06:49:23.7410634Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T06:49:23.7410664Z 2025-09-07T06:49:23.7410772Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T06:49:23.7410839Z most changes can be expressed by changes in a single method. 2025-09-07T06:49:23.7410870Z 2025-09-07T06:49:23.7410917Z There are 3 usual patterns of extension: 2025-09-07T06:49:23.7410947Z 2025-09-07T06:49:23.7411037Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T06:49:23.7411116Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T06:49:23.7411147Z 2025-09-07T06:49:23.7411193Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7411244Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T06:49:23.7411288Z >>> def set_up_planner( 2025-09-07T06:49:23.7411321Z >>> self, 2025-09-07T06:49:23.7411367Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T06:49:23.7411417Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T06:49:23.7411458Z >>> is_coordinator: bool, 2025-09-07T06:49:23.7411495Z >>> ) -> None: 2025-09-07T06:49:23.7411537Z >>> # prefix all keys with `foo_`` 2025-09-07T06:49:23.7411643Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T06:49:23.7413032Z 2025-09-07T06:49:23.7413150Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T06:49:23.7413180Z 2025-09-07T06:49:23.7413227Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7413276Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T06:49:23.7413322Z >>> def create_local_plan(self): 2025-09-07T06:49:23.7413391Z >>> plan = super().create_local_plan() 2025-09-07T06:49:23.7413434Z >>> for p in plan: 2025-09-07T06:49:23.7413480Z >>> if p.tensor_data is not None: 2025-09-07T06:49:23.7413543Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T06:49:23.7413581Z >>> return plan 2025-09-07T06:49:23.7413613Z >>> 2025-09-07T06:49:23.7413658Z >>> def resolve_data(self, write_item): 2025-09-07T06:49:23.7413709Z >>> item = super().resolve_data(write_item) 2025-09-07T06:49:23.7413806Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T06:49:23.7413837Z 2025-09-07T06:49:23.7413954Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T06:49:23.7413985Z 2025-09-07T06:49:23.7414028Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7414089Z >>> from itertools import zip_longest 2025-09-07T06:49:23.7414132Z >>> from dataclasses import replace 2025-09-07T06:49:23.7414209Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T06:49:23.7415758Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T06:49:23.7415812Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T06:49:23.7415863Z >>> def create_global_plan(self, all_plans): 2025-09-07T06:49:23.7415922Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T06:49:23.7415963Z >>> items_per_rank = [ 2025-09-07T06:49:23.7416018Z >>> [item for item in items if item is not None] 2025-09-07T06:49:23.7416078Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T06:49:23.7416141Z >>> ] 2025-09-07T06:49:23.7416181Z >>> all_plans = [ 2025-09-07T06:49:23.7416229Z >>> replace(plan, items=items) 2025-09-07T06:49:23.7416302Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T06:49:23.7416335Z >>> ] 2025-09-07T06:49:23.7416390Z >>> return super().create_global_plan(all_plans) 2025-09-07T06:49:23.7416420Z 2025-09-07T06:49:23.7416513Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T06:49:23.7416610Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T06:49:23.7416654Z the global planner aggregate them: 2025-09-07T06:49:23.7416683Z 2025-09-07T06:49:23.7416728Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7416785Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T06:49:23.7416837Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T06:49:23.7416883Z >>> plan = super().create_local_plan() 2025-09-07T06:49:23.7418323Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T06:49:23.7418359Z >>> 2025-09-07T06:49:23.7418468Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T06:49:23.7418540Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T06:49:23.7418600Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T06:49:23.7418664Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T06:49:23.7418709Z >>> return global_plan, metadata 2025-09-07T06:49:23.7418738Z 2025-09-07T06:49:23.7418825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7418856Z 2025-09-07T06:49:23.7418894Z warnings.warn(msg) 2025-09-07T06:49:23.7418924Z 2025-09-07T06:49:23.7419002Z --- Parse Warning: 102 / 146 --- 2025-09-07T06:49:23.7419350Z /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. 2025-09-07T06:49:23.7419445Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7419476Z 2025-09-07T06:49:23.7419575Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T06:49:23.7419606Z 2025-09-07T06:49:23.7419704Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T06:49:23.7419735Z 2025-09-07T06:49:23.7419831Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T06:49:23.7421241Z will be visible to the whole process. 2025-09-07T06:49:23.7421272Z 2025-09-07T06:49:23.7421374Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T06:49:23.7421431Z 2025-09-07T06:49:23.7421478Z 1) set_up_planner - called on all ranks. 2025-09-07T06:49:23.7421530Z Signals the start of loading a checkpoint. 2025-09-07T06:49:23.7421578Z 2025-09-07T06:49:23.7421628Z 2) create_local_plan - called on all ranks. 2025-09-07T06:49:23.7421729Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T06:49:23.7421759Z 2025-09-07T06:49:23.7421825Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T06:49:23.7421896Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T06:49:23.7421925Z 2025-09-07T06:49:23.7421982Z 4) load_bytes - called multiple times on each rank 2025-09-07T06:49:23.7422045Z This is called once per non-tensor value in state_dict. 2025-09-07T06:49:23.7422076Z 2025-09-07T06:49:23.7422168Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T06:49:23.7422237Z They are called in pair for each Tensor value in state_dict. 2025-09-07T06:49:23.7422269Z 2025-09-07T06:49:23.7422376Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T06:49:23.7422445Z most changes can be expressed by changes in a single method. 2025-09-07T06:49:23.7422475Z 2025-09-07T06:49:23.7423889Z There are two usual patterns of extension: 2025-09-07T06:49:23.7423922Z 2025-09-07T06:49:23.7424012Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T06:49:23.7424101Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T06:49:23.7424179Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T06:49:23.7424229Z we need to be able to perform it in place 2025-09-07T06:49:23.7424259Z 2025-09-07T06:49:23.7424305Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7424355Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T06:49:23.7424454Z >>> def set_up_planner( 2025-09-07T06:49:23.7424491Z >>> self, 2025-09-07T06:49:23.7424535Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T06:49:23.7424578Z >>> metadata: Metadata, 2025-09-07T06:49:23.7424619Z >>> is_coordinator: bool, 2025-09-07T06:49:23.7424655Z >>> ) -> None: 2025-09-07T06:49:23.7424706Z >>> self.original_state_dict = state_dict 2025-09-07T06:49:23.7424772Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T06:49:23.7424803Z >>> 2025-09-07T06:49:23.7424848Z >>> if self.flatten_sharded_tensors: 2025-09-07T06:49:23.7424905Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T06:49:23.7424936Z >>> 2025-09-07T06:49:23.7424978Z >>> if self.flatten_state_dict: 2025-09-07T06:49:23.7426416Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T06:49:23.7426449Z >>> 2025-09-07T06:49:23.7426492Z >>> self.state_dict = state_dict 2025-09-07T06:49:23.7426563Z >>> self.metadata = metadata 2025-09-07T06:49:23.7426615Z >>> self.is_coordinator = is_coordinator 2025-09-07T06:49:23.7426645Z >>> 2025-09-07T06:49:23.7426693Z >>> def load_bytes(self, read_item, value): 2025-09-07T06:49:23.7426736Z >>> # Remove the "foo_" prefix 2025-09-07T06:49:23.7426849Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T06:49:23.7426879Z 2025-09-07T06:49:23.7426910Z 2025-09-07T06:49:23.7426999Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T06:49:23.7427030Z 2025-09-07T06:49:23.7427073Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T06:49:23.7427132Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T06:49:23.7427178Z >>> def resolve_tensor(self, read_item): 2025-09-07T06:49:23.7427257Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T06:49:23.7427316Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T06:49:23.7427365Z >>> 2025-09-07T06:49:23.7427416Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T06:49:23.7427475Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T06:49:23.7428872Z 2025-09-07T06:49:23.7428965Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7428996Z 2025-09-07T06:49:23.7429033Z warnings.warn(msg) 2025-09-07T06:49:23.7429063Z 2025-09-07T06:49:23.7429137Z --- Parse Warning: 103 / 146 --- 2025-09-07T06:49:23.7429491Z /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. 2025-09-07T06:49:23.7429589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7429620Z 2025-09-07T06:49:23.7429681Z Return the model state_dict and optimizers state_dict. 2025-09-07T06:49:23.7429714Z 2025-09-07T06:49:23.7429794Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T06:49:23.7429883Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T06:49:23.7429970Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T06:49:23.7430047Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T06:49:23.7430122Z with a different number of trainers and/or different parallelisms. 2025-09-07T06:49:23.7430211Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T06:49:23.7430246Z these APIs. 2025-09-07T06:49:23.7430294Z 3.) sanity checking the result state_dict. 2025-09-07T06:49:23.7430327Z 2025-09-07T06:49:23.7430404Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T06:49:23.7430486Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T06:49:23.7431987Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T06:49:23.7432064Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T06:49:23.7432140Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T06:49:23.7432227Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T06:49:23.7432304Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T06:49:23.7432349Z canonical FQNs when calling this API. 2025-09-07T06:49:23.7432379Z 2025-09-07T06:49:23.7432460Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T06:49:23.7432539Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T06:49:23.7432614Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T06:49:23.7432645Z 2025-09-07T06:49:23.7432680Z Example: 2025-09-07T06:49:23.7432720Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7432757Z >>> import torch 2025-09-07T06:49:23.7432843Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:23.7432917Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T06:49:23.7432995Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T06:49:23.7433026Z 2025-09-07T06:49:23.7433075Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T06:49:23.7433144Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T06:49:23.7433193Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T06:49:23.7434681Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T06:49:23.7434742Z 2025-09-07T06:49:23.7434773Z 2025-09-07T06:49:23.7434862Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T06:49:23.7434944Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T06:49:23.7434987Z ... fsdp_model, fsdp_optim 2025-09-07T06:49:23.7435019Z ... ) 2025-09-07T06:49:23.7435049Z 2025-09-07T06:49:23.7435127Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T06:49:23.7435169Z >>> # the asserts will fail. 2025-09-07T06:49:23.7435220Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T06:49:23.7435278Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T06:49:23.7435308Z 2025-09-07T06:49:23.7435338Z 2025-09-07T06:49:23.7435370Z Args: 2025-09-07T06:49:23.7435442Z model (nn.Module): the nn.Module to the model. 2025-09-07T06:49:23.7435513Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T06:49:23.7435577Z The optimizers that are used to optimize ``model``. 2025-09-07T06:49:23.7435676Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T06:49:23.7435721Z that belong to the submodules. 2025-09-07T06:49:23.7435785Z options (StateDictOptions): the options to control how 2025-09-07T06:49:23.7435859Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T06:49:23.7437289Z `StateDictOptions` for the details. 2025-09-07T06:49:23.7437322Z 2025-09-07T06:49:23.7437354Z Returns: 2025-09-07T06:49:23.7437426Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T06:49:23.7437456Z 2025-09-07T06:49:23.7437537Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T06:49:23.7437568Z 2025-09-07T06:49:23.7437656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7437689Z 2025-09-07T06:49:23.7437728Z warnings.warn(msg) 2025-09-07T06:49:23.7437759Z 2025-09-07T06:49:23.7437834Z --- Parse Warning: 104 / 146 --- 2025-09-07T06:49:23.7438154Z /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=69. 2025-09-07T06:49:23.7438245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7438275Z 2025-09-07T06:49:23.7438346Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T06:49:23.7438377Z 2025-09-07T06:49:23.7438454Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T06:49:23.7438537Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T06:49:23.7438639Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T06:49:23.7438672Z costs). 2025-09-07T06:49:23.7438702Z 2025-09-07T06:49:23.7440139Z Each rank will try to read the least amount of data necessary 2025-09-07T06:49:23.7440221Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T06:49:23.7440309Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T06:49:23.7440339Z 2025-09-07T06:49:23.7440432Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T06:49:23.7440520Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T06:49:23.7440585Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T06:49:23.7440678Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T06:49:23.7440736Z it in the ``state_dict`` with the deserialized object. 2025-09-07T06:49:23.7440786Z 2025-09-07T06:49:23.7440826Z .. warning:: 2025-09-07T06:49:23.7440889Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T06:49:23.7440968Z destination device *prior to* calling this function. 2025-09-07T06:49:23.7440998Z 2025-09-07T06:49:23.7441078Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T06:49:23.7441113Z on state_dict. 2025-09-07T06:49:23.7441143Z 2025-09-07T06:49:23.7441176Z .. warning:: 2025-09-07T06:49:23.7441250Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T06:49:23.7441317Z pos-processing and non-tensor data properly propagates. 2025-09-07T06:49:23.7441347Z 2025-09-07T06:49:23.7442738Z .. note: 2025-09-07T06:49:23.7442836Z If no process group is initialized, this function will assume the intent 2025-09-07T06:49:23.7442917Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T06:49:23.7443010Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T06:49:23.7443052Z or ShardedTensor) 2025-09-07T06:49:23.7443082Z 2025-09-07T06:49:23.7443114Z .. note: 2025-09-07T06:49:23.7443168Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T06:49:23.7443199Z 2025-09-07T06:49:23.7443230Z Args: 2025-09-07T06:49:23.7443305Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T06:49:23.7443360Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T06:49:23.7443433Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T06:49:23.7443504Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T06:49:23.7443568Z It can also be a key if the storage is a key-value store. 2025-09-07T06:49:23.7443607Z (Default: ``None``) 2025-09-07T06:49:23.7443657Z storage_reader (Optional[StorageReader]): 2025-09-07T06:49:23.7443730Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T06:49:23.7443803Z specified, DCP will automatically infer the reader based on the 2025-09-07T06:49:23.7443874Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T06:49:23.7443916Z be raised. (Default: ``None``) 2025-09-07T06:49:23.7443961Z planner (Optional[LoadPlanner]): 2025-09-07T06:49:23.7445447Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T06:49:23.7445501Z planner will be used. (Default: ``None``) 2025-09-07T06:49:23.7445552Z process_group (Optional[ProcessGroup]): 2025-09-07T06:49:23.7445623Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T06:49:23.7445663Z (Default: ``None``) 2025-09-07T06:49:23.7445741Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T06:49:23.7445861Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T06:49:23.7445897Z Returns: 2025-09-07T06:49:23.7445929Z None. 2025-09-07T06:49:23.7445960Z 2025-09-07T06:49:23.7445992Z Examples 2025-09-07T06:49:23.7446033Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7446072Z >>> my_model = MyModule() 2025-09-07T06:49:23.7446125Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T06:49:23.7446174Z >>> model_state_dict = my_model.state_dict() 2025-09-07T06:49:23.7446259Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T06:49:23.7446297Z ... "/checkpoint/1" 2025-09-07T06:49:23.7446328Z ... ) 2025-09-07T06:49:23.7446357Z 2025-09-07T06:49:23.7446417Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T06:49:23.7446462Z >>> state_dict=model_state_dict, 2025-09-07T06:49:23.7447900Z >>> storage_reader=fs_storage_reader, 2025-09-07T06:49:23.7447936Z >>> ) 2025-09-07T06:49:23.7447966Z 2025-09-07T06:49:23.7448067Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T06:49:23.7448117Z >>> # to flush the state_dict, must call it to 2025-09-07T06:49:23.7448160Z >>> # ensure correct behavior. 2025-09-07T06:49:23.7448209Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T06:49:23.7448240Z 2025-09-07T06:49:23.7448272Z .. note:: 2025-09-07T06:49:23.7448348Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T06:49:23.7448423Z For NCCL-based process groups, internal tensor representations of 2025-09-07T06:49:23.7448505Z objects must be moved to the GPU device before communication takes place. 2025-09-07T06:49:23.7448602Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T06:49:23.7448686Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T06:49:23.7448756Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T06:49:23.7448789Z 2025-09-07T06:49:23.7448876Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7448906Z 2025-09-07T06:49:23.7448943Z warnings.warn(msg) 2025-09-07T06:49:23.7448974Z 2025-09-07T06:49:23.7449048Z --- Parse Warning: 105 / 146 --- 2025-09-07T06:49:23.7449404Z /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. 2025-09-07T06:49:23.7450857Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7450891Z 2025-09-07T06:49:23.7450968Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T06:49:23.7451001Z 2025-09-07T06:49:23.7451065Z This is the current recommended way to checkpoint FSDP. 2025-09-07T06:49:23.7451106Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7451163Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T06:49:23.7451196Z >>> # Save 2025-09-07T06:49:23.7451237Z >>> model: torch.nn.Model 2025-09-07T06:49:23.7451283Z >>> optim_params = model.parameters() 2025-09-07T06:49:23.7451339Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T06:49:23.7451370Z >>> # Save 2025-09-07T06:49:23.7451446Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T06:49:23.7451483Z >>> state_dict = { 2025-09-07T06:49:23.7451544Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T06:49:23.7451589Z >>> "model": model.state_dict() 2025-09-07T06:49:23.7451621Z >>> } 2025-09-07T06:49:23.7451662Z >>> dist_cp.save_state_dict( 2025-09-07T06:49:23.7451735Z >>> state_dict=optim_state, 2025-09-07T06:49:23.7451801Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T06:49:23.7451858Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T06:49:23.7451888Z >>> ) 2025-09-07T06:49:23.7453270Z >>> 2025-09-07T06:49:23.7453303Z >>> # Load 2025-09-07T06:49:23.7453384Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T06:49:23.7453434Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T06:49:23.7453474Z >>> checkpoint = { 2025-09-07T06:49:23.7453515Z >>> "model": model_state_dict 2025-09-07T06:49:23.7453545Z >>> } 2025-09-07T06:49:23.7453586Z >>> dist_cp.load_state_dict( 2025-09-07T06:49:23.7453627Z >>> state_dict=checkpoint, 2025-09-07T06:49:23.7453698Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T06:49:23.7453770Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T06:49:23.7453804Z >>> ) 2025-09-07T06:49:23.7453863Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T06:49:23.7453910Z >>> 2025-09-07T06:49:23.7453974Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T06:49:23.7454014Z >>> model_state_dict, 2025-09-07T06:49:23.7454057Z >>> optimizer_key="optimizer", 2025-09-07T06:49:23.7454122Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T06:49:23.7454152Z >>> ) 2025-09-07T06:49:23.7454183Z >>> 2025-09-07T06:49:23.7454240Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:23.7455707Z >>> model, optim, optim_state["optimizer"] 2025-09-07T06:49:23.7455739Z >>> ) 2025-09-07T06:49:23.7455771Z >>> 2025-09-07T06:49:23.7455846Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T06:49:23.7455878Z 2025-09-07T06:49:23.7455971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7456003Z 2025-09-07T06:49:23.7456041Z warnings.warn(msg) 2025-09-07T06:49:23.7456075Z 2025-09-07T06:49:23.7456149Z --- Parse Warning: 106 / 146 --- 2025-09-07T06:49:23.7456456Z /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. 2025-09-07T06:49:23.7456548Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7456578Z 2025-09-07T06:49:23.7456641Z This configures FSDP-native mixed precision training. 2025-09-07T06:49:23.7456671Z 2025-09-07T06:49:23.7456705Z Attributes: 2025-09-07T06:49:23.7456790Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T06:49:23.7456863Z parameters during forward and backward and thus the dtype for 2025-09-07T06:49:23.7456943Z forward and backward computation. Outside forward and backward, the 2025-09-07T06:49:23.7457016Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T06:49:23.7457089Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T06:49:23.7457149Z always saved in full precision. (Default: ``None``) 2025-09-07T06:49:23.7458594Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T06:49:23.7458673Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T06:49:23.7458738Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T06:49:23.7458809Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T06:49:23.7458886Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T06:49:23.7458960Z to force gradient reduction to run in full precision. (Default: 2025-09-07T06:49:23.7459027Z ``None``) 2025-09-07T06:49:23.7459104Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T06:49:23.7459177Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T06:49:23.7459248Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T06:49:23.7459320Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T06:49:23.7459387Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T06:49:23.7459420Z ``None``) 2025-09-07T06:49:23.7459493Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T06:49:23.7459568Z gradients to full precision after the backward pass in preparation 2025-09-07T06:49:23.7459643Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T06:49:23.7459732Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T06:49:23.7459805Z using a custom optimizer that supports running in low precision. 2025-09-07T06:49:23.7459863Z (Default: ``False``) 2025-09-07T06:49:23.7459937Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T06:49:23.7460009Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T06:49:23.7461450Z that parameter and input dtypes match for forward computation, as 2025-09-07T06:49:23.7461528Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T06:49:23.7461606Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T06:49:23.7461678Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T06:49:23.7461738Z (Default: ``False``) 2025-09-07T06:49:23.7461817Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T06:49:23.7461891Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T06:49:23.7461960Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T06:49:23.7462014Z this does not do anything. (Default: ``True``) 2025-09-07T06:49:23.7462091Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T06:49:23.7462158Z module classes to ignore for mixed precision when using an 2025-09-07T06:49:23.7462224Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T06:49:23.7462297Z applied to them separately with mixed precision disabled (meaning 2025-09-07T06:49:23.7462369Z that the final FSDP construction would deviate from the specified 2025-09-07T06:49:23.7462436Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T06:49:23.7462506Z not do anything. This API is experimental and subject to change. 2025-09-07T06:49:23.7462550Z (Default: ``(_BatchNorm,)``) 2025-09-07T06:49:23.7462582Z 2025-09-07T06:49:23.7462645Z .. note:: This API is experimental and subject to change. 2025-09-07T06:49:23.7462677Z 2025-09-07T06:49:23.7462752Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T06:49:23.7462783Z 2025-09-07T06:49:23.7464205Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T06:49:23.7464252Z precision, but buffers are not. 2025-09-07T06:49:23.7464282Z 2025-09-07T06:49:23.7464355Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T06:49:23.7464490Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T06:49:23.7464574Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T06:49:23.7464649Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T06:49:23.7464761Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T06:49:23.7464841Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T06:49:23.7464898Z to still apply mixed precision to those modules. 2025-09-07T06:49:23.7464928Z 2025-09-07T06:49:23.7465001Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T06:49:23.7465075Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T06:49:23.7465155Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T06:49:23.7465219Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T06:49:23.7465250Z 2025-09-07T06:49:23.7465326Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T06:49:23.7465401Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T06:49:23.7465485Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T06:49:23.7465551Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T06:49:23.7465642Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T06:49:23.7467091Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T06:49:23.7467171Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T06:49:23.7467239Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T06:49:23.7467269Z 2025-09-07T06:49:23.7467344Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T06:49:23.7467424Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T06:49:23.7467519Z values to configure casting inputs or not before each instance's 2025-09-07T06:49:23.7467592Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T06:49:23.7467672Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T06:49:23.7467754Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T06:49:23.7467827Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T06:49:23.7467858Z 2025-09-07T06:49:23.7467892Z Example:: 2025-09-07T06:49:23.7467923Z 2025-09-07T06:49:23.7467975Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7468040Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T06:49:23.7468079Z >>> model[1] = FSDP( 2025-09-07T06:49:23.7468116Z >>> model[1], 2025-09-07T06:49:23.7468223Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T06:49:23.7468255Z >>> ) 2025-09-07T06:49:23.7468294Z >>> model = FSDP( 2025-09-07T06:49:23.7468330Z >>> model, 2025-09-07T06:49:23.7469798Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T06:49:23.7469836Z >>> ) 2025-09-07T06:49:23.7469865Z 2025-09-07T06:49:23.7469941Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T06:49:23.7470012Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T06:49:23.7470090Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T06:49:23.7470166Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T06:49:23.7470199Z ones. 2025-09-07T06:49:23.7470229Z 2025-09-07T06:49:23.7470259Z 2025-09-07T06:49:23.7470347Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7470378Z 2025-09-07T06:49:23.7470414Z warnings.warn(msg) 2025-09-07T06:49:23.7470445Z 2025-09-07T06:49:23.7470540Z --- Parse Warning: 107 / 146 --- 2025-09-07T06:49:23.7470891Z /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. 2025-09-07T06:49:23.7470985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7471014Z 2025-09-07T06:49:23.7471089Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T06:49:23.7471159Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T06:49:23.7471232Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T06:49:23.7471309Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T06:49:23.7472763Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T06:49:23.7472817Z follows: 2025-09-07T06:49:23.7472848Z 2025-09-07T06:49:23.7472902Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7473004Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:23.7473055Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T06:49:23.7473128Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T06:49:23.7473206Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T06:49:23.7473250Z >>> state = fsdp.state_dict() 2025-09-07T06:49:23.7473327Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T06:49:23.7473411Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T06:49:23.7473515Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T06:49:23.7473558Z >>> if dist.get_rank() == 0: 2025-09-07T06:49:23.7473627Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T06:49:23.7473682Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T06:49:23.7473731Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:23.7473813Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T06:49:23.7473896Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T06:49:23.7473934Z >>> fsdp = FSDP( 2025-09-07T06:49:23.7473967Z ... model, 2025-09-07T06:49:23.7474020Z ... device_id=torch.cuda.current_device(), 2025-09-07T06:49:23.7475479Z ... auto_wrap_policy=..., 2025-09-07T06:49:23.7475524Z ... sync_module_states=True, 2025-09-07T06:49:23.7475555Z ... ) 2025-09-07T06:49:23.7475632Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T06:49:23.7475664Z 2025-09-07T06:49:23.7475697Z Attributes: 2025-09-07T06:49:23.7475772Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T06:49:23.7475846Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T06:49:23.7475907Z ranks save the full state dict. (Default: ``False``) 2025-09-07T06:49:23.7475937Z 2025-09-07T06:49:23.7476025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7476055Z 2025-09-07T06:49:23.7476092Z warnings.warn(msg) 2025-09-07T06:49:23.7476122Z 2025-09-07T06:49:23.7476197Z --- Parse Warning: 108 / 146 --- 2025-09-07T06:49:23.7476594Z /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=634. 2025-09-07T06:49:23.7476718Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7476806Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T06:49:23.7476838Z 2025-09-07T06:49:23.7476929Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T06:49:23.7477003Z The target module does not have to be a FSDP module. If the target 2025-09-07T06:49:23.7477078Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T06:49:23.7478481Z 2025-09-07T06:49:23.7478554Z .. note:: This API should be called for only the top-level (root) 2025-09-07T06:49:23.7478590Z module. 2025-09-07T06:49:23.7478620Z 2025-09-07T06:49:23.7478694Z .. note:: This API enables users to transparently use the conventional 2025-09-07T06:49:23.7478764Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T06:49:23.7478867Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T06:49:23.7478959Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T06:49:23.7479042Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T06:49:23.7479079Z for FSDP: 2025-09-07T06:49:23.7479110Z 2025-09-07T06:49:23.7479145Z Example:: 2025-09-07T06:49:23.7479176Z 2025-09-07T06:49:23.7479228Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7479271Z >>> model = DDP(FSDP(...)) 2025-09-07T06:49:23.7479316Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:23.7479354Z >>> model, 2025-09-07T06:49:23.7479423Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T06:49:23.7479501Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T06:49:23.7479587Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T06:49:23.7479620Z >>> ) 2025-09-07T06:49:23.7481029Z >>> param_state_dict = model.state_dict() 2025-09-07T06:49:23.7481095Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T06:49:23.7481126Z 2025-09-07T06:49:23.7481158Z Args: 2025-09-07T06:49:23.7481209Z module (torch.nn.Module): Root module. 2025-09-07T06:49:23.7481292Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T06:49:23.7481375Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T06:49:23.7481421Z target ``state_dict_type``. 2025-09-07T06:49:23.7481512Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T06:49:23.7481560Z for the optimizer state dict. 2025-09-07T06:49:23.7481594Z 2025-09-07T06:49:23.7481628Z Returns: 2025-09-07T06:49:23.7481706Z A StateDictSettings that include the previous state_dict type and 2025-09-07T06:49:23.7481753Z configuration for the module. 2025-09-07T06:49:23.7481786Z 2025-09-07T06:49:23.7481873Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7481904Z 2025-09-07T06:49:23.7481941Z warnings.warn(msg) 2025-09-07T06:49:23.7481971Z 2025-09-07T06:49:23.7482044Z --- Parse Warning: 109 / 146 --- 2025-09-07T06:49:23.7482429Z /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=792. 2025-09-07T06:49:23.7482520Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7483984Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T06:49:23.7484018Z 2025-09-07T06:49:23.7484132Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T06:49:23.7484184Z :meth:`set_state_dict_type` for the detail. 2025-09-07T06:49:23.7484215Z 2025-09-07T06:49:23.7484251Z Example:: 2025-09-07T06:49:23.7484280Z 2025-09-07T06:49:23.7484333Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7484429Z >>> model = DDP(FSDP(...)) 2025-09-07T06:49:23.7484476Z >>> with FSDP.state_dict_type( 2025-09-07T06:49:23.7484513Z >>> model, 2025-09-07T06:49:23.7484565Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T06:49:23.7484599Z >>> ): 2025-09-07T06:49:23.7484671Z >>> checkpoint = model.state_dict() 2025-09-07T06:49:23.7484702Z 2025-09-07T06:49:23.7484735Z Args: 2025-09-07T06:49:23.7484803Z module (torch.nn.Module): Root module. 2025-09-07T06:49:23.7484886Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T06:49:23.7484967Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T06:49:23.7485028Z configuration for the target ``state_dict_type``. 2025-09-07T06:49:23.7485109Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T06:49:23.7486559Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T06:49:23.7486593Z 2025-09-07T06:49:23.7486704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7486735Z 2025-09-07T06:49:23.7486777Z warnings.warn(msg) 2025-09-07T06:49:23.7486806Z 2025-09-07T06:49:23.7486883Z --- Parse Warning: 110 / 146 --- 2025-09-07T06:49:23.7487270Z /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=1805. 2025-09-07T06:49:23.7487364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7487394Z 2025-09-07T06:49:23.7487478Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T06:49:23.7487507Z 2025-09-07T06:49:23.7487579Z The given state-dict can be transformed to one of three types: 2025-09-07T06:49:23.7487684Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T06:49:23.7487714Z 2025-09-07T06:49:23.7487794Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T06:49:23.7487872Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T06:49:23.7487908Z avoid OOM. 2025-09-07T06:49:23.7487938Z 2025-09-07T06:49:23.7488020Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T06:49:23.7488094Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T06:49:23.7488126Z memory. 2025-09-07T06:49:23.7489524Z 2025-09-07T06:49:23.7489604Z For local state_dict, no transformation will be performed. But a state 2025-09-07T06:49:23.7489688Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T06:49:23.7489733Z nature (this is not supported yet). 2025-09-07T06:49:23.7489763Z 2025-09-07T06:49:23.7489797Z Example:: 2025-09-07T06:49:23.7489827Z 2025-09-07T06:49:23.7489879Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7489989Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:23.7490050Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T06:49:23.7490117Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T06:49:23.7490191Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T06:49:23.7490231Z >>> # Save a checkpoint 2025-09-07T06:49:23.7490270Z >>> model, optim = ... 2025-09-07T06:49:23.7490314Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:23.7490347Z >>> model, 2025-09-07T06:49:23.7490394Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:23.7490445Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7490502Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7490533Z >>> ) 2025-09-07T06:49:23.7490579Z >>> state_dict = model.state_dict() 2025-09-07T06:49:23.7490657Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T06:49:23.7492071Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T06:49:23.7492131Z >>> # Load a checkpoint 2025-09-07T06:49:23.7492170Z >>> model, optim = ... 2025-09-07T06:49:23.7492230Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T06:49:23.7492272Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:23.7492305Z >>> model, 2025-09-07T06:49:23.7492352Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:23.7492402Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7492457Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7492487Z >>> ) 2025-09-07T06:49:23.7492535Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:23.7492608Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:23.7492654Z >>> model, optim, optim_state_dict 2025-09-07T06:49:23.7492687Z >>> ) 2025-09-07T06:49:23.7492738Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T06:49:23.7492768Z 2025-09-07T06:49:23.7492801Z Args: 2025-09-07T06:49:23.7492874Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T06:49:23.7492946Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T06:49:23.7492999Z were passed into the optimizer ``optim``. 2025-09-07T06:49:23.7493067Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T06:49:23.7494501Z parameters. 2025-09-07T06:49:23.7494583Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T06:49:23.7494659Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T06:49:23.7494698Z Default: ``None``) 2025-09-07T06:49:23.7494786Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T06:49:23.7494857Z are sharded or ``None`` if using the default process group. ( 2025-09-07T06:49:23.7494896Z Default: ``None``) 2025-09-07T06:49:23.7494928Z 2025-09-07T06:49:23.7494962Z Returns: 2025-09-07T06:49:23.7495031Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T06:49:23.7495095Z ``model``. The sharding of the optimizer state is based on 2025-09-07T06:49:23.7495135Z ``state_dict_type``. 2025-09-07T06:49:23.7495166Z 2025-09-07T06:49:23.7495253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7495284Z 2025-09-07T06:49:23.7495320Z warnings.warn(msg) 2025-09-07T06:49:23.7495349Z 2025-09-07T06:49:23.7495423Z --- Parse Warning: 111 / 146 --- 2025-09-07T06:49:23.7495848Z /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=1903. 2025-09-07T06:49:23.7495943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7495974Z 2025-09-07T06:49:23.7496097Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T06:49:23.7497499Z 2025-09-07T06:49:23.7497564Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T06:49:23.7497641Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T06:49:23.7497715Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T06:49:23.7497785Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T06:49:23.7497816Z 2025-09-07T06:49:23.7497869Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7497953Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T06:49:23.7498038Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T06:49:23.7498105Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T06:49:23.7498207Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T06:49:23.7498247Z >>> # Save a checkpoint 2025-09-07T06:49:23.7498284Z >>> model, optim = ... 2025-09-07T06:49:23.7498327Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:23.7498361Z >>> model, 2025-09-07T06:49:23.7498407Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:23.7498459Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7498514Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7498546Z >>> ) 2025-09-07T06:49:23.7498609Z >>> state_dict = model.state_dict() 2025-09-07T06:49:23.7498656Z >>> original_osd = optim.state_dict() 2025-09-07T06:49:23.7500072Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T06:49:23.7500111Z >>> model, 2025-09-07T06:49:23.7500145Z >>> optim, 2025-09-07T06:49:23.7500193Z >>> optim_state_dict=original_osd 2025-09-07T06:49:23.7500225Z >>> ) 2025-09-07T06:49:23.7500279Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T06:49:23.7500317Z >>> # Load a checkpoint 2025-09-07T06:49:23.7500355Z >>> model, optim = ... 2025-09-07T06:49:23.7500414Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T06:49:23.7500456Z >>> FSDP.set_state_dict_type( 2025-09-07T06:49:23.7500489Z >>> model, 2025-09-07T06:49:23.7500536Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T06:49:23.7500586Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7500641Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T06:49:23.7500672Z >>> ) 2025-09-07T06:49:23.7500719Z >>> model.load_state_dict(state_dict) 2025-09-07T06:49:23.7500776Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T06:49:23.7500822Z >>> model, optim, optim_state_dict 2025-09-07T06:49:23.7500852Z >>> ) 2025-09-07T06:49:23.7500901Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T06:49:23.7500931Z 2025-09-07T06:49:23.7500963Z Args: 2025-09-07T06:49:23.7502388Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T06:49:23.7502464Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T06:49:23.7502516Z were passed into the optimizer ``optim``. 2025-09-07T06:49:23.7502584Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T06:49:23.7502620Z parameters. 2025-09-07T06:49:23.7502698Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T06:49:23.7502769Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T06:49:23.7502859Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T06:49:23.7502925Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T06:49:23.7502997Z load_directly (bool): If this is set to True, this API will also 2025-09-07T06:49:23.7503069Z call optim.load_state_dict(result) before returning the result. 2025-09-07T06:49:23.7503146Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T06:49:23.7503187Z (Default: ``False``) 2025-09-07T06:49:23.7503271Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T06:49:23.7503339Z are sharded or ``None`` if using the default process group. ( 2025-09-07T06:49:23.7503376Z Default: ``None``) 2025-09-07T06:49:23.7503407Z 2025-09-07T06:49:23.7503495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7503542Z 2025-09-07T06:49:23.7503579Z warnings.warn(msg) 2025-09-07T06:49:23.7503610Z 2025-09-07T06:49:23.7503683Z --- Parse Warning: 112 / 146 --- 2025-09-07T06:49:23.7505536Z /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. 2025-09-07T06:49:23.7505631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7505663Z 2025-09-07T06:49:23.7505781Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T06:49:23.7505813Z 2025-09-07T06:49:23.7505949Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T06:49:23.7506025Z by using different periods concurrently after the warm-up stage. 2025-09-07T06:49:23.7506173Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T06:49:23.7506294Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T06:49:23.7506399Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T06:49:23.7506521Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T06:49:23.7506626Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T06:49:23.7506723Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T06:49:23.7506754Z 2025-09-07T06:49:23.7506786Z Args: 2025-09-07T06:49:23.7506880Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T06:49:23.7506957Z process group size, used for initializing process groups of 2025-09-07T06:49:23.7507036Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T06:49:23.7507116Z Particularly, at each iteration, there will be at most a single 2025-09-07T06:49:23.7507197Z process group that runs averaging -- the period of such group should 2025-09-07T06:49:23.7507273Z have the largest period which the current step can be divided by. 2025-09-07T06:49:23.7508694Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T06:49:23.7508772Z then this means totally three process groups will be created to 2025-09-07T06:49:23.7508850Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T06:49:23.7508922Z At the 4th iteration, only the second process group will run 2025-09-07T06:49:23.7509015Z averaging, because the first process group should be a 2025-09-07T06:49:23.7509095Z subset of the second process group, and no need to execute the first 2025-09-07T06:49:23.7509144Z process group redundantly. 2025-09-07T06:49:23.7509215Z On the other hand, the third process group can only be triggered 2025-09-07T06:49:23.7509293Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T06:49:23.7509399Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T06:49:23.7509552Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T06:49:23.7509615Z If ``None``, the default process group, which is created 2025-09-07T06:49:23.7509711Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T06:49:23.7509780Z (default: ``None``) 2025-09-07T06:49:23.7509810Z 2025-09-07T06:49:23.7509845Z Example:: 2025-09-07T06:49:23.7509893Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T06:49:23.7509942Z >>> from collections import OrderedDict 2025-09-07T06:49:23.7509978Z >>> import torch 2025-09-07T06:49:23.7510025Z >>> import torch.distributed as dist 2025-09-07T06:49:23.7510120Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T06:49:23.7511566Z >>> PostLocalSGDState, 2025-09-07T06:49:23.7511611Z >>> post_localSGD_hook, 2025-09-07T06:49:23.7511663Z >>> ) 2025-09-07T06:49:23.7511794Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T06:49:23.7511840Z >>> import torch.nn as nn 2025-09-07T06:49:23.7511871Z >>> 2025-09-07T06:49:23.7511941Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T06:49:23.7511984Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:23.7512037Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T06:49:23.7512101Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:23.7512156Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:23.7512188Z >>> ) 2025-09-07T06:49:23.7512243Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:23.7512340Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T06:49:23.7512386Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T06:49:23.7512493Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T06:49:23.7512558Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:23.7512589Z >>> 2025-09-07T06:49:23.7512688Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T06:49:23.7512736Z >>> # the 16 processes every 16 iterations. 2025-09-07T06:49:23.7512805Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T06:49:23.7512888Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T06:49:23.7514354Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:23.7514506Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T06:49:23.7514567Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T06:49:23.7514610Z >>> for step in range(0, 200): 2025-09-07T06:49:23.7514653Z >>> optimizer.zero_grad() 2025-09-07T06:49:23.7514728Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:23.7514769Z >>> loss.backward() 2025-09-07T06:49:23.7514810Z >>> optimizer.step() 2025-09-07T06:49:23.7514869Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T06:49:23.7514968Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T06:49:23.7515029Z >>> averager.average_parameters(model.parameters()) 2025-09-07T06:49:23.7515059Z 2025-09-07T06:49:23.7515093Z .. warning :: 2025-09-07T06:49:23.7515184Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T06:49:23.7515266Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T06:49:23.7515375Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T06:49:23.7515425Z 2025-09-07T06:49:23.7515461Z .. warning :: 2025-09-07T06:49:23.7515543Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T06:49:23.7515590Z 2025-09-07T06:49:23.7515679Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7515709Z 2025-09-07T06:49:23.7517120Z warnings.warn(msg) 2025-09-07T06:49:23.7517150Z 2025-09-07T06:49:23.7517227Z --- Parse Warning: 113 / 146 --- 2025-09-07T06:49:23.7517597Z /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. 2025-09-07T06:49:23.7517714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7517745Z 2025-09-07T06:49:23.7517817Z Averages parameters periodically after the warm-up stage. 2025-09-07T06:49:23.7517850Z 2025-09-07T06:49:23.7517943Z This can be used for running `post-local SGD `_, 2025-09-07T06:49:23.7518016Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T06:49:23.7518100Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T06:49:23.7518130Z 2025-09-07T06:49:23.7518161Z Args: 2025-09-07T06:49:23.7518223Z period (int): The number of steps per model averaging. 2025-09-07T06:49:23.7518320Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T06:49:23.7518370Z Otherwise, only DDP needs to be used. 2025-09-07T06:49:23.7518445Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T06:49:23.7518494Z model averaging is skipped. 2025-09-07T06:49:23.7518565Z process_group: The process group to be used for all-reduce. 2025-09-07T06:49:23.7518622Z If ``None``, the default process group, which 2025-09-07T06:49:23.7518693Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T06:49:23.7520107Z will be used. (default: ``None``) 2025-09-07T06:49:23.7520141Z 2025-09-07T06:49:23.7520175Z Example:: 2025-09-07T06:49:23.7520205Z 2025-09-07T06:49:23.7520256Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T06:49:23.7520292Z >>> import torch 2025-09-07T06:49:23.7520339Z >>> import torch.distributed as dist 2025-09-07T06:49:23.7520451Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T06:49:23.7520546Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T06:49:23.7520588Z >>> import torch.nn as nn 2025-09-07T06:49:23.7520621Z >>> 2025-09-07T06:49:23.7520709Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T06:49:23.7520754Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:23.7520805Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T06:49:23.7520865Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T06:49:23.7520918Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T06:49:23.7520950Z >>> ) 2025-09-07T06:49:23.7521004Z >>> # Register a post-localSGD communication hook. 2025-09-07T06:49:23.7521107Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T06:49:23.7521166Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:23.7521198Z >>> 2025-09-07T06:49:23.7521291Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T06:49:23.7522707Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T06:49:23.7522848Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T06:49:23.7522953Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T06:49:23.7522994Z >>> for step in range(0, 200): 2025-09-07T06:49:23.7523036Z >>> optimizer.zero_grad() 2025-09-07T06:49:23.7523080Z >>> loss = loss_fn(output, labels) 2025-09-07T06:49:23.7523122Z >>> loss.backward() 2025-09-07T06:49:23.7523162Z >>> optimizer.step() 2025-09-07T06:49:23.7523233Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T06:49:23.7523306Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T06:49:23.7523356Z >>> # the initial ``warmup_steps`` period. 2025-09-07T06:49:23.7523430Z >>> averager.average_parameters(model.parameters()) 2025-09-07T06:49:23.7523461Z 2025-09-07T06:49:23.7523552Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7523581Z 2025-09-07T06:49:23.7523618Z warnings.warn(msg) 2025-09-07T06:49:23.7523649Z 2025-09-07T06:49:23.7523721Z --- Parse Warning: 114 / 146 --- 2025-09-07T06:49:23.7524085Z /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. 2025-09-07T06:49:23.7524177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7524209Z 2025-09-07T06:49:23.7525683Z Run post-localSGD algorithm. 2025-09-07T06:49:23.7525715Z 2025-09-07T06:49:23.7525802Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T06:49:23.7525860Z by combining with a model averaging component (e.g., 2025-09-07T06:49:23.7525983Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T06:49:23.7526026Z that runs after the optimizer step. 2025-09-07T06:49:23.7526059Z 2025-09-07T06:49:23.7526090Z Args: 2025-09-07T06:49:23.7526170Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T06:49:23.7526268Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T06:49:23.7526414Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T06:49:23.7526503Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T06:49:23.7526563Z only exactly one tensor is stored in this bucket. 2025-09-07T06:49:23.7526593Z 2025-09-07T06:49:23.7526627Z Returns: 2025-09-07T06:49:23.7526712Z Future handler of the communication, which updates the gradients in place. 2025-09-07T06:49:23.7526744Z 2025-09-07T06:49:23.7526804Z Example:: 2025-09-07T06:49:23.7526843Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7526933Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T06:49:23.7526980Z start_localSGD_iter=10) 2025-09-07T06:49:23.7527045Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T06:49:23.7528525Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T06:49:23.7528646Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T06:49:23.7528677Z 2025-09-07T06:49:23.7528765Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7528798Z 2025-09-07T06:49:23.7528836Z warnings.warn(msg) 2025-09-07T06:49:23.7528894Z 2025-09-07T06:49:23.7528970Z --- Parse Warning: 115 / 146 --- 2025-09-07T06:49:23.7529323Z /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. 2025-09-07T06:49:23.7529436Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7529465Z 2025-09-07T06:49:23.7529508Z Implement PowerSGD algorithm. 2025-09-07T06:49:23.7529537Z 2025-09-07T06:49:23.7529618Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T06:49:23.7529701Z algorithm described in the `paper `_. 2025-09-07T06:49:23.7529801Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T06:49:23.7529842Z compression as follows: 2025-09-07T06:49:23.7529875Z 2025-09-07T06:49:23.7530029Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T06:49:23.7530061Z 2025-09-07T06:49:23.7530204Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T06:49:23.7531592Z 2025-09-07T06:49:23.7531734Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T06:49:23.7531764Z 2025-09-07T06:49:23.7531807Z 2. Handles uncompressed tensors: 2025-09-07T06:49:23.7531837Z 2025-09-07T06:49:23.7532008Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T06:49:23.7532039Z 2025-09-07T06:49:23.7532156Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T06:49:23.7532188Z 2025-09-07T06:49:23.7532273Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T06:49:23.7532303Z 2025-09-07T06:49:23.7532389Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T06:49:23.7532495Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T06:49:23.7532526Z 2025-09-07T06:49:23.7532581Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T06:49:23.7532612Z 2025-09-07T06:49:23.7532653Z 3.3. Allreduces Ps as a batch; 2025-09-07T06:49:23.7532684Z 2025-09-07T06:49:23.7532728Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T06:49:23.7532758Z 2025-09-07T06:49:23.7532829Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T06:49:23.7532861Z 2025-09-07T06:49:23.7534245Z 3.6. Allreduces Qs as a batch; 2025-09-07T06:49:23.7534278Z 2025-09-07T06:49:23.7534462Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T06:49:23.7534495Z 2025-09-07T06:49:23.7534633Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T06:49:23.7534732Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T06:49:23.7534874Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T06:49:23.7534904Z 2025-09-07T06:49:23.7534935Z Args: 2025-09-07T06:49:23.7535083Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T06:49:23.7535207Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T06:49:23.7535271Z and ``min_compression_rate``. 2025-09-07T06:49:23.7535412Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T06:49:23.7535519Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T06:49:23.7535577Z only exactly one tensor is stored in this bucket. 2025-09-07T06:49:23.7535607Z 2025-09-07T06:49:23.7535639Z Returns: 2025-09-07T06:49:23.7535724Z Future handler of the communication, which updates the gradients in place. 2025-09-07T06:49:23.7535755Z 2025-09-07T06:49:23.7535789Z Example:: 2025-09-07T06:49:23.7535828Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7535924Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T06:49:23.7537382Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T06:49:23.7537444Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T06:49:23.7537478Z 2025-09-07T06:49:23.7537569Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7537602Z 2025-09-07T06:49:23.7537639Z warnings.warn(msg) 2025-09-07T06:49:23.7537669Z 2025-09-07T06:49:23.7537742Z --- Parse Warning: 116 / 146 --- 2025-09-07T06:49:23.7538087Z /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. 2025-09-07T06:49:23.7538178Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7538208Z 2025-09-07T06:49:23.7538290Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T06:49:23.7538350Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T06:49:23.7538380Z 2025-09-07T06:49:23.7538411Z Args: 2025-09-07T06:49:23.7538512Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T06:49:23.7538577Z of shards that represent the local shards on this rank. 2025-09-07T06:49:23.7538659Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T06:49:23.7538705Z shape of the overall sharded tensor. 2025-09-07T06:49:23.7538734Z 2025-09-07T06:49:23.7538768Z Keyword args: 2025-09-07T06:49:23.7540238Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T06:49:23.7540292Z the default process group will be used. 2025-09-07T06:49:23.7540358Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T06:49:23.7540435Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T06:49:23.7540504Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T06:49:23.7540565Z Default: ``False``. 2025-09-07T06:49:23.7540596Z 2025-09-07T06:49:23.7540628Z Returns: 2025-09-07T06:49:23.7540687Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T06:49:23.7540717Z 2025-09-07T06:49:23.7540747Z 2025-09-07T06:49:23.7540779Z Examples: 2025-09-07T06:49:23.7540871Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T06:49:23.7540940Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T06:49:23.7540970Z 2025-09-07T06:49:23.7541003Z on rank 0: 2025-09-07T06:49:23.7541049Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T06:49:23.7541096Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T06:49:23.7541138Z >>> shard_offsets=[0, 0], 2025-09-07T06:49:23.7541179Z >>> shard_lengths=[5, 5], 2025-09-07T06:49:23.7541225Z >>> placement="rank:0/cuda:0" 2025-09-07T06:49:23.7541271Z >>> ) 2025-09-07T06:49:23.7542700Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T06:49:23.7542911Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T06:49:23.7542942Z 2025-09-07T06:49:23.7542974Z on rank 1: 2025-09-07T06:49:23.7543019Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T06:49:23.7543066Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T06:49:23.7543106Z >>> shard_offsets=[5, 0], 2025-09-07T06:49:23.7543145Z >>> shard_lengths=[5, 5], 2025-09-07T06:49:23.7543187Z >>> placement="rank:1/cuda:1" 2025-09-07T06:49:23.7543218Z >>> ) 2025-09-07T06:49:23.7543286Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T06:49:23.7543368Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T06:49:23.7543399Z 2025-09-07T06:49:23.7543491Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7543524Z 2025-09-07T06:49:23.7543562Z warnings.warn(msg) 2025-09-07T06:49:23.7543592Z 2025-09-07T06:49:23.7543663Z --- Parse Warning: 117 / 146 --- 2025-09-07T06:49:23.7543983Z /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. 2025-09-07T06:49:23.7544075Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7544105Z 2025-09-07T06:49:23.7545595Z Representation of a sharding plan, describes how to shard a module 2025-09-07T06:49:23.7545695Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T06:49:23.7545801Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T06:49:23.7545893Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T06:49:23.7545925Z 2025-09-07T06:49:23.7545957Z Args: 2025-09-07T06:49:23.7546054Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T06:49:23.7546117Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T06:49:23.7546214Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T06:49:23.7546304Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T06:49:23.7546353Z a parameter to a `ShardingSpec`. 2025-09-07T06:49:23.7546444Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T06:49:23.7546488Z to a `Sharder` object. 2025-09-07T06:49:23.7546602Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T06:49:23.7546727Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T06:49:23.7546815Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T06:49:23.7546853Z Default: `None` 2025-09-07T06:49:23.7546943Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T06:49:23.7547025Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T06:49:23.7547114Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T06:49:23.7547151Z root module). 2025-09-07T06:49:23.7547188Z Default: None 2025-09-07T06:49:23.7548577Z Example: 2025-09-07T06:49:23.7548682Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T06:49:23.7548809Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T06:49:23.7548840Z 2025-09-07T06:49:23.7548904Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T06:49:23.7548969Z >>> class MyModule(nn.Module): 2025-09-07T06:49:23.7549015Z >>> def __init__(self) -> None: 2025-09-07T06:49:23.7549055Z >>> super().__init__() 2025-09-07T06:49:23.7549096Z >>> self.fc1 = nn.Linear() 2025-09-07T06:49:23.7549137Z >>> self.gelu = nn.GELU() 2025-09-07T06:49:23.7549175Z >>> self.fc2 = nn.Linear() 2025-09-07T06:49:23.7549216Z >>> self.relu = nn.Linear() 2025-09-07T06:49:23.7549247Z >>> 2025-09-07T06:49:23.7549289Z >>> def forward(self, input): 2025-09-07T06:49:23.7549354Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T06:49:23.7549402Z 2025-09-07T06:49:23.7549432Z 2025-09-07T06:49:23.7549487Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T06:49:23.7549533Z >>> sharding_plan = ShardingPlan( 2025-09-07T06:49:23.7549567Z >>> plan={ 2025-09-07T06:49:23.7549609Z >>> "fc1.weight": spec1, 2025-09-07T06:49:23.7551057Z >>> "fc2.weight": spec2 2025-09-07T06:49:23.7551093Z >>> }, 2025-09-07T06:49:23.7551133Z >>> output_plan={ 2025-09-07T06:49:23.7551173Z >>> "fc2": output_spec 2025-09-07T06:49:23.7551204Z >>> }, 2025-09-07T06:49:23.7551250Z >>> return_local_tensor=["fc2"] 2025-09-07T06:49:23.7551281Z >>> ) 2025-09-07T06:49:23.7551311Z 2025-09-07T06:49:23.7551402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7551432Z 2025-09-07T06:49:23.7551471Z warnings.warn(msg) 2025-09-07T06:49:23.7551501Z 2025-09-07T06:49:23.7551575Z --- Parse Warning: 118 / 146 --- 2025-09-07T06:49:23.7551938Z /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. 2025-09-07T06:49:23.7552033Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7552063Z 2025-09-07T06:49:23.7552152Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T06:49:23.7552196Z size and sharding spec on each rank. 2025-09-07T06:49:23.7552226Z 2025-09-07T06:49:23.7552257Z Args: 2025-09-07T06:49:23.7552338Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T06:49:23.7552432Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T06:49:23.7553854Z The specification describing how to shard the Tensor. 2025-09-07T06:49:23.7553924Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T06:49:23.7554032Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T06:49:23.7554073Z Default: None 2025-09-07T06:49:23.7554137Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T06:49:23.7554212Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T06:49:23.7554282Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T06:49:23.7554322Z Default: ``False``. 2025-09-07T06:49:23.7554353Z 2025-09-07T06:49:23.7554498Z Returns: 2025-09-07T06:49:23.7554584Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T06:49:23.7554630Z tensor stored in the current rank. 2025-09-07T06:49:23.7554661Z 2025-09-07T06:49:23.7554696Z Examples: 2025-09-07T06:49:23.7554736Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7554812Z >>> # All tensors below are of torch.int64 type. 2025-09-07T06:49:23.7554860Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7554925Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T06:49:23.7555021Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T06:49:23.7555056Z >>> local_tensor 2025-09-07T06:49:23.7555098Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T06:49:23.7555138Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T06:49:23.7556549Z >>> sharding_dim = 0 2025-09-07T06:49:23.7556601Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T06:49:23.7556642Z dim=sharding_dim, 2025-09-07T06:49:23.7556680Z placements=[ 2025-09-07T06:49:23.7556719Z "rank:0/cuda:0", 2025-09-07T06:49:23.7556757Z "rank:1/cuda:1", 2025-09-07T06:49:23.7556811Z ], 2025-09-07T06:49:23.7556846Z ) 2025-09-07T06:49:23.7556899Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T06:49:23.7556951Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T06:49:23.7556985Z ... ) 2025-09-07T06:49:23.7557017Z >>> st 2025-09-07T06:49:23.7557054Z ShardedTensor( 2025-09-07T06:49:23.7557100Z ShardedTensorMetadata( 2025-09-07T06:49:23.7557138Z shards_metadata=[ 2025-09-07T06:49:23.7557233Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T06:49:23.7557323Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T06:49:23.7557355Z ], 2025-09-07T06:49:23.7557396Z size=torch.Size([2, 4]) 2025-09-07T06:49:23.7557427Z ) 2025-09-07T06:49:23.7557465Z >>> st.local_tensor() 2025-09-07T06:49:23.7558875Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T06:49:23.7558916Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T06:49:23.7558949Z 2025-09-07T06:49:23.7559047Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T06:49:23.7559135Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T06:49:23.7559212Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T06:49:23.7559250Z sharding spec. 2025-09-07T06:49:23.7559281Z 2025-09-07T06:49:23.7559369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7559400Z 2025-09-07T06:49:23.7559436Z warnings.warn(msg) 2025-09-07T06:49:23.7559466Z 2025-09-07T06:49:23.7559539Z --- Parse Warning: 119 / 146 --- 2025-09-07T06:49:23.7559890Z /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. 2025-09-07T06:49:23.7560011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7560043Z 2025-09-07T06:49:23.7560132Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T06:49:23.7560169Z single local shard. 2025-09-07T06:49:23.7560198Z 2025-09-07T06:49:23.7560279Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T06:49:23.7560364Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T06:49:23.7560407Z we swap local shards directly. 2025-09-07T06:49:23.7561859Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T06:49:23.7561951Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T06:49:23.7561984Z 2025-09-07T06:49:23.7562016Z Args: 2025-09-07T06:49:23.7562141Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T06:49:23.7562205Z specification describing how the tensor is sharded. 2025-09-07T06:49:23.7562249Z 2025-09-07T06:49:23.7562282Z Returns: 2025-09-07T06:49:23.7562354Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T06:49:23.7562384Z 2025-09-07T06:49:23.7562417Z Examples: 2025-09-07T06:49:23.7562454Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7562505Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T06:49:23.7562570Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T06:49:23.7562618Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T06:49:23.7562650Z >>> tensor 2025-09-07T06:49:23.7562700Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T06:49:23.7562760Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T06:49:23.7562807Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T06:49:23.7562857Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T06:49:23.7562897Z >>> sharding_dim = 0 2025-09-07T06:49:23.7562941Z >>> spec = ChunkShardingSpec( 2025-09-07T06:49:23.7564336Z dim=sharding_dim, 2025-09-07T06:49:23.7564442Z placements=[ 2025-09-07T06:49:23.7564481Z "rank:0/cuda:0", 2025-09-07T06:49:23.7564518Z "rank:1/cuda:1", 2025-09-07T06:49:23.7564555Z "rank:2/cuda:2", 2025-09-07T06:49:23.7564591Z "rank:3/cuda:3", 2025-09-07T06:49:23.7564625Z ], 2025-09-07T06:49:23.7564657Z ) 2025-09-07T06:49:23.7564698Z >>> current_offsets = [0] * 2 2025-09-07T06:49:23.7564741Z >>> current_offsets[0] = rank * 2 2025-09-07T06:49:23.7564785Z >>> shard_metadata = ShardMetadata( 2025-09-07T06:49:23.7564846Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T06:49:23.7564892Z shard_sizes=tensor.size(), 2025-09-07T06:49:23.7564941Z placement=spec.placements[rank], 2025-09-07T06:49:23.7564973Z ) 2025-09-07T06:49:23.7565010Z >>> local_shards = [ 2025-09-07T06:49:23.7565044Z Shard( 2025-09-07T06:49:23.7565082Z tensor=tensor, 2025-09-07T06:49:23.7565125Z metadata=shard_metadata, 2025-09-07T06:49:23.7565157Z ) 2025-09-07T06:49:23.7565187Z ] 2025-09-07T06:49:23.7566626Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T06:49:23.7566665Z >>> sharding_dim = 1 2025-09-07T06:49:23.7566714Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T06:49:23.7566752Z dim=sharding_dim, 2025-09-07T06:49:23.7566789Z placements=[ 2025-09-07T06:49:23.7566827Z "rank:0/cuda:0", 2025-09-07T06:49:23.7566863Z "rank:1/cuda:1", 2025-09-07T06:49:23.7566903Z "rank:2/cuda:2", 2025-09-07T06:49:23.7566964Z "rank:3/cuda:3", 2025-09-07T06:49:23.7566999Z ], 2025-09-07T06:49:23.7567031Z ) 2025-09-07T06:49:23.7567074Z >>> st.reshard(resharding_spec) 2025-09-07T06:49:23.7567118Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T06:49:23.7567151Z >>> tensor 2025-09-07T06:49:23.7567207Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T06:49:23.7567260Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T06:49:23.7567310Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T06:49:23.7567365Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T06:49:23.7567395Z 2025-09-07T06:49:23.7567485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7567515Z 2025-09-07T06:49:23.7567573Z warnings.warn(msg) 2025-09-07T06:49:23.7568961Z 2025-09-07T06:49:23.7569044Z --- Parse Warning: 120 / 146 --- 2025-09-07T06:49:23.7569404Z /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. 2025-09-07T06:49:23.7569524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7569553Z 2025-09-07T06:49:23.7569652Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T06:49:23.7569735Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T06:49:23.7569825Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T06:49:23.7569935Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T06:49:23.7570035Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T06:49:23.7570065Z 2025-09-07T06:49:23.7570098Z Args: 2025-09-07T06:49:23.7570148Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T06:49:23.7570217Z An op or a list of ops to register the customized sharding function. 2025-09-07T06:49:23.7570248Z 2025-09-07T06:49:23.7570279Z Returns: 2025-09-07T06:49:23.7570375Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T06:49:23.7570471Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T06:49:23.7570567Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T06:49:23.7570670Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T06:49:23.7570755Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T06:49:23.7570851Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T06:49:23.7572314Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T06:49:23.7572358Z corresponding input placements. 2025-09-07T06:49:23.7572390Z 2025-09-07T06:49:23.7572422Z Example: 2025-09-07T06:49:23.7572470Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7572522Z >>> @register_sharding(aten._softmax.default) 2025-09-07T06:49:23.7572581Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T06:49:23.7572637Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T06:49:23.7572681Z >>> acceptable_shardings = [] 2025-09-07T06:49:23.7572712Z >>> 2025-09-07T06:49:23.7572785Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T06:49:23.7572842Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T06:49:23.7572892Z >>> 2025-09-07T06:49:23.7572940Z >>> for sharding_dim in range(x.ndim): 2025-09-07T06:49:23.7572988Z >>> if sharding_dim != softmax_dim: 2025-09-07T06:49:23.7573027Z >>> all_sharded = ( 2025-09-07T06:49:23.7573070Z >>> [Shard(sharding_dim)], 2025-09-07T06:49:23.7573120Z >>> [Shard(sharding_dim), None, None], 2025-09-07T06:49:23.7573155Z >>> ) 2025-09-07T06:49:23.7573210Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T06:49:23.7573240Z >>> 2025-09-07T06:49:23.7574702Z >>> return acceptable_shardings 2025-09-07T06:49:23.7574734Z 2025-09-07T06:49:23.7574809Z .. note:: This API is currently experimental and subject to change 2025-09-07T06:49:23.7574841Z 2025-09-07T06:49:23.7574931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7574985Z 2025-09-07T06:49:23.7575024Z warnings.warn(msg) 2025-09-07T06:49:23.7575054Z 2025-09-07T06:49:23.7575127Z --- Parse Warning: 121 / 146 --- 2025-09-07T06:49:23.7575475Z /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. 2025-09-07T06:49:23.7575568Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7575598Z 2025-09-07T06:49:23.7575690Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T06:49:23.7575789Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T06:49:23.7575900Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T06:49:23.7575963Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T06:49:23.7575996Z 2025-09-07T06:49:23.7576028Z Args: 2025-09-07T06:49:23.7576103Z func (Callable): the function to be applied on each local shard of 2025-09-07T06:49:23.7576143Z :class:`DTensor` s. 2025-09-07T06:49:23.7576225Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T06:49:23.7576318Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T06:49:23.7577786Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T06:49:23.7577875Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T06:49:23.7577961Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T06:49:23.7578010Z mapping to the flattened ``output``. 2025-09-07T06:49:23.7578084Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T06:49:23.7578185Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T06:49:23.7578226Z should be `None`. 2025-09-07T06:49:23.7578310Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T06:49:23.7578389Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T06:49:23.7578476Z should ignore the desired placements because the function is not running with 2025-09-07T06:49:23.7578514Z :class:`DTensor` s. 2025-09-07T06:49:23.7578576Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T06:49:23.7578675Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T06:49:23.7578758Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T06:49:23.7578864Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T06:49:23.7578931Z placements or not. If the placements are not the same and 2025-09-07T06:49:23.7579018Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T06:49:23.7579103Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T06:49:23.7579191Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T06:49:23.7579270Z The only exception is when required placements are not ``None`` and the 2025-09-07T06:49:23.7579354Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T06:49:23.7580794Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T06:49:23.7580876Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T06:49:23.7580931Z Default: None 2025-09-07T06:49:23.7581002Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T06:49:23.7581076Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T06:49:23.7581166Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T06:49:23.7581233Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T06:49:23.7581309Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T06:49:23.7581380Z If not specified, we will assume the gradient layout of the local 2025-09-07T06:49:23.7581456Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T06:49:23.7581518Z and use that for gradient computation. Default: None. 2025-09-07T06:49:23.7581588Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T06:49:23.7581669Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T06:49:23.7581760Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T06:49:23.7581803Z mesh. Default: None. 2025-09-07T06:49:23.7581833Z 2025-09-07T06:49:23.7581868Z Keyword Args: 2025-09-07T06:49:23.7581916Z redistribute_inputs (bool, optional): 2025-09-07T06:49:23.7582005Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T06:49:23.7582090Z their placements are different from the required input placements. If this 2025-09-07T06:49:23.7582170Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T06:49:23.7582224Z an exception will be raised. Default: False. 2025-09-07T06:49:23.7583763Z 2025-09-07T06:49:23.7583798Z Returns: 2025-09-07T06:49:23.7583895Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T06:49:23.7583984Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T06:49:23.7584016Z 2025-09-07T06:49:23.7584051Z Raises: 2025-09-07T06:49:23.7584137Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T06:49:23.7584227Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T06:49:23.7584268Z if this is not the case. 2025-09-07T06:49:23.7584298Z 2025-09-07T06:49:23.7584459Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T06:49:23.7584517Z a redistribution according to ``in_placements``. 2025-09-07T06:49:23.7584549Z 2025-09-07T06:49:23.7584581Z Example: 2025-09-07T06:49:23.7584626Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7584682Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T06:49:23.7584730Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T06:49:23.7584845Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T06:49:23.7584889Z >>> return reduced_tensor 2025-09-07T06:49:23.7584921Z >>> 2025-09-07T06:49:23.7584971Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T06:49:23.7586494Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T06:49:23.7586536Z >>> Y = torch.mm(W, X) 2025-09-07T06:49:23.7586609Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T06:49:23.7586676Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T06:49:23.7586708Z >>> 2025-09-07T06:49:23.7586804Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T06:49:23.7586855Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T06:49:23.7586896Z >>> mm_allreduce_forward, 2025-09-07T06:49:23.7586970Z >>> out_placements=[Replicate()], 2025-09-07T06:49:23.7587019Z >>> in_placements=[col_wise, row_wise], 2025-09-07T06:49:23.7587080Z >>> device_mesh=device_mesh, 2025-09-07T06:49:23.7587111Z >>> ) 2025-09-07T06:49:23.7587142Z >>> 2025-09-07T06:49:23.7587182Z >>> W_dt = distribute_tensor( 2025-09-07T06:49:23.7587224Z ... W, device_mesh, (col_wise) 2025-09-07T06:49:23.7587269Z ... ) # col-wisely sharded W tensor 2025-09-07T06:49:23.7587308Z >>> X_dt = distribute_tensor( 2025-09-07T06:49:23.7587350Z ... X, device_mesh, (row_wise) 2025-09-07T06:49:23.7587395Z ... ) # row-wisely sharded X tensor 2025-09-07T06:49:23.7587440Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T06:49:23.7587479Z ... device_mesh, W_dt, X_dt 2025-09-07T06:49:23.7587561Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T06:49:23.7588985Z 2025-09-07T06:49:23.7589066Z .. note:: This API is currently experimental and subject to change 2025-09-07T06:49:23.7589099Z 2025-09-07T06:49:23.7589189Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7589222Z 2025-09-07T06:49:23.7589260Z warnings.warn(msg) 2025-09-07T06:49:23.7589290Z 2025-09-07T06:49:23.7589369Z --- Parse Warning: 122 / 146 --- 2025-09-07T06:49:23.7589710Z /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. 2025-09-07T06:49:23.7589803Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7589833Z 2025-09-07T06:49:23.7589970Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T06:49:23.7590084Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T06:49:23.7590114Z 2025-09-07T06:49:23.7590148Z Keyword Args: 2025-09-07T06:49:23.7590223Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:23.7590336Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T06:49:23.7590460Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T06:49:23.7590506Z as a placeholder. default: None. 2025-09-07T06:49:23.7590588Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:23.7590716Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:23.7592273Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T06:49:23.7592348Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T06:49:23.7592480Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T06:49:23.7592519Z default: None 2025-09-07T06:49:23.7592582Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T06:49:23.7592705Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:23.7592761Z have the desired DTensor layouts. default: None. 2025-09-07T06:49:23.7592807Z use_local_output (bool, optional): 2025-09-07T06:49:23.7592928Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T06:49:23.7592961Z Returns: 2025-09-07T06:49:23.7593072Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T06:49:23.7593119Z 2025-09-07T06:49:23.7593155Z Example:: 2025-09-07T06:49:23.7593197Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:23.7593324Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T06:49:23.7593397Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:23.7593429Z >>> ... 2025-09-07T06:49:23.7593535Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:23.7593587Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:23.7593618Z >>> 2025-09-07T06:49:23.7593733Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T06:49:23.7593803Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T06:49:23.7595281Z >>> parallelize_module( 2025-09-07T06:49:23.7595340Z >>> block, # this can be a submodule or module 2025-09-07T06:49:23.7595378Z >>> tp_mesh, 2025-09-07T06:49:23.7595422Z >>> parallelize_plan={ 2025-09-07T06:49:23.7595474Z >>> "attn": PrepareModuleInput( 2025-09-07T06:49:23.7595530Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T06:49:23.7595596Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T06:49:23.7595630Z >>> ), 2025-09-07T06:49:23.7595662Z >>> } 2025-09-07T06:49:23.7595694Z >>> ) 2025-09-07T06:49:23.7595723Z 2025-09-07T06:49:23.7595813Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7595842Z 2025-09-07T06:49:23.7595879Z warnings.warn(msg) 2025-09-07T06:49:23.7595908Z 2025-09-07T06:49:23.7595982Z --- Parse Warning: 123 / 146 --- 2025-09-07T06:49:23.7596327Z /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. 2025-09-07T06:49:23.7596424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7596453Z 2025-09-07T06:49:23.7596588Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T06:49:23.7596703Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T06:49:23.7598095Z 2025-09-07T06:49:23.7598131Z Keyword Args: 2025-09-07T06:49:23.7598194Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:23.7598311Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T06:49:23.7598471Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T06:49:23.7598528Z ``None`` need to be specified as a placeholder. 2025-09-07T06:49:23.7598603Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:23.7598734Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T06:49:23.7598781Z have the desired DTensor layouts. 2025-09-07T06:49:23.7598826Z use_local_output (bool, optional): 2025-09-07T06:49:23.7598947Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T06:49:23.7598980Z Returns: 2025-09-07T06:49:23.7599084Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T06:49:23.7599116Z 2025-09-07T06:49:23.7599150Z Example:: 2025-09-07T06:49:23.7599212Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:23.7599325Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T06:49:23.7599417Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:23.7599448Z >>> ... 2025-09-07T06:49:23.7599553Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:23.7599604Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:23.7599636Z >>> 2025-09-07T06:49:23.7601133Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T06:49:23.7601188Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T06:49:23.7601228Z >>> parallelize_module( 2025-09-07T06:49:23.7601301Z >>> block, # this can be a submodule or module 2025-09-07T06:49:23.7601338Z >>> tp_mesh, 2025-09-07T06:49:23.7601395Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T06:49:23.7601444Z >>> output_layouts=Replicate(), 2025-09-07T06:49:23.7601494Z >>> desired_output_layouts=Shard(0) 2025-09-07T06:49:23.7601525Z >>> ) 2025-09-07T06:49:23.7601556Z >>> ) 2025-09-07T06:49:23.7601586Z 2025-09-07T06:49:23.7601675Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7601704Z 2025-09-07T06:49:23.7601740Z warnings.warn(msg) 2025-09-07T06:49:23.7601771Z 2025-09-07T06:49:23.7601842Z --- Parse Warning: 124 / 146 --- 2025-09-07T06:49:23.7602195Z /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. 2025-09-07T06:49:23.7602288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7602319Z 2025-09-07T06:49:23.7602469Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T06:49:23.7602617Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T06:49:23.7604128Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T06:49:23.7604210Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T06:49:23.7604240Z 2025-09-07T06:49:23.7604273Z Keyword Args: 2025-09-07T06:49:23.7604346Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:23.7604518Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T06:49:23.7604640Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T06:49:23.7604717Z as a placeholder. default: None. 2025-09-07T06:49:23.7604801Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T06:49:23.7604928Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:23.7605060Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T06:49:23.7605112Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T06:49:23.7605237Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T06:49:23.7605275Z default: None 2025-09-07T06:49:23.7605336Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T06:49:23.7605460Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T06:49:23.7605535Z have the desired DTensor layouts. default: None. 2025-09-07T06:49:23.7605604Z use_local_input (bool, optional): 2025-09-07T06:49:23.7605726Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T06:49:23.7605786Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:23.7605899Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T06:49:23.7606028Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T06:49:23.7607452Z ``None`` need to be specified as a placeholder. 2025-09-07T06:49:23.7607549Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T06:49:23.7607683Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T06:49:23.7607729Z have the desired DTensor layouts. 2025-09-07T06:49:23.7607775Z use_local_output (bool, optional): 2025-09-07T06:49:23.7607896Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T06:49:23.7607927Z Returns: 2025-09-07T06:49:23.7608053Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T06:49:23.7608083Z 2025-09-07T06:49:23.7608118Z Example:: 2025-09-07T06:49:23.7608160Z >>> # xdoctest: +SKIP(failing) 2025-09-07T06:49:23.7608279Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T06:49:23.7608350Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T06:49:23.7608382Z >>> ... 2025-09-07T06:49:23.7608489Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T06:49:23.7608541Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T06:49:23.7608573Z >>> 2025-09-07T06:49:23.7608687Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T06:49:23.7608808Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T06:49:23.7608884Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T06:49:23.7608924Z >>> parallelize_module( 2025-09-07T06:49:23.7610335Z >>> block, # this can be a submodule or module 2025-09-07T06:49:23.7610373Z >>> tp_mesh, 2025-09-07T06:49:23.7610416Z >>> parallelize_plan={ 2025-09-07T06:49:23.7610472Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T06:49:23.7610529Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T06:49:23.7610614Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T06:49:23.7610663Z >>> output_layouts=Replicate(), 2025-09-07T06:49:23.7610712Z >>> desired_output_layouts=Shard(0), 2025-09-07T06:49:23.7610747Z >>> ), 2025-09-07T06:49:23.7610778Z >>> } 2025-09-07T06:49:23.7610809Z >>> ) 2025-09-07T06:49:23.7610839Z 2025-09-07T06:49:23.7610927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7610959Z 2025-09-07T06:49:23.7610995Z warnings.warn(msg) 2025-09-07T06:49:23.7611024Z 2025-09-07T06:49:23.7611097Z --- Parse Warning: 125 / 146 --- 2025-09-07T06:49:23.7611447Z /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. 2025-09-07T06:49:23.7611553Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7611596Z 2025-09-07T06:49:23.7611671Z Initialize rendezvous event object and record its operations. 2025-09-07T06:49:23.7611701Z 2025-09-07T06:49:23.7613098Z Args: 2025-09-07T06:49:23.7613150Z run_id (str): The run id of the rendezvous. 2025-09-07T06:49:23.7613208Z message (str): The message describing the event. 2025-09-07T06:49:23.7613300Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T06:49:23.7613368Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T06:49:23.7613415Z hostname (str): Hostname of the node. 2025-09-07T06:49:23.7613486Z pid (Optional[int]): The process id of the node. 2025-09-07T06:49:23.7613575Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T06:49:23.7613674Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T06:49:23.7613738Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T06:49:23.7613769Z Returns: 2025-09-07T06:49:23.7613801Z None 2025-09-07T06:49:23.7613833Z Example: 2025-09-07T06:49:23.7613881Z >>> # See DynamicRendezvousHandler class 2025-09-07T06:49:23.7613917Z >>> def _record( 2025-09-07T06:49:23.7613950Z ... self, 2025-09-07T06:49:23.7613989Z ... message: str, 2025-09-07T06:49:23.7614043Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T06:49:23.7614087Z ... rank: Optional[int] = None, 2025-09-07T06:49:23.7614122Z ... ) -> None: 2025-09-07T06:49:23.7614169Z ... construct_and_record_rdzv_event( 2025-09-07T06:49:23.7614232Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T06:49:23.7615700Z ... run_id=self._settings.run_id, 2025-09-07T06:49:23.7615742Z ... message=message, 2025-09-07T06:49:23.7615787Z ... node_state=node_state, 2025-09-07T06:49:23.7615836Z ... hostname=self._this_node.addr, 2025-09-07T06:49:23.7615879Z ... pid=self._this_node.pid, 2025-09-07T06:49:23.7615928Z ... local_id=self._this_node.local_id, 2025-09-07T06:49:23.7615966Z ... rank=rank, 2025-09-07T06:49:23.7615997Z ... ) 2025-09-07T06:49:23.7616027Z 2025-09-07T06:49:23.7616116Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7616146Z 2025-09-07T06:49:23.7616182Z warnings.warn(msg) 2025-09-07T06:49:23.7616211Z 2025-09-07T06:49:23.7616284Z --- Parse Warning: 126 / 146 --- 2025-09-07T06:49:23.7616645Z /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. 2025-09-07T06:49:23.7616740Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7616770Z 2025-09-07T06:49:23.7616853Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T06:49:23.7616882Z 2025-09-07T06:49:23.7616955Z It creates a user-specified module on a specified remote node. 2025-09-07T06:49:23.7617038Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T06:49:23.7618449Z executed on the remote node. 2025-09-07T06:49:23.7618533Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T06:49:23.7618592Z gradients back to the corresponding remote module. 2025-09-07T06:49:23.7618717Z It can be shared across processors using `RPC framework `__, 2025-09-07T06:49:23.7618820Z without incurring any overheads of copying the actual module, 2025-09-07T06:49:23.7618894Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T06:49:23.7618960Z pointing to the remote module. 2025-09-07T06:49:23.7618990Z 2025-09-07T06:49:23.7619063Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T06:49:23.7619135Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T06:49:23.7619166Z 2025-09-07T06:49:23.7619272Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T06:49:23.7619302Z 2025-09-07T06:49:23.7619390Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T06:49:23.7619533Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T06:49:23.7619570Z Hybrid Example: 2025-09-07T06:49:23.7619616Z >>> class HybridModel(nn.Module): 2025-09-07T06:49:23.7619665Z >>> def __init__(self) -> None: 2025-09-07T06:49:23.7619710Z >>> nn.Module.__init__(self) 2025-09-07T06:49:23.7619764Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T06:49:23.7619813Z >>> self.local_linear = nn.Linear(...) 2025-09-07T06:49:23.7619843Z 2025-09-07T06:49:23.7621271Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T06:49:23.7621367Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T06:49:23.7621442Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T06:49:23.7621494Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T06:49:23.7621558Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T06:49:23.7621589Z 2025-09-07T06:49:23.7621624Z .. note:: 2025-09-07T06:49:23.7621680Z If the remote module is placed on a cuda device, 2025-09-07T06:49:23.7621766Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T06:49:23.7621902Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T06:49:23.7621932Z 2025-09-07T06:49:23.7621965Z Args: 2025-09-07T06:49:23.7622069Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:23.7622170Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T06:49:23.7622203Z formats: 2025-09-07T06:49:23.7622234Z 2025-09-07T06:49:23.7622288Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T06:49:23.7622348Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T06:49:23.7622378Z 2025-09-07T06:49:23.7622484Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:23.7622530Z module_cls (nn.Module): For example, 2025-09-07T06:49:23.7623929Z >>> class MyModule(nn.Module): 2025-09-07T06:49:23.7623972Z >>> def forward(input): 2025-09-07T06:49:23.7624014Z >>> return input + 1 2025-09-07T06:49:23.7624046Z >>> 2025-09-07T06:49:23.7624087Z >>> module_cls = MyModule 2025-09-07T06:49:23.7624162Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T06:49:23.7624233Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T06:49:23.7624330Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T06:49:23.7624484Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T06:49:23.7624565Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T06:49:23.7624678Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T06:49:23.7624708Z 2025-09-07T06:49:23.7624758Z Returns: 2025-09-07T06:49:23.7624846Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:23.7624931Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T06:49:23.7625030Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:23.7625085Z on the user-provided module on the remote side. 2025-09-07T06:49:23.7625116Z 2025-09-07T06:49:23.7625150Z Example:: 2025-09-07T06:49:23.7625207Z Run the following code in two different processes: 2025-09-07T06:49:23.7625237Z 2025-09-07T06:49:23.7625282Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7626713Z >>> # On worker 0: 2025-09-07T06:49:23.7626754Z >>> import torch 2025-09-07T06:49:23.7626806Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7626852Z >>> from torch import nn, Tensor 2025-09-07T06:49:23.7626933Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:23.7626968Z >>> 2025-09-07T06:49:23.7627023Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:23.7627070Z >>> remote_linear_module = RemoteModule( 2025-09-07T06:49:23.7627118Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:23.7627150Z >>> ) 2025-09-07T06:49:23.7627191Z >>> input = torch.randn(128, 20) 2025-09-07T06:49:23.7627251Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T06:49:23.7627291Z >>> ret = ret_fut.wait() 2025-09-07T06:49:23.7627329Z >>> rpc.shutdown() 2025-09-07T06:49:23.7627360Z 2025-09-07T06:49:23.7627396Z >>> # On worker 1: 2025-09-07T06:49:23.7627432Z >>> import torch 2025-09-07T06:49:23.7627481Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7627514Z >>> 2025-09-07T06:49:23.7627567Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:23.7627606Z >>> rpc.shutdown() 2025-09-07T06:49:23.7627636Z 2025-09-07T06:49:23.7629083Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7629115Z 2025-09-07T06:49:23.7629152Z warnings.warn(msg) 2025-09-07T06:49:23.7629182Z 2025-09-07T06:49:23.7629257Z --- Parse Warning: 127 / 146 --- 2025-09-07T06:49:23.7629611Z /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. 2025-09-07T06:49:23.7629706Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7629737Z 2025-09-07T06:49:23.7629872Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T06:49:23.7629903Z 2025-09-07T06:49:23.7630021Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T06:49:23.7630129Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T06:49:23.7630160Z 2025-09-07T06:49:23.7630260Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T06:49:23.7630328Z which is not supported. The recommended way is as follows: 2025-09-07T06:49:23.7630357Z 2025-09-07T06:49:23.7630404Z 1. the sender creates a RemoteModule; 2025-09-07T06:49:23.7630459Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T06:49:23.7630574Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T06:49:23.7630625Z 2025-09-07T06:49:23.7630659Z Example:: 2025-09-07T06:49:23.7632127Z Run the following code in two different processes: 2025-09-07T06:49:23.7632160Z 2025-09-07T06:49:23.7632223Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7632260Z >>> # On worker 0: 2025-09-07T06:49:23.7632296Z >>> import torch 2025-09-07T06:49:23.7632346Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7632388Z >>> from torch import nn, Tensor 2025-09-07T06:49:23.7632469Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:23.7632501Z >>> 2025-09-07T06:49:23.7632553Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:23.7632596Z >>> remote_module = RemoteModule( 2025-09-07T06:49:23.7632644Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:23.7632676Z >>> ) 2025-09-07T06:49:23.7632719Z >>> 2025-09-07T06:49:23.7632766Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T06:49:23.7632805Z >>> "worker1/cpu", 2025-09-07T06:49:23.7632857Z >>> RemoteModule.init_from_module_rref, 2025-09-07T06:49:23.7632918Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T06:49:23.7632949Z >>> ) 2025-09-07T06:49:23.7632987Z >>> rpc.shutdown() 2025-09-07T06:49:23.7633017Z 2025-09-07T06:49:23.7633052Z >>> # On worker 1: 2025-09-07T06:49:23.7634501Z >>> import torch 2025-09-07T06:49:23.7634555Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7634586Z >>> 2025-09-07T06:49:23.7634640Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:23.7634676Z >>> rpc.shutdown() 2025-09-07T06:49:23.7634707Z 2025-09-07T06:49:23.7634739Z Args: 2025-09-07T06:49:23.7634848Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:23.7634946Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T06:49:23.7634985Z formats: 2025-09-07T06:49:23.7635015Z 2025-09-07T06:49:23.7635073Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T06:49:23.7635132Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T06:49:23.7635163Z 2025-09-07T06:49:23.7635250Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:23.7635338Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T06:49:23.7635380Z the created remote module. 2025-09-07T06:49:23.7635477Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T06:49:23.7635561Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T06:49:23.7635641Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T06:49:23.7635752Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T06:49:23.7637153Z 2025-09-07T06:49:23.7637190Z Returns: 2025-09-07T06:49:23.7637280Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:23.7637365Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T06:49:23.7637460Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:23.7637517Z on the user-provided module on the remote side. 2025-09-07T06:49:23.7637547Z 2025-09-07T06:49:23.7637635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7637665Z 2025-09-07T06:49:23.7637702Z warnings.warn(msg) 2025-09-07T06:49:23.7637732Z 2025-09-07T06:49:23.7637807Z --- Parse Warning: 128 / 146 --- 2025-09-07T06:49:23.7638157Z /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. 2025-09-07T06:49:23.7638268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7638297Z 2025-09-07T06:49:23.7638378Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T06:49:23.7638408Z 2025-09-07T06:49:23.7638480Z It creates a user-specified module on a specified remote node. 2025-09-07T06:49:23.7638562Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T06:49:23.7638605Z executed on the remote node. 2025-09-07T06:49:23.7638685Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T06:49:23.7638759Z gradients back to the corresponding remote module. 2025-09-07T06:49:23.7640160Z 2025-09-07T06:49:23.7640246Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T06:49:23.7640323Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T06:49:23.7640413Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T06:49:23.7640485Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T06:49:23.7640528Z returned by the ``module_cls``. 2025-09-07T06:49:23.7640558Z 2025-09-07T06:49:23.7640629Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T06:49:23.7640718Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T06:49:23.7640796Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T06:49:23.7640826Z 2025-09-07T06:49:23.7640876Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T06:49:23.7640941Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T06:49:23.7640971Z 2025-09-07T06:49:23.7641003Z Args: 2025-09-07T06:49:23.7641107Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T06:49:23.7641230Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T06:49:23.7641286Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T06:49:23.7641372Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T06:49:23.7641457Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T06:49:23.7641488Z 2025-09-07T06:49:23.7641532Z >>> class MyModule(nn.Module): 2025-09-07T06:49:23.7642925Z >>> def forward(input): 2025-09-07T06:49:23.7642970Z >>> return input + 1 2025-09-07T06:49:23.7643004Z >>> 2025-09-07T06:49:23.7643046Z >>> module_cls = MyModule 2025-09-07T06:49:23.7643096Z 2025-09-07T06:49:23.7643170Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T06:49:23.7643243Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T06:49:23.7643273Z 2025-09-07T06:49:23.7643305Z Returns: 2025-09-07T06:49:23.7643392Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T06:49:23.7643476Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T06:49:23.7643571Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T06:49:23.7643627Z on the user-provided module on the remote side. 2025-09-07T06:49:23.7643657Z 2025-09-07T06:49:23.7643690Z Example:: 2025-09-07T06:49:23.7643751Z Run the following code in two different processes: 2025-09-07T06:49:23.7643984Z 2025-09-07T06:49:23.7644031Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7644067Z >>> # On worker 0: 2025-09-07T06:49:23.7644104Z >>> import torch 2025-09-07T06:49:23.7644169Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7645646Z >>> from torch import nn, Tensor 2025-09-07T06:49:23.7645731Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T06:49:23.7645763Z >>> 2025-09-07T06:49:23.7645816Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T06:49:23.7645863Z >>> remote_linear_module = RemoteModule( 2025-09-07T06:49:23.7645913Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T06:49:23.7645944Z >>> ) 2025-09-07T06:49:23.7645986Z >>> input = torch.randn(128, 20) 2025-09-07T06:49:23.7646045Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T06:49:23.7646109Z >>> ret = ret_fut.wait() 2025-09-07T06:49:23.7646150Z >>> rpc.shutdown() 2025-09-07T06:49:23.7646180Z 2025-09-07T06:49:23.7646218Z >>> # On worker 1: 2025-09-07T06:49:23.7646254Z >>> import torch 2025-09-07T06:49:23.7646304Z >>> import torch.distributed.rpc as rpc 2025-09-07T06:49:23.7646336Z >>> 2025-09-07T06:49:23.7646387Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T06:49:23.7646425Z >>> rpc.shutdown() 2025-09-07T06:49:23.7646455Z 2025-09-07T06:49:23.7646527Z Furthermore, a more practical example that is combined with 2025-09-07T06:49:23.7646695Z `DistributedDataParallel `__ (DDP) 2025-09-07T06:49:23.7646810Z can be found in this `tutorial `__. 2025-09-07T06:49:23.7648205Z 2025-09-07T06:49:23.7648298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7648330Z 2025-09-07T06:49:23.7648368Z warnings.warn(msg) 2025-09-07T06:49:23.7648400Z 2025-09-07T06:49:23.7648476Z --- Parse Warning: 129 / 146 --- 2025-09-07T06:49:23.7648765Z /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. 2025-09-07T06:49:23.7648860Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7648941Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T06:49:23.7648972Z 2025-09-07T06:49:23.7649012Z The values are as follows: 2025-09-07T06:49:23.7649042Z 2025-09-07T06:49:23.7649087Z ================= ==================================================== 2025-09-07T06:49:23.7649130Z nonlinearity gain 2025-09-07T06:49:23.7649174Z ================= ==================================================== 2025-09-07T06:49:23.7649217Z Linear / Identity :math:`1` 2025-09-07T06:49:23.7649279Z Conv{1,2,3}D :math:`1` 2025-09-07T06:49:23.7649317Z Sigmoid :math:`1` 2025-09-07T06:49:23.7649362Z Tanh :math:`\frac{5}{3}` 2025-09-07T06:49:23.7649406Z ReLU :math:`\sqrt{2}` 2025-09-07T06:49:23.7649477Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T06:49:23.7649520Z SELU :math:`\frac{3}{4}` 2025-09-07T06:49:23.7650944Z ================= ==================================================== 2025-09-07T06:49:23.7650974Z 2025-09-07T06:49:23.7651010Z .. warning:: 2025-09-07T06:49:23.7651080Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T06:49:23.7651170Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T06:49:23.7651234Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T06:49:23.7651344Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T06:49:23.7651424Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T06:49:23.7651509Z effect for more stable gradient flow in rectangular layers. 2025-09-07T06:49:23.7651539Z 2025-09-07T06:49:23.7651571Z Args: 2025-09-07T06:49:23.7651643Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T06:49:23.7651708Z param: optional parameter for the non-linear function 2025-09-07T06:49:23.7651738Z 2025-09-07T06:49:23.7651772Z Examples: 2025-09-07T06:49:23.7651818Z >>> gain = nn.init.calculate_gain( 2025-09-07T06:49:23.7651859Z ... "leaky_relu", 0.2 2025-09-07T06:49:23.7651922Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T06:49:23.7651954Z 2025-09-07T06:49:23.7652125Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T06:49:23.7652157Z 2025-09-07T06:49:23.7653615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7653648Z 2025-09-07T06:49:23.7653685Z warnings.warn(msg) 2025-09-07T06:49:23.7653714Z 2025-09-07T06:49:23.7653788Z --- Parse Warning: 130 / 146 --- 2025-09-07T06:49:23.7654124Z /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. 2025-09-07T06:49:23.7654217Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7654247Z 2025-09-07T06:49:23.7654332Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T06:49:23.7654363Z 2025-09-07T06:49:23.7654502Z This context manager will keep track of already-joined DDP processes, 2025-09-07T06:49:23.7654577Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T06:49:23.7654664Z communication operations to match with the ones created by non-joined 2025-09-07T06:49:23.7654745Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T06:49:23.7654824Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T06:49:23.7654897Z would otherwise happen when training with uneven inputs across 2025-09-07T06:49:23.7654980Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T06:49:23.7655054Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T06:49:23.7655127Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T06:49:23.7655172Z according to application logic. 2025-09-07T06:49:23.7655203Z 2025-09-07T06:49:23.7655313Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T06:49:23.7656776Z the model corresponding to the last joined process to all processes to 2025-09-07T06:49:23.7656836Z ensure the model is the same across all processes 2025-09-07T06:49:23.7656878Z (which is guaranteed by DDP). 2025-09-07T06:49:23.7656909Z 2025-09-07T06:49:23.7656981Z To use this to enable training with uneven inputs across processes, 2025-09-07T06:49:23.7657059Z simply wrap this context manager around your training loop. No further 2025-09-07T06:49:23.7657124Z modifications to the model or data loading is required. 2025-09-07T06:49:23.7657153Z 2025-09-07T06:49:23.7657188Z .. warning:: 2025-09-07T06:49:23.7657261Z If the model or training loop this context manager is wrapped around 2025-09-07T06:49:23.7657329Z has additional distributed collective operations, such as 2025-09-07T06:49:23.7657399Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T06:49:23.7657498Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T06:49:23.7657575Z context manager is not aware of non-DDP collective communication. 2025-09-07T06:49:23.7657663Z This flag will cause all ranks to throw when any one rank 2025-09-07T06:49:23.7657738Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T06:49:23.7657778Z from across all ranks. 2025-09-07T06:49:23.7657810Z 2025-09-07T06:49:23.7657841Z Args: 2025-09-07T06:49:23.7657908Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T06:49:23.7657984Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T06:49:23.7659414Z with. If ``False``, will compute the effective world size 2025-09-07T06:49:23.7659514Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T06:49:23.7659573Z divide gradients by that during allreduce. Set 2025-09-07T06:49:23.7659641Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T06:49:23.7659718Z sample including the uneven inputs have equal weight in terms of 2025-09-07T06:49:23.7659780Z how much they contribute to the global gradient. This is 2025-09-07T06:49:23.7659843Z achieved by always dividing the gradient by the initial 2025-09-07T06:49:23.7659910Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T06:49:23.7659974Z this to ``False``, we divide the gradient by the remaining 2025-09-07T06:49:23.7660043Z number of nodes. This ensures parity with training on a smaller 2025-09-07T06:49:23.7660109Z ``world_size`` although it also means the uneven inputs would 2025-09-07T06:49:23.7660178Z contribute more towards the global gradient. Typically, you 2025-09-07T06:49:23.7660244Z would want to set this to ``True`` for cases where the last few 2025-09-07T06:49:23.7660314Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T06:49:23.7660381Z there is a large discrepancy in the number of inputs, setting 2025-09-07T06:49:23.7660435Z this to ``False`` might provide better results. 2025-09-07T06:49:23.7660510Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T06:49:23.7660570Z in ``enable=False`` to disable in cases where you know that 2025-09-07T06:49:23.7660637Z inputs are even across participating processes. Default is 2025-09-07T06:49:23.7660671Z ``True``. 2025-09-07T06:49:23.7660738Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T06:49:23.7660802Z or continue training when at least one rank has exhausted 2025-09-07T06:49:23.7662229Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T06:49:23.7662295Z of data. If ``False``, will continue training with a smaller 2025-09-07T06:49:23.7662383Z effective world size until all ranks are joined. Note that if 2025-09-07T06:49:23.7662433Z this flag is specified, then the flag 2025-09-07T06:49:23.7662498Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T06:49:23.7662537Z is ``False``. 2025-09-07T06:49:23.7662568Z 2025-09-07T06:49:23.7662598Z 2025-09-07T06:49:23.7662631Z Example:: 2025-09-07T06:49:23.7662662Z 2025-09-07T06:49:23.7662706Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T06:49:23.7662743Z >>> import torch 2025-09-07T06:49:23.7662787Z >>> import torch.distributed as dist 2025-09-07T06:49:23.7662823Z >>> import os 2025-09-07T06:49:23.7662871Z >>> import torch.multiprocessing as mp 2025-09-07T06:49:23.7662913Z >>> import torch.nn as nn 2025-09-07T06:49:23.7662954Z >>> # On each spawned worker 2025-09-07T06:49:23.7663009Z >>> def worker(rank): 2025-09-07T06:49:23.7663079Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T06:49:23.7663140Z >>> torch.cuda.set_device(rank) 2025-09-07T06:49:23.7663193Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T06:49:23.7664678Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T06:49:23.7664739Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T06:49:23.7664771Z >>> ) 2025-09-07T06:49:23.7664822Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T06:49:23.7664891Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T06:49:23.7664931Z >>> with model.join(): 2025-09-07T06:49:23.7664971Z >>> for _ in range(5): 2025-09-07T06:49:23.7665041Z >>> for inp in inputs: 2025-09-07T06:49:23.7665089Z >>> loss = model(inp).sum() 2025-09-07T06:49:23.7665135Z >>> loss.backward() 2025-09-07T06:49:23.7665203Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T06:49:23.7665260Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T06:49:23.7665309Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T06:49:23.7665340Z 2025-09-07T06:49:23.7665428Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7665458Z 2025-09-07T06:49:23.7665495Z warnings.warn(msg) 2025-09-07T06:49:23.7665525Z 2025-09-07T06:49:23.7665598Z --- Parse Warning: 131 / 146 --- 2025-09-07T06:49:23.7665955Z /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. 2025-09-07T06:49:23.7666048Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7666079Z 2025-09-07T06:49:23.7667556Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T06:49:23.7667591Z 2025-09-07T06:49:23.7667665Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T06:49:23.7667740Z parameter will run immediately when that parameter's gradient is 2025-09-07T06:49:23.7667812Z finished with reduction, instead of waiting for all parameters' 2025-09-07T06:49:23.7667891Z gradients to finish reduction. This can result in a training speedup 2025-09-07T06:49:23.7667966Z depending on your workload since the optimizer can run while gradient 2025-09-07T06:49:23.7668047Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T06:49:23.7668127Z the potential to reduce peak memory consumption during training, as it 2025-09-07T06:49:23.7668200Z only needs to load the per-parameter optimizer states of a single 2025-09-07T06:49:23.7668297Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T06:49:23.7668333Z states at once. 2025-09-07T06:49:23.7668363Z 2025-09-07T06:49:23.7668395Z Args: 2025-09-07T06:49:23.7668465Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T06:49:23.7668506Z as a fused optimizer. 2025-09-07T06:49:23.7668570Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T06:49:23.7668644Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T06:49:23.7668724Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T06:49:23.7668795Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T06:49:23.7668830Z optimized. 2025-09-07T06:49:23.7670259Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T06:49:23.7670315Z 2025-09-07T06:49:23.7670349Z .. warning :: 2025-09-07T06:49:23.7670429Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T06:49:23.7670520Z and registering multiple fused optimizers for the same DDP model 2025-09-07T06:49:23.7670572Z is not currently supported. Please ping 2025-09-07T06:49:23.7670654Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:23.7670691Z for your use case. 2025-09-07T06:49:23.7670721Z 2025-09-07T06:49:23.7670755Z .. warning :: 2025-09-07T06:49:23.7670868Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T06:49:23.7670944Z compose together, meaning that custom DDP communication hooks are 2025-09-07T06:49:23.7671007Z not supported with overlapped optimizers. Please ping 2025-09-07T06:49:23.7671102Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:23.7671142Z for your use case. 2025-09-07T06:49:23.7671173Z 2025-09-07T06:49:23.7671208Z .. warning :: 2025-09-07T06:49:23.7671287Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T06:49:23.7671337Z with overlapped optimizer. Please ping 2025-09-07T06:49:23.7671414Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T06:49:23.7671450Z for your use case. 2025-09-07T06:49:23.7671480Z 2025-09-07T06:49:23.7672886Z Example:: 2025-09-07T06:49:23.7672918Z 2025-09-07T06:49:23.7672971Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T06:49:23.7673076Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T06:49:23.7673150Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T06:49:23.7673185Z >>> lr = 1e-2 2025-09-07T06:49:23.7673225Z >>> betas = (0.9, 0.99) 2025-09-07T06:49:23.7673262Z >>> eps = 1e-6 2025-09-07T06:49:23.7673347Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T06:49:23.7673394Z >>> # Example with subset of parameters 2025-09-07T06:49:23.7673451Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T06:49:23.7673493Z >>> net._register_fused_optim( 2025-09-07T06:49:23.7673577Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T06:49:23.7673609Z ... ) 2025-09-07T06:49:23.7673639Z 2025-09-07T06:49:23.7673726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7673757Z 2025-09-07T06:49:23.7673792Z warnings.warn(msg) 2025-09-07T06:49:23.7673823Z 2025-09-07T06:49:23.7673895Z --- Parse Warning: 132 / 146 --- 2025-09-07T06:49:23.7674214Z /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. 2025-09-07T06:49:23.7674309Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7675856Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T06:49:23.7675887Z 2025-09-07T06:49:23.7676000Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T06:49:23.7676043Z and with 2D inputs, this class 2025-09-07T06:49:23.7676073Z 2025-09-07T06:49:23.7676181Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T06:49:23.7676288Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T06:49:23.7676390Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T06:49:23.7676447Z 2025-09-07T06:49:23.7676569Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T06:49:23.7676625Z operations. 2025-09-07T06:49:23.7676654Z 2025-09-07T06:49:23.7676747Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T06:49:23.7676831Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T06:49:23.7676919Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T06:49:23.7677002Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T06:49:23.7677045Z :attr:`per_sample_weights`. 2025-09-07T06:49:23.7677076Z 2025-09-07T06:49:23.7677106Z Args: 2025-09-07T06:49:23.7677197Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T06:49:23.7677261Z embedding_dim (int): the size of each embedding vector 2025-09-07T06:49:23.7678731Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T06:49:23.7678795Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T06:49:23.7678914Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T06:49:23.7679025Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T06:49:23.7679087Z the words in the mini-batch. Default ``False``. 2025-09-07T06:49:23.7679154Z Note: this option is not supported when ``mode="max"``. 2025-09-07T06:49:23.7679246Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T06:49:23.7679329Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T06:49:23.7679408Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T06:49:23.7679473Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T06:49:23.7679522Z Default: ``"mean"`` 2025-09-07T06:49:23.7679636Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T06:49:23.7679725Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T06:49:23.7679775Z supported when ``mode="max"``. 2025-09-07T06:49:23.7679906Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T06:49:23.7680007Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T06:49:23.7680123Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T06:49:23.7680223Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T06:49:23.7680309Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T06:49:23.7680404Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T06:49:23.7680489Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T06:49:23.7681928Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T06:49:23.7681993Z reduction. 2025-09-07T06:49:23.7682026Z 2025-09-07T06:49:23.7682060Z Attributes: 2025-09-07T06:49:23.7682186Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T06:49:23.7682242Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T06:49:23.7682272Z 2025-09-07T06:49:23.7682309Z Examples:: 2025-09-07T06:49:23.7682338Z 2025-09-07T06:49:23.7682401Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T06:49:23.7682461Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T06:49:23.7682512Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T06:49:23.7682581Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T06:49:23.7682651Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T06:49:23.7682709Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T06:49:23.7682758Z >>> embedding_sum(input, offsets) 2025-09-07T06:49:23.7682801Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T06:49:23.7682845Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T06:49:23.7682875Z 2025-09-07T06:49:23.7682921Z >>> # Example with padding_idx 2025-09-07T06:49:23.7682995Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T06:49:23.7683061Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T06:49:23.7684531Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T06:49:23.7684581Z >>> embedding_sum(input, offsets) 2025-09-07T06:49:23.7684623Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T06:49:23.7684665Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T06:49:23.7684696Z 2025-09-07T06:49:23.7684764Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T06:49:23.7684822Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T06:49:23.7684883Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T06:49:23.7684926Z embedding.weight, 2025-09-07T06:49:23.7684977Z padding_idx=embedding.padding_idx, 2025-09-07T06:49:23.7685016Z mode='sum') 2025-09-07T06:49:23.7685048Z 2025-09-07T06:49:23.7685135Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7685165Z 2025-09-07T06:49:23.7685202Z warnings.warn(msg) 2025-09-07T06:49:23.7685232Z 2025-09-07T06:49:23.7685304Z --- Parse Warning: 133 / 146 --- 2025-09-07T06:49:23.7685621Z /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. 2025-09-07T06:49:23.7685739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7685797Z Take in and process masked source/target sequences. 2025-09-07T06:49:23.7685828Z 2025-09-07T06:49:23.7685863Z .. note:: 2025-09-07T06:49:23.7687266Z 2025-09-07T06:49:23.7687405Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T06:49:23.7687461Z not allowed to participate in the attention, 2025-09-07T06:49:23.7687531Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T06:49:23.7687600Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T06:49:23.7687630Z 2025-09-07T06:49:23.7687662Z Args: 2025-09-07T06:49:23.7687715Z src: the sequence to the encoder (required). 2025-09-07T06:49:23.7687792Z tgt: the sequence to the decoder (required). 2025-09-07T06:49:23.7687861Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T06:49:23.7687944Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T06:49:23.7688017Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T06:49:23.7688097Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T06:49:23.7688174Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T06:49:23.7688264Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T06:49:23.7688338Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T06:49:23.7688411Z Default: ``None``; try to detect a causal mask. 2025-09-07T06:49:23.7688450Z Warning: 2025-09-07T06:49:23.7688512Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T06:49:23.7688577Z the causal mask. Providing incorrect hints can result in 2025-09-07T06:49:23.7688639Z incorrect execution, including forward and backward 2025-09-07T06:49:23.7690042Z compatibility. 2025-09-07T06:49:23.7690121Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T06:49:23.7690177Z Default: ``None``; try to detect a causal mask. 2025-09-07T06:49:23.7690215Z Warning: 2025-09-07T06:49:23.7690273Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T06:49:23.7690336Z the causal mask. Providing incorrect hints can result in 2025-09-07T06:49:23.7690396Z incorrect execution, including forward and backward 2025-09-07T06:49:23.7690435Z compatibility. 2025-09-07T06:49:23.7690502Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T06:49:23.7690543Z ``memory_mask``. 2025-09-07T06:49:23.7690585Z Default: ``False``. 2025-09-07T06:49:23.7690621Z Warning: 2025-09-07T06:49:23.7690675Z ``memory_is_causal`` provides a hint that 2025-09-07T06:49:23.7690735Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T06:49:23.7690793Z hints can result in incorrect execution, including 2025-09-07T06:49:23.7690843Z forward and backward compatibility. 2025-09-07T06:49:23.7690874Z 2025-09-07T06:49:23.7690907Z Shape: 2025-09-07T06:49:23.7690998Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T06:49:23.7691046Z `(N, S, E)` if `batch_first=True`. 2025-09-07T06:49:23.7691131Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T06:49:23.7691195Z `(N, T, E)` if `batch_first=True`. 2025-09-07T06:49:23.7692625Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T06:49:23.7692695Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T06:49:23.7692742Z - memory_mask: :math:`(T, S)`. 2025-09-07T06:49:23.7692828Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T06:49:23.7692912Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T06:49:23.7693000Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T06:49:23.7693031Z 2025-09-07T06:49:23.7693142Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T06:49:23.7693240Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T06:49:23.7693330Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T06:49:23.7693406Z is provided, it will be added to the attention weight. 2025-09-07T06:49:23.7693512Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T06:49:23.7693588Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T06:49:23.7693696Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T06:49:23.7693727Z 2025-09-07T06:49:23.7693817Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T06:49:23.7693877Z `(N, T, E)` if `batch_first=True`. 2025-09-07T06:49:23.7693908Z 2025-09-07T06:49:23.7693999Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T06:49:23.7694082Z the output sequence length of a transformer is same as the input sequence 2025-09-07T06:49:23.7694129Z (i.e. target) length of the decoder. 2025-09-07T06:49:23.7695566Z 2025-09-07T06:49:23.7695681Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T06:49:23.7695733Z batch size, :math:`E` is the feature number 2025-09-07T06:49:23.7695764Z 2025-09-07T06:49:23.7695799Z Examples: 2025-09-07T06:49:23.7695841Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7695888Z >>> output = transformer_model( 2025-09-07T06:49:23.7695945Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T06:49:23.7695980Z ... ) 2025-09-07T06:49:23.7696011Z 2025-09-07T06:49:23.7696105Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7696137Z 2025-09-07T06:49:23.7696175Z warnings.warn(msg) 2025-09-07T06:49:23.7696206Z 2025-09-07T06:49:23.7696281Z --- Parse Warning: 134 / 146 --- 2025-09-07T06:49:23.7696577Z /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=410. 2025-09-07T06:49:23.7696670Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7696729Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T06:49:23.7696760Z 2025-09-07T06:49:23.7696852Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T06:49:23.7696883Z 2025-09-07T06:49:23.7696964Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T06:49:23.7698440Z including the indices of the maximal values and computes a partial inverse 2025-09-07T06:49:23.7698501Z in which all non-maximal values are set to zero. 2025-09-07T06:49:23.7698535Z 2025-09-07T06:49:23.7698567Z Note: 2025-09-07T06:49:23.7698676Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T06:49:23.7698807Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T06:49:23.7698838Z 2025-09-07T06:49:23.7698920Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T06:49:23.7698985Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T06:49:23.7699053Z To accommodate this, you can provide the needed output size 2025-09-07T06:49:23.7699129Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T06:49:23.7699195Z See the Inputs and Example below. 2025-09-07T06:49:23.7699228Z 2025-09-07T06:49:23.7699260Z Args: 2025-09-07T06:49:23.7699355Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T06:49:23.7699421Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T06:49:23.7699473Z It is set to :attr:`kernel_size` by default. 2025-09-07T06:49:23.7699541Z padding (int or tuple): Padding that was added to the input 2025-09-07T06:49:23.7699571Z 2025-09-07T06:49:23.7699604Z Inputs: 2025-09-07T06:49:23.7699653Z - `input`: the input Tensor to invert 2025-09-07T06:49:23.7701093Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T06:49:23.7701157Z - `output_size` (optional): the targeted output size 2025-09-07T06:49:23.7701210Z 2025-09-07T06:49:23.7701243Z Shape: 2025-09-07T06:49:23.7701317Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T06:49:23.7701396Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T06:49:23.7701429Z 2025-09-07T06:49:23.7701466Z .. math:: 2025-09-07T06:49:23.7701563Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T06:49:23.7701593Z 2025-09-07T06:49:23.7701628Z .. math:: 2025-09-07T06:49:23.7701716Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T06:49:23.7701746Z 2025-09-07T06:49:23.7701808Z or as given by :attr:`output_size` in the call operator 2025-09-07T06:49:23.7701837Z 2025-09-07T06:49:23.7701872Z Example:: 2025-09-07T06:49:23.7701902Z 2025-09-07T06:49:23.7701965Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T06:49:23.7702016Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T06:49:23.7702072Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T06:49:23.7702117Z [ 5., 6., 7., 8.], 2025-09-07T06:49:23.7702159Z [ 9., 10., 11., 12.], 2025-09-07T06:49:23.7703562Z [13., 14., 15., 16.]]]]) 2025-09-07T06:49:23.7703613Z >>> output, indices = pool(input) 2025-09-07T06:49:23.7703654Z >>> unpool(output, indices) 2025-09-07T06:49:23.7703698Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T06:49:23.7703738Z [ 0., 6., 0., 8.], 2025-09-07T06:49:23.7703776Z [ 0., 0., 0., 0.], 2025-09-07T06:49:23.7703815Z [ 0., 14., 0., 16.]]]]) 2025-09-07T06:49:23.7703894Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T06:49:23.7703952Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T06:49:23.7704016Z [ 6., 7., 8., 9., 10.], 2025-09-07T06:49:23.7704061Z [11., 12., 13., 14., 15.], 2025-09-07T06:49:23.7704106Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T06:49:23.7704152Z >>> output, indices = pool(input) 2025-09-07T06:49:23.7704217Z >>> # This call will not work without specifying output_size 2025-09-07T06:49:23.7704276Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T06:49:23.7704319Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T06:49:23.7704357Z [ 0., 7., 0., 9., 0.], 2025-09-07T06:49:23.7704488Z [ 0., 0., 0., 0., 0.], 2025-09-07T06:49:23.7704529Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T06:49:23.7704560Z 2025-09-07T06:49:23.7704613Z 2025-09-07T06:49:23.7706008Z 2025-09-07T06:49:23.7706104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7706156Z 2025-09-07T06:49:23.7706195Z warnings.warn(msg) 2025-09-07T06:49:23.7706225Z 2025-09-07T06:49:23.7706298Z --- Parse Warning: 135 / 146 --- 2025-09-07T06:49:23.7706624Z /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=1798. 2025-09-07T06:49:23.7706716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7706788Z Creates a criterion that measures the triplet loss given input 2025-09-07T06:49:23.7706879Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T06:49:23.7706957Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T06:49:23.7707051Z real-valued function ("distance function") used to compute the relationship 2025-09-07T06:49:23.7707130Z between the anchor and positive example ("positive distance") and the 2025-09-07T06:49:23.7707191Z anchor and negative example ("negative distance"). 2025-09-07T06:49:23.7707222Z 2025-09-07T06:49:23.7707299Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T06:49:23.7707338Z can be described as: 2025-09-07T06:49:23.7707369Z 2025-09-07T06:49:23.7707402Z .. math:: 2025-09-07T06:49:23.7707459Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T06:49:23.7707518Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T06:49:23.7707549Z 2025-09-07T06:49:23.7709003Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T06:49:23.7709111Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T06:49:23.7709201Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T06:49:23.7709290Z between the positive and negative distances that is required for the loss to 2025-09-07T06:49:23.7709372Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T06:49:23.7709420Z that the distance function can handle. 2025-09-07T06:49:23.7709450Z 2025-09-07T06:49:23.7709495Z If :attr:`reduction` is not ``'none'`` 2025-09-07T06:49:23.7709537Z (default ``'mean'``), then: 2025-09-07T06:49:23.7709568Z 2025-09-07T06:49:23.7709600Z .. math:: 2025-09-07T06:49:23.7709635Z \ell(x, y) = 2025-09-07T06:49:23.7709672Z \begin{cases} 2025-09-07T06:49:23.7709749Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T06:49:23.7709819Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T06:49:23.7709856Z \end{cases} 2025-09-07T06:49:23.7709908Z 2025-09-07T06:49:23.7709994Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T06:49:23.7710086Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T06:49:23.7710116Z 2025-09-07T06:49:23.7710148Z Args: 2025-09-07T06:49:23.7710244Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T06:49:23.7711734Z quantifies the closeness of two tensors. If not specified, 2025-09-07T06:49:23.7711801Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T06:49:23.7711896Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T06:49:23.7711995Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T06:49:23.7712113Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T06:49:23.7712178Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T06:49:23.7712283Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T06:49:23.7712373Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T06:49:23.7712458Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T06:49:23.7712550Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T06:49:23.7712604Z the loss computation. Default: ``False``. 2025-09-07T06:49:23.7712701Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T06:49:23.7712783Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T06:49:23.7712853Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T06:49:23.7712939Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T06:49:23.7712970Z 2025-09-07T06:49:23.7713001Z 2025-09-07T06:49:23.7713034Z Shape: 2025-09-07T06:49:23.7713120Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T06:49:23.7713171Z as supported by the distance function. 2025-09-07T06:49:23.7713262Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T06:49:23.7714716Z otherwise. 2025-09-07T06:49:23.7714749Z 2025-09-07T06:49:23.7714783Z Examples: 2025-09-07T06:49:23.7714813Z 2025-09-07T06:49:23.7714854Z >>> # Initialize embeddings 2025-09-07T06:49:23.7714901Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T06:49:23.7714954Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:23.7715007Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:23.7715060Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T06:49:23.7715104Z >>> anchor = embedding(anchor_ids) 2025-09-07T06:49:23.7715151Z >>> positive = embedding(positive_ids) 2025-09-07T06:49:23.7715196Z >>> negative = embedding(negative_ids) 2025-09-07T06:49:23.7715228Z >>> 2025-09-07T06:49:23.7715270Z >>> # Built-in Distance Function 2025-09-07T06:49:23.7715310Z >>> triplet_loss = \ 2025-09-07T06:49:23.7715409Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T06:49:23.7715468Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:23.7715509Z >>> output.backward() 2025-09-07T06:49:23.7715542Z >>> 2025-09-07T06:49:23.7715583Z >>> # Custom Distance Function 2025-09-07T06:49:23.7715625Z >>> def l_infinity(x1, x2): 2025-09-07T06:49:23.7715710Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T06:49:23.7717112Z >>> 2025-09-07T06:49:23.7717185Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T06:49:23.7717223Z >>> triplet_loss = ( 2025-09-07T06:49:23.7717320Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T06:49:23.7717378Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:23.7717418Z >>> output.backward() 2025-09-07T06:49:23.7717448Z >>> 2025-09-07T06:49:23.7717496Z >>> # Custom Distance Function (Lambda) 2025-09-07T06:49:23.7717532Z >>> triplet_loss = ( 2025-09-07T06:49:23.7717585Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T06:49:23.7717664Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T06:49:23.7717745Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T06:49:23.7717784Z >>> output.backward() 2025-09-07T06:49:23.7717815Z 2025-09-07T06:49:23.7717867Z Reference: 2025-09-07T06:49:23.7717975Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T06:49:23.7718056Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T06:49:23.7718088Z 2025-09-07T06:49:23.7718176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T06:49:23.7718207Z 2025-09-07T06:49:23.7718243Z warnings.warn(msg) 2025-09-07T06:49:23.7719633Z 2025-09-07T06:49:23.7719711Z --- Parse Warning: 136 / 146 --- 2025-09-07T06:49:23.7720023Z /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=1933. 2025-09-07T06:49:23.7720121Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7720177Z The Connectionist Temporal Classification loss. 2025-09-07T06:49:23.7720209Z 2025-09-07T06:49:23.7720338Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T06:49:23.7720459Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T06:49:23.7720574Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T06:49:23.7720677Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T06:49:23.7720708Z 2025-09-07T06:49:23.7720739Z Args: 2025-09-07T06:49:23.7720802Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T06:49:23.7720889Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T06:49:23.7720960Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T06:49:23.7721037Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T06:49:23.7721126Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T06:49:23.7721166Z Default: ``'mean'`` 2025-09-07T06:49:23.7721211Z zero_infinity (bool, optional): 2025-09-07T06:49:23.7721283Z Whether to zero infinite losses and the associated gradients. 2025-09-07T06:49:23.7721322Z Default: ``False`` 2025-09-07T06:49:23.7721391Z Infinite losses mainly occur when the inputs are too short 2025-09-07T06:49:23.7722800Z to be aligned to the targets. 2025-09-07T06:49:23.7722835Z 2025-09-07T06:49:23.7722869Z Shape: 2025-09-07T06:49:23.7722943Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T06:49:23.7723010Z where :math:`T = \text{input length}`, 2025-09-07T06:49:23.7723059Z :math:`N = \text{batch size}`, and 2025-09-07T06:49:23.7723119Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T06:49:23.7723199Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T06:49:23.7723251Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T06:49:23.7723302Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T06:49:23.7723364Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T06:49:23.7723412Z where :math:`N = \text{batch size}` and 2025-09-07T06:49:23.7723472Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T06:49:23.7723545Z It represents the target sequences. Each element in the target 2025-09-07T06:49:23.7723650Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T06:49:23.7723711Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T06:49:23.7723777Z length of the longest sequence, and stacked. 2025-09-07T06:49:23.7723846Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T06:49:23.7723899Z the targets are assumed to be un-padded and 2025-09-07T06:49:23.7723945Z concatenated within 1 dimension. 2025-09-07T06:49:23.7724019Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T06:49:23.7725504Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T06:49:23.7725582Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T06:49:23.7725688Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T06:49:23.7725736Z are padded to equal lengths. 2025-09-07T06:49:23.7725814Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T06:49:23.7725892Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T06:49:23.7725970Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T06:49:23.7726058Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T06:49:23.7726122Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T06:49:23.7726212Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T06:49:23.7726279Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T06:49:23.7726366Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T06:49:23.7726451Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T06:49:23.7726521Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T06:49:23.7726609Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T06:49:23.7726680Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T06:49:23.7726712Z 2025-09-07T06:49:23.7726745Z Examples: 2025-09-07T06:49:23.7726774Z 2025-09-07T06:49:23.7726819Z >>> # Target are to be padded 2025-09-07T06:49:23.7726864Z >>> T = 50 # Input sequence length 2025-09-07T06:49:23.7726917Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:23.7728316Z >>> N = 16 # Batch size 2025-09-07T06:49:23.7728405Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T06:49:23.7728472Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T06:49:23.7728532Z >>> 2025-09-07T06:49:23.7728604Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T06:49:23.7728684Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T06:49:23.7728716Z >>> 2025-09-07T06:49:23.7728785Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:23.7728861Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T06:49:23.7728893Z >>> 2025-09-07T06:49:23.7728970Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T06:49:23.7729017Z >>> target_lengths = torch.randint( 2025-09-07T06:49:23.7729056Z ... low=S_min, 2025-09-07T06:49:23.7729095Z ... high=S, 2025-09-07T06:49:23.7729132Z ... size=(N,), 2025-09-07T06:49:23.7729193Z ... dtype=torch.long, 2025-09-07T06:49:23.7729224Z ... ) 2025-09-07T06:49:23.7729266Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:23.7729351Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:23.7729391Z >>> loss.backward() 2025-09-07T06:49:23.7729422Z >>> 2025-09-07T06:49:23.7730811Z >>> 2025-09-07T06:49:23.7730860Z >>> # Target are to be un-padded 2025-09-07T06:49:23.7730906Z >>> T = 50 # Input sequence length 2025-09-07T06:49:23.7730957Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:23.7730996Z >>> N = 16 # Batch size 2025-09-07T06:49:23.7731028Z >>> 2025-09-07T06:49:23.7731098Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T06:49:23.7731191Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T06:49:23.7731269Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T06:49:23.7731304Z >>> 2025-09-07T06:49:23.7731373Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:23.7731460Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T06:49:23.7731499Z >>> target = torch.randint( 2025-09-07T06:49:23.7731536Z ... low=1, 2025-09-07T06:49:23.7731572Z ... high=C, 2025-09-07T06:49:23.7731617Z ... size=(sum(target_lengths),), 2025-09-07T06:49:23.7731657Z ... dtype=torch.long, 2025-09-07T06:49:23.7731689Z ... ) 2025-09-07T06:49:23.7731727Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:23.7731793Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:23.7731832Z >>> loss.backward() 2025-09-07T06:49:23.7731864Z >>> 2025-09-07T06:49:23.7733253Z >>> 2025-09-07T06:49:23.7733327Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T06:49:23.7733370Z >>> T = 50 # Input sequence length 2025-09-07T06:49:23.7733425Z >>> C = 20 # Number of classes (including blank) 2025-09-07T06:49:23.7733456Z >>> 2025-09-07T06:49:23.7733526Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T06:49:23.7733579Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T06:49:23.7733651Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T06:49:23.7733710Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T06:49:23.7733741Z >>> 2025-09-07T06:49:23.7733809Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T06:49:23.7733891Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T06:49:23.7733933Z >>> target = torch.randint( 2025-09-07T06:49:23.7733990Z ... low=1, 2025-09-07T06:49:23.7734027Z ... high=C, 2025-09-07T06:49:23.7734071Z ... size=(target_lengths,), 2025-09-07T06:49:23.7734110Z ... dtype=torch.long, 2025-09-07T06:49:23.7734141Z ... ) 2025-09-07T06:49:23.7734181Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T06:49:23.7734245Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T06:49:23.7734283Z >>> loss.backward() 2025-09-07T06:49:23.7734313Z 2025-09-07T06:49:23.7735757Z Reference: 2025-09-07T06:49:23.7735827Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T06:49:23.7735909Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T06:49:23.7735974Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T06:49:23.7736033Z 2025-09-07T06:49:23.7736064Z Note: 2025-09-07T06:49:23.7736156Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T06:49:23.7736264Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T06:49:23.7736345Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T06:49:23.7736387Z dtype :attr:`torch.int32`. 2025-09-07T06:49:23.7736419Z 2025-09-07T06:49:23.7736514Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T06:49:23.7736544Z 2025-09-07T06:49:23.7736574Z 2025-09-07T06:49:23.7736605Z Note: 2025-09-07T06:49:23.7736690Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T06:49:23.7736796Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T06:49:23.7736883Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T06:49:23.7736972Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T06:49:23.7737007Z True``. 2025-09-07T06:49:23.7737078Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T06:49:23.7738472Z 2025-09-07T06:49:23.7738564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7738595Z 2025-09-07T06:49:23.7738632Z warnings.warn(msg) 2025-09-07T06:49:23.7738663Z 2025-09-07T06:49:23.7738739Z --- Parse Warning: 137 / 146 --- 2025-09-07T06:49:23.7739045Z /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=603. 2025-09-07T06:49:23.7739138Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7739208Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T06:49:23.7739238Z 2025-09-07T06:49:23.7739363Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T06:49:23.7739441Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T06:49:23.7739517Z Internal Covariate Shift `__ . 2025-09-07T06:49:23.7739547Z 2025-09-07T06:49:23.7739581Z .. math:: 2025-09-07T06:49:23.7739610Z 2025-09-07T06:49:23.7739690Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T06:49:23.7739721Z 2025-09-07T06:49:23.7739801Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T06:49:23.7739889Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T06:49:23.7739975Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T06:49:23.7740065Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T06:49:23.7741503Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T06:49:23.7741597Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T06:49:23.7741644Z `torch.var(input, unbiased=False)`. 2025-09-07T06:49:23.7741675Z 2025-09-07T06:49:23.7741757Z Also by default, during training this layer keeps running estimates of its 2025-09-07T06:49:23.7741839Z computed mean and variance, which are then used for normalization during 2025-09-07T06:49:23.7741923Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T06:49:23.7741956Z of 0.1. 2025-09-07T06:49:23.7741986Z 2025-09-07T06:49:23.7742067Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T06:49:23.7742168Z keep running estimates, and batch statistics are instead used during 2025-09-07T06:49:23.7742210Z evaluation time as well. 2025-09-07T06:49:23.7742256Z 2025-09-07T06:49:23.7742289Z .. note:: 2025-09-07T06:49:23.7742368Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T06:49:23.7742447Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T06:49:23.7742499Z update rule for running statistics here is 2025-09-07T06:49:23.7742596Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T06:49:23.7742669Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T06:49:23.7742707Z new observed value. 2025-09-07T06:49:23.7742737Z 2025-09-07T06:49:23.7744213Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T06:49:23.7744313Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T06:49:23.7744429Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T06:49:23.7744462Z 2025-09-07T06:49:23.7744519Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T06:49:23.7744619Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T06:49:23.7744696Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T06:49:23.7744770Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T06:49:23.7744809Z Network with DDP. 2025-09-07T06:49:23.7744840Z 2025-09-07T06:49:23.7744871Z Args: 2025-09-07T06:49:23.7744935Z num_features: :math:`C` from an expected input of size 2025-09-07T06:49:23.7744975Z :math:`(N, C, +)` 2025-09-07T06:49:23.7745044Z eps: a value added to the denominator for numerical stability. 2025-09-07T06:49:23.7745086Z Default: ``1e-5`` 2025-09-07T06:49:23.7745156Z momentum: the value used for the running_mean and running_var 2025-09-07T06:49:23.7745232Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T06:49:23.7745281Z (i.e. simple average). Default: 0.1 2025-09-07T06:49:23.7745354Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T06:49:23.7745412Z learnable affine parameters. Default: ``True`` 2025-09-07T06:49:23.7745486Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T06:49:23.7745568Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T06:49:23.7747017Z this module does not track such statistics, and initializes statistics 2025-09-07T06:49:23.7747092Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T06:49:23.7747202Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T06:49:23.7747262Z in both training and eval modes. Default: ``True`` 2025-09-07T06:49:23.7747349Z process_group: synchronization of stats happen within each process group 2025-09-07T06:49:23.7747429Z individually. Default behavior is synchronization across the whole 2025-09-07T06:49:23.7747463Z world 2025-09-07T06:49:23.7747494Z 2025-09-07T06:49:23.7747526Z Shape: 2025-09-07T06:49:23.7747569Z - Input: :math:`(N, C, +)` 2025-09-07T06:49:23.7747626Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T06:49:23.7747657Z 2025-09-07T06:49:23.7747689Z .. note:: 2025-09-07T06:49:23.7747774Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T06:49:23.7747845Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T06:49:23.7747916Z ``self.training`` is otherwise ``False``. 2025-09-07T06:49:23.7747946Z 2025-09-07T06:49:23.7747998Z Examples:: 2025-09-07T06:49:23.7748029Z 2025-09-07T06:49:23.7748068Z >>> # xdoctest: +SKIP 2025-09-07T06:49:23.7748114Z >>> # With Learnable Parameters 2025-09-07T06:49:23.7748157Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T06:49:23.7749567Z >>> # creating process group (optional) 2025-09-07T06:49:23.7749623Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T06:49:23.7749664Z >>> ranks = list(range(8)) 2025-09-07T06:49:23.7749707Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T06:49:23.7749766Z >>> # Note: every rank calls into new_group for every 2025-09-07T06:49:23.7749844Z >>> # process group created, even if that rank is not 2025-09-07T06:49:23.7749886Z >>> # part of the group. 2025-09-07T06:49:23.7749981Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T06:49:23.7750055Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T06:49:23.7750103Z >>> # Without Learnable Parameters 2025-09-07T06:49:23.7750178Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T06:49:23.7750227Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T06:49:23.7750266Z >>> output = m(input) 2025-09-07T06:49:23.7750296Z 2025-09-07T06:49:23.7750343Z >>> # network is nn.BatchNorm layer 2025-09-07T06:49:23.7750440Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T06:49:23.7750502Z >>> # only single gpu per process is currently supported 2025-09-07T06:49:23.7750582Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T06:49:23.7750630Z >>> sync_bn_network, 2025-09-07T06:49:23.7750682Z >>> device_ids=[args.local_rank], 2025-09-07T06:49:23.7750734Z >>> output_device=args.local_rank) 2025-09-07T06:49:23.7752351Z 2025-09-07T06:49:23.7752444Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7752475Z 2025-09-07T06:49:23.7752514Z warnings.warn(msg) 2025-09-07T06:49:23.7752544Z 2025-09-07T06:49:23.7752619Z --- Parse Warning: 138 / 146 --- 2025-09-07T06:49:23.7752958Z /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=830. 2025-09-07T06:49:23.7753052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7753182Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T06:49:23.7753213Z 2025-09-07T06:49:23.7753248Z Args: 2025-09-07T06:49:23.7753334Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T06:49:23.7753411Z process_group (optional): process group to scope synchronization, 2025-09-07T06:49:23.7753456Z default is the whole world 2025-09-07T06:49:23.7753487Z 2025-09-07T06:49:23.7753520Z Returns: 2025-09-07T06:49:23.7753611Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T06:49:23.7753686Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T06:49:23.7753765Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T06:49:23.7753800Z instead. 2025-09-07T06:49:23.7753846Z 2025-09-07T06:49:23.7753881Z Example:: 2025-09-07T06:49:23.7755335Z 2025-09-07T06:49:23.7755388Z >>> # Network with nn.BatchNorm layer 2025-09-07T06:49:23.7755472Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.7755519Z >>> module = torch.nn.Sequential( 2025-09-07T06:49:23.7755567Z >>> torch.nn.Linear(20, 100), 2025-09-07T06:49:23.7755616Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T06:49:23.7755655Z >>> ).cuda() 2025-09-07T06:49:23.7755706Z >>> # creating process group (optional) 2025-09-07T06:49:23.7755762Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T06:49:23.7755805Z >>> ranks = list(range(8)) 2025-09-07T06:49:23.7755849Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T06:49:23.7755923Z >>> # Note: every rank calls into new_group for every 2025-09-07T06:49:23.7755986Z >>> # process group created, even if that rank is not 2025-09-07T06:49:23.7756029Z >>> # part of the group. 2025-09-07T06:49:23.7756079Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T06:49:23.7756171Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T06:49:23.7756245Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T06:49:23.7756350Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T06:49:23.7756380Z 2025-09-07T06:49:23.7756411Z 2025-09-07T06:49:23.7756498Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7757888Z 2025-09-07T06:49:23.7757928Z warnings.warn(msg) 2025-09-07T06:49:23.7757960Z 2025-09-07T06:49:23.7758033Z --- Parse Warning: 139 / 146 --- 2025-09-07T06:49:23.7758330Z /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=66. 2025-09-07T06:49:23.7758422Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7758453Z 2025-09-07T06:49:23.7758563Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T06:49:23.7758594Z 2025-09-07T06:49:23.7758689Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T06:49:23.7758771Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T06:49:23.7758801Z 2025-09-07T06:49:23.7758914Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T06:49:23.7759004Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T06:49:23.7759093Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T06:49:23.7759124Z 2025-09-07T06:49:23.7759159Z Shape: 2025-09-07T06:49:23.7759239Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T06:49:23.7759328Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T06:49:23.7759408Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T06:49:23.7759460Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T06:49:23.7759489Z 2025-09-07T06:49:23.7760881Z Args: 2025-09-07T06:49:23.7760943Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T06:49:23.7761068Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T06:49:23.7761098Z 2025-09-07T06:49:23.7761156Z Examples: 2025-09-07T06:49:23.7761202Z >>> input = torch.randn(2, 50) 2025-09-07T06:49:23.7761242Z >>> # With tuple of ints 2025-09-07T06:49:23.7761297Z >>> m = nn.Sequential( 2025-09-07T06:49:23.7761336Z >>> nn.Linear(50, 50), 2025-09-07T06:49:23.7761380Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T06:49:23.7761411Z >>> ) 2025-09-07T06:49:23.7761451Z >>> output = m(input) 2025-09-07T06:49:23.7761488Z >>> output.size() 2025-09-07T06:49:23.7761528Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:23.7761564Z >>> # With torch.Size 2025-09-07T06:49:23.7761602Z >>> m = nn.Sequential( 2025-09-07T06:49:23.7761639Z >>> nn.Linear(50, 50), 2025-09-07T06:49:23.7761690Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T06:49:23.7761720Z >>> ) 2025-09-07T06:49:23.7761769Z >>> output = m(input) 2025-09-07T06:49:23.7761808Z >>> output.size() 2025-09-07T06:49:23.7763205Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:23.7763260Z >>> # With namedshape (tuple of tuples) 2025-09-07T06:49:23.7763318Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T06:49:23.7763396Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T06:49:23.7763439Z >>> output = unflatten(input) 2025-09-07T06:49:23.7763476Z >>> output.size() 2025-09-07T06:49:23.7763513Z torch.Size([2, 2, 5, 5]) 2025-09-07T06:49:23.7763543Z 2025-09-07T06:49:23.7763631Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7763662Z 2025-09-07T06:49:23.7763698Z warnings.warn(msg) 2025-09-07T06:49:23.7763728Z 2025-09-07T06:49:23.7763801Z --- Parse Warning: 140 / 146 --- 2025-09-07T06:49:23.7764123Z /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. 2025-09-07T06:49:23.7764217Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7764279Z Register a parametrization to a tensor in a module. 2025-09-07T06:49:23.7764309Z 2025-09-07T06:49:23.7764450Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T06:49:23.7764549Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T06:49:23.7764641Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T06:49:23.7764742Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T06:49:23.7764773Z 2025-09-07T06:49:23.7766244Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T06:49:23.7766335Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T06:49:23.7766394Z 2025-09-07T06:49:23.7766484Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T06:49:23.7766536Z ``module.parametrizations.weight``. 2025-09-07T06:49:23.7766567Z 2025-09-07T06:49:23.7766620Z The original tensor will be accessible under 2025-09-07T06:49:23.7766678Z ``module.parametrizations.weight.original``. 2025-09-07T06:49:23.7766707Z 2025-09-07T06:49:23.7766800Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T06:49:23.7766840Z on the same attribute. 2025-09-07T06:49:23.7766870Z 2025-09-07T06:49:23.7766954Z The training mode of a registered parametrization is updated on registration 2025-09-07T06:49:23.7767006Z to match the training mode of the host module 2025-09-07T06:49:23.7767038Z 2025-09-07T06:49:23.7767144Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T06:49:23.7767213Z using the context manager :func:`cached`. 2025-09-07T06:49:23.7767242Z 2025-09-07T06:49:23.7767346Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T06:49:23.7767376Z 2025-09-07T06:49:23.7767418Z .. code-block:: python 2025-09-07T06:49:23.7767447Z 2025-09-07T06:49:23.7768888Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T06:49:23.7768920Z 2025-09-07T06:49:23.7769013Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T06:49:23.7769088Z is registered to compute the initial value of the original tensor. 2025-09-07T06:49:23.7769194Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T06:49:23.7769252Z 2025-09-07T06:49:23.7769362Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T06:49:23.7769466Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T06:49:23.7769499Z 2025-09-07T06:49:23.7769579Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T06:49:23.7769665Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T06:49:23.7769747Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T06:49:23.7769778Z 2025-09-07T06:49:23.7769893Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T06:49:23.7769943Z with names ``original0``, ``original1``,... 2025-09-07T06:49:23.7769974Z 2025-09-07T06:49:23.7770007Z .. note:: 2025-09-07T06:49:23.7770038Z 2025-09-07T06:49:23.7770135Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T06:49:23.7770194Z once to perform a number of consistency checks. 2025-09-07T06:49:23.7770288Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T06:49:23.7771693Z and nothing will be called otherwise. 2025-09-07T06:49:23.7771725Z 2025-09-07T06:49:23.7771759Z .. note:: 2025-09-07T06:49:23.7771788Z 2025-09-07T06:49:23.7771863Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T06:49:23.7771909Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T06:49:23.7772010Z `right inverse `_). 2025-09-07T06:49:23.7772098Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T06:49:23.7772136Z to relax this. 2025-09-07T06:49:23.7772165Z 2025-09-07T06:49:23.7772202Z .. warning:: 2025-09-07T06:49:23.7772233Z 2025-09-07T06:49:23.7772345Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T06:49:23.7772437Z will register a number of new parameters. If such parametrization is registered 2025-09-07T06:49:23.7772533Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T06:49:23.7772605Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T06:49:23.7772635Z 2025-09-07T06:49:23.7772666Z Args: 2025-09-07T06:49:23.7772742Z module (nn.Module): module on which to register the parametrization 2025-09-07T06:49:23.7772820Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T06:49:23.7772862Z the parametrization 2025-09-07T06:49:23.7772937Z parametrization (nn.Module): the parametrization to register 2025-09-07T06:49:23.7774325Z Keyword args: 2025-09-07T06:49:23.7774473Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T06:49:23.7774546Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T06:49:23.7774665Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T06:49:23.7774712Z Enable this flag at your own risk. 2025-09-07T06:49:23.7774743Z 2025-09-07T06:49:23.7774774Z Raises: 2025-09-07T06:49:23.7774876Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T06:49:23.7774906Z 2025-09-07T06:49:23.7774940Z Examples: 2025-09-07T06:49:23.7774996Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T06:49:23.7775036Z >>> import torch 2025-09-07T06:49:23.7775077Z >>> import torch.nn as nn 2025-09-07T06:49:23.7775147Z >>> import torch.nn.utils.parametrize as P 2025-09-07T06:49:23.7775181Z >>> 2025-09-07T06:49:23.7775225Z >>> class Symmetric(nn.Module): 2025-09-07T06:49:23.7775270Z >>> def forward(self, X): 2025-09-07T06:49:23.7775337Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T06:49:23.7775369Z >>> 2025-09-07T06:49:23.7775415Z >>> def right_inverse(self, A): 2025-09-07T06:49:23.7775455Z >>> return A.triu() 2025-09-07T06:49:23.7776849Z >>> 2025-09-07T06:49:23.7776891Z >>> m = nn.Linear(5, 5) 2025-09-07T06:49:23.7776957Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T06:49:23.7777044Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T06:49:23.7777076Z True 2025-09-07T06:49:23.7777115Z >>> A = torch.rand(5, 5) 2025-09-07T06:49:23.7777161Z >>> A = A + A.T # A is now symmetric 2025-09-07T06:49:23.7777235Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T06:49:23.7777287Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T06:49:23.7777320Z True 2025-09-07T06:49:23.7777352Z 2025-09-07T06:49:23.7777394Z >>> class RankOne(nn.Module): 2025-09-07T06:49:23.7777437Z >>> def forward(self, x, y): 2025-09-07T06:49:23.7777494Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T06:49:23.7777547Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T06:49:23.7777579Z >>> 2025-09-07T06:49:23.7777622Z >>> def right_inverse(self, Z): 2025-09-07T06:49:23.7777673Z >>> # Project Z onto the rank 1 matrices 2025-09-07T06:49:23.7777729Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T06:49:23.7777774Z >>> # Return rescaled singular vectors 2025-09-07T06:49:23.7777824Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T06:49:23.7777904Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T06:49:23.7779291Z >>> 2025-09-07T06:49:23.7779353Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T06:49:23.7779403Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T06:49:23.7779436Z ... ) 2025-09-07T06:49:23.7779507Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T06:49:23.7779539Z 1 2025-09-07T06:49:23.7779569Z 2025-09-07T06:49:23.7779599Z 2025-09-07T06:49:23.7779688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7779718Z 2025-09-07T06:49:23.7779756Z warnings.warn(msg) 2025-09-07T06:49:23.7779785Z 2025-09-07T06:49:23.7779861Z --- Parse Warning: 141 / 146 --- 2025-09-07T06:49:23.7780195Z /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. 2025-09-07T06:49:23.7780323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7780402Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T06:49:23.7780433Z 2025-09-07T06:49:23.7780528Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T06:49:23.7780628Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T06:49:23.7780716Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T06:49:23.7780824Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T06:49:23.7780867Z 2025-09-07T06:49:23.7782265Z .. note:: 2025-09-07T06:49:23.7782354Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T06:49:23.7782436Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T06:49:23.7782513Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T06:49:23.7782592Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T06:49:23.7782670Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T06:49:23.7782747Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T06:49:23.7782817Z even in cases where we have to apply permutation to input tensors. 2025-09-07T06:49:23.7782848Z 2025-09-07T06:49:23.7782926Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T06:49:23.7782974Z channels_last. This ensures that; 2025-09-07T06:49:23.7783050Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T06:49:23.7783134Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T06:49:23.7783216Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T06:49:23.7783260Z from memory_format conversion. 2025-09-07T06:49:23.7783290Z 2025-09-07T06:49:23.7783369Z The optimal case is that, layers between convolution layers are channels 2025-09-07T06:49:23.7783450Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T06:49:23.7783530Z encounters the first convolution layer and stay in that memory format. 2025-09-07T06:49:23.7783612Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T06:49:23.7783642Z 2025-09-07T06:49:23.7785128Z In case where a channels last incompatible layer is between convolution 2025-09-07T06:49:23.7785211Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T06:49:23.7785324Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T06:49:23.7785405Z contiguous format and be permuted to channels last when it encounters 2025-09-07T06:49:23.7785479Z another convolution layer. There's no point in propagating that 2025-09-07T06:49:23.7785556Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T06:49:23.7785595Z ``memory_format``. 2025-09-07T06:49:23.7785625Z 2025-09-07T06:49:23.7785707Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T06:49:23.7785785Z there might have been a better spot to fuse the permutation other than 2025-09-07T06:49:23.7785831Z immediately before a convolution. 2025-09-07T06:49:23.7785862Z 2025-09-07T06:49:23.7785894Z Args: 2025-09-07T06:49:23.7785989Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T06:49:23.7786033Z ``nn.Module`` 2025-09-07T06:49:23.7786091Z memory_format: user specified ``memory_format``, 2025-09-07T06:49:23.7786176Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T06:49:23.7786206Z 2025-09-07T06:49:23.7786238Z Returns: 2025-09-07T06:49:23.7786293Z The original module with updated ``nn.Conv2d`` 2025-09-07T06:49:23.7786323Z 2025-09-07T06:49:23.7786356Z Example: 2025-09-07T06:49:23.7787779Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.7787843Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T06:49:23.7787886Z >>> input = torch.randint( 2025-09-07T06:49:23.7787963Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T06:49:23.7787996Z ... ) 2025-09-07T06:49:23.7788038Z >>> model = nn.Sequential( 2025-09-07T06:49:23.7788090Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T06:49:23.7788130Z >>> # This is identical to: 2025-09-07T06:49:23.7788219Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T06:49:23.7788283Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T06:49:23.7788328Z ... model, torch.channels_last 2025-09-07T06:49:23.7788360Z ... ) 2025-09-07T06:49:23.7788400Z >>> out = model(input) 2025-09-07T06:49:23.7788431Z 2025-09-07T06:49:23.7788521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7788551Z 2025-09-07T06:49:23.7788588Z warnings.warn(msg) 2025-09-07T06:49:23.7788619Z 2025-09-07T06:49:23.7788695Z --- Parse Warning: 142 / 146 --- 2025-09-07T06:49:23.7789029Z /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. 2025-09-07T06:49:23.7789123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7790565Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T06:49:23.7790665Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T06:49:23.7790761Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T06:49:23.7790884Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T06:49:23.7790992Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T06:49:23.7791024Z 2025-09-07T06:49:23.7791058Z .. note:: 2025-09-07T06:49:23.7791165Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T06:49:23.7791245Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T06:49:23.7791320Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T06:49:23.7791398Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T06:49:23.7791479Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T06:49:23.7791556Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T06:49:23.7791627Z even in cases where we have to apply permutation to input tensors. 2025-09-07T06:49:23.7791659Z 2025-09-07T06:49:23.7791738Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T06:49:23.7791786Z channels_last_3d. This ensures that; 2025-09-07T06:49:23.7791879Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T06:49:23.7791961Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T06:49:23.7792064Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T06:49:23.7792109Z from memory_format conversion. 2025-09-07T06:49:23.7792139Z 2025-09-07T06:49:23.7793591Z The optimal case is that, layers between convolution layers are channels 2025-09-07T06:49:23.7793677Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T06:49:23.7793758Z encounters the first convolution layer and stay in that memory format. 2025-09-07T06:49:23.7793839Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T06:49:23.7793871Z 2025-09-07T06:49:23.7793965Z In case where a channels last incompatible layer is between convolution 2025-09-07T06:49:23.7794047Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T06:49:23.7794125Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T06:49:23.7794204Z contiguous format and be permuted to channels last when it encounters 2025-09-07T06:49:23.7794278Z another convolution layer. There's no point in propagating that 2025-09-07T06:49:23.7794353Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T06:49:23.7794436Z ``memory_format``. 2025-09-07T06:49:23.7794466Z 2025-09-07T06:49:23.7794547Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T06:49:23.7794623Z there might have been a better spot to fuse the permutation other than 2025-09-07T06:49:23.7794669Z immediately before a convolution. 2025-09-07T06:49:23.7794700Z 2025-09-07T06:49:23.7794733Z Args: 2025-09-07T06:49:23.7794813Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T06:49:23.7794855Z ``nn.Module`` 2025-09-07T06:49:23.7794912Z memory_format: user specified ``memory_format``, 2025-09-07T06:49:23.7796344Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T06:49:23.7796376Z 2025-09-07T06:49:23.7796409Z Returns: 2025-09-07T06:49:23.7796465Z The original module with updated ``nn.Conv3d`` 2025-09-07T06:49:23.7796497Z 2025-09-07T06:49:23.7796529Z Example: 2025-09-07T06:49:23.7796583Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T06:49:23.7796643Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T06:49:23.7796686Z >>> input = torch.randint( 2025-09-07T06:49:23.7796743Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T06:49:23.7796775Z ... ) 2025-09-07T06:49:23.7796816Z >>> model = nn.Sequential( 2025-09-07T06:49:23.7796893Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T06:49:23.7796935Z >>> # This is identical to: 2025-09-07T06:49:23.7797026Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T06:49:23.7797089Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T06:49:23.7797134Z ... model, torch.channels_last_3d 2025-09-07T06:49:23.7797166Z ... ) 2025-09-07T06:49:23.7797206Z >>> out = model(input) 2025-09-07T06:49:23.7797237Z 2025-09-07T06:49:23.7797325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7797355Z 2025-09-07T06:49:23.7798746Z warnings.warn(msg) 2025-09-07T06:49:23.7798779Z 2025-09-07T06:49:23.7798854Z --- Parse Warning: 143 / 146 --- 2025-09-07T06:49:23.7799177Z /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=350. 2025-09-07T06:49:23.7799286Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7799344Z Pad a packed batch of variable length sequences. 2025-09-07T06:49:23.7799374Z 2025-09-07T06:49:23.7799441Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T06:49:23.7799471Z 2025-09-07T06:49:23.7799571Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T06:49:23.7799659Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T06:49:23.7799704Z sequence and ``B`` is the batch size. 2025-09-07T06:49:23.7799750Z 2025-09-07T06:49:23.7799784Z Example: 2025-09-07T06:49:23.7799873Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T06:49:23.7799933Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T06:49:23.7799973Z >>> lens = [2, 1, 3] 2025-09-07T06:49:23.7800020Z >>> packed = pack_padded_sequence( 2025-09-07T06:49:23.7800078Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T06:49:23.7800109Z ... ) 2025-09-07T06:49:23.7800144Z >>> packed 2025-09-07T06:49:23.7801602Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T06:49:23.7801686Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T06:49:23.7801778Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T06:49:23.7801818Z >>> seq_unpacked 2025-09-07T06:49:23.7801855Z tensor([[1, 2, 0], 2025-09-07T06:49:23.7801893Z [3, 0, 0], 2025-09-07T06:49:23.7801931Z [4, 5, 6]]) 2025-09-07T06:49:23.7801969Z >>> lens_unpacked 2025-09-07T06:49:23.7802007Z tensor([2, 1, 3]) 2025-09-07T06:49:23.7802038Z 2025-09-07T06:49:23.7802070Z .. note:: 2025-09-07T06:49:23.7802128Z :attr:`total_length` is useful to implement the 2025-09-07T06:49:23.7802209Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T06:49:23.7802289Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T06:49:23.7802376Z See :ref:`this FAQ section ` for 2025-09-07T06:49:23.7802411Z details. 2025-09-07T06:49:23.7802440Z 2025-09-07T06:49:23.7802472Z Args: 2025-09-07T06:49:23.7802523Z sequence (PackedSequence): batch to pad 2025-09-07T06:49:23.7802611Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T06:49:23.7802674Z format, ``T x B x *`` otherwise. 2025-09-07T06:49:23.7802747Z padding_value (float, optional): values for padded elements. 2025-09-07T06:49:23.7804179Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T06:49:23.7804269Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T06:49:23.7804338Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T06:49:23.7804428Z :attr:`sequence`. 2025-09-07T06:49:23.7804458Z 2025-09-07T06:49:23.7804490Z Returns: 2025-09-07T06:49:23.7804560Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T06:49:23.7804632Z containing the list of lengths of each sequence in the batch. 2025-09-07T06:49:23.7804713Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T06:49:23.7804817Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T06:49:23.7804849Z 2025-09-07T06:49:23.7804938Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7804987Z 2025-09-07T06:49:23.7805025Z warnings.warn(msg) 2025-09-07T06:49:23.7805055Z 2025-09-07T06:49:23.7805128Z --- Parse Warning: 144 / 146 --- 2025-09-07T06:49:23.7805419Z /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. 2025-09-07T06:49:23.7805509Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7805636Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T06:49:23.7805667Z 2025-09-07T06:49:23.7805751Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T06:49:23.7805830Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T06:49:23.7807268Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T06:49:23.7807343Z Modifies module in place (and also return the modified module) 2025-09-07T06:49:23.7807376Z by: 2025-09-07T06:49:23.7807406Z 2025-09-07T06:49:23.7807480Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:23.7807558Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:23.7807634Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:23.7807708Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:23.7807747Z ``name+'_orig'``. 2025-09-07T06:49:23.7807778Z 2025-09-07T06:49:23.7807811Z Args: 2025-09-07T06:49:23.7807880Z module (nn.Module): module containing the tensor to prune 2025-09-07T06:49:23.7807948Z name (str): parameter name within ``module`` on which pruning 2025-09-07T06:49:23.7807988Z will act. 2025-09-07T06:49:23.7808053Z amount (int or float): quantity of parameters to prune. 2025-09-07T06:49:23.7808117Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T06:49:23.7808189Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T06:49:23.7808242Z absolute number of parameters to prune. 2025-09-07T06:49:23.7808312Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T06:49:23.7808372Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T06:49:23.7808447Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T06:49:23.7809881Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T06:49:23.7809981Z shape as module parameter) used to compute mask for pruning. 2025-09-07T06:49:23.7810063Z The values in this tensor indicate the importance of the corresponding 2025-09-07T06:49:23.7810114Z elements in the parameter being pruned. 2025-09-07T06:49:23.7810197Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T06:49:23.7810227Z 2025-09-07T06:49:23.7810260Z Returns: 2025-09-07T06:49:23.7810338Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T06:49:23.7810369Z 2025-09-07T06:49:23.7810403Z Examples: 2025-09-07T06:49:23.7810450Z >>> from torch.nn.utils import prune 2025-09-07T06:49:23.7810492Z >>> m = prune.ln_structured( 2025-09-07T06:49:23.7810564Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T06:49:23.7810611Z ... ) 2025-09-07T06:49:23.7810642Z 2025-09-07T06:49:23.7810731Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7810776Z 2025-09-07T06:49:23.7810813Z warnings.warn(msg) 2025-09-07T06:49:23.7810842Z 2025-09-07T06:49:23.7810915Z --- Parse Warning: 145 / 146 --- 2025-09-07T06:49:23.7811215Z /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. 2025-09-07T06:49:23.7811307Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7812702Z 2025-09-07T06:49:23.7812877Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T06:49:23.7812909Z 2025-09-07T06:49:23.7812952Z Modifies modules in place by: 2025-09-07T06:49:23.7812985Z 2025-09-07T06:49:23.7813063Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:23.7813141Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:23.7813220Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:23.7813296Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:23.7813333Z ``name+'_orig'``. 2025-09-07T06:49:23.7813363Z 2025-09-07T06:49:23.7813395Z Args: 2025-09-07T06:49:23.7813469Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T06:49:23.7813538Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T06:49:23.7813614Z weights prior to deciding which ones to prune. module must be of 2025-09-07T06:49:23.7813674Z type :class:`nn.Module`, and name must be a string. 2025-09-07T06:49:23.7813754Z pruning_method (function): a valid pruning function from this module, 2025-09-07T06:49:23.7813819Z or a custom one implemented by the user that satisfies the 2025-09-07T06:49:23.7813901Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T06:49:23.7813983Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T06:49:23.7814059Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T06:49:23.7815552Z should be the same shape as the parameter, and is used for computing 2025-09-07T06:49:23.7815596Z mask for pruning. 2025-09-07T06:49:23.7815671Z If unspecified or None, the parameter will be used in place of its 2025-09-07T06:49:23.7815710Z importance scores. 2025-09-07T06:49:23.7815759Z kwargs: other keyword arguments such as: 2025-09-07T06:49:23.7815831Z amount (int or float): quantity of parameters to prune across the 2025-09-07T06:49:23.7815875Z specified parameters. 2025-09-07T06:49:23.7815963Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T06:49:23.7816037Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T06:49:23.7816087Z absolute number of parameters to prune. 2025-09-07T06:49:23.7816118Z 2025-09-07T06:49:23.7816149Z Raises: 2025-09-07T06:49:23.7816207Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T06:49:23.7816238Z 2025-09-07T06:49:23.7816270Z Note: 2025-09-07T06:49:23.7816347Z Since global structured pruning doesn't make much sense unless the 2025-09-07T06:49:23.7816418Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T06:49:23.7816476Z scope of global pruning to unstructured methods. 2025-09-07T06:49:23.7816506Z 2025-09-07T06:49:23.7816541Z Examples: 2025-09-07T06:49:23.7816587Z >>> from torch.nn.utils import prune 2025-09-07T06:49:23.7816659Z >>> from collections import OrderedDict 2025-09-07T06:49:23.7818065Z >>> net = nn.Sequential( 2025-09-07T06:49:23.7818106Z ... OrderedDict( 2025-09-07T06:49:23.7818162Z ... [ 2025-09-07T06:49:23.7818212Z ... ("first", nn.Linear(10, 4)), 2025-09-07T06:49:23.7818260Z ... ("second", nn.Linear(4, 1)), 2025-09-07T06:49:23.7818293Z ... ] 2025-09-07T06:49:23.7818323Z ... ) 2025-09-07T06:49:23.7818355Z ... ) 2025-09-07T06:49:23.7818397Z >>> parameters_to_prune = ( 2025-09-07T06:49:23.7818438Z ... (net.first, "weight"), 2025-09-07T06:49:23.7818480Z ... (net.second, "weight"), 2025-09-07T06:49:23.7818511Z ... ) 2025-09-07T06:49:23.7818555Z >>> prune.global_unstructured( 2025-09-07T06:49:23.7818595Z ... parameters_to_prune, 2025-09-07T06:49:23.7818663Z ... pruning_method=prune.L1Unstructured, 2025-09-07T06:49:23.7818703Z ... amount=10, 2025-09-07T06:49:23.7818734Z ... ) 2025-09-07T06:49:23.7818816Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T06:49:23.7818851Z tensor(10) 2025-09-07T06:49:23.7818880Z 2025-09-07T06:49:23.7818910Z 2025-09-07T06:49:23.7818998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7820390Z 2025-09-07T06:49:23.7820429Z warnings.warn(msg) 2025-09-07T06:49:23.7820459Z 2025-09-07T06:49:23.7820532Z --- Parse Warning: 146 / 146 --- 2025-09-07T06:49:23.7820825Z /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. 2025-09-07T06:49:23.7820918Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T06:49:23.7821062Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T06:49:23.7821092Z 2025-09-07T06:49:23.7821173Z Modifies module in place (and also return the modified module) by: 2025-09-07T06:49:23.7821202Z 2025-09-07T06:49:23.7821277Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T06:49:23.7821353Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T06:49:23.7821427Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T06:49:23.7821504Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T06:49:23.7821541Z ``name+'_orig'``. 2025-09-07T06:49:23.7821571Z 2025-09-07T06:49:23.7821603Z Args: 2025-09-07T06:49:23.7821673Z module (nn.Module): module containing the tensor to prune 2025-09-07T06:49:23.7821739Z name (str): parameter name within ``module`` on which pruning 2025-09-07T06:49:23.7821777Z will act. 2025-09-07T06:49:23.7821860Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T06:49:23.7823253Z 2025-09-07T06:49:23.7823288Z Returns: 2025-09-07T06:49:23.7823367Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T06:49:23.7823397Z 2025-09-07T06:49:23.7823431Z Examples: 2025-09-07T06:49:23.7823477Z >>> from torch.nn.utils import prune 2025-09-07T06:49:23.7823522Z >>> m = prune.custom_from_mask( 2025-09-07T06:49:23.7823586Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T06:49:23.7823618Z ... ) 2025-09-07T06:49:23.7823658Z >>> print(m.bias_mask) 2025-09-07T06:49:23.7823695Z tensor([0., 1., 0.]) 2025-09-07T06:49:23.7823725Z 2025-09-07T06:49:23.7823757Z 2025-09-07T06:49:23.7823847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T06:49:23.7823898Z 2025-09-07T06:49:23.7823937Z warnings.warn(msg) 2025-09-07T06:49:23.7823967Z 2025-09-07T06:49:23.7824025Z  2025-09-07T06:49:23.7824093Z === Found 8 run-time warnings === 2025-09-07T06:49:23.7824161Z --- Runtime Warning: 1 / 8 --- 2025-09-07T06:49:23.7824255Z example = 2025-09-07T06:49:23.7824752Z /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:1971.) 2025-09-07T06:49:23.7826169Z return super().refine_names(names) 2025-09-07T06:49:23.7826228Z 2025-09-07T06:49:23.7826300Z --- Runtime Warning: 2 / 8 --- 2025-09-07T06:49:23.7826414Z example = 2025-09-07T06:49:23.7826626Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:282: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-09-07T06:49:23.7826739Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-09-07T06:49:23.7826815Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-09-07T06:49:23.7826923Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-09-07T06:49:23.7826961Z dispatch key: CPU 2025-09-07T06:49:23.7827108Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-09-07T06:49:23.7827439Z new kernel: registered at :1 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:218.) 2025-09-07T06:49:23.7827505Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-09-07T06:49:23.7827537Z 2025-09-07T06:49:23.7827605Z --- Runtime Warning: 3 / 8 --- 2025-09-07T06:49:23.7827689Z example = 2025-09-07T06:49:23.7828292Z /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.) 2025-09-07T06:49:23.7828381Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-09-07T06:49:23.7828413Z 2025-09-07T06:49:23.7828496Z --- Runtime Warning: 4 / 8 --- 2025-09-07T06:49:23.7828602Z example = 2025-09-07T06:49:23.7829123Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:278: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2025-09-07T06:49:23.7830554Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-09-07T06:49:23.7830588Z 2025-09-07T06:49:23.7830657Z --- Runtime Warning: 5 / 8 --- 2025-09-07T06:49:23.7830762Z example = 2025-09-07T06:49:23.7831187Z /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) 2025-09-07T06:49:23.7831267Z warnings.warn( 2025-09-07T06:49:23.7831297Z 2025-09-07T06:49:23.7831364Z --- Runtime Warning: 6 / 8 --- 2025-09-07T06:49:23.7831480Z example = 2025-09-07T06:49:23.7831834Z /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) 2025-09-07T06:49:23.7831870Z warnings.warn( 2025-09-07T06:49:23.7831914Z 2025-09-07T06:49:23.7831980Z --- Runtime Warning: 7 / 8 --- 2025-09-07T06:49:23.7832082Z example = 2025-09-07T06:49:23.7832355Z /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`. 2025-09-07T06:49:23.7832403Z WeightNorm.apply(module, name, dim) 2025-09-07T06:49:23.7832433Z 2025-09-07T06:49:23.7832498Z --- Runtime Warning: 8 / 8 --- 2025-09-07T06:49:23.7832603Z example = 2025-09-07T06:49:23.7832866Z /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`. 2025-09-07T06:49:23.7832912Z WeightNorm.apply(module, name, dim) 2025-09-07T06:49:23.7834320Z 2025-09-07T06:49:23.7834495Z === 338 passed, 394 skipped, 154 warnings in 17.07 seconds === 2025-09-07T06:49:23.7834597Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-09-07 06:49:23.655251] 2025-09-07T06:49:23.7834646Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:49:23.7834963Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:49:23.655506] 2025-09-07T06:49:46.2158942Z 2025-09-07T06:49:46.2160018Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_db11d99e9c8230c3_.log 2025-09-07T06:49:46.2167038Z Running 32 items in this shard: test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_autocast_flash_attention_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_custom_rule_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_inplace_op_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_invalid_context_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_list_ops_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_must_not_recompute_gemm_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_must_recompute_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_outplace_op_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_parametrization_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_partial_ctx_fn_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_random_op_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_tensor_subclass_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_compile_selective_checkpoint_triton_kernel_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_distributed_utils_checkpoint_wrapper_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_dynamo_does_not_trace_getattr_as_top_frame_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_error_msg_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_fallback_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_kwargs_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_list_inputs_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_pattern_matcher_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_symints_location_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_decomps_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_dropout_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_function_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_function_via_global_checkpoint_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_function_with_kwargs_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_module_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_multiple_checkpoints_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_must_save_tensor_that_has_backward_hook_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_rand_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_recomputed_rand_cuda, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTestsCUDA::test_tags_sequential_layers_cuda 2025-09-07T06:49:46.2173402Z 2025-09-07T06:49:46.2173498Z Running dynamo/test_aot_autograd 1/1 ... [2025-09-07 06:49:46.216068] 2025-09-07T06:49:46.2173675Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:49:46.2174099Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:49:46.216307] 2025-09-07T06:50:23.3636043Z 2025-09-07T06:50:23.3637755Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_cca424431e0af5d4_.log 2025-09-07T06:50:23.3651404Z Running 48 items in this shard: test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_LSTM, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_alias_inputs, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_backwards, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_error, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_expand_mutation_functionalizes, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_autograd_raises_invalid_leaf_set, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_export_joint_simple_repro, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_grad_mode_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_sequence_nr, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_args_param_non_tensor_arg_list, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_arg_dupe_via_dynamo_recompiles_many_with_global, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_autograd_function_tangent_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_safe, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_unsafe, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_unsafe_control_flow, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_copy, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_fails_in_backward, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_data_ptr_access_fails_in_forward, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_different_inputs_overlapping_set_with_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer2, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer3, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer4, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer5, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer6, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph2, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph3, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph4, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_double_backward_errors, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_eager_sequence_nr, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_grad_inputs_alias_inputs, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_inputs_overlapping_with_mutation_recompile, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_inputs_overlapping_with_mutation_stress, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_multiple_aot_autograd_calls_dupe_args, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_mutation1, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_negative_testing, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_negative_testing_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_nn_parameter_construction, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_no_storage_overlap_guards_no_aliasing, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_no_storage_overlap_guards_no_mutation, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_requires_grad_fake_via_dynamo_recompiles, test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_split_with_sizes_aot_autograd_cleans_up_traceback_meta 2025-09-07T06:50:23.3658911Z 2025-09-07T06:50:23.3658996Z Running dynamo/test_aot_compile 1/1 ... [2025-09-07 06:50:23.363641] 2025-09-07T06:50:23.3659172Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:50:23.3659566Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_compile.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:50:23.363849] 2025-09-07T06:50:25.5836325Z 2025-09-07T06:50:25.5838622Z dynamo/test_aot_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_compile_1.1_e45b84a962c277fa_.log 2025-09-07T06:50:25.5840866Z Running 5 items in this shard: test/dynamo/test_aot_compile.py::TestAOTCompile::test_aot_compile_basic_fn, test/dynamo/test_aot_compile.py::TestAOTCompile::test_aot_compile_basic_forward, test/dynamo/test_aot_compile.py::TestAOTCompile::test_aot_compile_graph_break_error_fmt, test/dynamo/test_aot_compile.py::TestAOTCompile::test_decorated_function_aot, test/dynamo/test_aot_compile.py::TestAOTCompile::test_guard_filter_override_aot 2025-09-07T06:50:25.5842110Z 2025-09-07T06:50:25.5842282Z Running dynamo/test_autograd_function 1/1 ... [2025-09-07 06:50:25.583739] 2025-09-07T06:50:25.5842580Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:50:25.5843238Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_autograd_function.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:50:25.583991] 2025-09-07T06:50:37.8773140Z 2025-09-07T06:50:37.8781893Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_2f28c2c4f083f6a0_.log 2025-09-07T06:50:37.8787908Z Running 40 items in this shard: test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_allow_in_graph, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_amp_custom_fwd_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_after_matmul, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_on_grad_output_directly, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_equivalence, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_has_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_backward_returns_none_for_tensor_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_classmethod, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_data_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_default_values, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_enum_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_forward_returns_constant, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_mark_and_save, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_save_and_mark, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_with_bound_free_variable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_no_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_propogation_correctness, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_linear_setup_context, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_multi_output_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_materialize_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multi_output, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multiple_different_non_tensor_inputs, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_needs_input_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_once_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_print_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_repeated_save_for_backward_calls, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_requires_grad_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_save_for_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_set_materialize_grads_no_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smoke_from_test_autograd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_symint_issue_111031, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_tensor_and_complex_structures, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_stride_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_list_as_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_subclass_intermediary_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_basic, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_multiple_out, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tuple_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_user_defined_object_as_input 2025-09-07T06:50:37.8793210Z 2025-09-07T06:50:37.8793294Z Running dynamo/test_backends 1/1 ... [2025-09-07 06:50:37.877234] 2025-09-07T06:50:37.8793483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:50:37.8793889Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backends.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:50:37.877467] 2025-09-07T06:50:47.8606748Z 2025-09-07T06:50:47.8608069Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_ce172d2422f73975_.log 2025-09-07T06:50:47.8615142Z Running 21 items in this shard: test/dynamo/test_backends.py::NormalizeIRTests::test_inplace_normalize, test/dynamo/test_backends.py::MPSSupportedTest::test_mps_supported, test/dynamo/test_backends.py::TestExplainWithBackend::test_explain_with_backend, test/dynamo/test_backends.py::TestCustomBackendAPI::test_aot_autograd_api, test/dynamo/test_backends.py::TestCustomBackendAPI::test_backend_graph_freeze, test/dynamo/test_backends.py::TestCustomBackendAPI::test_backend_recompilation, test/dynamo/test_backends.py::TestCustomBackendAPI::test_lookup_backend, test/dynamo/test_backends.py::TestCustomBackendAPI::test_lookup_custom_backend, test/dynamo/test_backends.py::TestCustomBackendAPI::test_register_backend_api, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_aot_cudagraphs_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_aot_eager_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_aot_eager_decomp_partition_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_aot_ts_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_eager_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_eager_noexcept_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_example_inputs_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_example_inputs_runtime_use_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_intel_gaudi_backend_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_list_backends_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_torchscript_cuda, test/dynamo/test_backends.py::TestOptimizationsCUDA::test_tvm_cuda 2025-09-07T06:50:47.8619064Z 2025-09-07T06:50:47.8619227Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-09-07 06:50:47.860485] 2025-09-07T06:50:47.8619591Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:50:47.8620240Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backward_higher_order_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:50:47.860812] 2025-09-07T06:50:56.8969020Z 2025-09-07T06:50:56.8971027Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_e804fadfe465486c_.log 2025-09-07T06:50:56.8974988Z Running 7 items in this shard: test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_eager, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_graph_breaks, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_side_effect, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_bw, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_fx_forward_contrived 2025-09-07T06:50:56.8977557Z 2025-09-07T06:50:56.8977773Z Running dynamo/test_base_hop 1/1 ... [2025-09-07 06:50:56.896853] 2025-09-07T06:50:56.8978157Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:50:56.8979035Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_base_hop.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:50:56.897154] 2025-09-07T06:51:00.2703111Z 2025-09-07T06:51:00.2704558Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_f309773e51784640_.log 2025-09-07T06:51:00.2708118Z Running 11 items in this shard: test/dynamo/test_base_hop.py::BaseHOPTest::test_aliasing_mutation_error, test/dynamo/test_base_hop.py::BaseHOPTest::test_aot_eager, test/dynamo/test_base_hop.py::BaseHOPTest::test_auto_functionalize, test/dynamo/test_base_hop.py::BaseHOPTest::test_dynamo, test/dynamo/test_base_hop.py::BaseHOPTest::test_eager_call, test/dynamo/test_base_hop.py::BaseHOPTest::test_int_input, test/dynamo/test_base_hop.py::BaseHOPTest::test_none_input, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_pytree_in_out, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_pytree_in_out_with_mutation, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_single_return, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_single_return_with_mutation 2025-09-07T06:51:00.2710838Z 2025-09-07T06:51:00.2711065Z Running dynamo/test_base_output 1/1 ... [2025-09-07 06:51:00.270239] 2025-09-07T06:51:00.2719303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:00.2720073Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_base_output.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:00.270536] 2025-09-07T06:51:02.1901276Z 2025-09-07T06:51:02.1903269Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_ab31ebed87091672_.log 2025-09-07T06:51:02.1905567Z Running 6 items in this shard: test/dynamo/test_base_output.py::TestBaseOutput::test_assign, test/dynamo/test_base_output.py::TestBaseOutput::test_create, test/dynamo/test_base_output.py::TestBaseOutput::test_getattr, test/dynamo/test_base_output.py::TestBaseOutput::test_getitem, test/dynamo/test_base_output.py::TestBaseOutput::test_index, test/dynamo/test_base_output.py::TestBaseOutput::test_tuple 2025-09-07T06:51:02.1907536Z 2025-09-07T06:51:02.1907775Z Running dynamo/test_buffers_override 1/1 ... [2025-09-07 06:51:02.190092] 2025-09-07T06:51:02.1908209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:02.1909170Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_buffers_override.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:02.190385] 2025-09-07T06:51:08.8704189Z 2025-09-07T06:51:08.8707101Z dynamo/test_buffers_override 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_buffers_override_1.1_4fa58f323ff96879_.log 2025-09-07T06:51:08.8708635Z Running 2 items in this shard: test/dynamo/test_buffers_override.py::TestBuffersOverride::test_buffers_override, test/dynamo/test_buffers_override.py::TestBuffersOverride::test_named_buffers_override 2025-09-07T06:51:08.8709460Z 2025-09-07T06:51:08.8709717Z Running dynamo/test_bytecode_utils 1/1 ... [2025-09-07 06:51:08.870341] 2025-09-07T06:51:08.8710182Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:08.8711381Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:08.870714] 2025-09-07T06:51:15.7494370Z 2025-09-07T06:51:15.7496431Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_ff78d02f8a1bde99_.log 2025-09-07T06:51:15.7509270Z Running 21 items in this shard: test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_analysis_jump_backward_no_interrupt, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_from_template, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_from_template_noprefix, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_from_template_noreturn1, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_from_template_noreturn2, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_bytecode_from_template_noreturn_const, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_compute_exception_table_nested, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_exception_table_e2e, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_exception_table_e2e_2, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_exception_table_encode_varint, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_exception_table_entry_propagation, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_exception_table_parsing, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_extended_args_starts_line, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_if_tensor_is_none, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_linetable_310_writer, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_linetable_311_writer1, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_linetable_311_writer2, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_lnotab_writer, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_py311_jump_offset, test/dynamo/test_bytecode_utils.py::BytecodeTests::test_remove_dead_code_with_exn_table_entries, test/dynamo/test_bytecode_utils.py::BytecodeHookTests::test_bytecode_hook 2025-09-07T06:51:15.7512247Z 2025-09-07T06:51:15.7512360Z Running dynamo/test_callback 1/1 ... [2025-09-07 06:51:15.749553] 2025-09-07T06:51:15.7512552Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:15.7518152Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_callback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:15.749848] 2025-09-07T06:51:21.1757354Z 2025-09-07T06:51:21.1757834Z dynamo/test_callback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_callback_1.1_aadb44b86ee1e914_.log 2025-09-07T06:51:21.1758751Z Running 4 items in this shard: test/dynamo/test_callback.py::CallbackTests::test_callbacks_with_duplicate_prevention, test/dynamo/test_callback.py::CallbackTests::test_counter, test/dynamo/test_callback.py::CallbackTests::test_counter_assertion, test/dynamo/test_callback.py::CallbackTests::test_triggers 2025-09-07T06:51:21.1759345Z 2025-09-07T06:51:21.1760222Z Running dynamo/test_compile 1/1 ... [2025-09-07 06:51:21.175723] 2025-09-07T06:51:21.1760738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:21.1761592Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_compile.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:21.175978] 2025-09-07T06:51:28.3554587Z 2025-09-07T06:51:28.3556380Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_ac7ef95206259689_.log 2025-09-07T06:51:28.3562568Z Running 17 items in this shard: test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_callback, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_callback_with_graph_break, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_constant_hasattr_fail, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_evnum_hasattr_fail, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_name_error, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_nn_module_invalid_method, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_tensor_invalid_method, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compile_eager_options, test/dynamo/test_compile.py::InPlaceCompilationTests::test_jit_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_list_bad_access, test/dynamo/test_compile.py::InPlaceCompilationTests::test_overwrite_call_impl, test/dynamo/test_compile.py::InPlaceCompilationTests::test_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_state_dict_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_to_sparse_to_dense_with_graph_break, test/dynamo/test_compile.py::InPlaceCompilationTests::test_torch_script_compilation, test/dynamo/test_compile.py::PublicTorchCompilerTests::test_dynamo_signatures 2025-09-07T06:51:28.3565169Z 2025-09-07T06:51:28.3565288Z Running dynamo/test_compiler_bisector 1/1 ... [2025-09-07 06:51:28.355472] 2025-09-07T06:51:28.3565489Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:28.3566192Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:28.355800] 2025-09-07T06:51:57.6569243Z 2025-09-07T06:51:57.6570195Z PRINTING LOG FILE of dynamo/test_compiler_bisector 1/1 (test/test-reports/dynamo.test_compiler_bisector_1.1_bb98c79dc00900ac_.log) 2025-09-07T06:51:57.6571282Z /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. 2025-09-07T06:51:57.6572044Z import pkg_resources 2025-09-07T06:51:57.6572780Z Test results will be stored in test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-4ff69d670474d194.xml 2025-09-07T06:51:57.6579634Z ============================= test session starts ============================== 2025-09-07T06:51:57.6580008Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T06:51:57.6580266Z cachedir: .pytest_cache 2025-09-07T06:51:57.6580602Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T06:51:57.6580896Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T06:51:57.6581036Z configfile: pytest.ini 2025-09-07T06:51:57.6581297Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T06:51:57.6581569Z collecting ... collected 7 items 2025-09-07T06:51:57.6581801Z stepcurrent: Cannot find last run test, not skipping 2025-09-07T06:51:57.6589404Z Running 7 items in this shard: test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_decomp, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_lowering, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_crossref, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_eager_backend, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_emulate_precision_casts, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_joint_graph, test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng 2025-09-07T06:51:57.6590318Z 2025-09-07T06:51:57.6590515Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_decomp PASSED [0.5672s] [ 14%] 2025-09-07T06:51:57.6590925Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_bad_lowering PASSED [1.5606s] [ 28%] 2025-09-07T06:51:57.6591206Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_crossref PASSED [0.1032s] [ 42%] 2025-09-07T06:51:57.6591498Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_eager_backend PASSED [0.0012s] [ 57%] 2025-09-07T06:51:57.6591800Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_emulate_precision_casts PASSED [1.0075s] [ 71%] 2025-09-07T06:51:57.6592094Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_joint_graph PASSED [0.8488s] [ 85%] 2025-09-07T06:51:57.6592385Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng ('RERUN', {'yellow': True}) [2.1835s] [100%] 2025-09-07T06:51:57.6596074Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng ('RERUN', {'yellow': True}) [1.7108s] [100%] 2025-09-07T06:51:57.6596359Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng FAILED [1.4903s] [100%] 2025-09-07T06:51:57.6596502Z 2025-09-07T06:51:57.6596565Z ==================================== RERUNS ==================================== 2025-09-07T06:51:57.6596744Z ________________________ TestCompilerBisector.test_rng _________________________ 2025-09-07T06:51:57.6596912Z Traceback (most recent call last): 2025-09-07T06:51:57.6597208Z File "/var/lib/jenkins/pytorch/test/dynamo/test_compiler_bisector.py", line 147, in test_rng 2025-09-07T06:51:57.6597441Z self.assertEqual(out.subsystem, "inductor_fallback_random") 2025-09-07T06:51:57.6597692Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T06:51:57.6597923Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T06:51:57.6598058Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T06:51:57.6598292Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T06:51:57.6598557Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T06:51:57.6598741Z AssertionError: None mismatch: None is not inductor_fallback_random 2025-09-07T06:51:57.6598859Z 2025-09-07T06:51:57.6598929Z To execute this test, run the following from the base repo dir: 2025-09-07T06:51:57.6599183Z PYTORCH_TEST_WITH_ROCM=1 python test/dynamo/test_compiler_bisector.py TestCompilerBisector.test_rng 2025-09-07T06:51:57.6599373Z 2025-09-07T06:51:57.6599462Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T06:51:57.6599671Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6599838Z No bisection status found. 2025-09-07T06:51:57.6599971Z Starting bisection process with system: eager 2025-09-07T06:51:57.6600114Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6600262Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6600440Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6600593Z Moving to the next system: inductor 2025-09-07T06:51:57.6600827Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6601060Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6601237Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6601402Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6601580Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6601768Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6601960Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6602164Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6602363Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6602559Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6602757Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6602960Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6603140Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6603287Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6603469Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6603662Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6603831Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6603995Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6604166Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6604449Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6604640Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6604843Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6605040Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6605235Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6605469Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6605668Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6605844Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6605988Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6606169Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6606356Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6606534Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6606711Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6606897Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6607093Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6607288Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6607500Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6607687Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6607902Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6608072Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6608217Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6608390Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6608582Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6608766Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6608962Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6609152Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6609360Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6609556Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6609749Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6609921Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6610061Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6610238Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6610441Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6610632Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6610820Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6611016Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6611208Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6611380Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6613106Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6613286Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6613478Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6613660Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6613842Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6614004Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6614136Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6614299Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6614582Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6614743Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6614878Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6616301Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6616512Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6616674Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6616867Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6617013Z Bisection status deleted. 2025-09-07T06:51:57.6617152Z ________________________ TestCompilerBisector.test_rng _________________________ 2025-09-07T06:51:57.6617307Z Traceback (most recent call last): 2025-09-07T06:51:57.6617498Z File "/var/lib/jenkins/pytorch/test/dynamo/test_compiler_bisector.py", line 147, in test_rng 2025-09-07T06:51:57.6617716Z self.assertEqual(out.subsystem, "inductor_fallback_random") 2025-09-07T06:51:57.6617957Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T06:51:57.6618184Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T06:51:57.6619526Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T06:51:57.6619768Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T06:51:57.6620059Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T06:51:57.6620239Z AssertionError: None mismatch: None is not inductor_fallback_random 2025-09-07T06:51:57.6620353Z 2025-09-07T06:51:57.6620422Z To execute this test, run the following from the base repo dir: 2025-09-07T06:51:57.6620644Z PYTORCH_TEST_WITH_ROCM=1 python test/dynamo/test_compiler_bisector.py TestCompilerBisector.test_rng 2025-09-07T06:51:57.6620801Z 2025-09-07T06:51:57.6620884Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T06:51:57.6621073Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6621258Z No bisection status found. 2025-09-07T06:51:57.6621373Z Starting bisection process with system: eager 2025-09-07T06:51:57.6621503Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6622768Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6622931Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6623074Z Moving to the next system: inductor 2025-09-07T06:51:57.6623269Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6623482Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6623639Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6623787Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6623945Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6624113Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6624290Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6624579Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6625874Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6626057Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6626237Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6626419Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6626580Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6626714Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6626877Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6627052Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6627205Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6627354Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6627509Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6629823Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6630011Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6630201Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6630384Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6630624Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6630807Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6630991Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6631151Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6631286Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6631455Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6631629Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6633313Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6633497Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6633706Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6633895Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6634075Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6634253Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6634531Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6634714Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6634873Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6635007Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6636387Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6636583Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6636758Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6636949Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6637131Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6637309Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6637487Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6637670Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6637831Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6637964Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6638128Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6639397Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6639587Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6639765Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6639946Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6640126Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6640286Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6640419Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6640582Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6640769Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6640947Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6641128Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6642328Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6642466Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6642675Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6642870Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6643029Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6643161Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6643322Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6643485Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6643644Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6643836Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6643983Z Bisection status deleted. 2025-09-07T06:51:57.6645284Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6645437Z No bisection status found. 2025-09-07T06:51:57.6645585Z Starting bisection process with system: eager 2025-09-07T06:51:57.6645715Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6645850Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6646032Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6646176Z Moving to the next system: inductor 2025-09-07T06:51:57.6646376Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6646591Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6646746Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6646894Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6648142Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6648344Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6648521Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6648746Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6648928Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6649109Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6649288Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6649468Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6649628Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6649761Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6649924Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6651149Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6651304Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6651455Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6651615Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6651783Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6651959Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6652145Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6652324Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6652500Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6652678Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6652860Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6654029Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6654164Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6654329Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6654602Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6654791Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6654959Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6655132Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6655317Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6655496Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6655672Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6656867Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6657057Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6657216Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6657349Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6657542Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6657729Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6657902Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6658113Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6658294Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6658472Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6658651Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6659843Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6660009Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6660144Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6660335Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6660530Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6660712Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6660891Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6661069Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6661250Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6661408Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6661541Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6662709Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6662902Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6663079Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6663263Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6663427Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6663561Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6663724Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6663916Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6664075Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6664207Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6664453Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6665628Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6665792Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6665982Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6666129Z Bisection status deleted. 2025-09-07T06:51:57.6666247Z =================================== FAILURES =================================== 2025-09-07T06:51:57.6666440Z ________________________ TestCompilerBisector.test_rng _________________________ 2025-09-07T06:51:57.6666593Z Traceback (most recent call last): 2025-09-07T06:51:57.6666787Z File "/var/lib/jenkins/pytorch/test/dynamo/test_compiler_bisector.py", line 147, in test_rng 2025-09-07T06:51:57.6667008Z self.assertEqual(out.subsystem, "inductor_fallback_random") 2025-09-07T06:51:57.6667249Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T06:51:57.6667477Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T06:51:57.6668643Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T06:51:57.6668880Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T06:51:57.6669142Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T06:51:57.6669324Z AssertionError: None mismatch: None is not inductor_fallback_random 2025-09-07T06:51:57.6669467Z 2025-09-07T06:51:57.6669541Z To execute this test, run the following from the base repo dir: 2025-09-07T06:51:57.6669763Z PYTORCH_TEST_WITH_ROCM=1 python test/dynamo/test_compiler_bisector.py TestCompilerBisector.test_rng 2025-09-07T06:51:57.6669948Z 2025-09-07T06:51:57.6670033Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T06:51:57.6670221Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6670368Z No bisection status found. 2025-09-07T06:51:57.6670560Z Starting bisection process with system: eager 2025-09-07T06:51:57.6670688Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6671873Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6672034Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6672206Z Moving to the next system: inductor 2025-09-07T06:51:57.6672401Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6672619Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6672774Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6672923Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6673083Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6673252Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6673426Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6673616Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6674927Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6675109Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6675293Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6675480Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6675640Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6675775Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6675945Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6676121Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6676273Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6676421Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6676578Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6677766Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6677942Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6678133Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6678314Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6678521Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6678701Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6678887Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6679047Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6679180Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6679342Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6679514Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6680762Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6680930Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6681104Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6681291Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6681524Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6681701Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6681902Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6682084Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6682244Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6682377Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6683574Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6683762Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6683935Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6684122Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6684330Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6684650Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6684830Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6685015Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6685175Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6685309Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6685471Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6686740Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6686927Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6687104Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6687286Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6687468Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6687633Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6687765Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6687931Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6688117Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6688294Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6688475Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6688634Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6689793Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6689958Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6690147Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6690309Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6690443Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6690641Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6690804Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6690966Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6691156Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6691301Z Bisection status deleted. 2025-09-07T06:51:57.6691433Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6692582Z No bisection status found. 2025-09-07T06:51:57.6692701Z Starting bisection process with system: eager 2025-09-07T06:51:57.6692829Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6692962Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6693122Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6693265Z Moving to the next system: inductor 2025-09-07T06:51:57.6693489Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6693701Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6693882Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6694030Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6694188Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6695465Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6695640Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6695829Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6696012Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6696220Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6696399Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6696588Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6696747Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6696882Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6697046Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6697220Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6698386Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6698536Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6698694Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6698860Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6699034Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6699222Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6699405Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6699581Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6699760Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6699942Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6700101Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6701257Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6701421Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6701593Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6701750Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6701916Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6702092Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6702279Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6702490Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6702671Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6702849Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6704042Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6704207Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6704435Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6704599Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6704779Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6704952Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6705142Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6705357Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6705536Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6705754Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6705936Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6707406Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6707545Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6707708Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6707901Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6708083Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6708260Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6708465Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6708651Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6708812Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6708947Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6709111Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6710301Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6710584Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6710768Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6710928Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6711062Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6711227Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6711420Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6711579Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6711717Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6711878Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6712045Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6713231Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6713426Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6713571Z Bisection status deleted. 2025-09-07T06:51:57.6713705Z ----------------------------- Captured stdout call ----------------------------- 2025-09-07T06:51:57.6713851Z No bisection status found. 2025-09-07T06:51:57.6713967Z Starting bisection process with system: eager 2025-09-07T06:51:57.6714094Z Moving to the next system: aot_eager 2025-09-07T06:51:57.6714226Z Moving to the next system: aot_eager_decomp_partition 2025-09-07T06:51:57.6714514Z Moving to the next system: aot_eager_decomp_partition_crossref 2025-09-07T06:51:57.6714657Z Moving to the next system: inductor 2025-09-07T06:51:57.6714881Z The issue is in the inductor system. Moving to the first subsystem: BisectSubsystem(name='joint_graph_passes') 2025-09-07T06:51:57.6716140Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6716296Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6716445Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6716604Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6716772Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6716947Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6717134Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6717315Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6717494Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6717713Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6717897Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6719088Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6719223Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6719387Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6719561Z Disabling joint_graph_passes did not fix the issue. 2025-09-07T06:51:57.6719713Z Moving to the next subsystem: inductor - post_grad_passes 2025-09-07T06:51:57.6719860Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6720017Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6720184Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6720384Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6720574Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6720758Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6721944Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6722128Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6722310Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6722471Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6722605Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6722769Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6722941Z Disabling post_grad_passes did not fix the issue. 2025-09-07T06:51:57.6723098Z Moving to the next subsystem: inductor - inductor_fallback_random 2025-09-07T06:51:57.6723268Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6723441Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6723634Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6724906Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6725088Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6725267Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6725449Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6725609Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6725742Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6725904Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6726086Z Disabling inductor_fallback_random did not fix the issue. 2025-09-07T06:51:57.6726261Z Moving to the next subsystem: inductor - inductor_emulate_precision_casts 2025-09-07T06:51:57.6726448Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6726661Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6727867Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6728051Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6728233Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6728392Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6728525Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6728688Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6728879Z Disabling inductor_emulate_precision_casts did not fix the issue. 2025-09-07T06:51:57.6729061Z Moving to the next subsystem: inductor - inductor_layout_optimization 2025-09-07T06:51:57.6729239Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6729416Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6730638Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6730803Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6730968Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6731130Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6731317Z Disabling inductor_layout_optimization did not fix the issue. 2025-09-07T06:51:57.6731494Z Moving to the next subsystem: inductor - inductor_comprehensive_padding 2025-09-07T06:51:57.6731676Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6731834Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6731966Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6732151Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6732341Z Disabling inductor_comprehensive_padding did not fix the issue. 2025-09-07T06:51:57.6733521Z Moving to the next subsystem: inductor - lowerings 2025-09-07T06:51:57.6733658Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6733824Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6733987Z Disabling lowerings did not fix the issue. 2025-09-07T06:51:57.6734148Z All subsystems in inductor have been checked. The issue is not in this system. 2025-09-07T06:51:57.6734433Z The issue is in the inductor system, but could not identify subsystem. 2025-09-07T06:51:57.6734579Z Bisection status deleted. 2025-09-07T06:51:57.6734866Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-4ff69d670474d194.xml - 2025-09-07T06:51:57.6735180Z =========================== short test summary info ============================ 2025-09-07T06:51:57.6735466Z FAILED [1.4903s] dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng - AssertionError: None mismatch: None is not inductor_fallback_random 2025-09-07T06:51:57.6735691Z 2025-09-07T06:51:57.6735761Z To execute this test, run the following from the base repo dir: 2025-09-07T06:51:57.6737022Z PYTORCH_TEST_WITH_ROCM=1 python test/dynamo/test_compiler_bisector.py TestCompilerBisector.test_rng 2025-09-07T06:51:57.6737186Z 2025-09-07T06:51:57.6737269Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T06:51:57.6737448Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-09-07T06:51:57.6737607Z ===================== 1 failed, 6 passed, 2 rerun in 9.49s ===================== 2025-09-07T06:51:57.6737739Z Got exit code 1 2025-09-07T06:51:57.6737831Z Retrying single test... 2025-09-07T06:51:57.6738356Z /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. 2025-09-07T06:51:57.6738850Z import pkg_resources 2025-09-07T06:51:57.6739088Z Test results will be stored in test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-50865cbb67b33e71.xml 2025-09-07T06:51:57.6739353Z ============================= test session starts ============================== 2025-09-07T06:51:57.6739557Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T06:51:57.6740795Z cachedir: .pytest_cache 2025-09-07T06:51:57.6741013Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T06:51:57.6741242Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T06:51:57.6741354Z configfile: pytest.ini 2025-09-07T06:51:57.6741576Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T06:51:57.6741869Z collecting ... collected 7 items / 6 deselected / 1 selected 2025-09-07T06:51:57.6742126Z stepcurrent: skipping 6 already run items. Running only test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng 2025-09-07T06:51:57.6742378Z Running 1 items in this shard 2025-09-07T06:51:57.6742447Z 2025-09-07T06:51:57.6742554Z dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng PASSED [1.6814s] [100%] 2025-09-07T06:51:57.6742696Z 2025-09-07T06:51:57.6742922Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-50865cbb67b33e71.xml - 2025-09-07T06:51:57.6743234Z ======================= 1 passed, 6 deselected in 1.69s ======================== 2025-09-07T06:51:57.6744528Z Got exit code 0 2025-09-07T06:51:57.6744689Z Test succeeeded in new process, continuing with the rest of the tests 2025-09-07T06:51:57.6745226Z /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. 2025-09-07T06:51:57.6745712Z import pkg_resources 2025-09-07T06:51:57.6745943Z Test results will be stored in test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-d01567d3854474c8.xml 2025-09-07T06:51:57.6746201Z ============================= test session starts ============================== 2025-09-07T06:51:57.6746400Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T06:51:57.6746577Z cachedir: .pytest_cache 2025-09-07T06:51:57.6746791Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T06:51:57.6747019Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T06:51:57.6747129Z configfile: pytest.ini 2025-09-07T06:51:57.6748419Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T06:51:57.6748679Z collecting ... collected 7 items / 7 deselected / 0 selected 2025-09-07T06:51:57.6748827Z stepcurrent: skipping 7 already run items. 2025-09-07T06:51:57.6748948Z Running 0 items in this shard 2025-09-07T06:51:57.6749016Z 2025-09-07T06:51:57.6749232Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/dynamo.test_compiler_bisector/dynamo.test_compiler_bisector-d01567d3854474c8.xml - 2025-09-07T06:51:57.6749537Z ============================ 7 deselected in 0.01s ============================= 2025-09-07T06:51:57.6749803Z The following tests failed and then succeeded when run in a new process['test/dynamo/test_compiler_bisector.py::TestCompilerBisector::test_rng'] 2025-09-07T06:51:57.6750008Z 2025-09-07T06:51:57.6750203Z FINISHED PRINTING LOG FILE of dynamo/test_compiler_bisector 1/1 (test/test-reports/dynamo.test_compiler_bisector_1.1_bb98c79dc00900ac_.log) 2025-09-07T06:51:57.6750406Z 2025-09-07T06:51:57.6750606Z Running dynamo/test_comptime 1/1 ... [2025-09-07 06:51:57.657160] 2025-09-07T06:51:57.6750755Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:51:57.6752168Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_comptime.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:51:57.657378] 2025-09-07T06:52:08.1910876Z 2025-09-07T06:52:08.1911772Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_57259f0085c56a6f_.log 2025-09-07T06:52:08.1913198Z Running 12 items in this shard: test/dynamo/test_comptime.py::ComptimeTests::test_get_local, test/dynamo/test_comptime.py::ComptimeTests::test_get_local_closure_variable, test/dynamo/test_comptime.py::ComptimeTests::test_graph_break, test/dynamo/test_comptime.py::ComptimeTests::test_print_bt, test/dynamo/test_comptime.py::ComptimeTests::test_print_direct, test/dynamo/test_comptime.py::ComptimeTests::test_print_disas, test/dynamo/test_comptime.py::ComptimeTests::test_print_graph, test/dynamo/test_comptime.py::ComptimeTests::test_print_guards, test/dynamo/test_comptime.py::ComptimeTests::test_print_locals, test/dynamo/test_comptime.py::ComptimeTests::test_print_single, test/dynamo/test_comptime.py::ComptimeTests::test_print_value_stack, test/dynamo/test_comptime.py::ComptimeTests::test_sleep 2025-09-07T06:52:08.1914995Z 2025-09-07T06:52:08.1921369Z Running dynamo/test_config 1/1 ... [2025-09-07 06:52:08.190893] 2025-09-07T06:52:08.1921555Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:08.1922030Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_config.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:08.191109] 2025-09-07T06:52:13.6145078Z 2025-09-07T06:52:13.6145969Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_fd5081cdc61d1862_.log 2025-09-07T06:52:13.6146859Z Running 5 items in this shard: test/dynamo/test_config.py::ConfigTests::test_automatic_dynamic, test/dynamo/test_config.py::ConfigTests::test_config_compile_ignored, test/dynamo/test_config.py::ConfigTests::test_config_hash, test/dynamo/test_config.py::ConfigTests::test_no_assume_static_by_default, test/dynamo/test_config.py::ConfigTests::test_no_automatic_dynamic 2025-09-07T06:52:13.6147429Z 2025-09-07T06:52:13.6147545Z Running dynamo/test_ctx_manager 1/1 ... [2025-09-07 06:52:13.614431] 2025-09-07T06:52:13.6147742Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:13.6154637Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:13.614691] 2025-09-07T06:52:27.5094986Z 2025-09-07T06:52:27.5096369Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_871c1fa78fdd0af8_.log 2025-09-07T06:52:27.5119176Z Running 101 items in this shard: test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_arguments_binding, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break_2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break_inner_fn, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_device, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_float64, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_graph_break_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_sdpa, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autograd_profiler, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autograd_profiler_enabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_grad_mode_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_grad_mode_nested_function_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_set_grad_enabled_nested_function, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_amp_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_device, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_across_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_created_outside_of_graph, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_method_create_stream_outside_of_compile, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_reconstruct, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_across_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_compared_with_constant, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_compared_with_stream, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_context_manager1, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_context_manager2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_prev_disabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_prev_disabled_nested, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager_CustomizedCtxManager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager_customized_ctx_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager_with_graph_break_CustomizedCtxManager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager_with_graph_break_customized_ctx_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_ctx_manager_with_graph_break_CustomizedCtxManagerWithGraphBreak, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_ctx_manager_with_graph_break_customized_ctx_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_grad_mode_guard, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_graph_break_inlining_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_graph_break_inlining_grad, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local_nullctx, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local_nullctx2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_stack, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_stack2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_is_autocast_cpu_enabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager_CustomizedCtxManager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager_customized_ctx_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager_with_graph_break_CustomizedCtxManager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager_with_graph_break_customized_ctx_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_grad_mode_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_no_grad, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_return_context_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_return_context_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager1, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager3, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager_as_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager_kwargs, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_sdpa_kernel_ctx_manager_set_priority, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_torch_profiler, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_torch_profiler_use_after_with_block, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_WITH_EXCEPT_START, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_advanced_contextmanager_as_argument, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_advanced_contextmanager_as_argument_error, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_0, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_1, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_global_0, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_global_1, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_nonlocal_0, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_change_parent_nonlocal_1, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextlib_nullcontext, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextlib_suppress_name_stderr, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextlib_suppress_name_stdout, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextlib_suppress_name_suppress, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextmanager_as_argument, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextmanager_as_argument_only___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_contextmanager_as_argument_only___exit__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_ctx_basic0, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_ctx_basic1, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_disable___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_disable___exit__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_disable_ctx_manager, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_disable_trace_contextmanager, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_dynamo_disable_ctx, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_globals_change_in_other_file, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_after___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_and_disable___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_before___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_before___enter___and_disable___exit__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_before_and_after___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_in_finally, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_inside___enter__, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_inside_ctx, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_inside_ctx_1, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_inside_ctx_2, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_graph_break_inside_ctx_with_side_effects, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_return_advanced_contextmanager, test/dynamo/test_ctx_manager.py::ContextlibContextManagerTests::test_return_new_contextmanager 2025-09-07T06:52:27.5138359Z 2025-09-07T06:52:27.5138447Z Running dynamo/test_cudagraphs 1/1 ... [2025-09-07 06:52:27.509336] 2025-09-07T06:52:27.5138610Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:27.5138991Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:27.509588] 2025-09-07T06:52:31.2320288Z 2025-09-07T06:52:31.2320859Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_631b520dc706133d_.log 2025-09-07T06:52:31.2323337Z Running 8 items in this shard: test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_basic, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_dead_fill, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_dtoh, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_factory, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_htod, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_mutate_constant, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_mutate_input, test/dynamo/test_cudagraphs.py::TestAotCudagraphs::test_mutated_metadata 2025-09-07T06:52:31.2331863Z 2025-09-07T06:52:31.2332248Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2025-09-07 06:52:31.231939] 2025-09-07T06:52:31.2332658Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:31.2333525Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:31.232223] 2025-09-07T06:52:35.0034568Z 2025-09-07T06:52:35.0036035Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_f76b9e48e6145486_.log 2025-09-07T06:52:35.0039688Z Running 8 items in this shard: test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_basic, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_dead_fill, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_dtoh, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_factory, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_htod, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_mutate_constant, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_mutate_input, test/dynamo/test_cudagraphs_expandable_segments.py::TestAotCudagraphs::test_mutated_metadata 2025-09-07T06:52:35.0042508Z 2025-09-07T06:52:35.0042765Z Running dynamo/test_debug_utils 1/1 ... [2025-09-07 06:52:35.003190] 2025-09-07T06:52:35.0043221Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:35.0044293Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_debug_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:35.003399] 2025-09-07T06:52:37.6250013Z 2025-09-07T06:52:37.6251643Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_4ddf6fa385553aeb_.log 2025-09-07T06:52:37.6252477Z Running 4 items in this shard: test/dynamo/test_debug_utils.py::TestDebugUtilsCUDA::test_cast_model_to_fp64_dtype_args_cuda, test/dynamo/test_debug_utils.py::TestDebugUtilsCUDA::test_generate_env_vars_string_cuda, test/dynamo/test_debug_utils.py::TestDebugUtilsDeviceCUDA::test_aot_graph_parser_cuda, test/dynamo/test_debug_utils.py::TestDebugUtilsDeviceCUDA::test_sym_aot_graph_parser_cuda 2025-09-07T06:52:37.6253061Z 2025-09-07T06:52:37.6253921Z Running dynamo/test_decorators 1/1 ... [2025-09-07 06:52:37.625168] 2025-09-07T06:52:37.6254109Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:37.6256650Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_decorators.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:37.625483] 2025-09-07T06:52:47.7109569Z 2025-09-07T06:52:47.7121510Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_ead08a4805549e74_.log 2025-09-07T06:52:47.7135029Z Running 67 items in this shard: test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph_no_id_reuse, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_computation_with_graph_input, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_user_defined_fn, test/dynamo/test_decorators.py::DecoratorTests::test_class_methods, test/dynamo/test_decorators.py::DecoratorTests::test_disable_for_custom_op, test/dynamo/test_decorators.py::DecoratorTests::test_disable_ignores_outer_wraps, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_module_with_class_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_modules_forward_hook, test/dynamo/test_decorators.py::DecoratorTests::test_disable_optimize, test/dynamo/test_decorators.py::DecoratorTests::test_disable_recursive_false, test/dynamo/test_decorators.py::DecoratorTests::test_disable_recursive_false_weird, test/dynamo/test_decorators.py::DecoratorTests::test_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_dont_skip_tracing, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_empty_graph, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_error, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_export, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_fullgraph, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_nested, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_nested_deep, test/dynamo/test_decorators.py::DecoratorTests::test_error_on_graph_break_nested_with_skip, test/dynamo/test_decorators.py::DecoratorTests::test_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_incorrect_usage_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_guarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_unguarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_nn_module, test/dynamo/test_decorators.py::DecoratorTests::test_nested_compile_error_on_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_nested_compile_fullgraph, test/dynamo/test_decorators.py::DecoratorTests::test_nested_disable_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_newly_constructed_trace_register_constant_type_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_captured_external_tensor, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_custom_class_output_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_kwarg, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_int_and_float_output, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_no_action_at_a_distance, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_object_in_context_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_on_method, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_register_constant_type_guard, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tensor_args, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tuple_and_sym_int_output, test/dynamo/test_decorators.py::DecoratorTests::test_patch_dynamo_config_errors, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_aot_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile_with_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_forbid_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_eager, test/dynamo/test_decorators.py::DecoratorTests::test_skip_frame, test/dynamo/test_decorators.py::DecoratorTests::test_substitute_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_disable, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_partially_disable 2025-09-07T06:52:47.7144365Z 2025-09-07T06:52:47.7144513Z Running dynamo/test_deque_reconstruct 1/1 ... [2025-09-07 06:52:47.710895] 2025-09-07T06:52:47.7144684Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:47.7145069Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deque_reconstruct.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:47.711120] 2025-09-07T06:52:49.8804322Z 2025-09-07T06:52:49.8805622Z dynamo/test_deque_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deque_reconstruct_1.1_03d9d24844ae5f1a_.log 2025-09-07T06:52:49.8808012Z Running 3 items in this shard: test/dynamo/test_deque_reconstruct.py::TestDequeReconstruct::test_deque_reconstruct_in_globals, test/dynamo/test_deque_reconstruct.py::TestDequeReconstruct::test_deque_reconstruct_not_in_globals, test/dynamo/test_deque_reconstruct.py::TestDequeReconstruct::test_deque_reconstruct_shallows_globals 2025-09-07T06:52:49.8816453Z 2025-09-07T06:52:49.8816672Z Running dynamo/test_deviceguard 1/1 ... [2025-09-07 06:52:49.880314] 2025-09-07T06:52:49.8817013Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:49.8817783Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deviceguard.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:49.880664] 2025-09-07T06:52:51.9498084Z 2025-09-07T06:52:51.9499054Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_6ade0eeb65c3cd5d_.log 2025-09-07T06:52:51.9500973Z Running 4 items in this shard: test/dynamo/test_deviceguard.py::TestDeviceGuard::test_device_guard, test/dynamo/test_deviceguard.py::TestDeviceGuard::test_device_guard_no_index, test/dynamo/test_deviceguard.py::TestCUDADeviceGuard::test_device_guard, test/dynamo/test_deviceguard.py::TestCUDADeviceGuard::test_device_guard_no_index 2025-09-07T06:52:51.9502779Z 2025-09-07T06:52:51.9502986Z Running dynamo/test_dicts 1/1 ... [2025-09-07 06:52:51.949695] 2025-09-07T06:52:51.9503416Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:52:51.9510611Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dicts.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:52:51.949954] 2025-09-07T06:53:00.9821513Z 2025-09-07T06:53:00.9822396Z dynamo/test_dicts 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dicts_1.1_ac715ae33cf23c53_.log 2025-09-07T06:53:00.9834274Z Running 126 items in this shard: test/dynamo/test_dicts.py::DictTests::test_builtin_ior_, test/dynamo/test_dicts.py::DictTests::test_builtin_or_with_diff_keys, test/dynamo/test_dicts.py::DictTests::test_builtin_or_with_invalid_types, test/dynamo/test_dicts.py::DictTests::test_builtin_or_with_same_keys, test/dynamo/test_dicts.py::DictTests::test_construct_user_dict_and_return, test/dynamo/test_dicts.py::DictTests::test_contains_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_contains_module_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_custom_iter_dict, test/dynamo/test_dicts.py::DictTests::test_custom_keys_iter_dict, test/dynamo/test_dicts.py::DictTests::test_dict_construction_from_mapping_proxy, test/dynamo/test_dicts.py::DictTests::test_dict_contains, test/dynamo/test_dicts.py::DictTests::test_dict_copy_alias, test/dynamo/test_dicts.py::DictTests::test_dict_guard_on_keys_order, test/dynamo/test_dicts.py::DictTests::test_dict_guard_on_keys_order2, test/dynamo/test_dicts.py::DictTests::test_dict_iter, test/dynamo/test_dicts.py::DictTests::test_dict_keys_binop_op_and_, test/dynamo/test_dicts.py::DictTests::test_dict_keys_binop_op_or_, test/dynamo/test_dicts.py::DictTests::test_dict_keys_binop_op_sub, test/dynamo/test_dicts.py::DictTests::test_dict_keys_binop_op_xor, test/dynamo/test_dicts.py::DictTests::test_dict_keys_inplace_binop_op_iand, test/dynamo/test_dicts.py::DictTests::test_dict_keys_inplace_binop_op_ior, test/dynamo/test_dicts.py::DictTests::test_dict_keys_inplace_binop_op_isub, test/dynamo/test_dicts.py::DictTests::test_dict_keys_inplace_binop_op_ixor, test/dynamo/test_dicts.py::DictTests::test_dict_list_values, test/dynamo/test_dicts.py::DictTests::test_dict_mutation_side_effect, test/dynamo/test_dicts.py::DictTests::test_dict_namedtuple, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys_modules, test/dynamo/test_dicts.py::DictTests::test_dict_order_keys_tensors, test/dynamo/test_dicts.py::DictTests::test_dict_reconstruct_keeps_original_order, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_contains, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_get_method, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_initialization_in_graph, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_instantiation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_instantiation_return, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_local_mutation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_local_with_non_dict_method, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_methods_fallback_mutation, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_methods_fallback_readonly, test/dynamo/test_dicts.py::DictTests::test_dict_subclass_setitem, test/dynamo/test_dicts.py::DictTests::test_dict_tag_guard, test/dynamo/test_dicts.py::DictTests::test_empty_dict_recompilation, test/dynamo/test_dicts.py::DictTests::test_fn_id, test/dynamo/test_dicts.py::DictTests::test_items_type, test/dynamo/test_dicts.py::DictTests::test_lazy_key_guarding, test/dynamo/test_dicts.py::DictTests::test_lazy_key_non_const_guarding, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_ban_muation_on_dict_realization, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing_local_mutation, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_existing_mutation, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_for_local, test/dynamo/test_dicts.py::DictTests::test_mapping_proxy_for_nonlocal, test/dynamo/test_dicts.py::DictTests::test_move_to_end, test/dynamo/test_dicts.py::DictTests::test_newly_constructed_default_dict, test/dynamo/test_dicts.py::DictTests::test_ordered_dict_reordered_keys, test/dynamo/test_dicts.py::DictTests::test_ordered_dict_subclass_reordered_keys, test/dynamo/test_dicts.py::DictTests::test_overridden_get_item, test/dynamo/test_dicts.py::DictTests::test_udf_dict_reconstruction, test/dynamo/test_dicts.py::DictTests::test_update_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_update_module_dunder_dict, test/dynamo/test_dicts.py::DictTests::test_weakref_dict, test/dynamo/test_dicts.py::DictGuardTests::test_cmp_eq, test/dynamo/test_dicts.py::DictGuardTests::test_cmp_ior, test/dynamo/test_dicts.py::DictGuardTests::test_cmp_ne, test/dynamo/test_dicts.py::DictGuardTests::test_cmp_or, test/dynamo/test_dicts.py::DictGuardTests::test_popitem, test/dynamo/test_dicts.py::DictMethodsTests::test_binop_ior, test/dynamo/test_dicts.py::DictMethodsTests::test_binop_ior_iterable, test/dynamo/test_dicts.py::DictMethodsTests::test_binop_or, test/dynamo/test_dicts.py::DictMethodsTests::test_clear, test/dynamo/test_dicts.py::DictMethodsTests::test_cmp_eq, test/dynamo/test_dicts.py::DictMethodsTests::test_cmp_ne, test/dynamo/test_dicts.py::DictMethodsTests::test_copy, test/dynamo/test_dicts.py::DictMethodsTests::test_dict_type_comparison, test/dynamo/test_dicts.py::DictMethodsTests::test_fromkeys, test/dynamo/test_dicts.py::DictMethodsTests::test_get, test/dynamo/test_dicts.py::DictMethodsTests::test_items, test/dynamo/test_dicts.py::DictMethodsTests::test_keys, test/dynamo/test_dicts.py::DictMethodsTests::test_pop, test/dynamo/test_dicts.py::DictMethodsTests::test_popitem, test/dynamo/test_dicts.py::DictMethodsTests::test_setdefault, test/dynamo/test_dicts.py::DictMethodsTests::test_type, test/dynamo/test_dicts.py::DictMethodsTests::test_update, test/dynamo/test_dicts.py::DictMethodsTests::test_values, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_binop_ior, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_binop_ior_iterable, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_binop_or, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_clear, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_cmp_eq, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_cmp_ne, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_copy, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_dict_type_comparison, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_fromkeys, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_get, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_items, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_keys, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_pop, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_popitem, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_setdefault, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_type, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_update, test/dynamo/test_dicts.py::DictSubclassMethodsTests::test_values, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_binop_ior, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_binop_ior_iterable, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_binop_ior_return_type, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_binop_or, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_binop_or_return_type, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_clear, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_cmp_eq, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_cmp_eq_order, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_cmp_ne, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_copy, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_dict_type_comparison, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_fromkeys, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_get, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_items, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_keys, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_move_to_end, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_pop, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_popitem, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_popitem_kwarg, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_setdefault, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_type, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_update, test/dynamo/test_dicts.py::OrderedDictMethodsTests::test_values, test/dynamo/test_dicts.py::OrderedDictSubclassOverload::test_move_to_end 2025-09-07T06:53:00.9851522Z 2025-09-07T06:53:00.9851613Z Running dynamo/test_dynamic_shapes 1/3 ... [2025-09-07 06:53:00.982130] 2025-09-07T06:53:00.9851787Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T06:53:00.9852170Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:53:00.982364] 2025-09-07T07:05:29.6935688Z 2025-09-07T07:05:29.6943442Z dynamo/test_dynamic_shapes 1/3 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.3_f902c3851400b0ee_.log 2025-09-07T07:05:29.7038823Z Running 632 items in this shard: test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_autocast_arguments_binding_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_autocast_cpu_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_autocast_decorator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_autocast_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_autograd_profiler_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_context_wrapping_grad_mode_decorator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_context_wrapping_grad_mode_nested_function_decorator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_cuda_amp_autocast_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_cuda_event_reconstruct_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_cuda_stream_compared_with_constant_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_cuda_stream_compared_with_stream_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_cuda_stream_context_manager2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_disable_saved_tensors_hooks_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_disable_saved_tensors_hooks_prev_disabled_nested_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_generic_context_manager_CustomizedCtxManager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_generic_context_manager_customized_ctx_manager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_generic_context_manager_with_graph_break_customized_ctx_manager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_grad_mode_guard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_inactive_context_graph_break_local_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_inactive_context_graph_break_local_nullctx_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_inactive_context_graph_break_stack_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_nested_generic_context_manager_CustomizedCtxManager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_nested_grad_mode_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_no_grad_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_sdpa_kernel_ctx_manager1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_sdpa_kernel_ctx_manager3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesCtxManagerTests::test_sdpa_kernel_ctx_manager_as_decorator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_T_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_addcdiv__dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_addcdiv_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_call_dict1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_callable_builtin_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_cls_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_compare_constant_and_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_constant2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_constant3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_constant_set_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_cublas_allow_tf32_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_default_dict_lambda_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_default_dict_list_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_defaultdict_setdefault3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_id_guard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_items_sorted_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_keys_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_mutable_map_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_setdefault1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dict_setdefault3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_distributed_is_initialized_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_dtype_compare_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_filter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_filter_graph_break_reconstruct_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_filter_infinite_iterator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_fstrings1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_fstrings2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_functools_cache_guard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_functools_partial_binding_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_generic_namedtuple_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_generic_namedtuple_subclass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_generic_namedtuple_user_methods_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_get_autocast_gpu_dtype_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_get_default_dtype_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_get_device_properties_tensor_device_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_globalfn_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_indexed_range_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_inline_jit__unwrap_optional_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_inline_with_default_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_inner_function_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_is_floating_point_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_is_not_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_is_not_null_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_islice_chain_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_itertools_chain_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_itertools_combinations_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_itertools_permutations_various_iterators_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_itertools_product_various_iterators_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_jit_annotate_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_len_constant_list_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_list_add_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_list_compare_polyfill_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_list_reversed_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_list_slice_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_list_truth_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_listarg1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_mT_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_manual_seed_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_call_function_ex_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_infinite_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_reconstruct_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_return_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_tuple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_unpack_twice_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_unpack_vars_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_map_zip_dict_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_math_radians_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_mean_sum_np_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_methodcall2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_methodcall3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_module_constant_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_namedtuple_fields_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_namedtuple_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_namedtuple_replace_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_namedtuple_subclass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_namedtuple_user_methods_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_ndim_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_no_recompile_inner_lambda_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_non_inlined_closure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_not_list_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_np_constant_collections_guards_float_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_np_iinfo_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_number_method_method_as_integer_ratio_num_type3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_number_method_method_bit_length_num_type1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_number_method_method_conjugate_num_type4_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_numpy_linalg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_numpy_random_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_as_input_partials_lambda_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_graph_break_reconstruct_args_and_kwargs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___annotations___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___class___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___delattr___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___dir___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___doc___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___getattribute___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___kwdefaults___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___ne___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___reduce___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___setattr___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_hasattr_attr___sizeof___dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_udf_arg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_udf_kwarg_method_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_partials_udf_kwarg_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_pop_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_rand_tensor_partial_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_range_iterator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_range_iterator_graph_break_2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_range_iterator_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_range_length_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_range_with_index_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_reduce_with_single_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_return_multiple_numpy_ndarray_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_return_tuple1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_set_keys_view_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_shape1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_size_tuple_add_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_slice2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_slice3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_sliced_range_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_sum_with_start_arg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tensor_dim_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tensor_element_size_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tensor_is_complex_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tensor_len_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tensor_type2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_to_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_truth_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_tuple_iadd_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_two_point_iter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_unary_fold_op_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_unpack3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_unsqueeze_inplace_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFunctionTests::test_viatorch_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_312_binary_slice_with_graph_break2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_RAISE_VARARGS_0_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_add_to_set_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_anomaly_aot_autograd_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_aot_autograd_propagate_unbacked_symints_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_argwhere_with_dynamic_shapes_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_builtin_bool_on_symbool_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_builtin_complex_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_callpacked_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_catch_watchings1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cell_captured_by_existing_func_but_not_root_frame_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cell_output1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_class_duner_mro_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_clone_sparse_input_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_closure_out_of_scope_cell_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_closure_with_mutation_and_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_compilation_metrics_size_limit_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cond_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cond_export_single_arg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cond_nested_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_conditional_list_comp_in_context_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_constant_getattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cross_entropy_loss_fancy_ctor1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cross_entropy_loss_fancy_ctor2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_cross_entropy_loss_simple_ctor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_custom_module_free_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dataclass_fields_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_deque_input_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_deterministic_algorithms_mutated_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dunder_methods_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dunder_new_function_inlining2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dunder_weakref_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamic_one_hot_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamic_sources_force_parameter_static_shapes_and_property_static_shapes_override_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamic_sources_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamic_sources_int_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamic_sources_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_dynamo_disabled_in_custom_op_kernels_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_enum_as_dict_key_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_enum_subclass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_error_on_nested_fx_trace_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_error_on_recompile_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_escaping_closure_var_with_nonlocal_var_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_fn_hasattr__name__3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_frozen_dataclass_default_factory_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_frozen_dataclass_hashable_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_frozen_dataclass_kw_only_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_frozenset_of_non_literals_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_fullgraph_capture_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_function_annotation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_generate_tensor_from_list_of_numpy_primitive_type_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_get_attr_function_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_get_custom_tensor_attribute_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_get_instruction_source_311_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_getattr_dict_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_getattrvariable_as_python_constant_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_global_state_guard_serialization_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_graph_break_compilation_metrics_on_failure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_guard_failure_fn_shape_control_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_guard_filter_fn_by_name_and_value_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_guard_filter_nn_modules_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_guards_cse_pass_single_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_hasattr_nn_module_guard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_id_guarded_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_if_cond_nn_mod3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_inline_closure_returned_by_another_function_and_captures_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_inline_dict_function_passed_as_arg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_inline_func_jump_on_tensor_condition_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_inline_list_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_inline_local_dict_clear_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_input_cell_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_int_int_comparisons_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_int_shape_binops_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_int_shape_comparisons_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_int_shape_inplace_binops_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_intermediary_tensor_grad_access_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_invalid_args_builtin_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_is_floating_point2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_is_tensor2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_is_tensor_like2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_item_changes_new_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_itertools_accumulate_symint_default_sum_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_itertools_accumulate_tensors_default_sum_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_itertools_accumulate_tensors_kwargs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_itertools_groupby_pure_python_key_func_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_itertools_infinite_repeat_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_list_hasattr1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_map_side_effects_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_map_with_quantization_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_mark_static_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_mark_unbacked_strict_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_matmul1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_min_max_over_iterable_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_module_complex_iter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_multiple_inheritance_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_namedtuple3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_namedtuple_with_custom_getitem_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_ne_operator_with_custom_eq_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_ne_operator_with_custom_ne_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nested_dataclass_reconstruct_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nested_optimize_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nested_optimize_run_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nested_sequential_try_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nn_module_getattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nn_module_getattribute_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_nn_sequential_invocation_reposition_indices_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_no_guard_for_unused_sym_node_fstring_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_gt_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_int_constant_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_ndarray_works_with_builtin_function_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_non_torch_dtype_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_random_config_to_numpy_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_ufunc_out_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_numpy_ufunc_out_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_object_classmethod_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_out_variants_with_resizing_on_graph_inputs_with_dynamic1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_overridden_getattribute_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_packaging_version_parse_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_param_shape_binops_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_parameter_free_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_precompile_entries_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_precompile_entry_miss_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_pure_python_accumulate_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_python_slice_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_raise_guard_indirect_full_constraint_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_raise_guard_partial_constraint_no_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_raises_importerror1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_range_iter_side_effects_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_range_with_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_recompile_on_disable_2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_recompile_on_global_state_change_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_reconstruct_frozen_dataclass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_reconstruct_set_across_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_recursion_depth_guards_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_recursive_inline_list_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_release_module_memory_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_repro_graph_breaks_in__get_item_by_idx_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_running_nested_func_with_captured_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_sample_input_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_scalar_tensor_is_equivalent_to_symint_argument_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_set_aliasing_recompiles_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_set_discard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_setattr_mutation1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_setattr_mutation2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_shape_env_equal_constructor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_shape_env_equal_evaluate_expr_refinement_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_shape_type_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_shape_unpack_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_side_effects_codegen_update_mutated_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_slice_input_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_source_non_input_grad_access_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_storage_return_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_str_format_assert1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_str_format_return2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_structseq2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_sym_constrain_range_on_replaced_unbacked_symbol_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_sym_max_unbacked_sizelike_simplification_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_symint_as_device_kwarg_multi_gpu_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tagging_tensors_simple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_data_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_dict1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_interacts_with_numpy_ndarray_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_is_contiguous_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_item_capture_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tensor_item_no_capture_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tolist_1d_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tolist_kd_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tolist_scalar_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_top_package_import_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_check_is_size_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_distributions_lazy_property_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_dtype_python_type_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_generator_set_state_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_guards_stack_frame_register_inlining_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_size_numel_dynamic_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_variable_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_trace_ndarray_frame_2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_tuple_iadd_with_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_typing_union_and_optional_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unbacked_2d_expand_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unbacked_repeat_cat_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unbacked_strict_mode_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unhandled_exception_in_dynamo2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unhandled_exception_in_dynamo_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unique_consecutive_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_unpack5_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_update_locals_and_stack_uses_shared_cache_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_user_code_statically_known_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_user_defined_setattr2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_user_getattr1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_usr_cls_staticmethod_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_variable_access_in_exception_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_write_to_closures_in_inlining_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_yield_from_in_a_loop_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_yield_from_user_stop_iteration_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_yield_gen_and_from_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_yield_send_to_subgenerator_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_abc_setattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_add_complex_conj_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_amp_foreach_fake_impl_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_aot_autograd_runtime_wrapper_prologue_profiled_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_as_strided_on_base_with_mutation_works_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_autograd_function_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_avoid_dupe_specialization_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_batchnorm_e2e_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_boxes_len_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_chunk_reformer_ff_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_compilation_metrics_on_error_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_convert_boxes_to_pooler_format_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_copy_weird_strides_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_dalle2_maybe_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_deferred_runtime_asserts_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_delattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_delete_local_error_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_delsubscr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_delsubscr_raises_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_detectron2_instances_cat_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_dynamic_shape_disable_duck_size_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_dynamic_shapes_double_not_equal_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_ellipsis_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_empty_graph_nested_calls_fullgraph_True_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_empty_list_contains_with_jump_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_empty_out_dynamic_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_enum_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_error_return_without_exception_set_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_fsdp_set_input_mutation_applied_when_input_gets_no_gradients_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_grad_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_graph_break_on_jit_isinstance_pep585_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_guard_default_device_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_guard_fail_tensor_bool_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_inductor_dynamic_shapes_broadcasting_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_isinstance_dtype_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_isinstance_storage_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_issue134451_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_list_index_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_list_reverse_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_list_self_reference_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_listcomp_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_maml_item_capture_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_many_overlapping_inputs_does_not_explode_guards_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_many_views_with_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_maybe_multiply_symint_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_merge_criteria_processor_list2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nanmean_out_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nn_module_stack_bc_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nn_parameter_ctor_graph_breaks_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nn_parameter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_no_tracing_into_eval_frame_ctx_manager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nonconst_issubclass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_nullcontext1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_numpy_not_ndarray_recompiles_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_omegaconf_dictconfig_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_omegaconf_listconfig_iter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_ones_out_dynamic_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_optimized_module_training_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_out_nested_cell_tuple_shape_change_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_out_none_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_output_aliases_intermediate_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_overwriting_params_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_partially_initialized_module_property_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_partitioner_activation_memory_budget_with_unbacked_symints_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_primtorch_no_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_reinplacing_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_requires_grad_guards_with_grad_mode2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_restricted_list_subclass2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_return_value_duplication_mixed_grad_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_return_value_duplication_scalar_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_rewrite_assert_with_msg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_rewrite_assert_with_non_string_msg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_rewrite_assert_without_msg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_setitem_tuple_boolean_mask_diff_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_sigmoid_out2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_size_typematch_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_slice_into_list_mutable_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_slicing_dynamic_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_specialized_stride_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_staticmethod_allow_in_graph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_string_format_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_super_classmethod_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_super_classmethod_inheritance_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_symint_bitwise_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_isinstance_tuple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_item_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_random_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_set_data_backend_aot_eager_func_name_func1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_set_data_backend_inductor_func_name_func2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_set_data_mismatched_dtype_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_split_within_device_cm_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_tensor_uniform_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_trace_functional_tensor_with_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_unbacked_arange_in_bounds_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_user_ctor_ctx_manager_custom_init_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_user_ctor_ctx_manager_custom_init_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_user_ctor_ctx_manager_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_user_defined_iter_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_validate_model_kwargs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_view_dtype_overload_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_weakref_del_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesReproTests::test_zeros_out_dynamic_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_call_fn_with_non_const_inputs_safe_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_cfgmod_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_conv_call_forward_directly_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_fnmembercmp2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_hasattr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_intarg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_iseval1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_isnonelayer_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_lazy_module5_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_lazy_module6_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_lazy_module_bad_params_call_function_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_attribute_precedence_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_class_method_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_forward_has_graph_break_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_guard_name_is_valid_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_property_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_module_static_method_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_moduledict_custom_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_parameterdict_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_parameters2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_sequential_with_duplicated_module2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_super1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_tensorlist_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesNNModuleTests::test_viamodulecall_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_cond_raise_user_error_on_branch_return_multiple_tensors_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_cond_raise_user_error_on_mismatch_return_length_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_cond_raise_user_error_on_mismatch_return_tensor_meta_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_cond_raise_user_error_on_non_tensor_operands_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_cond_raise_user_error_on_unsupported_pred_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dict_return_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dict_return_with_aten_graph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dupes_2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dupes_and_bypass_reorder_with_non_tensor_arg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dupes_and_bypass_with_non_tensor_output_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dupes_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dynamic_slicing_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_dynamic_slicing_simple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_enforce_equalities_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_decomp_asserts_bad_args_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_dynamic_dim_not_1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_graph_with_complex_reorder_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_mismatched_out_with_aten_graph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_no_tensor_computation_with_aten_graph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_raise_guard_partial_constraint_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_shape_control_flow_1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_specialized_int_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_symbolic_shape_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_args_with_default_None_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_args_with_default_float_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_cond_closure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_constant_free_function_and_class_method_multiarg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_constant_in_unspecialized_nn_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_constant_method_on_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_constant_none_control_flow_free_func_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_constant_not_none_control_flow_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_functools_wrapped_method_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_kwargs_and_empty_args_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_kwargs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_kwargs_with_default_tuple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_map_zero_sized_tensor_suppress_errors_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_symbool_inputs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_export_with_wrapped_fn_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_exported_graph_serialization_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_func_return_with_aten_graph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_invalid_input_nonlocal_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_list_contains_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_list_unpack_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_map_cond_param_buffer_lifted_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_no_tensor_computation_2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_no_tensor_computation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_param_buffer_safe_from_mutation_recurse_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_param_buffer_safe_from_mutation_simple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_predispatch_with_higher_order_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_preserve_fx_node_metadata_recompile_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_remove_redundant_dynamic_dim_in_error_message_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_retracibility_dict_container_inp_out_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_round_dynamic_shapes_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_strict_fake_tensor_prop_real_tensors_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_sym_contains_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_symbolic_tracing_within_fake_mode_with_constraints_with_parameters_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_torch_inference_mode_ctx_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_untracked_inputs_in_constraints_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_zeroes_in_and_out_different_shape_on_test_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_zeroes_in_new_shape_scalar_out_permute_dupe_and_bypass_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesExportTests::test_zeroes_in_new_shape_scalar_out_permute_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_capi_call1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_capi_call3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_control_flow1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_dynamic_duck_size_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_dynamic_getitem_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_dynamic_kwarg_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_graph_break_on_item_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_indirect_unsupported3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_resume2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_resume_freevars_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_resume_tuple_iterator_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_resume_with_no_grad3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_stack_state1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_stack_state2_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesSubGraphTests::test_start4_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_capture_global_num_adds_guard_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_capture_untracked_global_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_capture_untracked_global_nested_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_capture_value_created_in_subgraph_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_branches_no_arguments_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_branches_no_arguments_no_closure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_free_variable_in_both_branches_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_graph_break_in_one_branch_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_pytree_operands_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_cond_pytree_operands_with_non_tensor_leaves_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_fallback_on_graph_break_simple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_fn_with_kwargs_in_torch_ops_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_freevars_as_inputs_to_wrap_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_hints_wrapper_incorrect_type_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_hints_wrapper_pytree_inputs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_lift_tensors_with_shared_symbols_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_map_pytree_return_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_nested_wrap_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_no_freevars_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_return_captured_var_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_return_captured_vars_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_del_existing_attr_nonlocal_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_del_existing_attr_nonlocal_obj_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_mutate_global_list_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_mutate_global_num_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_mutate_nonlocal_num_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_mutate_nonlocal_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_set_existing_attr_global_obj_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_set_existing_attr_nonlocal_module_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_set_new_attr_global_obj_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_side_effect_set_new_attr_nonlocal_obj_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_tensor_with_unbacked_shape_closure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_unbacked_symbol_closure_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_vmap_source_fn_stack_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_wrap_allow_local_assign_in_body_fn_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_wrap_kwarg_default_else_branch_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_wrap_kwarg_int_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_wrap_pytree_args_not_const_symint_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesHigherOrderOpTests::test_wrap_source_fn_stack_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_functional_call_sequential_params_and_buffers_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_grad_call_torch_compile_fn_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_grad_has_aux_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_grad_non_tensor_input_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_grad_over_grad_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_jacrev_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_jvp_freevar_python_scalar_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_jvp_freevar_tensor_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_jvp_has_aux_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_linearize_jvp_fn_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vjp_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_multiple_outputs_out_dims_tuple_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_new_tensor_in_body_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_new_tensor_unused_in_body_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_side_effects_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesFuncTorchHigherOrderOpTests::test_vmap_with_graph_break_lambda_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_LSTM_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_alias_inputs_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_aot_grad_mode_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_aot_sequence_nr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_call_fn_with_non_const_inputs_aot_safe_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_different_inputs_overlapping_set_with_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_donated_buffer4_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph1_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_donated_buffer_with_retain_or_create_graph3_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_eager_sequence_nr_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_negative_testing_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_no_storage_overlap_guards_no_mutation_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_requires_grad_fake_via_dynamo_recompiles_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesAotAutogradFallbackTests::test_split_with_sizes_aot_autograd_cleans_up_traceback_meta_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesTestSDPA::test_intermediate_attr_access_SDPAParams_dynamic_shapes, test/dynamo/test_dynamic_shapes.py::DynamicShapesTestSDPA::test_returns_SDPAParams_dynamic_shapes 2025-09-07T07:05:29.7131617Z 2025-09-07T07:05:29.7131724Z Running export/test_functionalized_assertions 1/1 ... [2025-09-07 07:05:29.694202] 2025-09-07T07:05:29.7131911Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:05:29.7132314Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_functionalized_assertions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:29.694415] 2025-09-07T07:05:31.7634576Z 2025-09-07T07:05:31.7635773Z export/test_functionalized_assertions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_functionalized_assertions_1.1_6c75908deb8976f1_.log 2025-09-07T07:05:31.7643758Z Running 2 items in this shard: test/export/test_functionalized_assertions.py::TestFuntionalAssertions::test_functional_assert_async_msg, test/export/test_functionalized_assertions.py::TestFuntionalAssertions::test_functional_sym_constrain_range 2025-09-07T07:05:31.7644165Z 2025-09-07T07:05:31.7644254Z Running export/test_hop 1/1 ... [2025-09-07 07:05:31.763239] 2025-09-07T07:05:31.7644532Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:05:31.7644904Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_hop.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:31.763473] 2025-09-07T07:05:45.4529454Z 2025-09-07T07:05:45.4530496Z export/test_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_hop_1.1_494c84440505f01a_.log 2025-09-07T07:05:45.4538433Z Running 40 items in this shard: test/export/test_hop.py::TestHOPCUDA::test_aot_export_auto_functionalize_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_cond_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_flex_attention_backward_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_flex_attention_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_invoke_quant_packed_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_invoke_quant_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_invoke_subgraph_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_scan_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_while_loop_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_aot_export_while_loop_stack_output_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_auto_functionalize_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_cond_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_flex_attention_backward_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_flex_attention_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_invoke_quant_packed_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_invoke_quant_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_invoke_subgraph_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_scan_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_while_loop_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_pre_dispatch_export_while_loop_stack_output_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_auto_functionalize_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_cond_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_flex_attention_backward_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_flex_attention_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_invoke_quant_packed_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_invoke_quant_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_invoke_subgraph_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_scan_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_while_loop_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_retrace_export_while_loop_stack_output_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_auto_functionalize_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_cond_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_flex_attention_backward_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_flex_attention_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_invoke_quant_packed_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_invoke_quant_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_invoke_subgraph_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_scan_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_while_loop_simple_cuda_float32, test/export/test_hop.py::TestHOPCUDA::test_serialize_export_while_loop_stack_output_simple_cuda_float32 2025-09-07T07:05:45.4550244Z 2025-09-07T07:05:45.4550329Z Running export/test_package 1/1 ... [2025-09-07 07:05:45.452860] 2025-09-07T07:05:45.4550487Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:05:45.4550858Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_package.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:45.453146] 2025-09-07T07:05:47.5720863Z 2025-09-07T07:05:47.5722215Z export/test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_package_1.1_936e3f1926cbb869_.log 2025-09-07T07:05:47.5724340Z Running 4 items in this shard: test/export/test_package.py::TestPackage::test_basic, test/export/test_package.py::TestPackage::test_error, test/export/test_package.py::TestPackage::test_more_than_once, test/export/test_package.py::TestPackage::test_overloads 2025-09-07T07:05:47.5725065Z 2025-09-07T07:05:47.5725202Z Running export/test_pass_infra 1/1 ... [2025-09-07 07:05:47.571927] 2025-09-07T07:05:47.5725440Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:05:47.5732846Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_pass_infra.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:47.572160] 2025-09-07T07:05:54.0495250Z 2025-09-07T07:05:54.0497141Z export/test_pass_infra 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_pass_infra_1.1_f5f8e89c7c2023f0_.log 2025-09-07T07:05:54.0499325Z Running 5 items in this shard: test/export/test_pass_infra.py::TestPassInfra::test_cond, test/export/test_pass_infra.py::TestPassInfra::test_export_pass_base, test/export/test_pass_infra.py::TestPassInfra::test_graph_signature_updated_after_transformation, test/export/test_pass_infra.py::TestPassInfra::test_node_name_stability, test/export/test_pass_infra.py::TestPassInfra::test_replace_hook_basic 2025-09-07T07:05:54.0500428Z 2025-09-07T07:05:54.0500559Z Running export/test_passes 1/1 ... [2025-09-07 07:05:54.049452] 2025-09-07T07:05:54.0500789Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:05:54.0501373Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_passes.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:54.049740] 2025-09-07T07:06:15.2027215Z 2025-09-07T07:06:15.2032342Z export/test_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_passes_1.1_52f0f427076b0007_.log 2025-09-07T07:06:15.2037017Z Running 28 items in this shard: test/export/test_passes.py::TestPasses::test_constant_folding_pass, test/export/test_passes.py::TestPasses::test_custom_obj_tuple_out, test/export/test_passes.py::TestPasses::test_fakify_script_objects, test/export/test_passes.py::TestPasses::test_fakify_script_objects_properly_handle_containers, test/export/test_passes.py::TestPasses::test_functionalization_with_view_copy, test/export/test_passes.py::TestPasses::test_inline_, test/export/test_passes.py::TestPasses::test_math_ops, test/export/test_passes.py::TestPasses::test_move_device_example_inputs, test/export/test_passes.py::TestPasses::test_move_device_submod, test/export/test_passes.py::TestPasses::test_move_device_to, test/export/test_passes.py::TestPasses::test_move_to_device_pass, test/export/test_passes.py::TestPasses::test_predispatch_autocast, test/export/test_passes.py::TestPasses::test_predispatch_autocast_and_set_grad, test/export/test_passes.py::TestPasses::test_predispatch_set_grad, test/export/test_passes.py::TestPasses::test_remove_auto_functionalized_pass, test/export/test_passes.py::TestPasses::test_remove_auto_functionalized_pass_tuple, test/export/test_passes.py::TestPasses::test_remove_effect_token_kwargs, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_cond, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_item, test/export/test_passes.py::TestPasses::test_runtime_assert_inline_constraints_for_nonzero, test/export/test_passes.py::TestPasses::test_runtime_assert_multiple_dims, test/export/test_passes.py::TestPasses::test_runtime_assert_one_dim, test/export/test_passes.py::TestPasses::test_runtime_assert_some_dims_not_specified, test/export/test_passes.py::TestPasses::test_runtime_assert_some_inps_not_used, test/export/test_passes.py::TestPasses::test_sequential_split, test/export/test_passes.py::TestPasses::test_sequential_split_graph, test/export/test_passes.py::TestPasses::test_view_to_view_copy, test/export/test_passes.py::TestPasses::test_views_op_having_view_copy 2025-09-07T07:06:15.2040605Z 2025-09-07T07:06:15.2040712Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T07:06:15.2047307Z Uploading artifacts took 0.00 seconds 2025-09-07T07:06:15.2047508Z Running export/test_retraceability 1/1 ... [2025-09-07 07:06:15.202520] 2025-09-07T07:06:15.2047681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:06:15.2048103Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_retraceability.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:06:15.202736] 2025-09-07T07:09:29.8285077Z 2025-09-07T07:09:29.8286372Z export/test_retraceability 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_retraceability_1.1_f46a27ce64499be3_.log 2025-09-07T07:09:29.8441734Z Running 832 items in this shard: test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_assume_static_by_default_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_constraints_error_not_in_range_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_constraints_error_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_inline_constraints_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_slice_maxsize_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_slice_unbacked_dim1_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_export_strict_narrow_unbacked_expr_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_no_grad_param_inplace_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestDynamismExpression::test_reshape_view_backed_size_oblivious_retraceability_strict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_assume_static_by_default_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_constraints_error_not_in_range_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_constraints_error_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_inline_constraints_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_slice_maxsize_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_slice_unbacked_dim1_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_export_strict_narrow_unbacked_expr_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_no_grad_param_inplace_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestDynamismExpression::test_reshape_view_backed_size_oblivious_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportTestExport::test__scaled_dot_product_flash_attention_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_additional_inputs_constants_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_allow_explicit_guards_as_runtime_asserts_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_args_type_checked_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_aten_lift_fresh_copy_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_attention_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_attr_assignment_extra_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_automatic_constrain_size_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_automatic_dynamic_shapes_constant_relation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_automatic_dynamic_shapes_linear_relation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_automatic_dynamic_shapes_simple_equality_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_baddbmm_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_basic_non_strict_fake_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_basic_non_strict_real_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_bincount_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_buffer_util_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_capture_subclass_constructor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_capture_subclass_constructor_torch_ir_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_capture_subclass_wrong_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_ccode_python_mod_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_check_specialized_int_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_checks_to_constrain_range_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cleanup_dynamic_markers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_colin_unbacked_backed_vr_sub_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_colon_parameter_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_compiling_state_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_access_identical_symint_closure_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_branches_return_constant_int_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_branches_return_same_int_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_buffers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_contains_unbacked_no_escape_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_int_closure_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_unflatten_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_with_module_stack_export_with_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cond_with_module_stack_export_with_unflatten_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_aliasing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_input_naming_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_no_user_inp_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_output_dup_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_output_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_requires_grad_const_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_return_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_tensor_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_tensor_with_non_functional_nested_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constant_tensor_with_non_functional_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constrain_decomp_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constrain_size_in_eager_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constrain_size_with_constrain_value_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_constrain_size_with_various_cases_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_conv_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_crop_like_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_cse_for_symint_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_op_auto_functionalize_pre_dispatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_op_auto_functionalize_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_op_auto_warn_pre_dispatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_op_preserve_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_pytree_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_custom_tag_metadata_re_export_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_decomp_batch_norm_functional_predispatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_decomp_item_in_prim_after_decomposition_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_decomp_item_in_prim_before_decomposition_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_default_decomposition_core_cia_ops_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_1_2_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_integer_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_nested_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_out_of_order_repeat_derived_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_out_of_order_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_out_of_order_simplified_repeat_non_derived_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_out_of_order_simplified_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_derived_dim_repeat_derived_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_detect_leak_nonstrict_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_detect_leak_nonstrict_with_stacktrace_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_detect_leak_strict_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_device_to_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_device_to_gpu_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_device_to_mutation_float_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_device_to_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_device_to_static_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_1_2_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_auto_and_dim_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_dynamic_divisibility_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_dynamic_specialization_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_hint_range_violations_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dim_hint_ranges_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_disable_forced_specializations_errors_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_disable_forced_specializations_ok_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_distributed_all_gather_into_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_distributed_all_gather_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_distributed_all_reduce_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_distributed_all_to_all_single_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_distributed_reduce_scatter_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dont_duck_size_for_auto_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_double_lifted_constants_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_checks_aliasing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_checks_mutation_list_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_checks_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_checks_mutation_with_nan_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_fake_kernel_inference_errors_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_draft_export_infers_fake_kernel_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_duplicate_modules_with_non_persistent_buffers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_lr_shift_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_bounds_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_builder_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_builder_kwargs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_builder_pytree_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_dataclass_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_inferred_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_serdes_generic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_serdes_user_errors_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_serdes_various_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_shapes_spec_with_pytree_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_dynamic_sym_round_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_ends_of_bounds_oblivious_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_error_does_not_reference_eager_fallback_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_error_when_passing_mutating_primitive_op_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_exception_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_expand_copy_export_handles_implicit_true_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_api_with_dynamic_shapes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_as_backend_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_associative_scan_lifted_buffers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_associative_scan_symbol_dim_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_associative_scan_symbol_scandim_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_aten_to_unflatten_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_aten_to_unflatten_subclass_pre_dispatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_aten_to_unflatten_subclass_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_cond_preserve_torch_fn_for_subgraphs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_cond_symbool_pred_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_cond_warns_constant_pred_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_custom_decomp_table_basic_pop_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_custom_decomp_table_container_methods_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_custom_op_lib_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_custom_triton_kernel_mutable_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_custom_triton_kernel_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_cyclic_reference_leak_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_decomp_torture_case_1_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_decomp_torture_case_2_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_decomps_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_decomps_simple_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_dynamo_config_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_for_training_run_decomp_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_for_training_with_container_type_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_for_training_with_dynamic_shapes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_for_training_with_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_for_training_with_state_dict_hooks_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_default_kwargs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_keyword_only_args_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_kwargs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_pytree_kwargs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_var_keyword_args_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_var_keyword_pytree_args_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_func_with_var_postional_args_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_function_schema_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_graph_with_no_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_input_mutation_bug_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_input_mutation_dynamic_shape_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_input_mutation_static_shape_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_linear_preserve_dynamic_shape_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_max_nonstrict_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_max_onnx_reported_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_method_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_mod_constraints_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_preserve_linear_at_aot_level_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_preserve_linear_but_not_custom_op_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_scan_pytree_output_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_script_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_statically_known_true_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_then_compile_tensor_ctor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_autocast_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_fake_tensor_inputs_on_cuda_devices_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_fake_tensor_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_inline_constraints_complex_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_inline_constraints_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_set_grad_enabled_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_export_with_wrong_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_external_call_non_strict_real_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_fake_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_fake_weights_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_filter_traceback_frames_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_float_conversion_from_int_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_float_conversion_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_fqn_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_from_node_metadata_export_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_full_on_scalar_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_function_holding_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_hints_wrapper_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_hoo_inline_users_issue_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_if_functional_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_if_post_autograd_op_preserved_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_inline_script_class_method_recursive_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_inline_script_class_method_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_inline_script_function_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_inline_script_method_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_int_shape_specialization_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_intermediate_shape_comp_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_is_exporting_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_is_non_negative_check_function_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_is_nonzero_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_isnonzero_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_issue_113041_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_issue_157289_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_istft_op_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_keep_composite_ops_invalid_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_keep_composite_ops_linear_convd_for_training_ir_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_keep_composite_ops_linear_convd_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_kwarg_dynamic_shapes_diff_order_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_kwargs_reorder_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_layer_norm_unbacked_normalized_shape_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_layer_sharing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_lazy_module_kwargs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_lifted_constants_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_linear_conv_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_malformed_fqn_from_source_name_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_map_buffers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_map_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_mask_nonzero_static_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_masked_select_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_math_pow_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_mismatched_dynamic_shapes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_mixed_input_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_dict_key_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_input_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_input_subclasses_parameterization_nested_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_list_slice_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_module_with_dict_container_inp_out_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_modules_access_for_deleted_submodule_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_more_multidimensional_slicing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_multidimensional_slicing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_multinomial_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_multiple_definitions_same_name_dim_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nested_dynamic_shapes_spec_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nested_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nested_module_with_constant_buffer_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nested_module_with_init_buffer_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nested_module_with_parameter_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nn_module_stack_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nn_module_stack_shared_submodule_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_check_is_size_error_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_suggested_fixes_for_data_dependent_errors_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_tensor_computation_2_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_tensor_computation_3_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_tensor_computation_4_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_no_tensor_computation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_arg_name_dynamic_shapes_api_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_arg_name_dynamic_shapes_api_with_container_type_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_arg_name_dynamic_shapes_api_with_kwarg_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_persistent_buffer_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_strict_dynamic_shapes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_non_strict_dynamic_shapes_suggested_fixes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_none_buffers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nonstrict_retrace_preserves_metadata_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nonzero_2_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_nonzero_dynamic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_not_registered_parameter_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_operator_aten_tensor_mode_variant_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_output_node_name_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_pad_sequence_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_param_util_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_partial_patched_forward_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_placeholder_naming_collisions_hoo_subgraphs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_placeholder_naming_collisions_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_placeholder_naming_order_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_placeholder_naming_order_variadic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_placeholder_update_preserving_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_predispatch_cond_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_predispatch_grad_wrappers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_preserve_module_call_signature_unflatten_specialization_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_preserve_requires_grad_placeholders_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_preserve_shape_dynamism_for_unused_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_profiling_code_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_python_asserts_with_sym_int_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_pytree_register_data_class_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_pytree_register_nested_data_class_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_raise_user_error_when_guard_on_data_dependent_operation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_range_constraints_with_replacement_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_real_tensor_alias_dtype_mismatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_real_tensor_bool_cast_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_real_tensor_errors_on_aliasing_custom_op_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_real_tensor_for_max_op_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_real_tensor_size_mismatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_redundant_assert_max_upper_bound_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_redundant_asserts_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_refine_dynamic_shapes_from_suggested_fixes_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_register_constant_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_repeat_interleave_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_replace_unbacked_with_very_large_upperbound_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_replaced_unbacked_bindings_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_reshape_view_helper_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_retracable_ep_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_retrace_pre_autograd_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_run_decomposition_supports_user_input_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_run_decompositions_keep_metadata_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_run_decompositions_keep_tensor_constant_metadata_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_runtime_assert_for_prim_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_runtime_assert_for_prm_str_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_runtime_assert_with_size_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_sdpa_gqa_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_sequential_slicing_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_set_example_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_set_grad_as_side_effect_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_set_grad_empty_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_set_grad_unflatten_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_setgrad_lifted_tensor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_shared_submodule_nn_module_stack_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_simple_export_for_training_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_simple_unbacked_view_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_size_input_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_slice_nn_module_stack_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_solver_unsupported_sympy_function_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_specialize_derived_dim_roots_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_split_const_gm_with_lifted_constants_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_stack_trace_make_fx_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_stack_trace_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_state_primitives_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_state_shape_attribute_assignment_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_state_tensors_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_static_dim_constraints_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclass_nested_attr_access_complicated_metadata_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclass_nested_attr_access_const_metadata_not_top_level_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclass_nested_attr_access_const_metadata_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclass_nested_attr_access_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclass_nested_attr_access_submodule_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclasses_parameterization_nested_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_subclasses_parameterization_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_suggest_torch_checks_with_non_negative_check_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_suggest_torch_checks_with_regular_check_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_suggested_fixes_for_data_dependent_errors_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_suggested_fixes_for_data_dependent_errors_puzzlers_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_suggested_fixes_new_roots_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_sym_float_operators_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_sym_or_sym_and_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_sym_sqrt_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symbool_item_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symfloat_item_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_input_additional_inputs_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_input_basic_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_input_ranges_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_input_shapes_collection_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_input_specialization_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_item_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_output_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_symint_tensor_return_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_tensor_attribute_zero_args_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_tensor_constant_aten_to_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_tensor_constant_with_wrapped_method_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_to_module_with_mutated_buffer_multiple_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_to_module_with_mutated_buffer_multiple_update_sub_later_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_to_module_with_mutated_buffer_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_tolist_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_torch_check_eq_commutativity_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_torch_fn_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_trace_under_fake_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_train_eval_on_exported_preautograd_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_3d_matmul_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_bincount_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_bindings_for_divisible_u_symint_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_deferred_runtime_retrace_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_expand_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_infer_size_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_kth_value_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_linear_layer_norm_input_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_noncontig_lin_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_pad_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_scalar_constructor_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_slice_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_to_cond_passthrough_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_to_cond_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unbacked_unsqueeze_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_asserts_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_buffer_update_child2parent_swap_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_closure_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_isinstance_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_multiple_graphs_dispatch_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_multiple_graphs_preserve_signature_no_error_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_multiple_graphs_shared_submodule_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_multiple_graphs_state_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_no_unroll_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_placeholder_update_child2parent_swap_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_placeholder_update_grandchild2cousin_swap_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_5_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_6_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_buf_8_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_const_preserving_3_1_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_const_preserving_3_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_4_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_6_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_9_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_10_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_1_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_5_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_7_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unflatten_random_dag_preserving_4_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unused_aliases_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_unused_constant_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_use_embedding_twice_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_user_input_and_buffer_mutation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_vmap_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_while_loop_assert_separation_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_while_loop_index_assertions_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_while_loop_simple_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_while_loop_tensor_constant_idx_retraceability_strict, test/export/test_retraceability.py::RetraceExportTestExport::test_wrapper_module_retraceability_strict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test__scaled_dot_product_flash_attention_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_additional_inputs_constants_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_allow_explicit_guards_as_runtime_asserts_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_args_type_checked_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_aten_lift_fresh_copy_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_attention_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_attr_assignment_extra_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_automatic_constrain_size_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_automatic_dynamic_shapes_constant_relation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_automatic_dynamic_shapes_linear_relation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_automatic_dynamic_shapes_simple_equality_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_baddbmm_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_basic_non_strict_fake_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_basic_non_strict_real_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_bincount_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_buffer_util_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_capture_subclass_constructor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_capture_subclass_constructor_torch_ir_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_capture_subclass_wrong_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_ccode_python_mod_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_check_specialized_int_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_checks_to_constrain_range_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cleanup_dynamic_markers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_colin_unbacked_backed_vr_sub_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_colon_parameter_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_compiling_state_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_access_identical_symint_closure_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_branches_return_constant_int_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_branches_return_same_int_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_buffers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_contains_unbacked_no_escape_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_int_closure_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_unflatten_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_with_module_stack_export_with_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cond_with_module_stack_export_with_unflatten_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_aliasing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_input_naming_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_no_user_inp_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_output_dup_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_output_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_requires_grad_const_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_return_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_tensor_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_tensor_with_non_functional_nested_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constant_tensor_with_non_functional_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constrain_decomp_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constrain_size_in_eager_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constrain_size_with_constrain_value_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_constrain_size_with_various_cases_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_conv_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_crop_like_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_cse_for_symint_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_op_auto_functionalize_pre_dispatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_op_auto_functionalize_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_op_auto_warn_pre_dispatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_op_preserve_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_pytree_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_custom_tag_metadata_re_export_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_decomp_batch_norm_functional_predispatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_decomp_item_in_prim_after_decomposition_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_decomp_item_in_prim_before_decomposition_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_default_decomposition_core_cia_ops_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_1_2_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_integer_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_nested_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_out_of_order_repeat_derived_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_out_of_order_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_out_of_order_simplified_repeat_non_derived_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_out_of_order_simplified_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_derived_dim_repeat_derived_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_detect_leak_nonstrict_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_detect_leak_nonstrict_with_stacktrace_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_detect_leak_strict_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_device_to_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_device_to_gpu_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_device_to_mutation_float_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_device_to_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_device_to_static_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_1_2_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_auto_and_dim_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_dynamic_divisibility_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_dynamic_specialization_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_hint_range_violations_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dim_hint_ranges_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_disable_forced_specializations_errors_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_disable_forced_specializations_ok_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_distributed_all_gather_into_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_distributed_all_gather_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_distributed_all_reduce_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_distributed_all_to_all_single_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_distributed_reduce_scatter_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dont_duck_size_for_auto_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_double_lifted_constants_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_checks_aliasing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_checks_mutation_list_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_checks_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_checks_mutation_with_nan_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_fake_kernel_inference_errors_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_draft_export_infers_fake_kernel_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_duplicate_modules_with_non_persistent_buffers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_lr_shift_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_bounds_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_builder_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_builder_kwargs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_builder_pytree_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_dataclass_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_inferred_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_serdes_generic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_serdes_user_errors_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_serdes_various_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_shapes_spec_with_pytree_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_dynamic_sym_round_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_ends_of_bounds_oblivious_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_error_does_not_reference_eager_fallback_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_error_when_passing_mutating_primitive_op_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_exception_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_expand_copy_export_handles_implicit_true_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_api_with_dynamic_shapes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_as_backend_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_associative_scan_lifted_buffers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_associative_scan_symbol_dim_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_associative_scan_symbol_scandim_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_aten_to_unflatten_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_aten_to_unflatten_subclass_pre_dispatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_aten_to_unflatten_subclass_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_cond_preserve_torch_fn_for_subgraphs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_cond_symbool_pred_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_cond_warns_constant_pred_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_custom_decomp_table_basic_pop_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_custom_decomp_table_container_methods_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_custom_op_lib_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_custom_triton_kernel_mutable_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_custom_triton_kernel_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_cyclic_reference_leak_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_decomp_torture_case_1_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_decomp_torture_case_2_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_decomps_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_decomps_simple_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_dynamo_config_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_for_training_run_decomp_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_for_training_with_container_type_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_for_training_with_dynamic_shapes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_for_training_with_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_for_training_with_state_dict_hooks_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_default_kwargs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_keyword_only_args_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_kwargs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_pytree_kwargs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_var_keyword_args_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_var_keyword_pytree_args_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_func_with_var_postional_args_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_function_schema_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_graph_with_no_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_input_mutation_bug_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_input_mutation_dynamic_shape_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_input_mutation_static_shape_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_linear_preserve_dynamic_shape_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_max_nonstrict_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_max_onnx_reported_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_method_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_mod_constraints_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_module_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_preserve_linear_at_aot_level_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_preserve_linear_but_not_custom_op_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_scan_pytree_output_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_script_module_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_statically_known_true_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_then_compile_tensor_ctor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_autocast_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_fake_tensor_inputs_on_cuda_devices_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_fake_tensor_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_inline_constraints_complex_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_inline_constraints_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_set_grad_enabled_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_export_with_wrong_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_external_call_non_strict_real_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_fake_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_fake_weights_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_filter_traceback_frames_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_float_conversion_from_int_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_float_conversion_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_fqn_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_from_node_metadata_export_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_full_on_scalar_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_function_holding_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_hints_wrapper_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_hoo_inline_users_issue_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_if_functional_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_if_post_autograd_op_preserved_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_inline_script_class_method_recursive_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_inline_script_class_method_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_inline_script_function_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_inline_script_method_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_int_shape_specialization_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_intermediate_shape_comp_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_is_exporting_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_is_non_negative_check_function_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_is_nonzero_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_isnonzero_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_issue_113041_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_issue_157289_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_istft_op_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_keep_composite_ops_invalid_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_keep_composite_ops_linear_convd_for_training_ir_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_keep_composite_ops_linear_convd_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_kwarg_dynamic_shapes_diff_order_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_kwargs_reorder_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_layer_norm_unbacked_normalized_shape_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_layer_sharing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_lazy_module_kwargs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_lifted_constants_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_linear_conv_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_malformed_fqn_from_source_name_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_map_buffers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_map_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_mask_nonzero_static_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_masked_select_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_math_pow_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_mismatched_dynamic_shapes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_mixed_input_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_dict_key_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_input_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_input_subclasses_parameterization_nested_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_list_slice_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_module_with_dict_container_inp_out_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_modules_access_for_deleted_submodule_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_more_multidimensional_slicing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_multidimensional_slicing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_multinomial_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_multiple_definitions_same_name_dim_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nested_dynamic_shapes_spec_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nested_module_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nested_module_with_constant_buffer_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nested_module_with_init_buffer_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nested_module_with_parameter_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nn_module_stack_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nn_module_stack_shared_submodule_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_check_is_size_error_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_suggested_fixes_for_data_dependent_errors_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_tensor_computation_2_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_tensor_computation_3_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_tensor_computation_4_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_no_tensor_computation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_with_container_type_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_with_kwarg_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_persistent_buffer_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_strict_dynamic_shapes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_non_strict_dynamic_shapes_suggested_fixes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_none_buffers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nonstrict_retrace_preserves_metadata_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nonzero_2_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_nonzero_dynamic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_not_registered_parameter_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_operator_aten_tensor_mode_variant_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_output_node_name_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_pad_sequence_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_param_util_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_partial_patched_forward_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_placeholder_naming_collisions_hoo_subgraphs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_placeholder_naming_collisions_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_placeholder_naming_order_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_placeholder_naming_order_variadic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_placeholder_update_preserving_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_predispatch_cond_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_predispatch_grad_wrappers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_preserve_module_call_signature_unflatten_specialization_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_preserve_requires_grad_placeholders_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_preserve_shape_dynamism_for_unused_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_profiling_code_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_python_asserts_with_sym_int_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_pytree_register_data_class_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_pytree_register_nested_data_class_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_raise_user_error_when_guard_on_data_dependent_operation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_range_constraints_with_replacement_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_real_tensor_alias_dtype_mismatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_real_tensor_bool_cast_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_real_tensor_errors_on_aliasing_custom_op_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_real_tensor_for_max_op_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_real_tensor_size_mismatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_redundant_assert_max_upper_bound_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_redundant_asserts_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_refine_dynamic_shapes_from_suggested_fixes_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_register_constant_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_repeat_interleave_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_replace_unbacked_with_very_large_upperbound_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_replaced_unbacked_bindings_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_reshape_view_helper_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_retracable_ep_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_retrace_pre_autograd_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_run_decomposition_supports_user_input_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_run_decompositions_keep_metadata_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_run_decompositions_keep_tensor_constant_metadata_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_runtime_assert_for_prim_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_runtime_assert_for_prm_str_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_runtime_assert_with_size_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_sdpa_gqa_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_sequential_slicing_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_set_example_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_set_grad_as_side_effect_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_set_grad_empty_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_set_grad_unflatten_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_setgrad_lifted_tensor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_shared_submodule_nn_module_stack_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_simple_export_for_training_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_simple_unbacked_view_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_size_input_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_slice_nn_module_stack_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_solver_unsupported_sympy_function_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_specialize_derived_dim_roots_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_split_const_gm_with_lifted_constants_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_stack_trace_make_fx_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_stack_trace_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_state_primitives_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_state_shape_attribute_assignment_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_state_tensors_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_static_dim_constraints_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclass_nested_attr_access_complicated_metadata_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclass_nested_attr_access_const_metadata_not_top_level_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclass_nested_attr_access_const_metadata_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclass_nested_attr_access_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclass_nested_attr_access_submodule_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclasses_parameterization_nested_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_subclasses_parameterization_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_suggest_torch_checks_with_non_negative_check_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_suggest_torch_checks_with_regular_check_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_suggested_fixes_for_data_dependent_errors_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_suggested_fixes_for_data_dependent_errors_puzzlers_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_suggested_fixes_new_roots_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_sym_float_operators_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_sym_or_sym_and_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_sym_sqrt_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symbool_item_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symfloat_item_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_input_additional_inputs_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_input_basic_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_input_ranges_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_input_shapes_collection_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_input_specialization_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_item_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_output_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_symint_tensor_return_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_tensor_attribute_zero_args_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_tensor_constant_aten_to_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_tensor_constant_with_wrapped_method_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_to_module_with_mutated_buffer_multiple_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_to_module_with_mutated_buffer_multiple_update_sub_later_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_to_module_with_mutated_buffer_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_tolist_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_torch_check_eq_commutativity_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_torch_fn_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_trace_under_fake_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_train_eval_on_exported_preautograd_module_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_3d_matmul_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_bincount_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_bindings_for_divisible_u_symint_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_deferred_runtime_retrace_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_expand_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_infer_size_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_kth_value_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_linear_layer_norm_input_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_noncontig_lin_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_pad_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_scalar_constructor_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_slice_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_to_cond_passthrough_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_to_cond_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unbacked_unsqueeze_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_asserts_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_buffer_update_child2parent_swap_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_closure_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_isinstance_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_multiple_graphs_dispatch_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_multiple_graphs_preserve_signature_no_error_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_multiple_graphs_shared_submodule_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_multiple_graphs_state_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_no_unroll_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_placeholder_update_child2parent_swap_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_placeholder_update_grandchild2cousin_swap_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_5_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_6_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_buf_8_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_const_preserving_3_1_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_const_preserving_3_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_4_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_6_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_9_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_10_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_1_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_5_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_7_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unflatten_random_dag_preserving_4_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unused_aliases_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_unused_constant_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_use_embedding_twice_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_user_input_and_buffer_mutation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_vmap_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_while_loop_assert_separation_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_while_loop_index_assertions_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_while_loop_simple_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_while_loop_tensor_constant_idx_retraceability_nonstrict, test/export/test_retraceability.py::RetraceExportNonStrictTestExport::test_wrapper_module_retraceability_nonstrict 2025-09-07T07:09:29.8583681Z 2025-09-07T07:09:29.8583767Z Running functorch/test_vmap 1/1 ... [2025-09-07 07:09:29.829646] 2025-09-07T07:09:29.8583937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:09:29.8584317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_vmap.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:09:29.829859] 2025-09-07T07:13:02.7110582Z 2025-09-07T07:13:02.7111240Z PRINTING LOG FILE of functorch/test_vmap 1/1 (test/test-reports/functorch.test_vmap_1.1_f001b2d0622d0c76_.log) 2025-09-07T07:13:02.7116245Z /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. 2025-09-07T07:13:02.7117741Z import pkg_resources 2025-09-07T07:13:02.7125223Z Test results will be stored in test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-31a6d6a52b5f5036.xml 2025-09-07T07:13:02.7125626Z ============================= test session starts ============================== 2025-09-07T07:13:02.7125962Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:13:02.7126250Z cachedir: .pytest_cache 2025-09-07T07:13:02.7126594Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:13:02.7126974Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:13:02.7127152Z configfile: pytest.ini 2025-09-07T07:13:02.7127492Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:13:02.7127855Z collecting ... collected 2136 items 2025-09-07T07:13:02.7128063Z stepcurrent: Cannot find last run test, not skipping 2025-09-07T07:13:02.7449961Z Running 2136 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_accepts_nested_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_backward_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_batch_rule_does_not_need_to_handle_no_batched_input, test/functorch/test_vmap.py::TestVmapAPI::test_batched_gradient_basic, test/functorch/test_vmap.py::TestVmapAPI::test_checkpoint, test/functorch/test_vmap.py::TestVmapAPI::test_constant_function, test/functorch/test_vmap.py::TestVmapAPI::test_data_attribute, test/functorch/test_vmap.py::TestVmapAPI::test_data_dependent_control_flow_throws, test/functorch/test_vmap.py::TestVmapAPI::test_decomposition_under_python_dispatcher, test/functorch/test_vmap.py::TestVmapAPI::test_different_map_dim_size_raises, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_does_not_warn_by_default, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_masked_fill, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_multiple_returns, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warning, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warns_when_warnings_are_enabled, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_with_undefined_grad, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_zero_dim, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_tensors, test/functorch/test_vmap.py::TestVmapAPI::test_functools_partial, test/functorch/test_vmap.py::TestVmapAPI::test_grad_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_in_dim_not_in_tensor_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_in_dims_wrong_type_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_different_levels, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_same_levels, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_unary, test/functorch/test_vmap.py::TestVmapAPI::test_integer_in_dim_but_not_tensor_input_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_item_throws, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs2, test/functorch/test_vmap.py::TestVmapAPI::test_nested_negative_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_non_default_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_diag_embed, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_different_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_same_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_nn_module, test/functorch/test_vmap.py::TestVmapAPI::test_non_default_in_dims_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_non_tensor_output_raises, test/functorch/test_vmap.py::TestVmapAPI::test_non_zero_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_none_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nonzero_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_noop_in_inner_vmap, test/functorch/test_vmap.py::TestVmapAPI::test_not_enough_in_dims_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dim_out_of_bounds_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_and_num_outputs_mismatch_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_edge_case, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_must_be_int_or_collection_of_int_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_none, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_none_tuple, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_normal_tensor, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_odict_returns, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_nested, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_simple, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_outdims, test/functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_into, test/functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_outof, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_no_vmapped_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_pytree_input_output, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_unexpanded_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_single_input, test/functorch/test_vmap.py::TestVmapAPI::test_unsupported_op_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cpu, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cuda, test/functorch/test_vmap.py::TestVmapOperators::test_T_numpy, test/functorch/test_vmap.py::TestVmapOperators::test_adaptive_avg_pool2d, test/functorch/test_vmap.py::TestVmapOperators::test_argmax_dim, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_as_strided, test/functorch/test_vmap.py::TestVmapOperators::test_bmm, test/functorch/test_vmap.py::TestVmapOperators::test_cat, test/functorch/test_vmap.py::TestVmapOperators::test_chunk, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_clamp, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_max_, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_min_, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_max, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_min, test/functorch/test_vmap.py::TestVmapOperators::test_clone, test/functorch/test_vmap.py::TestVmapOperators::test_comparison_ops, test/functorch/test_vmap.py::TestVmapOperators::test_conj, test/functorch/test_vmap.py::TestVmapOperators::test_conj_bit, test/functorch/test_vmap.py::TestVmapOperators::test_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_conv2d, test/functorch/test_vmap.py::TestVmapOperators::test_copy_, test/functorch/test_vmap.py::TestVmapOperators::test_cross_batch_size_three, test/functorch/test_vmap.py::TestVmapOperators::test_diagonal, test/functorch/test_vmap.py::TestVmapOperators::test_dot, test/functorch/test_vmap.py::TestVmapOperators::test_expand_as, test/functorch/test_vmap.py::TestVmapOperators::test_fill_and_zero_inplace, test/functorch/test_vmap.py::TestVmapOperators::test_imag, test/functorch/test_vmap.py::TestVmapOperators::test_is_complex, test/functorch/test_vmap.py::TestVmapOperators::test_is_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_is_floating_point, test/functorch/test_vmap.py::TestVmapOperators::test_mean, test/functorch/test_vmap.py::TestVmapOperators::test_mean_dim, test/functorch/test_vmap.py::TestVmapOperators::test_mm, test/functorch/test_vmap.py::TestVmapOperators::test_mode_key, test/functorch/test_vmap.py::TestVmapOperators::test_movedim, test/functorch/test_vmap.py::TestVmapOperators::test_mv, test/functorch/test_vmap.py::TestVmapOperators::test_narrow, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty_strided, test/functorch/test_vmap.py::TestVmapOperators::test_new_zeros, test/functorch/test_vmap.py::TestVmapOperators::test_nll_loss, test/functorch/test_vmap.py::TestVmapOperators::test_one_hot, test/functorch/test_vmap.py::TestVmapOperators::test_real, test/functorch/test_vmap.py::TestVmapOperators::test_repeat, test/functorch/test_vmap.py::TestVmapOperators::test_reshape, test/functorch/test_vmap.py::TestVmapOperators::test_reshape_as, test/functorch/test_vmap.py::TestVmapOperators::test_result_type, test/functorch/test_vmap.py::TestVmapOperators::test_roll_no_dims, test/functorch/test_vmap.py::TestVmapOperators::test_select, test/functorch/test_vmap.py::TestVmapOperators::test_silu_backward, test/functorch/test_vmap.py::TestVmapOperators::test_slice, test/functorch/test_vmap.py::TestVmapOperators::test_slogdet, test/functorch/test_vmap.py::TestVmapOperators::test_split, test/functorch/test_vmap.py::TestVmapOperators::test_squeeze, test/functorch/test_vmap.py::TestVmapOperators::test_stack, test/functorch/test_vmap.py::TestVmapOperators::test_stride, test/functorch/test_vmap.py::TestVmapOperators::test_sum, test/functorch/test_vmap.py::TestVmapOperators::test_sum_dim, test/functorch/test_vmap.py::TestVmapOperators::test_t, test/functorch/test_vmap.py::TestVmapOperators::test_tensor_split, test/functorch/test_vmap.py::TestVmapOperators::test_to, test/functorch/test_vmap.py::TestVmapOperators::test_trace, test/functorch/test_vmap.py::TestVmapOperators::test_transpose, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_abs, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_acos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_asin, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_atan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_ceil, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cosh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_digamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_exp, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_expm1, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_floor, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_frac, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_lgamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log10, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log1p, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log2, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_neg, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_reciprocal, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_relu, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_round, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_rsqrt, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sigmoid, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sign, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sin, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sinh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sqrt, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tanh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_trunc, test/functorch/test_vmap.py::TestVmapOperators::test_unbind, test/functorch/test_vmap.py::TestVmapOperators::test_unfold, test/functorch/test_vmap.py::TestVmapOperators::test_unsafe_view, test/functorch/test_vmap.py::TestVmapOperators::test_unsqueeze, test/functorch/test_vmap.py::TestVmapOperators::test_view, test/functorch/test_vmap.py::TestVmapOperators::test_view_as, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_complex, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_real, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_same, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_pinv_singular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_qr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_slogdet_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_triangular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_svd_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_svdvals_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_tensorinv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_tensorsolve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vander_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vecdot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vector_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linspace_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linspace_tensor_overload_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log10_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log1p_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log2_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_normal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_softmax_with_dtype_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logaddexp2_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logaddexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logcumsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logdet_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_and_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_not_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_or_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_xor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logit_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logspace_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logspace_tensor_overload_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_long_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_long_functorch_no_channels_last_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lt_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_unpack_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mH_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mT_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_amax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_amin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_argmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_argmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_cumprod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_cumsum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_fill_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_fill_functorch_Scalar_only_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_log_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_logaddexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_logsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_median_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_normalize_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_prod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_scatter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_select_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_softmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_std_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_sum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_var_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_matmul_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_matrix_exp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_binary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_pool2d_with_indices_backward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_reduction_no_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_reduction_with_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_maximum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_median_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_meshgrid_list_of_tensors_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_binary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_reduction_no_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_reduction_with_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_minimum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mode_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_movedim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_msort_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mul_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_multinomial_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_3_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nan_to_num_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanmean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanmedian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanquantile_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nansum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_narrow_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_narrow_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_batch_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_dropout_backward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_layer_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ne_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_neg_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_empty_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_empty_strided_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_full_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_ones_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_zeros_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nextafter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_alpha_dropout_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_batch_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_batch_norm_without_cudnn_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_bilinear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_binary_cross_entropy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_celu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_channel_shuffle_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_groups_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_padding_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_padding_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cosine_embedding_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cosine_similarity_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cross_entropy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_ctc_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_elu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_bag_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_functorch_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_fractional_max_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_fractional_max_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_gaussian_nll_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_gelu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_glu_cuda_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_float_functorch_no_channels_last_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_float_power_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_floor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_floor_divide_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_frac_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_frexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_full_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_full_like_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gather_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gcd_cuda_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ge_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_geometric_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_geqrf_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gradient_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_grid_sampler_2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_grid_sampler_3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gt_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_half_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_half_functorch_no_channels_last_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hash_tensor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_heaviside_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_histc_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hsplit_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hstack_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hypot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_i0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_igamma_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_igammac_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_imag_cuda_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_add_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_fill_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_put_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_put_functorch_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_amax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_amin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_prod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_select_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_inner_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_int_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_int_functorch_no_channels_last_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isclose_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isfinite_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isinf_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isnan_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isneginf_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isposinf_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isreal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_istft_cuda_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_item_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_2inputs_2outputs_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_4inputs_with_extra_args_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_binary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_binary_return_by_ref_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_unary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_kron_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_kthvalue_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lcm_cuda_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ldexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_le_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lerp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lgamma_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cholesky_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cholesky_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cond_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cross_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_det_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_diagonal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eig_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigvals_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigvalsh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_householder_product_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_inv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_inv_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_factor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_factor_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lstsq_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lstsq_grad_oriented_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_factor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_factor_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_power_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_rank_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_multi_dot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_hermitian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_singular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_qr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_slogdet_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_triangular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_svd_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_svdvals_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_tensorinv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_tensorsolve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vander_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vecdot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vector_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linspace_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linspace_tensor_overload_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log10_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log1p_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log2_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_normal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_softmax_with_dtype_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logaddexp2_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logaddexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logcumsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logdet_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_and_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_not_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_or_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_xor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logit_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logspace_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logspace_tensor_overload_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_long_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_long_functorch_no_channels_last_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lt_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_unpack_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mH_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mT_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_amax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_amin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_argmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_argmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_cumprod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_cumsum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_fill_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_fill_functorch_Scalar_only_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_log_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_logaddexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_logsumexp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_median_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_normalize_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_prod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_scatter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_select_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_softmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_std_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_sum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_var_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_matmul_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_matrix_exp_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_binary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_pool2d_with_indices_backward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_reduction_no_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_reduction_with_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_maximum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_median_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_meshgrid_list_of_tensors_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_binary_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_reduction_no_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_reduction_with_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_minimum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mode_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_movedim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_msort_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mul_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_multinomial_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_3_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nan_to_num_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanmean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanmedian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanquantile_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nansum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_narrow_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_narrow_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_batch_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_dropout_backward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_layer_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ne_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_neg_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_empty_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_empty_strided_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_full_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_ones_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_zeros_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nextafter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_alpha_dropout_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_batch_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_batch_norm_without_cudnn_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_bilinear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_binary_cross_entropy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_celu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_channel_shuffle_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_groups_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_strided_padding_dilation_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cosine_embedding_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cosine_similarity_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cross_entropy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_ctc_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_elu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_bag_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_functorch_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_fractional_max_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_fractional_max_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_gaussian_nll_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_gelu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_glu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_grid_sample_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_group_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardshrink_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardsigmoid_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardswish_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardtanh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hinge_embedding_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_huber_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_instance_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_area_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_bicubic_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_bilinear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_linear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_nearest-exact_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_nearest_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_trilinear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_kl_div_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_l1_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_layer_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_leaky_relu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_linear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_local_response_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_logsigmoid_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_margin_ranking_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool1d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool1d_grad_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool2d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool2d_grad_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool3d_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool3d_grad_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mish_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mse_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mse_loss_functorch_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multi_head_attention_forward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multi_margin_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multilabel_margin_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_nll_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_normalize_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_one_hot_cuda_int64, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pixel_unshuffle_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_poisson_nll_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_prelu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_relu6_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_relu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_rms_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_rrelu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_scaled_dot_product_attention_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_selu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_silu_complex_cuda_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_silu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_smooth_l1_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_soft_margin_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softmin_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softmin_with_dtype_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softplus_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softshrink_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softsign_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_tanhshrink_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_threshold_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_triplet_margin_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_unfold_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_upsample_bilinear_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_upsample_nearest_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nonzero_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nonzero_static_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_fro_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_inf_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_nuc_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_normal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_normal_in_place_cuda_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_randn_like_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ravel_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_real_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_reciprocal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_remainder_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_renorm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_repeat_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_repeat_interleave_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_reshape_as_cuda_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_prod_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_sum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_searchsorted_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_select_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_select_scatter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sgn_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_short_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_short_functorch_no_channels_last_cuda_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_slice_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_slice_scatter_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_softmax_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_softmax_with_dtype_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sort_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sparse_mm_reduce_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sparse_sampled_addmm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_airy_ai_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_j0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_j1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_y0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_y1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_t_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_u_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_v_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_w_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_entr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_erfcx_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_hermite_polynomial_h_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_hermite_polynomial_he_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i0e_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i1e_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_laguerre_polynomial_l_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_legendre_polynomial_p_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_log_ndtr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_i0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_i1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_k0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_k1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_ndtr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_ndtri_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_polygamma_special_polygamma_n_0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_scaled_modified_bessel_k0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_scaled_modified_bessel_k1_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_t_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_u_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_v_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_w_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_spherical_bessel_j0_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_xlog1py_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_zeta_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_list_args_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_with_sizes_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_with_sizes_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sqrt_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_square_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_multiple_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_stack_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_mean_unbiased_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_unbiased_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_stft_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sub_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sum_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sum_to_size_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_svd_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_svd_lowrank_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_t_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_t_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_take_along_dim_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_take_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tan_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tanh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tensor_split_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tensordot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tile_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_to_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_to_sparse_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_topk_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch__scaled_mm_cuda_float8_e4m3fn, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__efficient_attention_forward_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__flash_attention_forward_cuda_float16, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__safe_softmax_default_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trace_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_transpose_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_transpose_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trapezoid_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trapz_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triangular_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tril_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tril_indices_cuda_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triu_indices_cuda_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_true_divide_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trunc_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unbind_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unbind_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unflatten_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unfold_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unfold_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_uniform_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unique_consecutive_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unique_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unravel_index_cuda_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsafe_chunk_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsafe_split_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsqueeze_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsqueeze_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_mean_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_mean_unbiased_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_unbiased_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vdot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_complex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_real_cuda_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_copy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vsplit_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vstack_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_where_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_xlogy_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zero__cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zeros_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zeros_like_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cholesky_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cholesky_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cond_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cross_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_det_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_diagonal_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eig_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigvals_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigvalsh_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_householder_product_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_inv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_inv_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_factor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_factor_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lstsq_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lstsq_grad_oriented_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_factor_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_factor_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_power_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_multi_dot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_hermitian_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_singular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_qr_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_slogdet_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_ex_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_triangular_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_svd_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_svdvals_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_tensorinv_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_tensorsolve_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vander_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vecdot_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vector_norm_cuda_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_multi_dot_failure_1D_input_cuda, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_with_anomaly_detection_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_add_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_binary_cross_entropy_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_diagonal_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_div_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_expand_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_index_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_inplace_manyview_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_inplace_view_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_lgamma_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log1p_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log_softmax_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_logsumexp_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_max_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_median_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_min_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_mul_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_permute_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_different_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_error_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_same_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_different_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_error_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_same_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_different_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_error_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_same_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_reshape_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend0_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend1_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend2_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_select_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sigmoid_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_slice_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_stack_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sub_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_threshold_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_trace_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_unrelated_output_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_unrelated_output_multiple_grad_cuda, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_where_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_grad_and_value_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_grad_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jacfwd_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jacrev_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jvp_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_vjp_cuda, test/functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_vmap_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_first_cuda, 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test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_different_batched_call_True_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_different_batched_call_True_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_different_batched_call_True_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_False_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_False_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_False_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_True_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_True_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_error_batched_call_True_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_False_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_False_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_False_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_True_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_True_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_multinomial_use_generator_True_randomness_same_batched_call_True_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_last_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_last_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_last_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_none_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_none_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_none_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_none_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_none_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_none_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_first_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_first_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_first_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_last_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_last_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_last_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_none_batched_other_first_cuda, 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test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_none_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_none_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_error_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_error_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_error_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_same_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_same_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_False_randomness_same_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_different_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_different_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_different_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_different_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_different_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_different_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_first_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_last_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_none_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_different_use_generator_False_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_different_use_generator_True_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_error_use_generator_False_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_error_use_generator_True_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_same_use_generator_False_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_same_use_generator_True_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_unsupported_random_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_0_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_1_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_2_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_0_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_1_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_2_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_0_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_1_cuda, test/functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_2_cuda, test/functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test__is_all_true_cuda, test/functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test__is_any_true_cuda, test/functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_check_tensor_cuda, test/functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_cat_batching_rule_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_nt_and_batched_dense_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_nt_and_unbatched_dense_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_unary_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_with_nt_and_batched_dense_with_nonzero_bdim_raises_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_multilevel_vmap_raises_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_acts_as_dense_in_vmap_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_with_nonzero_in_dim_raises_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_with_nonzero_out_dim_raises_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_shape_call_cuda, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_vmap_fallback_check_ok 2025-09-07T07:13:02.7760790Z 2025-09-07T07:13:02.7760915Z functorch/test_vmap.py::TestVmapAPI::test_accepts_nested_inputs PASSED [0.0039s] [ 0%] 2025-09-07T07:13:02.7766972Z functorch/test_vmap.py::TestVmapAPI::test_backward_unsupported_interaction SKIPPED [0.0006s] (error: element 0 of tensors does not require grad and does not have a grad_fn) [ 0%] 2025-09-07T07:13:02.7767383Z functorch/test_vmap.py::TestVmapAPI::test_batch_rule_does_not_need_to_handle_no_batched_input PASSED [0.0009s] [ 0%] 2025-09-07T07:13:02.7767726Z functorch/test_vmap.py::TestVmapAPI::test_batched_gradient_basic PASSED [0.0010s] [ 0%] 2025-09-07T07:13:02.7767957Z functorch/test_vmap.py::TestVmapAPI::test_checkpoint PASSED [0.0580s] [ 0%] 2025-09-07T07:13:02.7768181Z functorch/test_vmap.py::TestVmapAPI::test_constant_function PASSED [0.0012s] [ 0%] 2025-09-07T07:13:02.7768404Z functorch/test_vmap.py::TestVmapAPI::test_data_attribute PASSED [0.0010s] [ 0%] 2025-09-07T07:13:02.7768646Z functorch/test_vmap.py::TestVmapAPI::test_data_dependent_control_flow_throws PASSED [0.0007s] [ 0%] 2025-09-07T07:13:02.7768922Z functorch/test_vmap.py::TestVmapAPI::test_decomposition_under_python_dispatcher PASSED [0.0011s] [ 0%] 2025-09-07T07:13:02.7769202Z functorch/test_vmap.py::TestVmapAPI::test_different_map_dim_size_raises PASSED [0.0006s] [ 0%] 2025-09-07T07:13:02.7769465Z functorch/test_vmap.py::TestVmapAPI::test_fallback_does_not_warn_by_default PASSED [0.0008s] [ 0%] 2025-09-07T07:13:02.7769717Z functorch/test_vmap.py::TestVmapAPI::test_fallback_masked_fill SKIPPED [0.0001s] [ 0%] 2025-09-07T07:13:02.7769994Z functorch/test_vmap.py::TestVmapAPI::test_fallback_multiple_returns PASSED [0.0149s] [ 0%] 2025-09-07T07:13:02.7770252Z functorch/test_vmap.py::TestVmapAPI::test_fallback_warning PASSED [0.1812s] [ 0%] 2025-09-07T07:13:02.7770501Z functorch/test_vmap.py::TestVmapAPI::test_fallback_warns_when_warnings_are_enabled XFAIL [0.0008s] [ 0%] 2025-09-07T07:13:02.7770774Z functorch/test_vmap.py::TestVmapAPI::test_fallback_with_undefined_grad PASSED [0.0004s] [ 0%] 2025-09-07T07:13:02.7771015Z functorch/test_vmap.py::TestVmapAPI::test_fallback_zero_dim PASSED [0.0008s] [ 0%] 2025-09-07T07:13:02.7771247Z functorch/test_vmap.py::TestVmapAPI::test_func_with_no_inputs PASSED [0.0005s] [ 0%] 2025-09-07T07:13:02.7771479Z functorch/test_vmap.py::TestVmapAPI::test_func_with_no_tensors PASSED [0.0004s] [ 0%] 2025-09-07T07:13:02.7771724Z functorch/test_vmap.py::TestVmapAPI::test_functools_partial PASSED [0.0008s] [ 0%] 2025-09-07T07:13:02.7771970Z functorch/test_vmap.py::TestVmapAPI::test_grad_unsupported_interaction XFAIL [0.0007s] [ 0%] 2025-09-07T07:13:02.7772223Z functorch/test_vmap.py::TestVmapAPI::test_in_dim_not_in_tensor_err_msg PASSED [0.0007s] [ 1%] 2025-09-07T07:13:02.7772472Z functorch/test_vmap.py::TestVmapAPI::test_in_dims_wrong_type_err_msg PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7772733Z functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_different_levels XFAIL [0.0010s] [ 1%] 2025-09-07T07:13:02.7773004Z functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_same_levels PASSED [0.1639s] [ 1%] 2025-09-07T07:13:02.7773257Z functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_unary PASSED [0.1544s] [ 1%] 2025-09-07T07:13:02.7773519Z functorch/test_vmap.py::TestVmapAPI::test_integer_in_dim_but_not_tensor_input_err_msg PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7773770Z functorch/test_vmap.py::TestVmapAPI::test_item_throws PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7773988Z functorch/test_vmap.py::TestVmapAPI::test_multiple_inputs PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7774210Z functorch/test_vmap.py::TestVmapAPI::test_multiple_out_dims PASSED [0.0008s] [ 1%] 2025-09-07T07:13:02.7774493Z functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7774719Z functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs2 PASSED [0.0005s] [ 1%] 2025-09-07T07:13:02.7774952Z functorch/test_vmap.py::TestVmapAPI::test_nested_negative_in_dims PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7775197Z functorch/test_vmap.py::TestVmapAPI::test_nested_non_default_in_dims PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7775426Z functorch/test_vmap.py::TestVmapAPI::test_nested_out_dims PASSED [0.0010s] [ 1%] 2025-09-07T07:13:02.7775653Z functorch/test_vmap.py::TestVmapAPI::test_nested_with_diag_embed PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7775894Z functorch/test_vmap.py::TestVmapAPI::test_nested_with_different_map_dim PASSED [0.0008s] [ 1%] 2025-09-07T07:13:02.7776138Z functorch/test_vmap.py::TestVmapAPI::test_nested_with_same_map_dim PASSED [0.0007s] [ 1%] 2025-09-07T07:13:02.7776382Z functorch/test_vmap.py::TestVmapAPI::test_nn_module PASSED [0.0009s] [ 1%] 2025-09-07T07:13:02.7776607Z functorch/test_vmap.py::TestVmapAPI::test_non_default_in_dims_out_dims PASSED [0.0010s] [ 1%] 2025-09-07T07:13:02.7776844Z functorch/test_vmap.py::TestVmapAPI::test_non_tensor_output_raises PASSED [0.0006s] [ 1%] 2025-09-07T07:13:02.7777070Z functorch/test_vmap.py::TestVmapAPI::test_non_zero_in_dims PASSED [0.0007s] [ 1%] 2025-09-07T07:13:02.7777283Z functorch/test_vmap.py::TestVmapAPI::test_none_in_dims PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7777495Z functorch/test_vmap.py::TestVmapAPI::test_nonzero_out_dims PASSED [0.0015s] [ 2%] 2025-09-07T07:13:02.7777718Z functorch/test_vmap.py::TestVmapAPI::test_noop_in_inner_vmap PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7777957Z functorch/test_vmap.py::TestVmapAPI::test_not_enough_in_dims_err_msg PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7778228Z functorch/test_vmap.py::TestVmapAPI::test_out_dim_out_of_bounds_err_msg PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7778494Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_and_num_outputs_mismatch_err_msg PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7778767Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_edge_case PASSED [0.0005s] [ 2%] 2025-09-07T07:13:02.7779027Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_must_be_int_or_collection_of_int_err_msg PASSED [0.0005s] [ 2%] 2025-09-07T07:13:02.7779282Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_none PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7779500Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_none_tuple PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7779726Z functorch/test_vmap.py::TestVmapAPI::test_out_dims_normal_tensor PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7779972Z functorch/test_vmap.py::TestVmapAPI::test_pytree_odict_returns PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7780194Z functorch/test_vmap.py::TestVmapAPI::test_pytree_returns PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7780436Z functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_nested PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7780697Z functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_simple PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7780943Z functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_outdims PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7781169Z functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_into PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7781388Z functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_outof PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7781626Z functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_no_vmapped_inputs PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7781884Z functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_pytree_input_output PASSED [0.0007s] [ 2%] 2025-09-07T07:13:02.7782143Z functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_unexpanded_outputs PASSED [0.0006s] [ 2%] 2025-09-07T07:13:02.7782376Z functorch/test_vmap.py::TestVmapAPI::test_single_input PASSED [0.0005s] [ 2%] 2025-09-07T07:13:02.7782593Z functorch/test_vmap.py::TestVmapAPI::test_unsupported_op_err_msg PASSED [0.0007s] [ 3%] 2025-09-07T07:13:02.7782881Z functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cpu SKIPPED [0.0001s] (Somehow, vmap and autocast do not work on CPU) [ 3%] 2025-09-07T07:13:02.7783162Z functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cuda PASSED [1.4339s] [ 3%] 2025-09-07T07:13:02.7783380Z functorch/test_vmap.py::TestVmapOperators::test_T_numpy PASSED [0.0025s] [ 3%] 2025-09-07T07:13:02.7783609Z functorch/test_vmap.py::TestVmapOperators::test_adaptive_avg_pool2d PASSED [0.0223s] [ 3%] 2025-09-07T07:13:02.7783843Z functorch/test_vmap.py::TestVmapOperators::test_argmax_dim PASSED [0.0021s] [ 3%] 2025-09-07T07:13:02.7784069Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add PASSED [0.0046s] [ 3%] 2025-09-07T07:13:02.7784311Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add_dunder PASSED [0.0044s] [ 3%] 2025-09-07T07:13:02.7784613Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div PASSED [0.0045s] [ 3%] 2025-09-07T07:13:02.7784855Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_div_dunder PASSED [0.0046s] [ 3%] 2025-09-07T07:13:02.7785094Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul PASSED [0.0044s] [ 3%] 2025-09-07T07:13:02.7785331Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul_dunder PASSED [0.0044s] [ 3%] 2025-09-07T07:13:02.7785569Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow PASSED [0.0046s] [ 3%] 2025-09-07T07:13:02.7785810Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow_dunder PASSED [0.0046s] [ 3%] 2025-09-07T07:13:02.7786049Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub PASSED [0.0043s] [ 3%] 2025-09-07T07:13:02.7786288Z functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub_dunder PASSED [0.0044s] [ 3%] 2025-09-07T07:13:02.7786543Z functorch/test_vmap.py::TestVmapOperators::test_as_strided PASSED [0.0077s] [ 3%] 2025-09-07T07:13:02.7786757Z functorch/test_vmap.py::TestVmapOperators::test_bmm PASSED [0.0024s] [ 3%] 2025-09-07T07:13:02.7786986Z functorch/test_vmap.py::TestVmapOperators::test_cat PASSED [0.0022s] [ 3%] 2025-09-07T07:13:02.7787193Z functorch/test_vmap.py::TestVmapOperators::test_chunk PASSED [0.0374s] [ 3%] 2025-09-07T07:13:02.7787448Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_error PASSED [0.0012s] [ 3%] 2025-09-07T07:13:02.7787752Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_same PASSED [0.0012s] [ 4%] 2025-09-07T07:13:02.7788106Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7788430Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_same PASSED [0.0016s] [ 4%] 2025-09-07T07:13:02.7797800Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7798165Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_same PASSED [0.0012s] [ 4%] 2025-09-07T07:13:02.7798486Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_error PASSED [0.0012s] [ 4%] 2025-09-07T07:13:02.7798796Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_same PASSED [0.0012s] [ 4%] 2025-09-07T07:13:02.7799105Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7799414Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_same PASSED [0.0013s] [ 4%] 2025-09-07T07:13:02.7799727Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7800039Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_same PASSED [0.0013s] [ 4%] 2025-09-07T07:13:02.7800348Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7800657Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_same PASSED [0.0013s] [ 4%] 2025-09-07T07:13:02.7803016Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7803343Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_1_randomness_same PASSED [0.0014s] [ 4%] 2025-09-07T07:13:02.7803652Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_error PASSED [0.0011s] [ 4%] 2025-09-07T07:13:02.7803965Z functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_same PASSED [0.0013s] [ 4%] 2025-09-07T07:13:02.7804307Z functorch/test_vmap.py::TestVmapOperators::test_clamp PASSED [0.0053s] [ 4%] 2025-09-07T07:13:02.7804629Z functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_max_ PASSED [0.0021s] [ 4%] 2025-09-07T07:13:02.7804921Z functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_min_ PASSED [0.0020s] [ 4%] 2025-09-07T07:13:02.7805199Z functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_max PASSED [0.0028s] [ 4%] 2025-09-07T07:13:02.7805469Z functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_min PASSED [0.0027s] [ 5%] 2025-09-07T07:13:02.7805717Z functorch/test_vmap.py::TestVmapOperators::test_clone PASSED [0.0036s] [ 5%] 2025-09-07T07:13:02.7805952Z functorch/test_vmap.py::TestVmapOperators::test_comparison_ops PASSED [0.0175s] [ 5%] 2025-09-07T07:13:02.7807852Z functorch/test_vmap.py::TestVmapOperators::test_conj PASSED [0.0027s] [ 5%] 2025-09-07T07:13:02.7808157Z functorch/test_vmap.py::TestVmapOperators::test_conj_bit PASSED [0.0006s] [ 5%] 2025-09-07T07:13:02.7808447Z functorch/test_vmap.py::TestVmapOperators::test_contiguous PASSED [0.0016s] [ 5%] 2025-09-07T07:13:02.7808699Z functorch/test_vmap.py::TestVmapOperators::test_conv2d PASSED [4.8184s] [ 5%] 2025-09-07T07:13:02.7808921Z functorch/test_vmap.py::TestVmapOperators::test_copy_ PASSED [0.0016s] [ 5%] 2025-09-07T07:13:02.7809164Z functorch/test_vmap.py::TestVmapOperators::test_cross_batch_size_three PASSED [0.0023s] [ 5%] 2025-09-07T07:13:02.7809410Z functorch/test_vmap.py::TestVmapOperators::test_diagonal PASSED [0.0020s] [ 5%] 2025-09-07T07:13:02.7809632Z functorch/test_vmap.py::TestVmapOperators::test_dot PASSED [0.0026s] [ 5%] 2025-09-07T07:13:02.7809859Z functorch/test_vmap.py::TestVmapOperators::test_expand_as PASSED [0.0025s] [ 5%] 2025-09-07T07:13:02.7810127Z functorch/test_vmap.py::TestVmapOperators::test_fill_and_zero_inplace PASSED [0.0032s] [ 5%] 2025-09-07T07:13:02.7810369Z functorch/test_vmap.py::TestVmapOperators::test_imag PASSED [0.0033s] [ 5%] 2025-09-07T07:13:02.7812057Z functorch/test_vmap.py::TestVmapOperators::test_is_complex PASSED [0.0006s] [ 5%] 2025-09-07T07:13:02.7812303Z functorch/test_vmap.py::TestVmapOperators::test_is_contiguous PASSED [0.1212s] [ 5%] 2025-09-07T07:13:02.7812549Z functorch/test_vmap.py::TestVmapOperators::test_is_floating_point PASSED [0.0009s] [ 5%] 2025-09-07T07:13:02.7812782Z functorch/test_vmap.py::TestVmapOperators::test_mean PASSED [0.0020s] [ 5%] 2025-09-07T07:13:02.7813004Z functorch/test_vmap.py::TestVmapOperators::test_mean_dim PASSED [0.0022s] [ 5%] 2025-09-07T07:13:02.7813224Z functorch/test_vmap.py::TestVmapOperators::test_mm PASSED [0.1419s] [ 5%] 2025-09-07T07:13:02.7813443Z functorch/test_vmap.py::TestVmapOperators::test_mode_key PASSED [0.0019s] [ 5%] 2025-09-07T07:13:02.7813664Z functorch/test_vmap.py::TestVmapOperators::test_movedim PASSED [0.0033s] [ 5%] 2025-09-07T07:13:02.7813882Z functorch/test_vmap.py::TestVmapOperators::test_mv PASSED [0.0461s] [ 6%] 2025-09-07T07:13:02.7814103Z functorch/test_vmap.py::TestVmapOperators::test_narrow PASSED [0.0027s] [ 6%] 2025-09-07T07:13:02.7814327Z functorch/test_vmap.py::TestVmapOperators::test_new_empty PASSED [0.0017s] [ 6%] 2025-09-07T07:13:02.7816079Z functorch/test_vmap.py::TestVmapOperators::test_new_empty_strided PASSED [0.0034s] [ 6%] 2025-09-07T07:13:02.7816319Z functorch/test_vmap.py::TestVmapOperators::test_new_zeros PASSED [0.0012s] [ 6%] 2025-09-07T07:13:02.7837167Z functorch/test_vmap.py::TestVmapOperators::test_nll_loss PASSED [0.0450s] [ 6%] 2025-09-07T07:13:02.7837475Z functorch/test_vmap.py::TestVmapOperators::test_one_hot PASSED [0.0014s] [ 6%] 2025-09-07T07:13:02.7837701Z functorch/test_vmap.py::TestVmapOperators::test_real PASSED [0.0081s] [ 6%] 2025-09-07T07:13:02.7837912Z functorch/test_vmap.py::TestVmapOperators::test_repeat PASSED [0.0010s] [ 6%] 2025-09-07T07:13:02.7838125Z functorch/test_vmap.py::TestVmapOperators::test_reshape PASSED [0.0019s] [ 6%] 2025-09-07T07:13:02.7838388Z functorch/test_vmap.py::TestVmapOperators::test_reshape_as PASSED [0.0021s] [ 6%] 2025-09-07T07:13:02.7838613Z functorch/test_vmap.py::TestVmapOperators::test_result_type PASSED [0.0017s] [ 6%] 2025-09-07T07:13:02.7838847Z functorch/test_vmap.py::TestVmapOperators::test_roll_no_dims PASSED [0.0018s] [ 6%] 2025-09-07T07:13:02.7841050Z functorch/test_vmap.py::TestVmapOperators::test_select PASSED [0.0014s] [ 6%] 2025-09-07T07:13:02.7841281Z functorch/test_vmap.py::TestVmapOperators::test_silu_backward PASSED [0.0011s] [ 6%] 2025-09-07T07:13:02.7841501Z functorch/test_vmap.py::TestVmapOperators::test_slice PASSED [0.0015s] [ 6%] 2025-09-07T07:13:02.7841709Z functorch/test_vmap.py::TestVmapOperators::test_slogdet PASSED [0.0016s] [ 6%] 2025-09-07T07:13:02.7841917Z functorch/test_vmap.py::TestVmapOperators::test_split PASSED [1.1267s] [ 6%] 2025-09-07T07:13:02.7842126Z functorch/test_vmap.py::TestVmapOperators::test_squeeze PASSED [0.0061s] [ 6%] 2025-09-07T07:13:02.7842364Z functorch/test_vmap.py::TestVmapOperators::test_stack PASSED [0.0017s] [ 6%] 2025-09-07T07:13:02.7842572Z functorch/test_vmap.py::TestVmapOperators::test_stride PASSED [0.0005s] [ 6%] 2025-09-07T07:13:02.7842801Z functorch/test_vmap.py::TestVmapOperators::test_sum PASSED [0.0017s] [ 7%] 2025-09-07T07:13:02.7843007Z functorch/test_vmap.py::TestVmapOperators::test_sum_dim PASSED [0.0019s] [ 7%] 2025-09-07T07:13:02.7843212Z functorch/test_vmap.py::TestVmapOperators::test_t PASSED [0.0015s] [ 7%] 2025-09-07T07:13:02.7844894Z functorch/test_vmap.py::TestVmapOperators::test_tensor_split SKIPPED [0.0002s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 7%] 2025-09-07T07:13:02.7845196Z functorch/test_vmap.py::TestVmapOperators::test_to PASSED [0.0016s] [ 7%] 2025-09-07T07:13:02.7845404Z functorch/test_vmap.py::TestVmapOperators::test_trace PASSED [0.0015s] [ 7%] 2025-09-07T07:13:02.7845646Z functorch/test_vmap.py::TestVmapOperators::test_transpose PASSED [0.0017s] [ 7%] 2025-09-07T07:13:02.7845882Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_abs PASSED [0.0025s] [ 7%] 2025-09-07T07:13:02.7846130Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_acos PASSED [0.0413s] [ 7%] 2025-09-07T07:13:02.7846379Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_asin PASSED [0.0464s] [ 7%] 2025-09-07T07:13:02.7846622Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_atan PASSED [0.0504s] [ 7%] 2025-09-07T07:13:02.7846865Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_ceil PASSED [0.0515s] [ 7%] 2025-09-07T07:13:02.7847108Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cos PASSED [0.0444s] [ 7%] 2025-09-07T07:13:02.7847350Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cosh PASSED [0.0025s] [ 7%] 2025-09-07T07:13:02.7848841Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_digamma PASSED [0.0028s] [ 7%] 2025-09-07T07:13:02.7849099Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_exp PASSED [0.0408s] [ 7%] 2025-09-07T07:13:02.7849347Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_expm1 PASSED [0.0454s] [ 7%] 2025-09-07T07:13:02.7849595Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_floor PASSED [0.0474s] [ 7%] 2025-09-07T07:13:02.7849837Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_frac PASSED [0.0024s] [ 7%] 2025-09-07T07:13:02.7850083Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_lgamma PASSED [0.0424s] [ 7%] 2025-09-07T07:13:02.7850328Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log PASSED [0.0485s] [ 7%] 2025-09-07T07:13:02.7850573Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log10 PASSED [0.0455s] [ 8%] 2025-09-07T07:13:02.7850817Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log1p PASSED [0.0443s] [ 8%] 2025-09-07T07:13:02.7851059Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log2 PASSED [0.0275s] [ 8%] 2025-09-07T07:13:02.7851328Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_neg PASSED [0.0024s] [ 8%] 2025-09-07T07:13:02.7852819Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_reciprocal PASSED [0.0025s] [ 8%] 2025-09-07T07:13:02.7853072Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_relu PASSED [0.0026s] [ 8%] 2025-09-07T07:13:02.7853315Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_round PASSED [0.0429s] [ 8%] 2025-09-07T07:13:02.7853560Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_rsqrt PASSED [0.0025s] [ 8%] 2025-09-07T07:13:02.7853811Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sigmoid PASSED [0.0025s] [ 8%] 2025-09-07T07:13:02.7854058Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sign PASSED [0.0027s] [ 8%] 2025-09-07T07:13:02.7854301Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sin PASSED [0.0431s] [ 8%] 2025-09-07T07:13:02.7854660Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sinh PASSED [0.0047s] [ 8%] 2025-09-07T07:13:02.7854901Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sqrt PASSED [0.0446s] [ 8%] 2025-09-07T07:13:02.7855167Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tan PASSED [0.0493s] [ 8%] 2025-09-07T07:13:02.7855408Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tanh PASSED [0.0474s] [ 8%] 2025-09-07T07:13:02.7856899Z functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_trunc PASSED [0.0474s] [ 8%] 2025-09-07T07:13:02.7857126Z functorch/test_vmap.py::TestVmapOperators::test_unbind PASSED [0.7384s] [ 8%] 2025-09-07T07:13:02.7857336Z functorch/test_vmap.py::TestVmapOperators::test_unfold PASSED [0.0014s] [ 8%] 2025-09-07T07:13:02.7857577Z functorch/test_vmap.py::TestVmapOperators::test_unsafe_view PASSED [0.0013s] [ 8%] 2025-09-07T07:13:02.7857798Z functorch/test_vmap.py::TestVmapOperators::test_unsqueeze PASSED [0.0025s] [ 8%] 2025-09-07T07:13:02.7858012Z functorch/test_vmap.py::TestVmapOperators::test_view PASSED [0.0020s] [ 8%] 2025-09-07T07:13:02.7858219Z functorch/test_vmap.py::TestVmapOperators::test_view_as PASSED [0.0019s] [ 8%] 2025-09-07T07:13:02.7858441Z functorch/test_vmap.py::TestVmapOperators::test_view_as_complex PASSED [0.0057s] [ 9%] 2025-09-07T07:13:02.7858672Z functorch/test_vmap.py::TestVmapOperators::test_view_as_real PASSED [0.0032s] [ 9%] 2025-09-07T07:13:02.7858961Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_error PASSED [0.0079s] [ 9%] 2025-09-07T07:13:02.7859307Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_same PASSED [0.0089s] [ 9%] 2025-09-07T07:13:02.7860887Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_error PASSED [0.0086s] [ 9%] 2025-09-07T07:13:02.7861229Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_same PASSED [0.0099s] [ 9%] 2025-09-07T07:13:02.7861571Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_error PASSED [0.1891s] [ 9%] 2025-09-07T07:13:02.7861912Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_same PASSED [0.0123s] [ 9%] 2025-09-07T07:13:02.7862251Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_error PASSED [0.0107s] [ 9%] 2025-09-07T07:13:02.7862589Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_same PASSED [0.0127s] [ 9%] 2025-09-07T07:13:02.7862923Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_error PASSED [0.0007s] [ 9%] 2025-09-07T07:13:02.7863248Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_same PASSED [0.0006s] [ 9%] 2025-09-07T07:13:02.7863604Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_error PASSED [0.0005s] [ 9%] 2025-09-07T07:13:02.7863930Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_same PASSED [0.0006s] [ 9%] 2025-09-07T07:13:02.7864255Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_error PASSED [0.0005s] [ 9%] 2025-09-07T07:13:02.7866051Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_same PASSED [0.0007s] [ 9%] 2025-09-07T07:13:02.7866378Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_error PASSED [0.0005s] [ 9%] 2025-09-07T07:13:02.7866707Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_same PASSED [0.0006s] [ 9%] 2025-09-07T07:13:02.7867057Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_error PASSED [0.0055s] [ 9%] 2025-09-07T07:13:02.7867367Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_same PASSED [0.0060s] [ 9%] 2025-09-07T07:13:02.7867703Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_error PASSED [0.0059s] [ 9%] 2025-09-07T07:13:02.7868011Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_same PASSED [0.0064s] [ 10%] 2025-09-07T07:13:02.7868408Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_error PASSED [0.0060s] [ 10%] 2025-09-07T07:13:02.7868715Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_same PASSED [0.0065s] [ 10%] 2025-09-07T07:13:02.7869041Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_error PASSED [0.0063s] [ 10%] 2025-09-07T07:13:02.7869352Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_same PASSED [0.0066s] [ 10%] 2025-09-07T07:13:02.7870939Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_error PASSED [0.0061s] [ 10%] 2025-09-07T07:13:02.7871246Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_same PASSED [0.0067s] [ 10%] 2025-09-07T07:13:02.7871555Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_error PASSED [0.0064s] [ 10%] 2025-09-07T07:13:02.7871862Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_same PASSED [0.0070s] [ 10%] 2025-09-07T07:13:02.7872168Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_0_randomness_error PASSED [0.0063s] [ 10%] 2025-09-07T07:13:02.7872475Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_0_randomness_same PASSED [0.0069s] [ 10%] 2025-09-07T07:13:02.7872782Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_1_randomness_error PASSED [0.0075s] [ 10%] 2025-09-07T07:13:02.7873090Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_1_randomness_same PASSED [0.0071s] [ 10%] 2025-09-07T07:13:02.7873396Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_2_randomness_error PASSED [0.0063s] [ 10%] 2025-09-07T07:13:02.7873701Z functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_2_randomness_same PASSED [0.0068s] [ 10%] 2025-09-07T07:13:02.7875234Z functorch/test_vmap.py::TestVmapOperators::test_vmap_fallback_check XFAIL [0.0006s] [ 10%] 2025-09-07T07:13:02.7875489Z functorch/test_vmap.py::TestVmapOperators::test_vmap_fallback_check_ok PASSED [0.0005s] [ 10%] 2025-09-07T07:13:02.7875735Z functorch/test_vmap.py::TestVmapOperators::test_weird_matmul_case PASSED [0.0006s] [ 10%] 2025-09-07T07:13:02.7876046Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_0d_tensor_index_put_inplace_False_cuda PASSED [0.0266s] [ 10%] 2025-09-07T07:13:02.7876368Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_0d_tensor_index_put_inplace_True_cuda PASSED [0.0069s] [ 10%] 2025-09-07T07:13:02.7876673Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_advanced_indexing_cuda PASSED [0.0692s] [ 10%] 2025-09-07T07:13:02.7877021Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_False_track_running_stats_False_affine_False_cuda PASSED [0.0006s] [ 11%] 2025-09-07T07:13:02.7877426Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_False_track_running_stats_False_affine_True_cuda PASSED [0.0004s] [ 11%] 2025-09-07T07:13:02.7877822Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_False_track_running_stats_True_affine_False_cuda PASSED [0.0269s] [ 11%] 2025-09-07T07:13:02.7878216Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_False_track_running_stats_True_affine_True_cuda PASSED [0.0808s] [ 11%] 2025-09-07T07:13:02.7878630Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_True_track_running_stats_False_affine_False_cuda PASSED [0.0028s] [ 11%] 2025-09-07T07:13:02.7880268Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_True_track_running_stats_False_affine_True_cuda PASSED [0.1619s] [ 11%] 2025-09-07T07:13:02.7880668Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_True_track_running_stats_True_affine_False_cuda PASSED [0.0035s] [ 11%] 2025-09-07T07:13:02.7881059Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_batch_norm_training_True_track_running_stats_True_affine_True_cuda PASSED [0.1463s] [ 11%] 2025-09-07T07:13:02.7881427Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_conv_double_backward_cuda PASSED [1.0560s] [ 11%] 2025-09-07T07:13:02.7881713Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_fill__Tensor_cuda PASSED [0.0034s] [ 11%] 2025-09-07T07:13:02.7881986Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_flatten_cuda PASSED [0.0008s] [ 11%] 2025-09-07T07:13:02.7882250Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_foo_like_cuda PASSED [0.0006s] [ 11%] 2025-09-07T07:13:02.7882518Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_group_norm_cuda PASSED [0.0441s] [ 11%] 2025-09-07T07:13:02.7882788Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_index_fill_cuda PASSED [0.0116s] [ 11%] 2025-09-07T07:13:02.7883052Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_index_put_cuda PASSED [0.0035s] [ 11%] 2025-09-07T07:13:02.7883324Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_inplace_on_view_cuda PASSED [0.0328s] [ 11%] 2025-09-07T07:13:02.7884866Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_isinf_cuda PASSED [0.0008s] [ 11%] 2025-09-07T07:13:02.7885118Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_isnan_cuda PASSED [0.0006s] [ 11%] 2025-09-07T07:13:02.7885383Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_linalg_eigh_cuda PASSED [3.1558s] [ 11%] 2025-09-07T07:13:02.7885653Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_linalg_svd_cuda PASSED [0.4353s] [ 11%] 2025-09-07T07:13:02.7885928Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_namedtuple_returns_cuda PASSED [0.0013s] [ 11%] 2025-09-07T07:13:02.7886223Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_nested_advanced_indexing_cuda PASSED [0.0015s] [ 11%] 2025-09-07T07:13:02.7886563Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_CubeGenVmapAutogradFunction_cuda_float32 PASSED [0.0328s] [ 12%] 2025-09-07T07:13:02.7886962Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ForwardHasDefaultArgsAutogradFunction_cuda_float32 PASSED [0.0055s] [ 12%] 2025-09-07T07:13:02.7887321Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_H_cuda_float32 PASSED [0.0056s] [ 12%] 2025-09-07T07:13:02.7887686Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_MulGenVmapAutogradFunction_cuda_float32 PASSED [0.0077s] [ 12%] 2025-09-07T07:13:02.7888049Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyCatCustomOp_cuda_float32 PASSED [0.0032s] [ 12%] 2025-09-07T07:13:02.7889610Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyCubeAutogradFunction_cuda_float32 PASSED [0.0064s] [ 12%] 2025-09-07T07:13:02.7889970Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyCubeCustomOp_cuda_float32 PASSED [0.0062s] [ 12%] 2025-09-07T07:13:02.7890352Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyCubeNotComposableAutogradFunction_cuda_float32 XFAIL [0.0035s] [ 12%] 2025-09-07T07:13:02.7890764Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyExpMarkDirtyAutogradFunction_cuda_float32 PASSED [0.0069s] [ 12%] 2025-09-07T07:13:02.7891178Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyMulAutogradFunction_cuda_float32 PASSED [0.0079s] [ 12%] 2025-09-07T07:13:02.7891554Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyMulCustomOp_cuda_float32 PASSED [0.0068s] [ 12%] 2025-09-07T07:13:02.7891907Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyMulScalarCustomOp_cuda_float32 PASSED [0.0049s] [ 12%] 2025-09-07T07:13:02.7892258Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyNMSCustomOp_cuda_float32 XFAIL [0.0052s] [ 12%] 2025-09-07T07:13:02.7892605Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyNonzeroCustomOp_cuda_float32 XFAIL [0.0110s] [ 12%] 2025-09-07T07:13:02.7892987Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpySortAutogradFunction_cuda_float32 PASSED [0.0068s] [ 12%] 2025-09-07T07:13:02.7894614Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpySortCustomOp_cuda_float32 PASSED [0.0065s] [ 12%] 2025-09-07T07:13:02.7894981Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpySplitCopyCustomOp_cuda_float32 PASSED [0.0078s] [ 12%] 2025-09-07T07:13:02.7895358Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpySplitCopyWithIntCustomOp_cuda_float32 PASSED [0.0078s] [ 12%] 2025-09-07T07:13:02.7895738Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyTakeAutogradFunction_cuda_float32 PASSED [0.0138s] [ 12%] 2025-09-07T07:13:02.7896098Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyTakeCustomOp_cuda_float32 PASSED [0.0123s] [ 12%] 2025-09-07T07:13:02.7896453Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_NumpyViewCopyCustomOp_cuda_float32 PASSED [0.0053s] [ 12%] 2025-09-07T07:13:02.7896834Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ScaleGradGenVmapAutogradFunction_cuda_float32 PASSED [0.0052s] [ 13%] 2025-09-07T07:13:02.7897214Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_SelectAutogradFunction_cuda_float32 PASSED [0.0049s] [ 13%] 2025-09-07T07:13:02.7897588Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_SelectGenVmapAutogradFunction_cuda_float32 PASSED [0.0046s] [ 13%] 2025-09-07T07:13:02.7897970Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_SortGenVmapAutogradFunction_cuda_float32 PASSED [0.0281s] [ 13%] 2025-09-07T07:13:02.7898311Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_T_cuda_float32 PASSED [0.0061s] [ 13%] 2025-09-07T07:13:02.7899866Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ZeroGradientsGenVmapAutogradFunction_cuda_float32 PASSED [0.0088s] [ 13%] 2025-09-07T07:13:02.7900265Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___getitem___cuda_float32 XFAIL [0.0309s] [ 13%] 2025-09-07T07:13:02.7900604Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___getitem___functorch_cuda_float32 PASSED [0.1177s] [ 13%] 2025-09-07T07:13:02.7900940Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___radd___cuda_float32 PASSED [0.2382s] [ 13%] 2025-09-07T07:13:02.7901258Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rand___cuda_int64 PASSED [0.1410s] [ 13%] 2025-09-07T07:13:02.7901574Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rdiv___cuda_float32 PASSED [0.3098s] [ 13%] 2025-09-07T07:13:02.7902280Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rmatmul___cuda_float32 SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157003 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.) [ 13%] 2025-09-07T07:13:02.7902994Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rmod___cuda_float32 PASSED [0.2672s] [ 13%] 2025-09-07T07:13:02.7903334Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rmul___cuda_float32 PASSED [0.2414s] [ 13%] 2025-09-07T07:13:02.7903648Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___ror___cuda_int64 PASSED [0.1417s] [ 13%] 2025-09-07T07:13:02.7903960Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rpow___cuda_float32 PASSED [0.2435s] [ 13%] 2025-09-07T07:13:02.7905571Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rsub___cuda_float32 XFAIL [0.0966s] [ 13%] 2025-09-07T07:13:02.7905915Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule___rxor___cuda_int64 PASSED [0.1410s] [ 13%] 2025-09-07T07:13:02.7906254Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__batch_norm_with_update_cuda_float32 XFAIL [0.0763s] [ 13%] 2025-09-07T07:13:02.7906598Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__chunk_cat_cuda_float32 PASSED [0.0121s] [ 13%] 2025-09-07T07:13:02.7906938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__native_batch_norm_legit_cuda_float32 XFAIL [0.0144s] [ 13%] 2025-09-07T07:13:02.7907293Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__segment_reduce_lengths_cuda_float32 XFAIL [0.0249s] [ 13%] 2025-09-07T07:13:02.7907644Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__segment_reduce_offsets_cuda_float32 XFAIL [0.0038s] [ 14%] 2025-09-07T07:13:02.7908018Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__softmax_backward_data_cuda_float32 SKIPPED [0.0002s] (Skipped!) [ 14%] 2025-09-07T07:13:02.7908445Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__unsafe_masked_index_cuda_float32 PASSED [0.1424s] [ 14%] 2025-09-07T07:13:02.7908814Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__unsafe_masked_index_put_accumulate_cuda_float32 PASSED [0.4481s] [ 14%] 2025-09-07T07:13:02.7910398Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule__upsample_bilinear2d_aa_cuda_float32 PASSED [0.0160s] [ 14%] 2025-09-07T07:13:02.7910743Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_abs_cuda_float32 PASSED [0.1325s] [ 14%] 2025-09-07T07:13:02.7911056Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_acos_cuda_float32 PASSED [0.1520s] [ 14%] 2025-09-07T07:13:02.7911367Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_acosh_cuda_float32 PASSED [0.1544s] [ 14%] 2025-09-07T07:13:02.7911678Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_add_cuda_float32 PASSED [0.3552s] [ 14%] 2025-09-07T07:13:02.7912016Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addbmm_cuda_float32 PASSED [0.0467s] [ 14%] 2025-09-07T07:13:02.7912335Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addcdiv_cuda_float32 PASSED [0.4541s] [ 14%] 2025-09-07T07:13:02.7912651Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addcmul_cuda_float32 PASSED [0.4325s] [ 14%] 2025-09-07T07:13:02.7912964Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addmm_cuda_float32 PASSED [0.0365s] [ 14%] 2025-09-07T07:13:02.7913290Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addmm_decomposed_cuda_float32 PASSED [0.0350s] [ 14%] 2025-09-07T07:13:02.7913984Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addmv_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157037 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.) [ 14%] 2025-09-07T07:13:02.7915972Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_addr_cuda_float32 PASSED [0.0474s] [ 14%] 2025-09-07T07:13:02.7916295Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_alias_copy_cuda_float32 PASSED [0.0053s] [ 14%] 2025-09-07T07:13:02.7916614Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_all_cuda_float32 PASSED [0.0278s] [ 14%] 2025-09-07T07:13:02.7916928Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_allclose_cuda_float32 XFAIL [0.0035s] [ 14%] 2025-09-07T07:13:02.7917269Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_amax_cuda_float32 PASSED [0.0348s] [ 14%] 2025-09-07T07:13:02.7917580Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_amin_cuda_float32 PASSED [0.0346s] [ 14%] 2025-09-07T07:13:02.7917895Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_aminmax_cuda_float32 PASSED [0.0266s] [ 15%] 2025-09-07T07:13:02.7918212Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_angle_cuda_float32 PASSED [0.0458s] [ 15%] 2025-09-07T07:13:02.7918522Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_any_cuda_float32 PASSED [0.0274s] [ 15%] 2025-09-07T07:13:02.7918836Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_arange_cuda_float32 PASSED [0.0108s] [ 15%] 2025-09-07T07:13:02.7919152Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_argmax_cuda_float32 PASSED [0.0234s] [ 15%] 2025-09-07T07:13:02.7920674Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_argmin_cuda_float32 PASSED [0.0229s] [ 15%] 2025-09-07T07:13:02.7920995Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_argsort_cuda_float32 PASSED [0.0804s] [ 15%] 2025-09-07T07:13:02.7921318Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_argwhere_cuda_float32 XFAIL [0.0272s] [ 15%] 2025-09-07T07:13:02.7921650Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_as_strided_copy_cuda_float32 XFAIL [0.0170s] [ 15%] 2025-09-07T07:13:02.7921982Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_as_strided_cuda_float32 XFAIL [0.0044s] [ 15%] 2025-09-07T07:13:02.7922326Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_as_strided_partial_views_cuda_float32 XFAIL [0.0041s] [ 15%] 2025-09-07T07:13:02.7922678Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_as_strided_scatter_cuda_float32 XFAIL [0.0052s] [ 15%] 2025-09-07T07:13:02.7923007Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_asin_cuda_float32 PASSED [0.1402s] [ 15%] 2025-09-07T07:13:02.7923348Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_asinh_cuda_float32 PASSED [0.1386s] [ 15%] 2025-09-07T07:13:02.7923661Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atan2_cuda_float32 PASSED [0.5867s] [ 15%] 2025-09-07T07:13:02.7925232Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atan_cuda_float32 PASSED [0.1376s] [ 15%] 2025-09-07T07:13:02.7925551Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atanh_cuda_float32 PASSED [0.1385s] [ 15%] 2025-09-07T07:13:02.7925868Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atleast_1d_cuda_float32 PASSED [0.0089s] [ 15%] 2025-09-07T07:13:02.7926193Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atleast_2d_cuda_float32 PASSED [0.0089s] [ 15%] 2025-09-07T07:13:02.7926520Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_atleast_3d_cuda_float32 PASSED [0.0088s] [ 15%] 2025-09-07T07:13:02.7926877Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_baddbmm_cuda_float32 PASSED [0.0375s] [ 15%] 2025-09-07T07:13:02.7927214Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bernoulli_cuda_float32 XFAIL [0.0130s] [ 16%] 2025-09-07T07:13:02.7927531Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bfloat16_cuda_float32 XFAIL [0.0085s] [ 16%] 2025-09-07T07:13:02.7927884Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bfloat16_functorch_no_channels_last_cuda_float32 PASSED [0.0075s] [ 16%] 2025-09-07T07:13:02.7928241Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bincount_cuda_int64 XFAIL [0.0080s] [ 16%] 2025-09-07T07:13:02.7928578Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_and_cuda_int64 PASSED [0.1982s] [ 16%] 2025-09-07T07:13:02.7930126Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_left_shift_cuda_int64 PASSED [0.2080s] [ 16%] 2025-09-07T07:13:02.7930464Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_not_cuda_int64 PASSED [0.0696s] [ 16%] 2025-09-07T07:13:02.7930787Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_or_cuda_int64 PASSED [0.1975s] [ 16%] 2025-09-07T07:13:02.7931122Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_right_shift_cuda_int64 PASSED [0.1993s] [ 16%] 2025-09-07T07:13:02.7931459Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bitwise_xor_cuda_int64 PASSED [0.1991s] [ 16%] 2025-09-07T07:13:02.7931784Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_block_diag_cuda_float32 PASSED [0.0219s] [ 16%] 2025-09-07T07:13:02.7932104Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bmm_cuda_float32 PASSED [0.0055s] [ 16%] 2025-09-07T07:13:02.7932414Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bool_cuda_float32 XFAIL [0.0085s] [ 16%] 2025-09-07T07:13:02.7932756Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bool_functorch_no_channels_last_cuda_float32 PASSED [0.0074s] [ 16%] 2025-09-07T07:13:02.7933123Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_broadcast_shapes_cuda_float32 PASSED [0.0036s] [ 16%] 2025-09-07T07:13:02.7933468Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_broadcast_tensors_cuda_float32 PASSED [0.1176s] [ 16%] 2025-09-07T07:13:02.7935132Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_broadcast_to_cuda_float32 PASSED [0.0114s] [ 16%] 2025-09-07T07:13:02.7935467Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_bucketize_cuda_float32 PASSED [0.0989s] [ 16%] 2025-09-07T07:13:02.7935784Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_byte_cuda_float32 XFAIL [0.0083s] [ 16%] 2025-09-07T07:13:02.7936156Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_byte_functorch_no_channels_last_cuda_float32 PASSED [0.0075s] [ 16%] 2025-09-07T07:13:02.7936518Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cartesian_prod_cuda_float32 PASSED [0.0115s] [ 16%] 2025-09-07T07:13:02.7936843Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cat_cuda_float32 PASSED [0.0207s] [ 16%] 2025-09-07T07:13:02.7937192Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cauchy_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 17%] 2025-09-07T07:13:02.7937547Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cdist_cuda_float32 PASSED [0.5544s] [ 17%] 2025-09-07T07:13:02.7937862Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cdouble_cuda_float32 XFAIL [0.0087s] [ 17%] 2025-09-07T07:13:02.7938197Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ceil_cuda_float32 PASSED [0.0743s] [ 17%] 2025-09-07T07:13:02.7938507Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cfloat_cuda_float32 XFAIL [0.0099s] [ 17%] 2025-09-07T07:13:02.7940073Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_chalf_cuda_float32 XFAIL [0.0033s] [ 17%] 2025-09-07T07:13:02.7940382Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_char_cuda_float32 XFAIL [0.0082s] [ 17%] 2025-09-07T07:13:02.7940724Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_char_functorch_no_channels_last_cuda_float32 PASSED [0.0075s] [ 17%] 2025-09-07T07:13:02.7941075Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cholesky_cuda_float32 PASSED [0.0494s] [ 17%] 2025-09-07T07:13:02.7941432Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cholesky_inverse_cuda_float32 PASSED [0.0282s] [ 17%] 2025-09-07T07:13:02.7941779Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cholesky_solve_cuda_float32 PASSED [0.0885s] [ 17%] 2025-09-07T07:13:02.7942106Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_chunk_cuda_float32 PASSED [0.0608s] [ 17%] 2025-09-07T07:13:02.7942417Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_clamp_cuda_float32 PASSED [0.2180s] [ 17%] 2025-09-07T07:13:02.7942738Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_clamp_max_cuda_float32 XFAIL [0.0035s] [ 17%] 2025-09-07T07:13:02.7943061Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_clamp_min_cuda_float32 XFAIL [0.0032s] [ 17%] 2025-09-07T07:13:02.7944650Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_clone_cuda_float32 XFAIL [0.0661s] [ 17%] 2025-09-07T07:13:02.7944978Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_column_stack_cuda_float32 PASSED [0.0030s] [ 17%] 2025-09-07T07:13:02.7945315Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_combinations_cuda_float32 PASSED [0.0458s] [ 17%] 2025-09-07T07:13:02.7945644Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_complex_cuda_float32 PASSED [0.2290s] [ 17%] 2025-09-07T07:13:02.7945959Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_conj_cuda_float32 PASSED [0.0405s] [ 17%] 2025-09-07T07:13:02.7946284Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_conj_physical_cuda_float32 PASSED [0.0368s] [ 17%] 2025-09-07T07:13:02.7946619Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_constant_pad_nd_cuda_float32 PASSED [0.0574s] [ 18%] 2025-09-07T07:13:02.7946951Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_contiguous_cuda_float32 XFAIL [0.0634s] [ 18%] 2025-09-07T07:13:02.7947313Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_copysign_cuda_float32 PASSED [0.3051s] [ 18%] 2025-09-07T07:13:02.7947633Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_corrcoef_cuda_float32 PASSED [0.1804s] [ 18%] 2025-09-07T07:13:02.7947951Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cos_cuda_float32 PASSED [0.0697s] [ 18%] 2025-09-07T07:13:02.7949547Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cosh_cuda_float32 PASSED [0.0786s] [ 18%] 2025-09-07T07:13:02.7949872Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_count_nonzero_cuda_float32 XFAIL [0.0034s] [ 18%] 2025-09-07T07:13:02.7950197Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cov_cuda_float32 PASSED [0.1209s] [ 18%] 2025-09-07T07:13:02.7950511Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cross_cuda_float32 PASSED [0.0116s] [ 18%] 2025-09-07T07:13:02.7950864Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cummax_cuda_float32 PASSED [0.0104s] [ 18%] 2025-09-07T07:13:02.7951179Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cummin_cuda_float32 PASSED [0.0075s] [ 18%] 2025-09-07T07:13:02.7951515Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cumprod_cuda_float32 PASSED [0.0175s] [ 18%] 2025-09-07T07:13:02.7951830Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cumsum_cuda_float32 PASSED [0.0071s] [ 18%] 2025-09-07T07:13:02.7952169Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_cumulative_trapezoid_cuda_float32 PASSED [0.0216s] [ 18%] 2025-09-07T07:13:02.7952512Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_deg2rad_cuda_float32 PASSED [0.0678s] [ 18%] 2025-09-07T07:13:02.7952855Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diag_cuda_float32 PASSED [0.0224s] [ 18%] 2025-09-07T07:13:02.7954487Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diag_embed_cuda_float32 PASSED [0.0365s] [ 18%] 2025-09-07T07:13:02.7954811Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diagflat_cuda_float32 XFAIL [0.0032s] [ 18%] 2025-09-07T07:13:02.7955137Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diagonal_copy_cuda_float32 PASSED [0.0346s] [ 18%] 2025-09-07T07:13:02.7955463Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diagonal_cuda_float32 PASSED [0.0332s] [ 18%] 2025-09-07T07:13:02.7955794Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diagonal_scatter_cuda_float32 PASSED [0.0340s] [ 18%] 2025-09-07T07:13:02.7956122Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_diff_cuda_float32 PASSED [0.1864s] [ 19%] 2025-09-07T07:13:02.7956434Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_digamma_cuda_float32 PASSED [0.5785s] [ 19%] 2025-09-07T07:13:02.7956749Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_dist_cuda_float32 PASSED [0.1214s] [ 19%] 2025-09-07T07:13:02.7957075Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_div_floor_rounding_cuda_float32 PASSED [0.5590s] [ 19%] 2025-09-07T07:13:02.7957422Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_div_no_rounding_mode_cuda_float32 PASSED [0.5365s] [ 19%] 2025-09-07T07:13:02.7957768Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_div_trunc_rounding_cuda_float32 PASSED [0.5727s] [ 19%] 2025-09-07T07:13:02.7959746Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_dot_cuda_float32 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157069 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.) [ 19%] 2025-09-07T07:13:02.7960420Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_double_cuda_float32 XFAIL [0.0084s] [ 19%] 2025-09-07T07:13:02.7960771Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_double_functorch_no_channels_last_cuda_float32 PASSED [0.0074s] [ 19%] 2025-09-07T07:13:02.7961123Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_dsplit_cuda_float32 PASSED [0.0102s] [ 19%] 2025-09-07T07:13:02.7961440Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_dstack_cuda_float32 PASSED [0.0057s] [ 19%] 2025-09-07T07:13:02.7961758Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_einsum_cuda_float32 PASSED [0.0040s] [ 19%] 2025-09-07T07:13:02.7962075Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_empty_cuda_float32 PASSED [0.0031s] [ 19%] 2025-09-07T07:13:02.7962417Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_empty_like_cuda_float32 PASSED [0.0208s] [ 19%] 2025-09-07T07:13:02.7962771Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_empty_permuted_cuda_float32 PASSED [0.0099s] [ 19%] 2025-09-07T07:13:02.7963109Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_empty_strided_cuda_float32 PASSED [0.0029s] [ 19%] 2025-09-07T07:13:02.7963428Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_eq_cuda_float32 XFAIL [0.1045s] [ 19%] 2025-09-07T07:13:02.7965055Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_equal_cuda_float32 XFAIL [0.0033s] [ 19%] 2025-09-07T07:13:02.7965368Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_erf_cuda_float32 PASSED [0.1102s] [ 19%] 2025-09-07T07:13:02.7965706Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_erfc_cuda_float32 PASSED [0.4674s] [ 19%] 2025-09-07T07:13:02.7966024Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_erfinv_cuda_float32 PASSED [0.5628s] [ 19%] 2025-09-07T07:13:02.7966341Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_exp2_cuda_float32 PASSED [0.5460s] [ 19%] 2025-09-07T07:13:02.7966655Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_exp_cuda_float32 PASSED [0.0737s] [ 20%] 2025-09-07T07:13:02.7966974Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_expand_as_cuda_float32 PASSED [0.0082s] [ 20%] 2025-09-07T07:13:02.7967302Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_expand_copy_cuda_float32 PASSED [0.0132s] [ 20%] 2025-09-07T07:13:02.7967624Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_expand_cuda_float32 PASSED [0.0128s] [ 20%] 2025-09-07T07:13:02.7967943Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_expm1_cuda_float32 PASSED [0.0913s] [ 20%] 2025-09-07T07:13:02.7968266Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_exponential_cuda_float32 XFAIL [0.0091s] [ 20%] 2025-09-07T07:13:02.7972445Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_eye_cuda_float32 PASSED [0.0160s] [ 20%] 2025-09-07T07:13:02.7972765Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_fft2_cuda_float32 PASSED [2.4688s] [ 20%] 2025-09-07T07:13:02.7973087Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_fft_cuda_float32 PASSED [1.0591s] [ 20%] 2025-09-07T07:13:02.7973403Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_fftn_cuda_float32 PASSED [2.0531s] [ 20%] 2025-09-07T07:13:02.7973730Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_fftshift_cuda_float32 PASSED [0.0129s] [ 20%] 2025-09-07T07:13:02.7974086Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_hfft2_cuda_float32 PASSED [1.4770s] [ 20%] 2025-09-07T07:13:02.7974461Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_hfft_cuda_float32 PASSED [0.5895s] [ 20%] 2025-09-07T07:13:02.7974782Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_hfftn_cuda_float32 PASSED [0.0184s] [ 20%] 2025-09-07T07:13:02.7975100Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ifft2_cuda_float32 PASSED [0.0149s] [ 20%] 2025-09-07T07:13:02.7975418Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ifft_cuda_float32 PASSED [0.0179s] [ 20%] 2025-09-07T07:13:02.7975734Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ifftn_cuda_float32 PASSED [0.0181s] [ 20%] 2025-09-07T07:13:02.7977295Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ifftshift_cuda_float32 PASSED [0.0091s] [ 20%] 2025-09-07T07:13:02.7977655Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ihfft2_cuda_float32 XFAIL [0.0079s] [ 20%] 2025-09-07T07:13:02.7977975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ihfft_cuda_float32 PASSED [0.0705s] [ 20%] 2025-09-07T07:13:02.7978319Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_ihfftn_cuda_float32 XFAIL [0.0056s] [ 20%] 2025-09-07T07:13:02.7978648Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_irfft2_cuda_float32 PASSED [0.6112s] [ 21%] 2025-09-07T07:13:02.7978969Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_irfft_cuda_float32 PASSED [0.0164s] [ 21%] 2025-09-07T07:13:02.7979290Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_irfftn_cuda_float32 PASSED [0.0175s] [ 21%] 2025-09-07T07:13:02.7979628Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_rfft2_cuda_float32 PASSED [0.7700s] [ 21%] 2025-09-07T07:13:02.7979949Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_rfft_cuda_float32 PASSED [0.0163s] [ 21%] 2025-09-07T07:13:02.7980268Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fft_rfftn_cuda_float32 PASSED [0.0228s] [ 21%] 2025-09-07T07:13:02.7981727Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fill_cuda_float32 XFAIL [0.0033s] [ 21%] 2025-09-07T07:13:02.7982045Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_flatten_cuda_float32 PASSED [0.0531s] [ 21%] 2025-09-07T07:13:02.7982361Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_flip_cuda_float32 PASSED [0.0130s] [ 21%] 2025-09-07T07:13:02.7982675Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fliplr_cuda_float32 PASSED [0.0063s] [ 21%] 2025-09-07T07:13:02.7982994Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_flipud_cuda_float32 PASSED [0.0062s] [ 21%] 2025-09-07T07:13:02.7983308Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_float_cuda_float32 XFAIL [0.0080s] [ 21%] 2025-09-07T07:13:02.7983658Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_float_functorch_no_channels_last_cuda_float32 PASSED [0.0073s] [ 21%] 2025-09-07T07:13:02.7984019Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_float_power_cuda_float32 PASSED [0.2530s] [ 21%] 2025-09-07T07:13:02.7984340Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_floor_cuda_float32 PASSED [0.0663s] [ 21%] 2025-09-07T07:13:02.7984714Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_floor_divide_cuda_float32 PASSED [0.2160s] [ 21%] 2025-09-07T07:13:02.7985033Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fmax_cuda_float32 PASSED [0.2193s] [ 21%] 2025-09-07T07:13:02.7986505Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fmin_cuda_float32 PASSED [0.1951s] [ 21%] 2025-09-07T07:13:02.7986816Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_fmod_cuda_float32 PASSED [0.2248s] [ 21%] 2025-09-07T07:13:02.7987126Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_frac_cuda_float32 PASSED [0.0745s] [ 21%] 2025-09-07T07:13:02.7987436Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_frexp_cuda_float32 PASSED [0.0686s] [ 21%] 2025-09-07T07:13:02.7987746Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_full_cuda_float32 PASSED [0.0030s] [ 22%] 2025-09-07T07:13:02.7988109Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_full_like_cuda_float32 PASSED [0.0111s] [ 22%] 2025-09-07T07:13:02.7988427Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_gather_cuda_float32 PASSED [0.0261s] [ 22%] 2025-09-07T07:13:02.7988759Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_gcd_cuda_int64 PASSED [0.7000s] [ 22%] 2025-09-07T07:13:02.7989059Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ge_cuda_float32 XFAIL [0.1757s] [ 22%] 2025-09-07T07:13:02.7989431Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_geometric_cuda_float32 SKIPPED [0.0003s] (Test expects tensor input) [ 22%] 2025-09-07T07:13:02.7989792Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_geqrf_cuda_float32 PASSED [0.0793s] [ 22%] 2025-09-07T07:13:02.7991256Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_gradient_cuda_float32 PASSED [0.0497s] [ 22%] 2025-09-07T07:13:02.7991587Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_grid_sampler_2d_cuda_float32 PASSED [0.0537s] [ 22%] 2025-09-07T07:13:02.7991964Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_grid_sampler_3d_cuda_float32 SKIPPED [0.0002s] (Skipped!) [ 22%] 2025-09-07T07:13:02.7992303Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_gt_cuda_float32 XFAIL [0.1766s] [ 22%] 2025-09-07T07:13:02.7992606Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_half_cuda_float32 XFAIL [0.0085s] [ 22%] 2025-09-07T07:13:02.7992946Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_half_functorch_no_channels_last_cuda_float32 PASSED [0.0075s] [ 22%] 2025-09-07T07:13:02.7993314Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_hash_tensor_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 22%] 2025-09-07T07:13:02.7993655Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_heaviside_cuda_float32 PASSED [0.2501s] [ 22%] 2025-09-07T07:13:02.7993971Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_histc_cuda_float32 XFAIL [0.0038s] [ 22%] 2025-09-07T07:13:02.7994285Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_hsplit_cuda_float32 PASSED [0.0112s] [ 22%] 2025-09-07T07:13:02.7995820Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_hstack_cuda_float32 PASSED [0.0059s] [ 22%] 2025-09-07T07:13:02.7996142Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_hypot_cuda_float32 PASSED [0.2280s] [ 22%] 2025-09-07T07:13:02.7996453Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_i0_cuda_float32 PASSED [0.4983s] [ 22%] 2025-09-07T07:13:02.7996764Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_igamma_cuda_float32 PASSED [0.5173s] [ 22%] 2025-09-07T07:13:02.7997081Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_igammac_cuda_float32 PASSED [0.5409s] [ 22%] 2025-09-07T07:13:02.7997398Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_imag_cuda_complex64 PASSED [0.0589s] [ 23%] 2025-09-07T07:13:02.7997742Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_add_cuda_float32 PASSED [0.0675s] [ 23%] 2025-09-07T07:13:02.7998068Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_copy_cuda_float32 PASSED [0.0142s] [ 23%] 2025-09-07T07:13:02.7998394Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_fill_cuda_float32 PASSED [0.0376s] [ 23%] 2025-09-07T07:13:02.7998714Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_put_cuda_float32 XFAIL [0.0112s] [ 23%] 2025-09-07T07:13:02.7999051Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_put_functorch_cuda_float32 PASSED [0.0744s] [ 23%] 2025-09-07T07:13:02.8000509Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_reduce_amax_cuda_float32 XFAIL [0.0034s] [ 23%] 2025-09-07T07:13:02.8000853Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_reduce_amin_cuda_float32 XFAIL [0.0034s] [ 23%] 2025-09-07T07:13:02.8001222Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_reduce_mean_cuda_float32 XFAIL [0.0034s] [ 23%] 2025-09-07T07:13:02.8001581Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_reduce_prod_cuda_float32 XFAIL [0.0033s] [ 23%] 2025-09-07T07:13:02.8001917Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_index_select_cuda_float32 PASSED [0.0106s] [ 23%] 2025-09-07T07:13:02.8002611Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_inner_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157090 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.) [ 23%] 2025-09-07T07:13:02.8003311Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_int_cuda_float32 XFAIL [0.0077s] [ 23%] 2025-09-07T07:13:02.8003659Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_int_functorch_no_channels_last_cuda_float32 PASSED [0.0071s] [ 23%] 2025-09-07T07:13:02.8004008Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isclose_cuda_float32 XFAIL [0.0087s] [ 23%] 2025-09-07T07:13:02.8004325Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isfinite_cuda_float32 PASSED [0.0456s] [ 23%] 2025-09-07T07:13:02.8004700Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isin_cuda_float32 XFAIL [0.0035s] [ 23%] 2025-09-07T07:13:02.8006148Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isinf_cuda_float32 PASSED [0.0388s] [ 23%] 2025-09-07T07:13:02.8006465Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isnan_cuda_float32 PASSED [0.0376s] [ 23%] 2025-09-07T07:13:02.8006783Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isneginf_cuda_float32 PASSED [0.0376s] [ 23%] 2025-09-07T07:13:02.8007105Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isposinf_cuda_float32 PASSED [0.0376s] [ 23%] 2025-09-07T07:13:02.8007423Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_isreal_cuda_float32 PASSED [0.0414s] [ 24%] 2025-09-07T07:13:02.8007737Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_istft_cuda_complex64 XFAIL [0.0130s] [ 24%] 2025-09-07T07:13:02.8008051Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_item_cuda_float32 XFAIL [0.0092s] [ 24%] 2025-09-07T07:13:02.8008389Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_jiterator_2inputs_2outputs_cuda_float32 XFAIL [0.2197s] [ 24%] 2025-09-07T07:13:02.8008766Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_jiterator_4inputs_with_extra_args_cuda_float32 XFAIL [0.2252s] [ 24%] 2025-09-07T07:13:02.8009154Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_jiterator_binary_cuda_float32 XFAIL [0.2259s] [ 24%] 2025-09-07T07:13:02.8009511Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_jiterator_binary_return_by_ref_cuda_float32 XFAIL [0.2225s] [ 24%] 2025-09-07T07:13:02.8011013Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_jiterator_unary_cuda_float32 XFAIL [0.1132s] [ 24%] 2025-09-07T07:13:02.8011339Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_kron_cuda_float32 PASSED [0.0063s] [ 24%] 2025-09-07T07:13:02.8011654Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_kthvalue_cuda_float32 PASSED [0.0338s] [ 24%] 2025-09-07T07:13:02.8011965Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lcm_cuda_int64 PASSED [0.7570s] [ 24%] 2025-09-07T07:13:02.8012270Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ldexp_cuda_float32 PASSED [0.2145s] [ 24%] 2025-09-07T07:13:02.8012605Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_le_cuda_float32 XFAIL [0.1483s] [ 24%] 2025-09-07T07:13:02.8012926Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lerp_cuda_float32 PASSED [0.0492s] [ 24%] 2025-09-07T07:13:02.8013237Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lgamma_cuda_float32 PASSED [0.7103s] [ 24%] 2025-09-07T07:13:02.8013562Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_cholesky_cuda_float32 PASSED [0.0376s] [ 24%] 2025-09-07T07:13:02.8013905Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_cholesky_ex_cuda_float32 PASSED [0.0395s] [ 24%] 2025-09-07T07:13:02.8015442Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_cond_cuda_float32 PASSED [0.0119s] [ 24%] 2025-09-07T07:13:02.8015780Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_cross_cuda_float32 PASSED [0.0136s] [ 24%] 2025-09-07T07:13:02.8016114Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_det_cuda_float32 PASSED [0.1836s] [ 24%] 2025-09-07T07:13:02.8016451Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_diagonal_cuda_float32 PASSED [0.0247s] [ 24%] 2025-09-07T07:13:02.8016785Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_eig_cuda_float32 PASSED [0.0203s] [ 25%] 2025-09-07T07:13:02.8017129Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_eigh_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 25%] 2025-09-07T07:13:02.8017477Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_eigvals_cuda_float32 PASSED [0.0155s] [ 25%] 2025-09-07T07:13:02.8017815Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_eigvalsh_cuda_float32 PASSED [0.0232s] [ 25%] 2025-09-07T07:13:02.8018254Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_householder_product_cuda_float32 SKIPPED [0.0005s] (skipCUDAIfRocm: test doesn't currently work on the ROCm stack) [ 25%] 2025-09-07T07:13:02.8018689Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_inv_cuda_float32 PASSED [0.0264s] [ 25%] 2025-09-07T07:13:02.8019022Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_inv_ex_cuda_float32 PASSED [0.0166s] [ 25%] 2025-09-07T07:13:02.8020485Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_ldl_factor_cuda_float32 PASSED [0.0139s] [ 25%] 2025-09-07T07:13:02.8020833Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_ldl_factor_ex_cuda_float32 PASSED [0.0099s] [ 25%] 2025-09-07T07:13:02.8021196Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_ldl_solve_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 25%] 2025-09-07T07:13:02.8021572Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lstsq_cuda_float32 PASSED [0.8773s] [ 25%] 2025-09-07T07:13:02.8021922Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lstsq_grad_oriented_cuda_float32 PASSED [3.6188s] [ 25%] 2025-09-07T07:13:02.8022270Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lu_cuda_float32 XFAIL [0.0042s] [ 25%] 2025-09-07T07:13:02.8022602Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lu_factor_cuda_float32 PASSED [0.0779s] [ 25%] 2025-09-07T07:13:02.8022949Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lu_factor_ex_cuda_float32 PASSED [0.0708s] [ 25%] 2025-09-07T07:13:02.8023293Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_lu_solve_cuda_float32 PASSED [0.3279s] [ 25%] 2025-09-07T07:13:02.8023658Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_matrix_norm_cuda_float32 PASSED [0.1102s] [ 25%] 2025-09-07T07:13:02.8024010Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_matrix_power_cuda_float32 PASSED [0.0522s] [ 25%] 2025-09-07T07:13:02.8025598Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_matrix_rank_cuda_float32 PASSED [0.1505s] [ 25%] 2025-09-07T07:13:02.8025960Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_matrix_rank_hermitian_cuda_float32 PASSED [0.0210s] [ 25%] 2025-09-07T07:13:02.8026320Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_multi_dot_cuda_float32 PASSED [0.0036s] [ 25%] 2025-09-07T07:13:02.8026683Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_norm_cuda_float32 PASSED [0.1500s] [ 25%] 2025-09-07T07:13:02.8027040Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_norm_subgradients_at_zero_cuda_float32 PASSED [0.1120s] [ 26%] 2025-09-07T07:13:02.8027403Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_pinv_cuda_float32 PASSED [0.0629s] [ 26%] 2025-09-07T07:13:02.8027749Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_pinv_hermitian_cuda_float32 PASSED [0.0273s] [ 26%] 2025-09-07T07:13:02.8028231Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_pinv_singular_cuda_float32 SKIPPED [0.0006s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 26%] 2025-09-07T07:13:02.8028647Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_qr_cuda_float32 PASSED [0.0773s] [ 26%] 2025-09-07T07:13:02.8028981Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_slogdet_cuda_float32 PASSED [0.0349s] [ 26%] 2025-09-07T07:13:02.8030440Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_cuda_float32 PASSED [0.0729s] [ 26%] 2025-09-07T07:13:02.8030781Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_ex_cuda_float32 PASSED [0.0879s] [ 26%] 2025-09-07T07:13:02.8031130Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_solve_triangular_cuda_float32 PASSED [0.3569s] [ 26%] 2025-09-07T07:13:02.8031474Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_svd_cuda_float32 PASSED [0.5287s] [ 26%] 2025-09-07T07:13:02.8031807Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_svdvals_cuda_float32 PASSED [0.0548s] [ 26%] 2025-09-07T07:13:02.8032147Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_tensorinv_cuda_float32 PASSED [0.0082s] [ 26%] 2025-09-07T07:13:02.8032492Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_tensorsolve_cuda_float32 XFAIL [0.0036s] [ 26%] 2025-09-07T07:13:02.8032860Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vander_cuda_float32 PASSED [0.0162s] [ 26%] 2025-09-07T07:13:02.8033574Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vecdot_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142924 for platform(s) linux, rocm, 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.) [ 26%] 2025-09-07T07:13:02.8034295Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linalg_vector_norm_cuda_float32 PASSED [0.2094s] [ 26%] 2025-09-07T07:13:02.8034680Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linspace_cuda_float32 PASSED [0.0133s] [ 26%] 2025-09-07T07:13:02.8036165Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_linspace_tensor_overload_cuda_float32 PASSED [0.2963s] [ 26%] 2025-09-07T07:13:02.8036537Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log10_cuda_float32 PASSED [0.0801s] [ 26%] 2025-09-07T07:13:02.8036870Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log1p_cuda_float32 PASSED [0.0995s] [ 26%] 2025-09-07T07:13:02.8037183Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log2_cuda_float32 PASSED [0.0795s] [ 26%] 2025-09-07T07:13:02.8037492Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_cuda_float32 PASSED [0.0798s] [ 27%] 2025-09-07T07:13:02.8037847Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_normal_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 27%] 2025-09-07T07:13:02.8038233Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_softmax_cuda_float32 PASSED [0.0427s] [ 27%] 2025-09-07T07:13:02.8038579Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_log_softmax_with_dtype_cuda_float32 PASSED [0.0234s] [ 27%] 2025-09-07T07:13:02.8038922Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logaddexp2_cuda_float32 PASSED [0.0164s] [ 27%] 2025-09-07T07:13:02.8039251Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logaddexp_cuda_float32 PASSED [0.2203s] [ 27%] 2025-09-07T07:13:02.8039581Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logcumsumexp_cuda_float32 PASSED [0.0210s] [ 27%] 2025-09-07T07:13:02.8041031Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logdet_cuda_float32 PASSED [0.0211s] [ 27%] 2025-09-07T07:13:02.8041360Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_and_cuda_float32 PASSED [0.2198s] [ 27%] 2025-09-07T07:13:02.8041692Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_not_cuda_float32 PASSED [0.0454s] [ 27%] 2025-09-07T07:13:02.8042024Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_or_cuda_float32 PASSED [0.2227s] [ 27%] 2025-09-07T07:13:02.8042355Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logical_xor_cuda_float32 PASSED [0.2201s] [ 27%] 2025-09-07T07:13:02.8042675Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logit_cuda_float32 PASSED [0.1175s] [ 27%] 2025-09-07T07:13:02.8042992Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logspace_cuda_float32 PASSED [0.0516s] [ 27%] 2025-09-07T07:13:02.8043333Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logspace_tensor_overload_cuda_float32 PASSED [2.0992s] [ 27%] 2025-09-07T07:13:02.8043681Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_logsumexp_cuda_float32 PASSED [0.0564s] [ 27%] 2025-09-07T07:13:02.8043999Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_long_cuda_float32 XFAIL [0.0088s] [ 27%] 2025-09-07T07:13:02.8045549Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_long_functorch_no_channels_last_cuda_float32 PASSED [0.0074s] [ 27%] 2025-09-07T07:13:02.8045899Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lt_cuda_float32 XFAIL [0.1782s] [ 27%] 2025-09-07T07:13:02.8046203Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_cuda_float32 PASSED [0.0944s] [ 27%] 2025-09-07T07:13:02.8046516Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_solve_cuda_float32 XFAIL [0.0041s] [ 27%] 2025-09-07T07:13:02.8046837Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_lu_unpack_cuda_float32 PASSED [0.1677s] [ 27%] 2025-09-07T07:13:02.8047153Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mH_cuda_float32 PASSED [0.0132s] [ 28%] 2025-09-07T07:13:02.8047477Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mT_cuda_float32 PASSED [0.0086s] [ 28%] 2025-09-07T07:13:02.8047792Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_amax_cuda_float32 PASSED [0.1612s] [ 28%] 2025-09-07T07:13:02.8048144Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_amin_cuda_float32 PASSED [0.1614s] [ 28%] 2025-09-07T07:13:02.8048475Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_argmax_cuda_float32 PASSED [0.1016s] [ 28%] 2025-09-07T07:13:02.8048810Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_argmin_cuda_float32 PASSED [0.1013s] [ 28%] 2025-09-07T07:13:02.8050259Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_cumprod_cuda_float32 PASSED [0.0317s] [ 28%] 2025-09-07T07:13:02.8050622Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_cumsum_cuda_float32 PASSED [0.0315s] [ 28%] 2025-09-07T07:13:02.8050957Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_fill_cuda_float32 XFAIL [0.0136s] [ 28%] 2025-09-07T07:13:02.8051316Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_fill_functorch_Scalar_only_cuda_float32 PASSED [0.0216s] [ 28%] 2025-09-07T07:13:02.8051693Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_log_softmax_cuda_float32 PASSED [0.0412s] [ 28%] 2025-09-07T07:13:02.8052044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_logaddexp_cuda_float32 PASSED [0.0453s] [ 28%] 2025-09-07T07:13:02.8052390Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_logsumexp_cuda_float32 PASSED [0.1987s] [ 28%] 2025-09-07T07:13:02.8052731Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_mean_cuda_float32 PASSED [0.1818s] [ 28%] 2025-09-07T07:13:02.8053062Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_median_cuda_float32 PASSED [0.0322s] [ 28%] 2025-09-07T07:13:02.8053393Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_norm_cuda_float32 PASSED [0.8080s] [ 28%] 2025-09-07T07:13:02.8053731Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_normalize_cuda_float32 PASSED [0.0548s] [ 28%] 2025-09-07T07:13:02.8055251Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_prod_cuda_float32 PASSED [0.1649s] [ 28%] 2025-09-07T07:13:02.8055583Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_scatter_cuda_float32 XFAIL [0.0112s] [ 28%] 2025-09-07T07:13:02.8055917Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_select_cuda_float32 XFAIL [0.0073s] [ 28%] 2025-09-07T07:13:02.8056255Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_softmax_cuda_float32 PASSED [0.0408s] [ 28%] 2025-09-07T07:13:02.8056623Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_softmin_cuda_float32 PASSED [0.0417s] [ 29%] 2025-09-07T07:13:02.8056961Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_std_cuda_float32 PASSED [0.1155s] [ 29%] 2025-09-07T07:13:02.8057290Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_sum_cuda_float32 PASSED [0.1615s] [ 29%] 2025-09-07T07:13:02.8057616Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_masked_var_cuda_float32 PASSED [0.1127s] [ 29%] 2025-09-07T07:13:02.8058305Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_matmul_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157163 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.) [ 29%] 2025-09-07T07:13:02.8059011Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_matrix_exp_cuda_float32 PASSED [0.0407s] [ 29%] 2025-09-07T07:13:02.8059355Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_binary_cuda_float32 PASSED [0.4142s] [ 29%] 2025-09-07T07:13:02.8060857Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_pool2d_with_indices_backward_cuda_float32 PASSED [2.2004s] [ 29%] 2025-09-07T07:13:02.8061226Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_reduction_no_dim_cuda_float32 PASSED [0.0053s] [ 29%] 2025-09-07T07:13:02.8061579Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_max_reduction_with_dim_cuda_float32 PASSED [0.0101s] [ 29%] 2025-09-07T07:13:02.8061943Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_maximum_cuda_float32 PASSED [0.2287s] [ 29%] 2025-09-07T07:13:02.8062265Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mean_cuda_float32 PASSED [0.0296s] [ 29%] 2025-09-07T07:13:02.8062583Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_median_cuda_float32 PASSED [0.0259s] [ 29%] 2025-09-07T07:13:02.8062928Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_meshgrid_list_of_tensors_cuda_float32 PASSED [0.0040s] [ 29%] 2025-09-07T07:13:02.8063293Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_meshgrid_variadic_tensors_cuda_float32 PASSED [0.0780s] [ 29%] 2025-09-07T07:13:02.8063642Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_binary_cuda_float32 PASSED [0.4167s] [ 29%] 2025-09-07T07:13:02.8063985Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_reduction_no_dim_cuda_float32 PASSED [0.0052s] [ 29%] 2025-09-07T07:13:02.8065519Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_min_reduction_with_dim_cuda_float32 PASSED [0.0101s] [ 29%] 2025-09-07T07:13:02.8065863Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_minimum_cuda_float32 PASSED [0.2304s] [ 29%] 2025-09-07T07:13:02.8066178Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mm_cuda_float32 PASSED [0.0094s] [ 29%] 2025-09-07T07:13:02.8066489Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mode_cuda_float32 PASSED [0.0462s] [ 29%] 2025-09-07T07:13:02.8066809Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_movedim_cuda_float32 XFAIL [0.0203s] [ 30%] 2025-09-07T07:13:02.8067125Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_msort_cuda_float32 PASSED [0.0059s] [ 30%] 2025-09-07T07:13:02.8067439Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mul_cuda_float32 PASSED [0.6092s] [ 30%] 2025-09-07T07:13:02.8067790Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_multinomial_cuda_float32 XFAIL [0.0266s] [ 30%] 2025-09-07T07:13:02.8068207Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32 (idx=0, sample='') SUBFAIL [0.0000s] [ 30%] 2025-09-07T07:13:02.8068539Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32 PASSED [0.0561s] [ 30%] 2025-09-07T07:13:02.8068713Z 2025-09-07T07:13:02.8068770Z =================================== FAILURES =================================== 2025-09-07T07:13:02.8070100Z _ TestVmapOperatorsOpInfoCUDA.test_op_has_batch_rule_mv_cuda_float32 (idx=0, sample='') _ 2025-09-07T07:13:02.8070292Z Traceback (most recent call last): 2025-09-07T07:13:02.8070475Z File "/var/lib/jenkins/pytorch/test/functorch/test_vmap.py", line 4172, in test 2025-09-07T07:13:02.8070649Z self.vmap_outplace_test( 2025-09-07T07:13:02.8070840Z File "/var/lib/jenkins/pytorch/test/functorch/test_vmap.py", line 4069, in vmap_outplace_test 2025-09-07T07:13:02.8071069Z self.assertEqual(vmap_out, loop_out) 2025-09-07T07:13:02.8071320Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:13:02.8071602Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:13:02.8071758Z AssertionError: Tensor-likes are not close! 2025-09-07T07:13:02.8071843Z 2025-09-07T07:13:02.8071887Z Mismatched elements: 10 / 10 (100.0%) 2025-09-07T07:13:02.8072063Z Greatest absolute difference: 0.110626220703125 at index (0, 1) (up to 0.0001 allowed) 2025-09-07T07:13:02.8073420Z Greatest relative difference: 0.0042924522422254086 at index (0, 4) (up to 0.0001 allowed) 2025-09-07T07:13:02.8073752Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-31a6d6a52b5f5036.xml - 2025-09-07T07:13:02.8074059Z =========================== short test summary info ============================ 2025-09-07T07:13:02.8074367Z (idx=0, sample='') SUBFAIL [0.0000s] functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32 - AssertionError: Tensor-likes are not close! 2025-09-07T07:13:02.8074662Z 2025-09-07T07:13:02.8074705Z Mismatched elements: 10 / 10 (100.0%) 2025-09-07T07:13:02.8074876Z Greatest absolute difference: 0.110626220703125 at index (0, 1) (up to 0.0001 allowed) 2025-09-07T07:13:02.8075104Z Greatest relative difference: 0.0042924522422254086 at index (0, 4) (up to 0.0001 allowed) 2025-09-07T07:13:02.8075299Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-09-07T07:13:02.8075469Z ======= 1 failed, 553 passed, 20 skipped, 72 xfailed in 68.32s (0:01:08) ======= 2025-09-07T07:13:02.8075612Z Got exit code 1 2025-09-07T07:13:02.8075704Z Retrying single test... 2025-09-07T07:13:02.8077334Z /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. 2025-09-07T07:13:02.8077820Z import pkg_resources 2025-09-07T07:13:02.8078029Z Test results will be stored in test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-43c5299c27209daa.xml 2025-09-07T07:13:02.8078266Z ============================= test session starts ============================== 2025-09-07T07:13:02.8078472Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:13:02.8078653Z cachedir: .pytest_cache 2025-09-07T07:13:02.8078867Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:13:02.8079095Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:13:02.8079208Z configfile: pytest.ini 2025-09-07T07:13:02.8079428Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:13:02.8080846Z collecting ... collected 2136 items / 2135 deselected / 1 selected 2025-09-07T07:13:02.8081159Z stepcurrent: skipping 644 already run items. Running only test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32 2025-09-07T07:13:02.8081428Z Running 1 items in this shard 2025-09-07T07:13:02.8081499Z 2025-09-07T07:13:02.8081642Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32 PASSED [0.4206s] [100%] 2025-09-07T07:13:02.8081816Z 2025-09-07T07:13:02.8082008Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-43c5299c27209daa.xml - 2025-09-07T07:13:02.8082290Z ====================== 1 passed, 2135 deselected in 0.47s ====================== 2025-09-07T07:13:02.8082423Z Got exit code 0 2025-09-07T07:13:02.8082556Z Test succeeeded in new process, continuing with the rest of the tests 2025-09-07T07:13:02.8083108Z /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. 2025-09-07T07:13:02.8083605Z import pkg_resources 2025-09-07T07:13:02.8083810Z Test results will be stored in test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-386d48e47c150c53.xml 2025-09-07T07:13:02.8085240Z ============================= test session starts ============================== 2025-09-07T07:13:02.8085447Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:13:02.8085628Z cachedir: .pytest_cache 2025-09-07T07:13:02.8085870Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:13:02.8086101Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:13:02.8086213Z configfile: pytest.ini 2025-09-07T07:13:02.8086428Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:13:02.8086696Z collecting ... collected 2136 items / 645 deselected / 1491 selected 2025-09-07T07:13:02.8086856Z stepcurrent: skipping 645 already run items. 2025-09-07T07:13:02.8086981Z Running 1491 items in this shard 2025-09-07T07:13:02.8087051Z 2025-09-07T07:13:02.8087218Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_1_cuda_float32 PASSED [0.3302s] [ 0%] 2025-09-07T07:13:02.8088721Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_3_cuda_float32 PASSED [0.1827s] [ 0%] 2025-09-07T07:13:02.8089082Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mvlgamma_mvlgamma_p_5_cuda_float32 PASSED [0.1864s] [ 0%] 2025-09-07T07:13:02.8089425Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nan_to_num_cuda_float32 PASSED [0.0971s] [ 0%] 2025-09-07T07:13:02.8089752Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanmean_cuda_float32 PASSED [0.0755s] [ 0%] 2025-09-07T07:13:02.8090074Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanmedian_cuda_float32 PASSED [0.0586s] [ 0%] 2025-09-07T07:13:02.8090402Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nanquantile_cuda_float32 XFAIL [0.0882s] [ 0%] 2025-09-07T07:13:02.8090727Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nansum_cuda_float32 PASSED [0.0438s] [ 0%] 2025-09-07T07:13:02.8091053Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_narrow_copy_cuda_float32 PASSED [0.0350s] [ 0%] 2025-09-07T07:13:02.8091377Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_narrow_cuda_float32 XFAIL [0.0208s] [ 0%] 2025-09-07T07:13:02.8091731Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_batch_norm_cuda_float32 XFAIL [0.0300s] [ 0%] 2025-09-07T07:13:02.8092085Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_dropout_backward_cuda_float32 PASSED [0.0313s] [ 0%] 2025-09-07T07:13:02.8093587Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_native_layer_norm_cuda_float32 PASSED [0.1053s] [ 0%] 2025-09-07T07:13:02.8093917Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ne_cuda_float32 XFAIL [0.1624s] [ 0%] 2025-09-07T07:13:02.8094225Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_neg_cuda_float32 PASSED [0.0678s] [ 1%] 2025-09-07T07:13:02.8094598Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_empty_cuda_float32 PASSED [0.0078s] [ 1%] 2025-09-07T07:13:02.8094983Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_empty_strided_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 1%] 2025-09-07T07:13:02.8095335Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_full_cuda_float32 PASSED [0.0114s] [ 1%] 2025-09-07T07:13:02.8095675Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_ones_cuda_float32 PASSED [0.0109s] [ 1%] 2025-09-07T07:13:02.8095996Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_new_zeros_cuda_float32 PASSED [0.0109s] [ 1%] 2025-09-07T07:13:02.8096317Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nextafter_cuda_float32 PASSED [0.2310s] [ 1%] 2025-09-07T07:13:02.8096673Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool1d_cuda_float32 PASSED [0.0159s] [ 1%] 2025-09-07T07:13:02.8098225Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool2d_cuda_float32 PASSED [0.0189s] [ 1%] 2025-09-07T07:13:02.8098618Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_avg_pool3d_cuda_float32 PASSED [0.0237s] [ 1%] 2025-09-07T07:13:02.8099003Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool1d_cuda_float32 PASSED [0.0199s] [ 1%] 2025-09-07T07:13:02.8099386Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool2d_cuda_float32 PASSED [0.0432s] [ 1%] 2025-09-07T07:13:02.8099769Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_adaptive_max_pool3d_cuda_float32 PASSED [0.0399s] [ 1%] 2025-09-07T07:13:02.8100145Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_alpha_dropout_cuda_float32 XFAIL [0.0214s] [ 1%] 2025-09-07T07:13:02.8100508Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool1d_cuda_float32 PASSED [0.0197s] [ 1%] 2025-09-07T07:13:02.8100869Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool2d_cuda_float32 PASSED [0.0171s] [ 2%] 2025-09-07T07:13:02.8101228Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_avg_pool3d_cuda_float32 PASSED [0.0219s] [ 2%] 2025-09-07T07:13:02.8101588Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_batch_norm_cuda_float32 PASSED [0.3837s] [ 2%] 2025-09-07T07:13:02.8101971Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_batch_norm_without_cudnn_cuda_float32 PASSED [0.1651s] [ 2%] 2025-09-07T07:13:02.8103484Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_bilinear_cuda_float32 XFAIL [0.3884s] [ 2%] 2025-09-07T07:13:02.8103859Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_binary_cross_entropy_cuda_float32 PASSED [0.0397s] [ 2%] 2025-09-07T07:13:02.8104288Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_binary_cross_entropy_with_logits_cuda_float32 PASSED [0.0996s] [ 2%] 2025-09-07T07:13:02.8104729Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_celu_cuda_float32 PASSED [0.0814s] [ 2%] 2025-09-07T07:13:02.8105092Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_channel_shuffle_cuda_float32 PASSED [0.0062s] [ 2%] 2025-09-07T07:13:02.8105458Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv1d_cuda_float32 PASSED [0.8283s] [ 2%] 2025-09-07T07:13:02.8106190Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_cuda_float32 SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157278 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.) [ 2%] 2025-09-07T07:13:02.8106958Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_no_bias_cuda_float32 PASSED [0.2770s] [ 2%] 2025-09-07T07:13:02.8107355Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32 PASSED [0.1028s] [ 2%] 2025-09-07T07:13:02.8107769Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_groups_with_bias_cuda_float32 PASSED [0.0240s] [ 2%] 2025-09-07T07:13:02.8108349Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_no_bias_cuda_float32 PASSED [0.1092s] [ 2%] 2025-09-07T07:13:02.8109950Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_padding_no_bias_cuda_float32 PASSED [0.0272s] [ 3%] 2025-09-07T07:13:02.8110366Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_padding_with_bias_cuda_float32 PASSED [0.0089s] [ 3%] 2025-09-07T07:13:02.8110767Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_stride_with_bias_cuda_float32 PASSED [0.0081s] [ 3%] 2025-09-07T07:13:02.8111543Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157315 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.) [ 3%] 2025-09-07T07:13:02.8112690Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_with_bias_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157335 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.) [ 3%] 2025-09-07T07:13:02.8113454Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv2d_with_bias_cuda_float32 PASSED [0.0081s] [ 3%] 2025-09-07T07:13:02.8114001Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv3d_cuda_float32 MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 52800, provided ptr: 0x7f3f9c660800 size: 11008 2025-09-07T07:13:02.8114667Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 52800, provided ptr: 0x7f3f9c660800 size: 11008 2025-09-07T07:13:02.8115096Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 52800, provided ptr: 0x7f3f9c66b200 size: 21760 2025-09-07T07:13:02.8115502Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 52800, provided ptr: 0x7f3f9c66b200 size: 21760 2025-09-07T07:13:02.8117082Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 52800, provided ptr: 0x7f3f9c669c00 size: 16384 2025-09-07T07:13:02.8117490Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 52800, provided ptr: 0x7f3f9c669c00 size: 16384 2025-09-07T07:13:02.8117897Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 337920, provided ptr: 0x7f3f9c662400 size: 12544 2025-09-07T07:13:02.8118305Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 337920, provided ptr: 0x7f3f9c662400 size: 12544 2025-09-07T07:13:02.8118737Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 337920, provided ptr: 0x7f3f9c66c400 size: 24832 2025-09-07T07:13:02.8119164Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 337920, provided ptr: 0x7f3f9c66c400 size: 24832 2025-09-07T07:13:02.8119569Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 337920, provided ptr: 0x7f3f9c66b800 size: 18176 2025-09-07T07:13:02.8119976Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 337920, provided ptr: 0x7f3f9c66b800 size: 18176 2025-09-07T07:13:02.8120229Z PASSED [0.3120s] [ 3%] 2025-09-07T07:13:02.8120867Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose1d_cuda_float32 SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142566 for platform(s) linux, rocm, 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.) [ 3%] 2025-09-07T07:13:02.8121975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose2d_cuda_float32 SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157368 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.) [ 3%] 2025-09-07T07:13:02.8124449Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_conv_transpose3d_cuda_float32 SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/82340 for platform(s) linux, 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.) [ 3%] 2025-09-07T07:13:02.8125209Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cosine_embedding_loss_cuda_float32 PASSED [0.0519s] [ 3%] 2025-09-07T07:13:02.8125600Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cosine_similarity_cuda_float32 PASSED [0.0437s] [ 3%] 2025-09-07T07:13:02.8125977Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_cross_entropy_cuda_float32 PASSED [0.0897s] [ 3%] 2025-09-07T07:13:02.8126341Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_ctc_loss_cuda_float32 XFAIL [0.0066s] [ 3%] 2025-09-07T07:13:02.8126723Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout2d_cuda_float32 XFAIL [0.0136s] [ 3%] 2025-09-07T07:13:02.8127078Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout3d_cuda_float32 XFAIL [0.0135s] [ 4%] 2025-09-07T07:13:02.8127430Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_dropout_cuda_float32 XFAIL [0.0131s] [ 4%] 2025-09-07T07:13:02.8127779Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_elu_cuda_float32 PASSED [0.0762s] [ 4%] 2025-09-07T07:13:02.8128132Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_bag_cuda_float32 XFAIL [0.0064s] [ 4%] 2025-09-07T07:13:02.8129753Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_cuda_float32 XFAIL [0.0340s] [ 4%] 2025-09-07T07:13:02.8130128Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_embedding_functorch_cuda_float32 PASSED [0.0168s] [ 4%] 2025-09-07T07:13:02.8130558Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_feature_alpha_dropout_with_train_cuda_float32 XFAIL [0.0089s] [ 4%] 2025-09-07T07:13:02.8130995Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_feature_alpha_dropout_without_train_cuda_float32 PASSED [0.0487s] [ 4%] 2025-09-07T07:13:02.8131402Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_fractional_max_pool2d_cuda_float32 XFAIL [0.0056s] [ 4%] 2025-09-07T07:13:02.8131791Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_fractional_max_pool3d_cuda_float32 XFAIL [0.0054s] [ 4%] 2025-09-07T07:13:02.8132195Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_gaussian_nll_loss_cuda_float32 XFAIL [0.0356s] [ 4%] 2025-09-07T07:13:02.8132559Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_gelu_cuda_float32 PASSED [0.0250s] [ 4%] 2025-09-07T07:13:02.8132901Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_glu_cuda_float32 PASSED [0.0595s] [ 4%] 2025-09-07T07:13:02.8133252Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_grid_sample_cuda_float32 PASSED [0.0550s] [ 4%] 2025-09-07T07:13:02.8133612Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_group_norm_cuda_float32 PASSED [0.1081s] [ 4%] 2025-09-07T07:13:02.8135223Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_hardshrink_cuda_float32 PASSED [0.0530s] [ 5%] 2025-09-07T07:13:02.8135590Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_hardsigmoid_cuda_float32 PASSED [0.0804s] [ 5%] 2025-09-07T07:13:02.8135950Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_hardswish_cuda_float32 PASSED [0.0125s] [ 5%] 2025-09-07T07:13:02.8136307Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_hardtanh_cuda_float32 PASSED [0.0504s] [ 5%] 2025-09-07T07:13:02.8136679Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_hinge_embedding_loss_cuda_float32 PASSED [0.0617s] [ 5%] 2025-09-07T07:13:02.8137054Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_huber_loss_cuda_float32 PASSED [0.0491s] [ 5%] 2025-09-07T07:13:02.8137419Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_instance_norm_cuda_float32 PASSED [0.0042s] [ 5%] 2025-09-07T07:13:02.8137794Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_area_cuda_float32 PASSED [0.0303s] [ 5%] 2025-09-07T07:13:02.8138213Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_bicubic_cuda_float32 PASSED [0.0455s] [ 5%] 2025-09-07T07:13:02.8138603Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_bilinear_cuda_float32 PASSED [0.0372s] [ 5%] 2025-09-07T07:13:02.8138992Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_linear_cuda_float32 PASSED [0.0263s] [ 5%] 2025-09-07T07:13:02.8140565Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_nearest-exact_cuda_float32 XFAIL [0.0053s] [ 5%] 2025-09-07T07:13:02.8140962Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_nearest_cuda_float32 PASSED [0.0348s] [ 5%] 2025-09-07T07:13:02.8141352Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_interpolate_trilinear_cuda_float32 PASSED [0.0336s] [ 5%] 2025-09-07T07:13:02.8141757Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_kl_div_cuda_float32 XFAIL [0.0032s] [ 5%] 2025-09-07T07:13:02.8142105Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_l1_loss_cuda_float32 PASSED [0.0194s] [ 6%] 2025-09-07T07:13:02.8142481Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_layer_norm_cuda_float32 PASSED [0.0450s] [ 6%] 2025-09-07T07:13:02.8142839Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_leaky_relu_cuda_float32 PASSED [0.0264s] [ 6%] 2025-09-07T07:13:02.8143194Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_linear_cuda_float32 PASSED [0.0667s] [ 6%] 2025-09-07T07:13:02.8143579Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_local_response_norm_cuda_float32 PASSED [0.0198s] [ 6%] 2025-09-07T07:13:02.8143954Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_logsigmoid_cuda_float32 PASSED [0.0440s] [ 6%] 2025-09-07T07:13:02.8145563Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_margin_ranking_loss_cuda_float32 XFAIL [0.0062s] [ 6%] 2025-09-07T07:13:02.8145939Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_pool1d_cuda_float32 PASSED [1.3171s] [ 6%] 2025-09-07T07:13:02.8146296Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_pool2d_cuda_float32 PASSED [2.2004s] [ 6%] 2025-09-07T07:13:02.8146651Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_pool3d_cuda_float32 PASSED [0.8956s] [ 6%] 2025-09-07T07:13:02.8147012Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool1d_cuda_float32 XFAIL [0.0075s] [ 6%] 2025-09-07T07:13:02.8147380Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool1d_grad_cuda_float32 XFAIL [0.0142s] [ 6%] 2025-09-07T07:13:02.8147746Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool2d_cuda_float32 XFAIL [0.0033s] [ 6%] 2025-09-07T07:13:02.8148155Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool2d_grad_cuda_float32 XFAIL [0.0043s] [ 6%] 2025-09-07T07:13:02.8148521Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool3d_cuda_float32 XFAIL [0.0032s] [ 6%] 2025-09-07T07:13:02.8148886Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_max_unpool3d_grad_cuda_float32 XFAIL [0.0037s] [ 7%] 2025-09-07T07:13:02.8149249Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_mish_cuda_float32 PASSED [0.0851s] [ 7%] 2025-09-07T07:13:02.8150816Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_mse_loss_cuda_float32 PASSED [0.0159s] [ 7%] 2025-09-07T07:13:02.8151188Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_mse_loss_functorch_cuda_float32 PASSED [0.0226s] [ 7%] 2025-09-07T07:13:02.8151582Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_multi_head_attention_forward_cuda_float32 XFAIL [1.4126s] [ 7%] 2025-09-07T07:13:02.8151975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_multi_margin_loss_cuda_float32 XFAIL [0.0155s] [ 7%] 2025-09-07T07:13:02.8152359Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_multilabel_margin_loss_cuda_float32 XFAIL [0.0106s] [ 7%] 2025-09-07T07:13:02.8152761Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_multilabel_soft_margin_loss_cuda_float32 PASSED [0.0159s] [ 7%] 2025-09-07T07:13:02.8153164Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_nll_loss_cuda_float32 PASSED [0.1999s] [ 7%] 2025-09-07T07:13:02.8153521Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_normalize_cuda_float32 PASSED [0.0132s] [ 7%] 2025-09-07T07:13:02.8153892Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_one_hot_cuda_int64 XFAIL [0.0087s] [ 7%] 2025-09-07T07:13:02.8154248Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pad_circular_cuda_float32 PASSED [0.0124s] [ 7%] 2025-09-07T07:13:02.8154654Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pad_constant_cuda_float32 PASSED [0.0578s] [ 7%] 2025-09-07T07:13:02.8156279Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pad_reflect_cuda_float32 PASSED [0.0195s] [ 7%] 2025-09-07T07:13:02.8156654Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pad_replicate_cuda_float32 PASSED [0.0187s] [ 7%] 2025-09-07T07:13:02.8157037Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pad_replicate_negative_cuda_float32 PASSED [0.0100s] [ 8%] 2025-09-07T07:13:02.8157424Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pairwise_distance_cuda_float32 PASSED [0.0182s] [ 8%] 2025-09-07T07:13:02.8157788Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pdist_cuda_float32 XFAIL [0.0031s] [ 8%] 2025-09-07T07:13:02.8158147Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pixel_shuffle_cuda_float32 PASSED [0.0072s] [ 8%] 2025-09-07T07:13:02.8158518Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_pixel_unshuffle_cuda_float32 PASSED [0.0071s] [ 8%] 2025-09-07T07:13:02.8158895Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_poisson_nll_loss_cuda_float32 PASSED [0.2164s] [ 8%] 2025-09-07T07:13:02.8159259Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_prelu_cuda_float32 PASSED [0.1920s] [ 8%] 2025-09-07T07:13:02.8159609Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_relu6_cuda_float32 PASSED [0.0430s] [ 8%] 2025-09-07T07:13:02.8159958Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_relu_cuda_float32 PASSED [0.0454s] [ 8%] 2025-09-07T07:13:02.8160311Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_rms_norm_cuda_float32 PASSED [0.0306s] [ 8%] 2025-09-07T07:13:02.8161834Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_rrelu_cuda_float32 XFAIL [0.0075s] [ 8%] 2025-09-07T07:13:02.8162211Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_scaled_dot_product_attention_cuda_float32 XFAIL [0.0400s] [ 8%] 2025-09-07T07:13:02.8162619Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_selu_cuda_float32 PASSED [0.0749s] [ 8%] 2025-09-07T07:13:02.8162980Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_silu_complex_cuda_complex64 PASSED [0.1040s] [ 8%] 2025-09-07T07:13:02.8163340Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_silu_cuda_float32 PASSED [0.0745s] [ 8%] 2025-09-07T07:13:02.8163697Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_smooth_l1_loss_cuda_float32 PASSED [0.0203s] [ 9%] 2025-09-07T07:13:02.8164068Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_soft_margin_loss_cuda_float32 XFAIL [0.0059s] [ 9%] 2025-09-07T07:13:02.8164478Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_softmin_cuda_float32 PASSED [0.0104s] [ 9%] 2025-09-07T07:13:02.8164879Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_softmin_with_dtype_cuda_float32 PASSED [0.0110s] [ 9%] 2025-09-07T07:13:02.8165275Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_softplus_cuda_float32 PASSED [0.0527s] [ 9%] 2025-09-07T07:13:02.8167046Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_softshrink_cuda_float32 PASSED [0.0543s] [ 9%] 2025-09-07T07:13:02.8167411Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_softsign_cuda_float32 PASSED [0.0467s] [ 9%] 2025-09-07T07:13:02.8167772Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_tanhshrink_cuda_float32 PASSED [0.0494s] [ 9%] 2025-09-07T07:13:02.8168160Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_threshold_cuda_float32 PASSED [0.0949s] [ 9%] 2025-09-07T07:13:02.8168534Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_triplet_margin_loss_cuda_float32 XFAIL [0.0130s] [ 9%] 2025-09-07T07:13:02.8168938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_triplet_margin_with_distance_loss_cuda_float32 PASSED [0.0796s] [ 9%] 2025-09-07T07:13:02.8169325Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_unfold_cuda_float32 PASSED [0.1832s] [ 9%] 2025-09-07T07:13:02.8169695Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_upsample_bilinear_cuda_float32 PASSED [0.0102s] [ 9%] 2025-09-07T07:13:02.8170078Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nn_functional_upsample_nearest_cuda_float32 PASSED [0.0172s] [ 9%] 2025-09-07T07:13:02.8170434Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nonzero_cuda_float32 XFAIL [0.0134s] [ 9%] 2025-09-07T07:13:02.8170789Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_nonzero_static_cuda_float32 SKIPPED [0.0005s] (Only runs on cpu) [ 10%] 2025-09-07T07:13:02.8172275Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_norm_cuda_float32 PASSED [0.0511s] [ 10%] 2025-09-07T07:13:02.8172595Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_norm_fro_cuda_float32 PASSED [0.0060s] [ 10%] 2025-09-07T07:13:02.8172914Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_norm_inf_cuda_float32 PASSED [0.0071s] [ 10%] 2025-09-07T07:13:02.8173235Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_norm_nuc_cuda_float32 PASSED [0.0681s] [ 10%] 2025-09-07T07:13:02.8173552Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_normal_cuda_float32 XFAIL [0.0119s] [ 10%] 2025-09-07T07:13:02.8173941Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_normal_in_place_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 10%] 2025-09-07T07:13:02.8174322Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_normal_number_mean_cuda_float32 XFAIL [0.0044s] [ 10%] 2025-09-07T07:13:02.8174706Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ones_cuda_float32 PASSED [0.0026s] [ 10%] 2025-09-07T07:13:02.8175023Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ones_like_cuda_float32 PASSED [0.0107s] [ 10%] 2025-09-07T07:13:02.8175409Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cuda_float32 PASSED [0.0203s] [ 10%] 2025-09-07T07:13:02.8175814Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ops_aten_index_put_functorch_cuda_float32 PASSED [0.2524s] [ 10%] 2025-09-07T07:13:02.8177286Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ormqr_cuda_float32 XFAIL [0.0093s] [ 10%] 2025-09-07T07:13:02.8177632Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_outer_cuda_float32 PASSED [0.0063s] [ 10%] 2025-09-07T07:13:02.8177969Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_pca_lowrank_cuda_float32 XFAIL [0.0377s] [ 10%] 2025-09-07T07:13:02.8178302Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_permute_copy_cuda_float32 XFAIL [0.0035s] [ 11%] 2025-09-07T07:13:02.8178625Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_permute_cuda_float32 PASSED [0.0723s] [ 11%] 2025-09-07T07:13:02.8178943Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_pinverse_cuda_float32 PASSED [0.1424s] [ 11%] 2025-09-07T07:13:02.8179277Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polar_cuda_float32 PASSED [0.2342s] [ 11%] 2025-09-07T07:13:02.8179615Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polygamma_polygamma_n_0_cuda_float32 PASSED [1.0189s] [ 11%] 2025-09-07T07:13:02.8179978Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polygamma_polygamma_n_1_cuda_float32 PASSED [0.3066s] [ 11%] 2025-09-07T07:13:02.8180338Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polygamma_polygamma_n_2_cuda_float32 PASSED [0.1638s] [ 11%] 2025-09-07T07:13:02.8181807Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polygamma_polygamma_n_3_cuda_float32 PASSED [0.1633s] [ 11%] 2025-09-07T07:13:02.8182172Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_polygamma_polygamma_n_4_cuda_float32 PASSED [0.1633s] [ 11%] 2025-09-07T07:13:02.8182514Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_positive_cuda_float32 PASSED [0.0373s] [ 11%] 2025-09-07T07:13:02.8182828Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_pow_cuda_float32 PASSED [0.2229s] [ 11%] 2025-09-07T07:13:02.8183140Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_prod_cuda_float32 PASSED [0.0488s] [ 11%] 2025-09-07T07:13:02.8183452Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_put_cuda_float32 XFAIL [0.0136s] [ 11%] 2025-09-07T07:13:02.8183758Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_qr_cuda_float32 PASSED [0.0771s] [ 11%] 2025-09-07T07:13:02.8184072Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_quantile_cuda_float32 XFAIL [0.0037s] [ 12%] 2025-09-07T07:13:02.8184435Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_rad2deg_cuda_float32 PASSED [0.0692s] [ 12%] 2025-09-07T07:13:02.8184757Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_rand_like_cuda_float32 XFAIL [0.0037s] [ 12%] 2025-09-07T07:13:02.8185144Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_randint_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 12%] 2025-09-07T07:13:02.8186645Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_randint_like_cuda_float32 XFAIL [0.0036s] [ 12%] 2025-09-07T07:13:02.8187014Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_randn_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 12%] 2025-09-07T07:13:02.8187372Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_randn_like_cuda_float32 XFAIL [0.0035s] [ 12%] 2025-09-07T07:13:02.8187692Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_ravel_cuda_float32 PASSED [0.0059s] [ 12%] 2025-09-07T07:13:02.8188005Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_real_cuda_float32 PASSED [0.0414s] [ 12%] 2025-09-07T07:13:02.8188376Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_reciprocal_cuda_float32 PASSED [0.0752s] [ 12%] 2025-09-07T07:13:02.8188731Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_remainder_cuda_float32 PASSED [0.2525s] [ 12%] 2025-09-07T07:13:02.8189065Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_renorm_cuda_float32 XFAIL [0.0082s] [ 12%] 2025-09-07T07:13:02.8189378Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_repeat_cuda_float32 PASSED [0.0399s] [ 12%] 2025-09-07T07:13:02.8189710Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_repeat_interleave_cuda_float32 PASSED [0.0199s] [ 12%] 2025-09-07T07:13:02.8190049Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_reshape_as_cuda_float32 PASSED [0.0585s] [ 12%] 2025-09-07T07:13:02.8191496Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_reshape_cuda_float32 PASSED [0.0539s] [ 13%] 2025-09-07T07:13:02.8191841Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_resize__cuda_float32 XFAIL [0.0043s] [ 13%] 2025-09-07T07:13:02.8192162Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_resize_as__cuda_float32 XFAIL [0.0031s] [ 13%] 2025-09-07T07:13:02.8192492Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_resolve_conj_cuda_float32 PASSED [0.0046s] [ 13%] 2025-09-07T07:13:02.8192821Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_resolve_neg_cuda_float32 PASSED [0.0047s] [ 13%] 2025-09-07T07:13:02.8193139Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_roll_cuda_float32 PASSED [0.0267s] [ 13%] 2025-09-07T07:13:02.8193449Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_rot90_cuda_float32 PASSED [0.0473s] [ 13%] 2025-09-07T07:13:02.8193759Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_round_cuda_float32 PASSED [0.0949s] [ 13%] 2025-09-07T07:13:02.8194086Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_round_decimals_0_cuda_float32 PASSED [0.1039s] [ 13%] 2025-09-07T07:13:02.8194481Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_round_decimals_3_cuda_float32 PASSED [0.1044s] [ 13%] 2025-09-07T07:13:02.8194828Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_round_decimals_neg_3_cuda_float32 PASSED [0.1037s] [ 13%] 2025-09-07T07:13:02.8196339Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_rsqrt_cuda_float32 PASSED [0.0751s] [ 13%] 2025-09-07T07:13:02.8196654Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_rsub_cuda_float32 PASSED [0.2296s] [ 13%] 2025-09-07T07:13:02.8196976Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scalar_tensor_cuda_float32 PASSED [0.0027s] [ 13%] 2025-09-07T07:13:02.8197313Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_add_cuda_float32 PASSED [0.0598s] [ 13%] 2025-09-07T07:13:02.8197665Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_cuda_float32 PASSED [0.1120s] [ 14%] 2025-09-07T07:13:02.8198002Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_reduce_amax_cuda_float32 PASSED [0.1667s] [ 14%] 2025-09-07T07:13:02.8198353Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_reduce_amin_cuda_float32 PASSED [0.1661s] [ 14%] 2025-09-07T07:13:02.8198704Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_reduce_mean_cuda_float32 PASSED [0.2086s] [ 14%] 2025-09-07T07:13:02.8199056Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_reduce_prod_cuda_float32 PASSED [0.1657s] [ 14%] 2025-09-07T07:13:02.8199405Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_scatter_reduce_sum_cuda_float32 PASSED [0.1666s] [ 14%] 2025-09-07T07:13:02.8200852Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_searchsorted_cuda_float32 XFAIL [0.5349s] [ 14%] 2025-09-07T07:13:02.8201210Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_select_cuda_float32 PASSED [0.0093s] [ 14%] 2025-09-07T07:13:02.8201561Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_select_scatter_cuda_float32 PASSED [0.0142s] [ 14%] 2025-09-07T07:13:02.8201888Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sgn_cuda_float32 PASSED [0.0691s] [ 14%] 2025-09-07T07:13:02.8202202Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_short_cuda_float32 XFAIL [0.0077s] [ 14%] 2025-09-07T07:13:02.8202551Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_short_functorch_no_channels_last_cuda_float32 PASSED [0.0068s] [ 14%] 2025-09-07T07:13:02.8202922Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sigmoid_cuda_float32 PASSED [0.1383s] [ 14%] 2025-09-07T07:13:02.8203240Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sign_cuda_float32 PASSED [0.0693s] [ 14%] 2025-09-07T07:13:02.8203589Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_bartlett_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 14%] 2025-09-07T07:13:02.8203979Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_blackman_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8204366Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_cosine_cuda_float32 SKIPPED [0.0002s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8205951Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_exponential_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8206352Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_gaussian_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8206748Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_general_cosine_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8207153Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_general_hamming_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8207549Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_hamming_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8207930Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_hann_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8208308Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_kaiser_cuda_float32 SKIPPED [0.0002s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8208691Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signal_windows_nuttall_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 15%] 2025-09-07T07:13:02.8209069Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_signbit_cuda_float32 PASSED [0.0398s] [ 15%] 2025-09-07T07:13:02.8209386Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sin_cuda_float32 PASSED [0.0694s] [ 15%] 2025-09-07T07:13:02.8209696Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sinc_cuda_float32 PASSED [0.6618s] [ 15%] 2025-09-07T07:13:02.8211121Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sinh_cuda_float32 PASSED [0.0735s] [ 15%] 2025-09-07T07:13:02.8211434Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_slice_cuda_float32 PASSED [0.0072s] [ 15%] 2025-09-07T07:13:02.8211757Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_slice_scatter_cuda_float32 PASSED [0.0239s] [ 16%] 2025-09-07T07:13:02.8212081Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_softmax_cuda_float32 PASSED [0.0215s] [ 16%] 2025-09-07T07:13:02.8212450Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_softmax_with_dtype_cuda_float32 PASSED [0.0225s] [ 16%] 2025-09-07T07:13:02.8212800Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sort_cuda_float32 PASSED [0.0889s] [ 16%] 2025-09-07T07:13:02.8213149Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sparse_mm_reduce_cuda_float32 SKIPPED [0.0006s] (Only runs on cpu) [ 16%] 2025-09-07T07:13:02.8213524Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sparse_sampled_addmm_cuda_float32 XFAIL [0.0036s] [ 16%] 2025-09-07T07:13:02.8213874Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_airy_ai_cuda_float32 XFAIL [0.2252s] [ 16%] 2025-09-07T07:13:02.8214238Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_bessel_j0_cuda_float32 PASSED [0.7694s] [ 16%] 2025-09-07T07:13:02.8215761Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_bessel_j1_cuda_float32 PASSED [0.5798s] [ 16%] 2025-09-07T07:13:02.8216112Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_bessel_y0_cuda_float32 PASSED [0.7532s] [ 16%] 2025-09-07T07:13:02.8216453Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_bessel_y1_cuda_float32 PASSED [0.8436s] [ 16%] 2025-09-07T07:13:02.8216813Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_chebyshev_polynomial_t_cuda_float32 XFAIL [0.3144s] [ 16%] 2025-09-07T07:13:02.8217190Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_chebyshev_polynomial_u_cuda_float32 XFAIL [0.4750s] [ 16%] 2025-09-07T07:13:02.8217567Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_chebyshev_polynomial_v_cuda_float32 XFAIL [0.4385s] [ 16%] 2025-09-07T07:13:02.8217943Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_chebyshev_polynomial_w_cuda_float32 XFAIL [0.4357s] [ 16%] 2025-09-07T07:13:02.8218298Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_entr_cuda_float32 PASSED [0.7009s] [ 17%] 2025-09-07T07:13:02.8218632Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_erfcx_cuda_float32 PASSED [0.7183s] [ 17%] 2025-09-07T07:13:02.8218982Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_hermite_polynomial_h_cuda_float32 XFAIL [0.2375s] [ 17%] 2025-09-07T07:13:02.8219352Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_hermite_polynomial_he_cuda_float32 XFAIL [0.2333s] [ 17%] 2025-09-07T07:13:02.8220803Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_i0e_cuda_float32 PASSED [0.5343s] [ 17%] 2025-09-07T07:13:02.8221136Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_i1_cuda_float32 PASSED [0.5486s] [ 17%] 2025-09-07T07:13:02.8221490Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_i1e_cuda_float32 PASSED [0.5204s] [ 17%] 2025-09-07T07:13:02.8221847Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_laguerre_polynomial_l_cuda_float32 XFAIL [0.2418s] [ 17%] 2025-09-07T07:13:02.8222224Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_legendre_polynomial_p_cuda_float32 XFAIL [0.2462s] [ 17%] 2025-09-07T07:13:02.8222585Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_log_ndtr_cuda_float32 XFAIL [0.1711s] [ 17%] 2025-09-07T07:13:02.8222938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_modified_bessel_i0_cuda_float32 PASSED [0.5514s] [ 17%] 2025-09-07T07:13:02.8223308Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_modified_bessel_i1_cuda_float32 PASSED [0.5729s] [ 17%] 2025-09-07T07:13:02.8223696Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_modified_bessel_k0_cuda_float32 PASSED [0.6605s] [ 17%] 2025-09-07T07:13:02.8224081Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_modified_bessel_k1_cuda_float32 PASSED [0.6159s] [ 17%] 2025-09-07T07:13:02.8224482Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_ndtr_cuda_float32 PASSED [0.0474s] [ 17%] 2025-09-07T07:13:02.8225947Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_ndtri_cuda_float32 PASSED [0.5664s] [ 18%] 2025-09-07T07:13:02.8226317Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_polygamma_special_polygamma_n_0_cuda_float32 PASSED [0.0619s] [ 18%] 2025-09-07T07:13:02.8226735Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_scaled_modified_bessel_k0_cuda_float32 PASSED [0.6107s] [ 18%] 2025-09-07T07:13:02.8227127Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_scaled_modified_bessel_k1_cuda_float32 PASSED [0.6088s] [ 18%] 2025-09-07T07:13:02.8227520Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_shifted_chebyshev_polynomial_t_cuda_float32 XFAIL [0.3176s] [ 18%] 2025-09-07T07:13:02.8227919Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_shifted_chebyshev_polynomial_u_cuda_float32 XFAIL [0.4854s] [ 18%] 2025-09-07T07:13:02.8228363Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_shifted_chebyshev_polynomial_v_cuda_float32 XFAIL [0.4421s] [ 18%] 2025-09-07T07:13:02.8228765Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_shifted_chebyshev_polynomial_w_cuda_float32 XFAIL [0.4337s] [ 18%] 2025-09-07T07:13:02.8229152Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_spherical_bessel_j0_cuda_float32 PASSED [0.6206s] [ 18%] 2025-09-07T07:13:02.8229512Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_xlog1py_cuda_float32 PASSED [0.2225s] [ 18%] 2025-09-07T07:13:02.8229850Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_special_zeta_cuda_float32 PASSED [2.2568s] [ 18%] 2025-09-07T07:13:02.8231332Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_split_cuda_float32 PASSED [0.0062s] [ 18%] 2025-09-07T07:13:02.8231660Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_split_list_args_cuda_float32 PASSED [0.0096s] [ 18%] 2025-09-07T07:13:02.8232009Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_split_with_sizes_copy_cuda_float32 PASSED [0.0119s] [ 18%] 2025-09-07T07:13:02.8232363Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_split_with_sizes_cuda_float32 PASSED [0.0116s] [ 18%] 2025-09-07T07:13:02.8232724Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sqrt_cuda_float32 PASSED [0.0708s] [ 19%] 2025-09-07T07:13:02.8233040Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_square_cuda_float32 PASSED [0.0436s] [ 19%] 2025-09-07T07:13:02.8233366Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_squeeze_copy_cuda_float32 XFAIL [0.0033s] [ 19%] 2025-09-07T07:13:02.8233689Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_squeeze_cuda_float32 PASSED [0.0192s] [ 19%] 2025-09-07T07:13:02.8234022Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_squeeze_multiple_cuda_float32 PASSED [0.0153s] [ 19%] 2025-09-07T07:13:02.8234350Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_stack_cuda_float32 PASSED [0.0035s] [ 19%] 2025-09-07T07:13:02.8235845Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_std_cuda_float32 PASSED [0.0154s] [ 19%] 2025-09-07T07:13:02.8236193Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_std_mean_cuda_float32 PASSED [0.0218s] [ 19%] 2025-09-07T07:13:02.8236545Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_std_mean_unbiased_cuda_float32 PASSED [0.0062s] [ 19%] 2025-09-07T07:13:02.8236884Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_std_unbiased_cuda_float32 PASSED [0.0048s] [ 19%] 2025-09-07T07:13:02.8237204Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_stft_cuda_float32 XFAIL [0.3028s] [ 19%] 2025-09-07T07:13:02.8237511Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sub_cuda_float32 PASSED [0.4167s] [ 19%] 2025-09-07T07:13:02.8237817Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sum_cuda_float32 PASSED [0.0257s] [ 19%] 2025-09-07T07:13:02.8238153Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_sum_to_size_cuda_float32 PASSED [0.0238s] [ 19%] 2025-09-07T07:13:02.8238472Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_svd_cuda_float32 PASSED [0.5073s] [ 19%] 2025-09-07T07:13:02.8238790Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_svd_lowrank_cuda_float32 XFAIL [0.0059s] [ 20%] 2025-09-07T07:13:02.8238929Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_t_copy_cuda_float32 XFAIL [0.0048s] [ 20%] 2025-09-07T07:13:02.8239063Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_t_cuda_float32 PASSED [0.0100s] [ 20%] 2025-09-07T07:13:02.8240323Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_take_along_dim_cuda_float32 PASSED [0.0118s] [ 20%] 2025-09-07T07:13:02.8240465Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_take_cuda_float32 XFAIL [0.0070s] [ 20%] 2025-09-07T07:13:02.8240600Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tan_cuda_float32 PASSED [0.0751s] [ 20%] 2025-09-07T07:13:02.8240741Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tanh_cuda_float32 PASSED [0.0949s] [ 20%] 2025-09-07T07:13:02.8240891Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tensor_split_cuda_float32 XFAIL [0.0032s] [ 20%] 2025-09-07T07:13:02.8241427Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tensordot_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142769 for platform(s) linux, rocm, 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.) [ 20%] 2025-09-07T07:13:02.8241568Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tile_cuda_float32 PASSED [0.0526s] [ 20%] 2025-09-07T07:13:02.8241743Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_to_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 20%] 2025-09-07T07:13:02.8241887Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_to_sparse_cuda_float32 XFAIL [0.0037s] [ 20%] 2025-09-07T07:13:02.8242024Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_topk_cuda_float32 PASSED [0.0394s] [ 20%] 2025-09-07T07:13:02.8242202Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_torch__scaled_mm_cuda_float8_e4m3fn SKIPPED [0.0001s] (Skipped!) [ 20%] 2025-09-07T07:13:02.8242477Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_torch_ops_aten__efficient_attention_forward_cuda_float32 SKIPPED [0.0006s] (Efficient attention on ROCM doesn't support custom_mask_type==2) [ 20%] 2025-09-07T07:13:02.8242661Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_torch_ops_aten__flash_attention_forward_cuda_float16 XFAIL [0.0066s] [ 21%] 2025-09-07T07:13:02.8242855Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_torch_ops_aten__safe_softmax_default_cuda_float32 PASSED [0.0137s] [ 21%] 2025-09-07T07:13:02.8243010Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_trace_cuda_float32 PASSED [0.0055s] [ 21%] 2025-09-07T07:13:02.8243162Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_transpose_copy_cuda_float32 XFAIL [0.0031s] [ 21%] 2025-09-07T07:13:02.8243308Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_transpose_cuda_float32 PASSED [0.0455s] [ 21%] 2025-09-07T07:13:02.8243452Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_trapezoid_cuda_float32 PASSED [0.0222s] [ 21%] 2025-09-07T07:13:02.8243604Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_trapz_cuda_float32 PASSED [0.0223s] [ 21%] 2025-09-07T07:13:02.8243760Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_triangular_solve_cuda_float32 PASSED [0.1516s] [ 21%] 2025-09-07T07:13:02.8243898Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tril_cuda_float32 XFAIL [0.0034s] [ 21%] 2025-09-07T07:13:02.8245188Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_tril_indices_cuda_int64 PASSED [0.0031s] [ 21%] 2025-09-07T07:13:02.8245330Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_triu_cuda_float32 XFAIL [0.0031s] [ 21%] 2025-09-07T07:13:02.8245475Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_triu_indices_cuda_int64 PASSED [0.0030s] [ 21%] 2025-09-07T07:13:02.8245620Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_true_divide_cuda_float32 PASSED [0.3146s] [ 21%] 2025-09-07T07:13:02.8245761Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_trunc_cuda_float32 PASSED [0.0653s] [ 21%] 2025-09-07T07:13:02.8245910Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unbind_copy_cuda_float32 XFAIL [0.0057s] [ 21%] 2025-09-07T07:13:02.8246052Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unbind_cuda_float32 PASSED [0.0228s] [ 22%] 2025-09-07T07:13:02.8246198Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unflatten_cuda_float32 PASSED [0.0136s] [ 22%] 2025-09-07T07:13:02.8246347Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unfold_copy_cuda_float32 PASSED [0.0291s] [ 22%] 2025-09-07T07:13:02.8246486Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unfold_cuda_float32 PASSED [0.0281s] [ 22%] 2025-09-07T07:13:02.8246627Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_uniform_cuda_float32 XFAIL [0.0055s] [ 22%] 2025-09-07T07:13:02.8246783Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unique_consecutive_cuda_float32 XFAIL [0.0098s] [ 22%] 2025-09-07T07:13:02.8246963Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unique_cuda_float32 XFAIL [0.0035s] [ 22%] 2025-09-07T07:13:02.8247111Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unravel_index_cuda_int64 PASSED [0.0415s] [ 22%] 2025-09-07T07:13:02.8247260Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unsafe_chunk_cuda_float32 PASSED [0.0550s] [ 22%] 2025-09-07T07:13:02.8247407Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unsafe_split_cuda_float32 PASSED [0.0059s] [ 22%] 2025-09-07T07:13:02.8247556Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unsqueeze_copy_cuda_float32 XFAIL [0.0032s] [ 22%] 2025-09-07T07:13:02.8247698Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_unsqueeze_cuda_float32 PASSED [0.0210s] [ 22%] 2025-09-07T07:13:02.8247835Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_var_cuda_float32 PASSED [0.0154s] [ 22%] 2025-09-07T07:13:02.8247998Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_var_mean_cuda_float32 PASSED [0.0218s] [ 22%] 2025-09-07T07:13:02.8248170Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_var_mean_unbiased_cuda_float32 PASSED [0.0060s] [ 23%] 2025-09-07T07:13:02.8249399Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_var_unbiased_cuda_float32 PASSED [0.0049s] [ 23%] 2025-09-07T07:13:02.8249927Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_vdot_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157450 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.) [ 23%] 2025-09-07T07:13:02.8250082Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_view_as_complex_cuda_float32 XFAIL [0.0037s] [ 23%] 2025-09-07T07:13:02.8250227Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_view_as_cuda_float32 PASSED [0.0574s] [ 23%] 2025-09-07T07:13:02.8250378Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_view_as_real_cuda_complex64 PASSED [0.0046s] [ 23%] 2025-09-07T07:13:02.8250522Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_view_copy_cuda_float32 PASSED [0.0187s] [ 23%] 2025-09-07T07:13:02.8250660Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_view_cuda_float32 PASSED [0.0533s] [ 23%] 2025-09-07T07:13:02.8250800Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_vsplit_cuda_float32 PASSED [0.0105s] [ 23%] 2025-09-07T07:13:02.8250941Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_vstack_cuda_float32 PASSED [0.0055s] [ 23%] 2025-09-07T07:13:02.8251080Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_where_cuda_float32 PASSED [0.0665s] [ 23%] 2025-09-07T07:13:02.8251220Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_xlogy_cuda_float32 PASSED [0.3994s] [ 23%] 2025-09-07T07:13:02.8251358Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_zero__cuda_float32 PASSED [0.0093s] [ 23%] 2025-09-07T07:13:02.8251496Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_zeros_cuda_float32 PASSED [0.0025s] [ 23%] 2025-09-07T07:13:02.8251642Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_zeros_like_cuda_float32 PASSED [0.0102s] [ 23%] 2025-09-07T07:13:02.8251776Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_searchsorted_bucketize_cuda PASSED [0.0017s] [ 24%] 2025-09-07T07:13:02.8251894Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_slogdet_cuda PASSED [0.1286s] [ 24%] 2025-09-07T07:13:02.8252013Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_sum_scalar_cuda PASSED [0.0016s] [ 24%] 2025-09-07T07:13:02.8252165Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_torch_return_types_returns_cuda PASSED [0.0016s] [ 24%] 2025-09-07T07:13:02.8252295Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_escaped_error_cuda PASSED [0.0015s] [ 24%] 2025-09-07T07:13:02.8252472Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_CubeGenVmapAutogradFunction_cuda_float32 PASSED [0.0061s] [ 24%] 2025-09-07T07:13:02.8252666Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ForwardHasDefaultArgsAutogradFunction_cuda_float32 PASSED [0.0043s] [ 24%] 2025-09-07T07:13:02.8253879Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_H_cuda_float32 PASSED [0.0046s] [ 24%] 2025-09-07T07:13:02.8254059Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_MulGenVmapAutogradFunction_cuda_float32 PASSED [0.0066s] [ 24%] 2025-09-07T07:13:02.8254236Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyCatCustomOp_cuda_float32 PASSED [0.0024s] [ 24%] 2025-09-07T07:13:02.8254482Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyCubeAutogradFunction_cuda_float32 PASSED [0.0055s] [ 24%] 2025-09-07T07:13:02.8254664Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyCubeCustomOp_cuda_float32 PASSED [0.0053s] [ 24%] 2025-09-07T07:13:02.8254857Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyCubeNotComposableAutogradFunction_cuda_float32 XFAIL [0.0034s] [ 24%] 2025-09-07T07:13:02.8255044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyExpMarkDirtyAutogradFunction_cuda_float32 PASSED [0.0064s] [ 24%] 2025-09-07T07:13:02.8255232Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyMulAutogradFunction_cuda_float32 PASSED [0.0076s] [ 24%] 2025-09-07T07:13:02.8255393Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyMulCustomOp_cuda_float32 PASSED [0.0060s] [ 25%] 2025-09-07T07:13:02.8255560Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyMulScalarCustomOp_cuda_float32 PASSED [0.0041s] [ 25%] 2025-09-07T07:13:02.8255717Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyNMSCustomOp_cuda_float32 XFAIL [0.0051s] [ 25%] 2025-09-07T07:13:02.8255877Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyNonzeroCustomOp_cuda_float32 XFAIL [0.0033s] [ 25%] 2025-09-07T07:13:02.8256049Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpySortAutogradFunction_cuda_float32 PASSED [0.0064s] [ 25%] 2025-09-07T07:13:02.8256206Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpySortCustomOp_cuda_float32 PASSED [0.0072s] [ 25%] 2025-09-07T07:13:02.8256375Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpySplitCopyCustomOp_cuda_float32 PASSED [0.0093s] [ 25%] 2025-09-07T07:13:02.8256555Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpySplitCopyWithIntCustomOp_cuda_float32 PASSED [0.0079s] [ 25%] 2025-09-07T07:13:02.8256728Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyTakeAutogradFunction_cuda_float32 PASSED [0.0146s] [ 25%] 2025-09-07T07:13:02.8256885Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyTakeCustomOp_cuda_float32 PASSED [0.0116s] [ 25%] 2025-09-07T07:13:02.8257051Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_NumpyViewCopyCustomOp_cuda_float32 PASSED [0.0045s] [ 25%] 2025-09-07T07:13:02.8257234Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ScaleGradGenVmapAutogradFunction_cuda_float32 PASSED [0.0045s] [ 25%] 2025-09-07T07:13:02.8257401Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_SelectAutogradFunction_cuda_float32 PASSED [0.0042s] [ 25%] 2025-09-07T07:13:02.8258689Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_SelectGenVmapAutogradFunction_cuda_float32 PASSED [0.0045s] [ 25%] 2025-09-07T07:13:02.8258868Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_SortGenVmapAutogradFunction_cuda_float32 PASSED [0.0069s] [ 25%] 2025-09-07T07:13:02.8259004Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_T_cuda_float32 PASSED [0.0053s] [ 26%] 2025-09-07T07:13:02.8259196Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ZeroGradientsGenVmapAutogradFunction_cuda_float32 PASSED [0.0078s] [ 26%] 2025-09-07T07:13:02.8259344Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___getitem___cuda_float32 XFAIL [0.0277s] [ 26%] 2025-09-07T07:13:02.8259505Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___getitem___functorch_cuda_float32 PASSED [0.1059s] [ 26%] 2025-09-07T07:13:02.8259669Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___radd___cuda_float32 PASSED [0.2238s] [ 26%] 2025-09-07T07:13:02.8259810Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rand___cuda_int64 PASSED [0.1317s] [ 26%] 2025-09-07T07:13:02.8259970Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rdiv___cuda_float32 PASSED [0.2361s] [ 26%] 2025-09-07T07:13:02.8260481Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rmatmul___cuda_float32 SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157534 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.) [ 26%] 2025-09-07T07:13:02.8260641Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rmod___cuda_float32 PASSED [0.2222s] [ 26%] 2025-09-07T07:13:02.8260785Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rmul___cuda_float32 PASSED [0.2209s] [ 26%] 2025-09-07T07:13:02.8260923Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___ror___cuda_int64 PASSED [0.1321s] [ 26%] 2025-09-07T07:13:02.8261065Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rpow___cuda_float32 PASSED [0.2205s] [ 26%] 2025-09-07T07:13:02.8261206Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rsub___cuda_float32 XFAIL [0.0874s] [ 26%] 2025-09-07T07:13:02.8261345Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive___rxor___cuda_int64 PASSED [0.1312s] [ 26%] 2025-09-07T07:13:02.8261506Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__batch_norm_with_update_cuda_float32 XFAIL [0.0035s] [ 26%] 2025-09-07T07:13:02.8261653Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__chunk_cat_cuda_float32 PASSED [0.0110s] [ 27%] 2025-09-07T07:13:02.8261818Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__native_batch_norm_legit_cuda_float32 XFAIL [0.0063s] [ 27%] 2025-09-07T07:13:02.8261982Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__segment_reduce_lengths_cuda_float32 PASSED [0.1992s] [ 27%] 2025-09-07T07:13:02.8262144Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__segment_reduce_offsets_cuda_float32 PASSED [0.1319s] [ 27%] 2025-09-07T07:13:02.8263406Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__softmax_backward_data_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 27%] 2025-09-07T07:13:02.8263571Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__unsafe_masked_index_cuda_float32 PASSED [0.1167s] [ 27%] 2025-09-07T07:13:02.8263751Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__unsafe_masked_index_put_accumulate_cuda_float32 PASSED [0.3990s] [ 27%] 2025-09-07T07:13:02.8263934Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive__upsample_bilinear2d_aa_cuda_float32 PASSED [0.0113s] [ 27%] 2025-09-07T07:13:02.8264074Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_abs_cuda_float32 PASSED [0.1116s] [ 27%] 2025-09-07T07:13:02.8264214Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_acos_cuda_float32 PASSED [0.1318s] [ 27%] 2025-09-07T07:13:02.8264354Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_acosh_cuda_float32 PASSED [0.1419s] [ 27%] 2025-09-07T07:13:02.8264555Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_add_cuda_float32 PASSED [0.3258s] [ 27%] 2025-09-07T07:13:02.8264696Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addbmm_cuda_float32 PASSED [0.0587s] [ 27%] 2025-09-07T07:13:02.8264836Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addcdiv_cuda_float32 XFAIL [0.2403s] [ 27%] 2025-09-07T07:13:02.8264998Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addcmul_cuda_float32 XFAIL [0.2145s] [ 27%] 2025-09-07T07:13:02.8265154Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addmm_cuda_float32 PASSED [0.0444s] [ 28%] 2025-09-07T07:13:02.8265313Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addmm_decomposed_cuda_float32 PASSED [0.0436s] [ 28%] 2025-09-07T07:13:02.8265812Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addmv_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157617 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.) [ 28%] 2025-09-07T07:13:02.8265978Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_addr_cuda_float32 PASSED [0.0502s] [ 28%] 2025-09-07T07:13:02.8266128Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_alias_copy_cuda_float32 PASSED [0.0049s] [ 28%] 2025-09-07T07:13:02.8266265Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_all_cuda_float32 PASSED [0.0249s] [ 28%] 2025-09-07T07:13:02.8266405Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_allclose_cuda_float32 XFAIL [0.0033s] [ 28%] 2025-09-07T07:13:02.8266544Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_amax_cuda_float32 PASSED [0.0320s] [ 28%] 2025-09-07T07:13:02.8266680Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_amin_cuda_float32 PASSED [0.0315s] [ 28%] 2025-09-07T07:13:02.8266823Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_aminmax_cuda_float32 PASSED [0.0193s] [ 28%] 2025-09-07T07:13:02.8268097Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_angle_cuda_float32 PASSED [0.0425s] [ 28%] 2025-09-07T07:13:02.8268239Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_any_cuda_float32 PASSED [0.0251s] [ 28%] 2025-09-07T07:13:02.8268382Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_arange_cuda_float32 PASSED [0.0100s] [ 28%] 2025-09-07T07:13:02.8268522Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_argmax_cuda_float32 PASSED [0.0218s] [ 28%] 2025-09-07T07:13:02.8268661Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_argmin_cuda_float32 PASSED [0.0216s] [ 28%] 2025-09-07T07:13:02.8268802Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_argsort_cuda_float32 PASSED [0.0453s] [ 29%] 2025-09-07T07:13:02.8268947Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_argwhere_cuda_float32 PASSED [0.0116s] [ 29%] 2025-09-07T07:13:02.8269102Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_as_strided_copy_cuda_float32 XFAIL [0.0056s] [ 29%] 2025-09-07T07:13:02.8269272Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_as_strided_cuda_float32 XFAIL [0.0061s] [ 29%] 2025-09-07T07:13:02.8269436Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_as_strided_partial_views_cuda_float32 XFAIL [0.0038s] [ 29%] 2025-09-07T07:13:02.8269590Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_as_strided_scatter_cuda_float32 XFAIL [0.0050s] [ 29%] 2025-09-07T07:13:02.8269728Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_asin_cuda_float32 PASSED [0.1263s] [ 29%] 2025-09-07T07:13:02.8269866Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_asinh_cuda_float32 PASSED [0.1287s] [ 29%] 2025-09-07T07:13:02.8270003Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atan2_cuda_float32 PASSED [0.4789s] [ 29%] 2025-09-07T07:13:02.8270140Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atan_cuda_float32 PASSED [0.1139s] [ 29%] 2025-09-07T07:13:02.8270295Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atanh_cuda_float32 PASSED [0.1146s] [ 29%] 2025-09-07T07:13:02.8270459Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atleast_1d_cuda_float32 PASSED [0.0076s] [ 29%] 2025-09-07T07:13:02.8270604Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atleast_2d_cuda_float32 PASSED [0.0076s] [ 29%] 2025-09-07T07:13:02.8270749Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_atleast_3d_cuda_float32 PASSED [0.0076s] [ 29%] 2025-09-07T07:13:02.8270889Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_baddbmm_cuda_float32 PASSED [0.0395s] [ 29%] 2025-09-07T07:13:02.8271048Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bernoulli_cuda_float32 XFAIL [0.0055s] [ 30%] 2025-09-07T07:13:02.8271189Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bfloat16_cuda_float32 XFAIL [0.0073s] [ 30%] 2025-09-07T07:13:02.8272456Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bfloat16_functorch_no_channels_last_cuda_float32 PASSED [0.0066s] [ 30%] 2025-09-07T07:13:02.8272599Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bincount_cuda_int64 PASSED [0.0349s] [ 30%] 2025-09-07T07:13:02.8272744Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_and_cuda_int64 PASSED [0.1768s] [ 30%] 2025-09-07T07:13:02.8272902Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_left_shift_cuda_int64 PASSED [0.1939s] [ 30%] 2025-09-07T07:13:02.8273047Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_not_cuda_int64 PASSED [0.0445s] [ 30%] 2025-09-07T07:13:02.8273192Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_or_cuda_int64 PASSED [0.1819s] [ 30%] 2025-09-07T07:13:02.8273352Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_right_shift_cuda_int64 PASSED [0.1812s] [ 30%] 2025-09-07T07:13:02.8273495Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bitwise_xor_cuda_int64 PASSED [0.1817s] [ 30%] 2025-09-07T07:13:02.8273642Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_block_diag_cuda_float32 PASSED [0.0199s] [ 30%] 2025-09-07T07:13:02.8273780Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bmm_cuda_float32 PASSED [0.0052s] [ 30%] 2025-09-07T07:13:02.8273915Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bool_cuda_float32 XFAIL [0.0079s] [ 30%] 2025-09-07T07:13:02.8274090Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bool_functorch_no_channels_last_cuda_float32 PASSED [0.0070s] [ 30%] 2025-09-07T07:13:02.8274247Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_broadcast_shapes_cuda_float32 PASSED [0.0034s] [ 30%] 2025-09-07T07:13:02.8274482Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_broadcast_tensors_cuda_float32 PASSED [0.1077s] [ 31%] 2025-09-07T07:13:02.8274635Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_broadcast_to_cuda_float32 PASSED [0.0106s] [ 31%] 2025-09-07T07:13:02.8274779Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_bucketize_cuda_float32 PASSED [0.0874s] [ 31%] 2025-09-07T07:13:02.8274914Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_byte_cuda_float32 XFAIL [0.0078s] [ 31%] 2025-09-07T07:13:02.8275088Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_byte_functorch_no_channels_last_cuda_float32 PASSED [0.0069s] [ 31%] 2025-09-07T07:13:02.8275241Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cartesian_prod_cuda_float32 PASSED [0.0106s] [ 31%] 2025-09-07T07:13:02.8275378Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cat_cuda_float32 PASSED [0.0197s] [ 31%] 2025-09-07T07:13:02.8275578Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cauchy_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 31%] 2025-09-07T07:13:02.8276818Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cdist_cuda_float32 PASSED [0.5142s] [ 31%] 2025-09-07T07:13:02.8276960Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cdouble_cuda_float32 XFAIL [0.0082s] [ 31%] 2025-09-07T07:13:02.8277098Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ceil_cuda_float32 PASSED [0.0685s] [ 31%] 2025-09-07T07:13:02.8277236Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cfloat_cuda_float32 XFAIL [0.0082s] [ 31%] 2025-09-07T07:13:02.8277397Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_chalf_cuda_float32 PASSED [0.0565s] [ 31%] 2025-09-07T07:13:02.8277535Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_char_cuda_float32 XFAIL [0.0076s] [ 31%] 2025-09-07T07:13:02.8277710Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_char_functorch_no_channels_last_cuda_float32 PASSED [0.0069s] [ 31%] 2025-09-07T07:13:02.8277856Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cholesky_cuda_float32 PASSED [0.0396s] [ 32%] 2025-09-07T07:13:02.8278013Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cholesky_inverse_cuda_float32 PASSED [0.0261s] [ 32%] 2025-09-07T07:13:02.8278166Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cholesky_solve_cuda_float32 PASSED [0.0833s] [ 32%] 2025-09-07T07:13:02.8278305Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_chunk_cuda_float32 PASSED [0.0546s] [ 32%] 2025-09-07T07:13:02.8278445Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_clamp_cuda_float32 XFAIL [0.1000s] [ 32%] 2025-09-07T07:13:02.8278595Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_clamp_max_cuda_float32 XFAIL [0.0032s] [ 32%] 2025-09-07T07:13:02.8278742Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_clamp_min_cuda_float32 XFAIL [0.0031s] [ 32%] 2025-09-07T07:13:02.8278881Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_clone_cuda_float32 XFAIL [0.0615s] [ 32%] 2025-09-07T07:13:02.8279033Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_column_stack_cuda_float32 PASSED [0.0027s] [ 32%] 2025-09-07T07:13:02.8279184Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_combinations_cuda_float32 PASSED [0.0422s] [ 32%] 2025-09-07T07:13:02.8279327Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_complex_cuda_float32 PASSED [0.2080s] [ 32%] 2025-09-07T07:13:02.8279467Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_conj_cuda_float32 PASSED [0.0378s] [ 32%] 2025-09-07T07:13:02.8279635Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_conj_physical_cuda_float32 PASSED [0.0612s] [ 32%] 2025-09-07T07:13:02.8279790Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_constant_pad_nd_cuda_float32 PASSED [0.0534s] [ 32%] 2025-09-07T07:13:02.8281012Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_contiguous_cuda_float32 XFAIL [0.0584s] [ 32%] 2025-09-07T07:13:02.8281160Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_copysign_cuda_float32 PASSED [0.2871s] [ 33%] 2025-09-07T07:13:02.8281304Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_corrcoef_cuda_float32 PASSED [0.0117s] [ 33%] 2025-09-07T07:13:02.8281443Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cos_cuda_float32 PASSED [0.0698s] [ 33%] 2025-09-07T07:13:02.8281582Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cosh_cuda_float32 PASSED [0.0731s] [ 33%] 2025-09-07T07:13:02.8281753Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_count_nonzero_cuda_float32 PASSED [0.0253s] [ 33%] 2025-09-07T07:13:02.8281904Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cov_cuda_float32 PASSED [0.1129s] [ 33%] 2025-09-07T07:13:02.8282042Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cross_cuda_float32 PASSED [0.0108s] [ 33%] 2025-09-07T07:13:02.8282182Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cummax_cuda_float32 PASSED [0.0100s] [ 33%] 2025-09-07T07:13:02.8282321Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cummin_cuda_float32 PASSED [0.0072s] [ 33%] 2025-09-07T07:13:02.8282479Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cumprod_cuda_float32 PASSED [0.0270s] [ 33%] 2025-09-07T07:13:02.8282619Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cumsum_cuda_float32 PASSED [0.0115s] [ 33%] 2025-09-07T07:13:02.8282786Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_cumulative_trapezoid_cuda_float32 PASSED [0.0217s] [ 33%] 2025-09-07T07:13:02.8282928Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_deg2rad_cuda_float32 PASSED [0.0715s] [ 33%] 2025-09-07T07:13:02.8283067Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diag_cuda_float32 PASSED [0.0231s] [ 33%] 2025-09-07T07:13:02.8283213Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diag_embed_cuda_float32 PASSED [0.0372s] [ 34%] 2025-09-07T07:13:02.8283358Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diagflat_cuda_float32 PASSED [0.0090s] [ 34%] 2025-09-07T07:13:02.8283512Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diagonal_copy_cuda_float32 PASSED [0.0348s] [ 34%] 2025-09-07T07:13:02.8283659Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diagonal_cuda_float32 PASSED [0.0336s] [ 34%] 2025-09-07T07:13:02.8283815Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diagonal_scatter_cuda_float32 PASSED [0.0351s] [ 34%] 2025-09-07T07:13:02.8283955Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_diff_cuda_float32 PASSED [0.1935s] [ 34%] 2025-09-07T07:13:02.8285225Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_digamma_cuda_float32 PASSED [0.1300s] [ 34%] 2025-09-07T07:13:02.8285366Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_dist_cuda_float32 PASSED [0.1125s] [ 34%] 2025-09-07T07:13:02.8285523Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_div_floor_rounding_cuda_float32 PASSED [0.5666s] [ 34%] 2025-09-07T07:13:02.8285686Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_div_no_rounding_mode_cuda_float32 PASSED [0.5719s] [ 34%] 2025-09-07T07:13:02.8285870Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_div_trunc_rounding_cuda_float32 PASSED [0.5726s] [ 34%] 2025-09-07T07:13:02.8286370Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_dot_cuda_float32 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157644 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.) [ 34%] 2025-09-07T07:13:02.8286512Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_double_cuda_float32 XFAIL [0.0079s] [ 34%] 2025-09-07T07:13:02.8286689Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_double_functorch_no_channels_last_cuda_float32 PASSED [0.0070s] [ 34%] 2025-09-07T07:13:02.8286832Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_dsplit_cuda_float32 PASSED [0.0096s] [ 34%] 2025-09-07T07:13:02.8286992Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_dstack_cuda_float32 PASSED [0.0055s] [ 35%] 2025-09-07T07:13:02.8287156Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_einsum_cuda_float32 PASSED [0.0038s] [ 35%] 2025-09-07T07:13:02.8287294Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_empty_cuda_float32 PASSED [0.0029s] [ 35%] 2025-09-07T07:13:02.8287442Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_empty_like_cuda_float32 PASSED [0.0201s] [ 35%] 2025-09-07T07:13:02.8287593Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_empty_permuted_cuda_float32 PASSED [0.0092s] [ 35%] 2025-09-07T07:13:02.8287743Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_empty_strided_cuda_float32 PASSED [0.0027s] [ 35%] 2025-09-07T07:13:02.8287895Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_eq_cuda_float32 XFAIL [0.0977s] [ 35%] 2025-09-07T07:13:02.8288036Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_equal_cuda_float32 XFAIL [0.0031s] [ 35%] 2025-09-07T07:13:02.8288172Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_erf_cuda_float32 PASSED [0.0947s] [ 35%] 2025-09-07T07:13:02.8288310Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_erfc_cuda_float32 PASSED [0.1062s] [ 35%] 2025-09-07T07:13:02.8288450Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_erfinv_cuda_float32 PASSED [0.0972s] [ 35%] 2025-09-07T07:13:02.8288588Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_exp2_cuda_float32 PASSED [0.1060s] [ 35%] 2025-09-07T07:13:02.8289809Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_exp_cuda_float32 PASSED [0.0753s] [ 35%] 2025-09-07T07:13:02.8289961Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_expand_as_cuda_float32 PASSED [0.0084s] [ 35%] 2025-09-07T07:13:02.8290114Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_expand_copy_cuda_float32 PASSED [0.0131s] [ 35%] 2025-09-07T07:13:02.8290256Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_expand_cuda_float32 PASSED [0.0129s] [ 36%] 2025-09-07T07:13:02.8290394Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_expm1_cuda_float32 PASSED [0.0949s] [ 36%] 2025-09-07T07:13:02.8290542Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_exponential_cuda_float32 XFAIL [0.0086s] [ 36%] 2025-09-07T07:13:02.8290677Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_eye_cuda_float32 PASSED [0.0143s] [ 36%] 2025-09-07T07:13:02.8290822Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_fft2_cuda_float32 PASSED [2.4332s] [ 36%] 2025-09-07T07:13:02.8290963Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_fft_cuda_float32 PASSED [0.7678s] [ 36%] 2025-09-07T07:13:02.8291127Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_fftn_cuda_float32 PASSED [2.0507s] [ 36%] 2025-09-07T07:13:02.8291279Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_fftshift_cuda_float32 PASSED [0.0086s] [ 36%] 2025-09-07T07:13:02.8291423Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_hfft2_cuda_float32 PASSED [1.4747s] [ 36%] 2025-09-07T07:13:02.8291567Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_hfft_cuda_float32 PASSED [0.4780s] [ 36%] 2025-09-07T07:13:02.8291709Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_hfftn_cuda_float32 PASSED [0.0168s] [ 36%] 2025-09-07T07:13:02.8291853Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ifft2_cuda_float32 PASSED [0.0137s] [ 36%] 2025-09-07T07:13:02.8291996Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ifft_cuda_float32 PASSED [0.0164s] [ 36%] 2025-09-07T07:13:02.8292156Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ifftn_cuda_float32 PASSED [0.0164s] [ 36%] 2025-09-07T07:13:02.8292320Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ifftshift_cuda_float32 PASSED [0.0083s] [ 36%] 2025-09-07T07:13:02.8292466Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ihfft2_cuda_float32 PASSED [0.3451s] [ 37%] 2025-09-07T07:13:02.8292609Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ihfft_cuda_float32 PASSED [0.0500s] [ 37%] 2025-09-07T07:13:02.8292753Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_ihfftn_cuda_float32 PASSED [0.0184s] [ 37%] 2025-09-07T07:13:02.8293992Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_irfft2_cuda_float32 PASSED [0.5982s] [ 37%] 2025-09-07T07:13:02.8294144Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_irfft_cuda_float32 PASSED [0.0140s] [ 37%] 2025-09-07T07:13:02.8294287Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_irfftn_cuda_float32 PASSED [0.0149s] [ 37%] 2025-09-07T07:13:02.8294486Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_rfft2_cuda_float32 PASSED [0.6056s] [ 37%] 2025-09-07T07:13:02.8294629Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_rfft_cuda_float32 PASSED [0.0163s] [ 37%] 2025-09-07T07:13:02.8294774Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fft_rfftn_cuda_float32 PASSED [0.0195s] [ 37%] 2025-09-07T07:13:02.8294911Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fill_cuda_float32 PASSED [0.0860s] [ 37%] 2025-09-07T07:13:02.8295055Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_flatten_cuda_float32 PASSED [0.0527s] [ 37%] 2025-09-07T07:13:02.8295194Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_flip_cuda_float32 PASSED [0.0126s] [ 37%] 2025-09-07T07:13:02.8295335Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fliplr_cuda_float32 PASSED [0.0061s] [ 37%] 2025-09-07T07:13:02.8295477Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_flipud_cuda_float32 PASSED [0.0061s] [ 37%] 2025-09-07T07:13:02.8295615Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_float_cuda_float32 XFAIL [0.0079s] [ 37%] 2025-09-07T07:13:02.8295791Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_float_functorch_no_channels_last_cuda_float32 PASSED [0.0071s] [ 38%] 2025-09-07T07:13:02.8295939Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_float_power_cuda_float32 PASSED [0.2731s] [ 38%] 2025-09-07T07:13:02.8296079Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_floor_cuda_float32 PASSED [0.0743s] [ 38%] 2025-09-07T07:13:02.8296250Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_floor_divide_cuda_float32 PASSED [0.3556s] [ 38%] 2025-09-07T07:13:02.8296389Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmax_cuda_float32 PASSED [0.2356s] [ 38%] 2025-09-07T07:13:02.8296527Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmin_cuda_float32 PASSED [0.2360s] [ 38%] 2025-09-07T07:13:02.8296664Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_fmod_cuda_float32 PASSED [0.3554s] [ 38%] 2025-09-07T07:13:02.8296799Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_frac_cuda_float32 PASSED [0.0757s] [ 38%] 2025-09-07T07:13:02.8296938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_frexp_cuda_float32 PASSED [0.0701s] [ 38%] 2025-09-07T07:13:02.8298159Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_full_cuda_float32 PASSED [0.0027s] [ 38%] 2025-09-07T07:13:02.8298332Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_full_like_cuda_float32 PASSED [0.0112s] [ 38%] 2025-09-07T07:13:02.8298489Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gather_cuda_float32 PASSED [0.0254s] [ 38%] 2025-09-07T07:13:02.8298622Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gcd_cuda_int64 PASSED [0.1833s] [ 38%] 2025-09-07T07:13:02.8298756Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ge_cuda_float32 XFAIL [0.1801s] [ 38%] 2025-09-07T07:13:02.8298945Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_geometric_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 38%] 2025-09-07T07:13:02.8299084Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_geqrf_cuda_float32 PASSED [0.0686s] [ 39%] 2025-09-07T07:13:02.8299248Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gradient_cuda_float32 PASSED [0.0499s] [ 39%] 2025-09-07T07:13:02.8299404Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_grid_sampler_2d_cuda_float32 PASSED [0.0471s] [ 39%] 2025-09-07T07:13:02.8299574Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_grid_sampler_3d_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 39%] 2025-09-07T07:13:02.8299707Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_gt_cuda_float32 XFAIL [0.1774s] [ 39%] 2025-09-07T07:13:02.8299843Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_half_cuda_float32 XFAIL [0.0083s] [ 39%] 2025-09-07T07:13:02.8300017Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_half_functorch_no_channels_last_cuda_float32 PASSED [0.0074s] [ 39%] 2025-09-07T07:13:02.8300182Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hash_tensor_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 39%] 2025-09-07T07:13:02.8300329Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_heaviside_cuda_float32 PASSED [0.3203s] [ 39%] 2025-09-07T07:13:02.8300467Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_histc_cuda_float32 PASSED [0.1601s] [ 39%] 2025-09-07T07:13:02.8300606Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hsplit_cuda_float32 PASSED [0.0109s] [ 39%] 2025-09-07T07:13:02.8300747Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hstack_cuda_float32 PASSED [0.0054s] [ 39%] 2025-09-07T07:13:02.8300884Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_hypot_cuda_float32 PASSED [0.3084s] [ 39%] 2025-09-07T07:13:02.8301020Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_i0_cuda_float32 PASSED [0.1073s] [ 39%] 2025-09-07T07:13:02.8301162Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_igamma_cuda_float32 PASSED [0.6583s] [ 39%] 2025-09-07T07:13:02.8302393Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_igammac_cuda_float32 PASSED [0.7077s] [ 40%] 2025-09-07T07:13:02.8302537Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_imag_cuda_complex64 PASSED [0.0561s] [ 40%] 2025-09-07T07:13:02.8302683Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_add_cuda_float32 PASSED [0.0772s] [ 40%] 2025-09-07T07:13:02.8302829Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_copy_cuda_float32 PASSED [0.0265s] [ 40%] 2025-09-07T07:13:02.8302975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_fill_cuda_float32 PASSED [0.0453s] [ 40%] 2025-09-07T07:13:02.8303118Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_put_cuda_float32 XFAIL [0.0119s] [ 40%] 2025-09-07T07:13:02.8303280Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_put_functorch_cuda_float32 PASSED [0.0848s] [ 40%] 2025-09-07T07:13:02.8303452Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_amax_cuda_float32 PASSED [0.0725s] [ 40%] 2025-09-07T07:13:02.8303624Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_amin_cuda_float32 PASSED [0.0722s] [ 40%] 2025-09-07T07:13:02.8303779Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_mean_cuda_float32 PASSED [0.0976s] [ 40%] 2025-09-07T07:13:02.8303933Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_reduce_prod_cuda_float32 PASSED [0.0736s] [ 40%] 2025-09-07T07:13:02.8304084Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_index_select_cuda_float32 PASSED [0.0111s] [ 40%] 2025-09-07T07:13:02.8304684Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_inner_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157726 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.) [ 40%] 2025-09-07T07:13:02.8304826Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_int_cuda_float32 XFAIL [0.0080s] [ 40%] 2025-09-07T07:13:02.8304998Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_int_functorch_no_channels_last_cuda_float32 PASSED [0.0072s] [ 40%] 2025-09-07T07:13:02.8305141Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isclose_cuda_float32 PASSED [0.4667s] [ 41%] 2025-09-07T07:13:02.8305285Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isfinite_cuda_float32 PASSED [0.0502s] [ 41%] 2025-09-07T07:13:02.8305425Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isin_cuda_float32 PASSED [0.0183s] [ 41%] 2025-09-07T07:13:02.8305564Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isinf_cuda_float32 PASSED [0.0435s] [ 41%] 2025-09-07T07:13:02.8305705Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isnan_cuda_float32 PASSED [0.0409s] [ 41%] 2025-09-07T07:13:02.8305849Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isneginf_cuda_float32 PASSED [0.0412s] [ 41%] 2025-09-07T07:13:02.8307076Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isposinf_cuda_float32 PASSED [0.0410s] [ 41%] 2025-09-07T07:13:02.8307218Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_isreal_cuda_float32 PASSED [0.0451s] [ 41%] 2025-09-07T07:13:02.8307361Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_istft_cuda_complex64 PASSED [1.3646s] [ 41%] 2025-09-07T07:13:02.8307499Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_item_cuda_float32 XFAIL [0.0093s] [ 41%] 2025-09-07T07:13:02.8307690Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_2inputs_2outputs_cuda_float32 XFAIL [0.0044s] [ 41%] 2025-09-07T07:13:02.8307867Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_4inputs_with_extra_args_cuda_float32 XFAIL [0.0041s] [ 41%] 2025-09-07T07:13:02.8308023Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_binary_cuda_float32 XFAIL [0.0074s] [ 41%] 2025-09-07T07:13:02.8308234Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_binary_return_by_ref_cuda_float32 XFAIL [0.0074s] [ 41%] 2025-09-07T07:13:02.8308388Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_jiterator_unary_cuda_float32 XFAIL [0.0037s] [ 41%] 2025-09-07T07:13:02.8308528Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_kron_cuda_float32 PASSED [0.0049s] [ 42%] 2025-09-07T07:13:02.8308672Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_kthvalue_cuda_float32 PASSED [0.0262s] [ 42%] 2025-09-07T07:13:02.8308833Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lcm_cuda_int64 PASSED [0.1807s] [ 42%] 2025-09-07T07:13:02.8308990Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ldexp_cuda_float32 PASSED [0.3451s] [ 42%] 2025-09-07T07:13:02.8309124Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_le_cuda_float32 XFAIL [0.1741s] [ 42%] 2025-09-07T07:13:02.8309262Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lerp_cuda_float32 PASSED [0.0665s] [ 42%] 2025-09-07T07:13:02.8309402Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lgamma_cuda_float32 PASSED [0.1120s] [ 42%] 2025-09-07T07:13:02.8309555Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cholesky_cuda_float32 PASSED [0.0403s] [ 42%] 2025-09-07T07:13:02.8309729Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cholesky_ex_cuda_float32 PASSED [0.0462s] [ 42%] 2025-09-07T07:13:02.8309880Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cond_cuda_float32 PASSED [0.0135s] [ 42%] 2025-09-07T07:13:02.8310032Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_cross_cuda_float32 PASSED [0.0140s] [ 42%] 2025-09-07T07:13:02.8310176Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_det_cuda_float32 PASSED [0.0297s] [ 42%] 2025-09-07T07:13:02.8311413Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_diagonal_cuda_float32 PASSED [0.0259s] [ 42%] 2025-09-07T07:13:02.8311562Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eig_cuda_float32 PASSED [0.0249s] [ 42%] 2025-09-07T07:13:02.8311727Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigh_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 42%] 2025-09-07T07:13:02.8311881Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigvals_cuda_float32 PASSED [0.0188s] [ 43%] 2025-09-07T07:13:02.8312033Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_eigvalsh_cuda_float32 PASSED [0.1025s] [ 43%] 2025-09-07T07:13:02.8312290Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_householder_product_cuda_float32 SKIPPED [0.0007s] (skipCUDAIfRocm: test doesn't currently work on the ROCm stack) [ 43%] 2025-09-07T07:13:02.8312440Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_inv_cuda_float32 PASSED [0.0295s] [ 43%] 2025-09-07T07:13:02.8312591Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_inv_ex_cuda_float32 PASSED [0.0184s] [ 43%] 2025-09-07T07:13:02.8312751Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_factor_cuda_float32 PASSED [0.0146s] [ 43%] 2025-09-07T07:13:02.8312930Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_factor_ex_cuda_float32 PASSED [0.0110s] [ 43%] 2025-09-07T07:13:02.8313168Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_ldl_solve_cuda_float32 SKIPPED [0.0005s] (skipCUDAIfRocm: test doesn't currently work on the ROCm stack) [ 43%] 2025-09-07T07:13:02.8313320Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lstsq_cuda_float32 PASSED [0.8764s] [ 43%] 2025-09-07T07:13:02.8313488Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lstsq_grad_oriented_cuda_float32 PASSED [3.6202s] [ 43%] 2025-09-07T07:13:02.8313634Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_cuda_float32 PASSED [0.1114s] [ 43%] 2025-09-07T07:13:02.8313789Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_factor_cuda_float32 PASSED [0.0958s] [ 43%] 2025-09-07T07:13:02.8313949Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_factor_ex_cuda_float32 PASSED [0.0895s] [ 43%] 2025-09-07T07:13:02.8314116Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_lu_solve_cuda_float32 PASSED [0.3689s] [ 43%] 2025-09-07T07:13:02.8314289Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_norm_cuda_float32 PASSED [0.1264s] [ 43%] 2025-09-07T07:13:02.8314502Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_power_cuda_float32 PASSED [0.0585s] [ 44%] 2025-09-07T07:13:02.8314661Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_rank_cuda_float32 PASSED [0.1715s] [ 44%] 2025-09-07T07:13:02.8314830Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_matrix_rank_hermitian_cuda_float32 PASSED [0.0246s] [ 44%] 2025-09-07T07:13:02.8316092Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_multi_dot_cuda_float32 PASSED [0.0034s] [ 44%] 2025-09-07T07:13:02.8316248Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_norm_cuda_float32 PASSED [0.1437s] [ 44%] 2025-09-07T07:13:02.8316427Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_norm_subgradients_at_zero_cuda_float32 PASSED [0.1042s] [ 44%] 2025-09-07T07:13:02.8316576Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_cuda_float32 PASSED [0.0590s] [ 44%] 2025-09-07T07:13:02.8316738Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_hermitian_cuda_float32 PASSED [0.0253s] [ 44%] 2025-09-07T07:13:02.8316974Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_pinv_singular_cuda_float32 SKIPPED [0.0005s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 44%] 2025-09-07T07:13:02.8317121Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_qr_cuda_float32 PASSED [0.0725s] [ 44%] 2025-09-07T07:13:02.8317274Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_slogdet_cuda_float32 PASSED [0.0234s] [ 44%] 2025-09-07T07:13:02.8317426Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_cuda_float32 PASSED [0.0695s] [ 44%] 2025-09-07T07:13:02.8317578Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_ex_cuda_float32 PASSED [0.0851s] [ 44%] 2025-09-07T07:13:02.8317742Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_solve_triangular_cuda_float32 PASSED [0.2454s] [ 44%] 2025-09-07T07:13:02.8317888Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_svd_cuda_float32 PASSED [0.5242s] [ 45%] 2025-09-07T07:13:02.8318041Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_svdvals_cuda_float32 PASSED [0.0546s] [ 45%] 2025-09-07T07:13:02.8318195Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_tensorinv_cuda_float32 PASSED [0.0082s] [ 45%] 2025-09-07T07:13:02.8318378Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_tensorsolve_cuda_float32 PASSED [0.0267s] [ 45%] 2025-09-07T07:13:02.8318529Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vander_cuda_float32 PASSED [0.0164s] [ 45%] 2025-09-07T07:13:02.8319044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vecdot_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157762 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.) [ 45%] 2025-09-07T07:13:02.8319205Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linalg_vector_norm_cuda_float32 PASSED [0.2091s] [ 45%] 2025-09-07T07:13:02.8319378Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linspace_cuda_float32 PASSED [0.0121s] [ 45%] 2025-09-07T07:13:02.8319546Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_linspace_tensor_overload_cuda_float32 PASSED [0.2900s] [ 45%] 2025-09-07T07:13:02.8319702Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log10_cuda_float32 PASSED [0.0789s] [ 45%] 2025-09-07T07:13:02.8320925Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log1p_cuda_float32 PASSED [0.0984s] [ 45%] 2025-09-07T07:13:02.8321066Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log2_cuda_float32 PASSED [0.0786s] [ 45%] 2025-09-07T07:13:02.8321202Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_cuda_float32 PASSED [0.0783s] [ 45%] 2025-09-07T07:13:02.8321408Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_normal_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 45%] 2025-09-07T07:13:02.8321558Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_softmax_cuda_float32 PASSED [0.0220s] [ 45%] 2025-09-07T07:13:02.8321722Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_log_softmax_with_dtype_cuda_float32 PASSED [0.0232s] [ 46%] 2025-09-07T07:13:02.8321869Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logaddexp2_cuda_float32 PASSED [0.0155s] [ 46%] 2025-09-07T07:13:02.8322014Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logaddexp_cuda_float32 PASSED [0.2207s] [ 46%] 2025-09-07T07:13:02.8322165Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logcumsumexp_cuda_float32 PASSED [0.0218s] [ 46%] 2025-09-07T07:13:02.8322306Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logdet_cuda_float32 PASSED [0.0217s] [ 46%] 2025-09-07T07:13:02.8322454Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_and_cuda_float32 PASSED [0.2234s] [ 46%] 2025-09-07T07:13:02.8322603Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_not_cuda_float32 PASSED [0.0449s] [ 46%] 2025-09-07T07:13:02.8322749Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_or_cuda_float32 PASSED [0.2219s] [ 46%] 2025-09-07T07:13:02.8322895Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logical_xor_cuda_float32 PASSED [0.2209s] [ 46%] 2025-09-07T07:13:02.8323033Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logit_cuda_float32 PASSED [0.1156s] [ 46%] 2025-09-07T07:13:02.8323177Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logspace_cuda_float32 PASSED [0.0465s] [ 46%] 2025-09-07T07:13:02.8323344Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logspace_tensor_overload_cuda_float32 PASSED [2.0814s] [ 46%] 2025-09-07T07:13:02.8323506Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_logsumexp_cuda_float32 PASSED [0.0534s] [ 46%] 2025-09-07T07:13:02.8323643Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_long_cuda_float32 XFAIL [0.0079s] [ 46%] 2025-09-07T07:13:02.8323819Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_long_functorch_no_channels_last_cuda_float32 PASSED [0.0070s] [ 46%] 2025-09-07T07:13:02.8323952Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lt_cuda_float32 XFAIL [0.1733s] [ 47%] 2025-09-07T07:13:02.8325223Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_cuda_float32 PASSED [0.0936s] [ 47%] 2025-09-07T07:13:02.8325372Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_solve_cuda_float32 PASSED [0.1077s] [ 47%] 2025-09-07T07:13:02.8325517Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_lu_unpack_cuda_float32 PASSED [0.1668s] [ 47%] 2025-09-07T07:13:02.8325696Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mH_cuda_float32 PASSED [0.0123s] [ 47%] 2025-09-07T07:13:02.8325830Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mT_cuda_float32 PASSED [0.0082s] [ 47%] 2025-09-07T07:13:02.8325999Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_amax_cuda_float32 PASSED [0.1504s] [ 47%] 2025-09-07T07:13:02.8326149Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_amin_cuda_float32 PASSED [0.1494s] [ 47%] 2025-09-07T07:13:02.8326298Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_argmax_cuda_float32 PASSED [0.0940s] [ 47%] 2025-09-07T07:13:02.8326449Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_argmin_cuda_float32 PASSED [0.0947s] [ 47%] 2025-09-07T07:13:02.8326619Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_cumprod_cuda_float32 PASSED [0.0289s] [ 47%] 2025-09-07T07:13:02.8326771Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_cumsum_cuda_float32 PASSED [0.0288s] [ 47%] 2025-09-07T07:13:02.8326918Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_fill_cuda_float32 PASSED [0.0598s] [ 47%] 2025-09-07T07:13:02.8327096Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_fill_functorch_Scalar_only_cuda_float32 PASSED [0.0208s] [ 47%] 2025-09-07T07:13:02.8327253Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_log_softmax_cuda_float32 PASSED [0.0381s] [ 47%] 2025-09-07T07:13:02.8327408Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_logaddexp_cuda_float32 PASSED [0.0423s] [ 48%] 2025-09-07T07:13:02.8327564Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_logsumexp_cuda_float32 PASSED [0.1922s] [ 48%] 2025-09-07T07:13:02.8327713Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_mean_cuda_float32 PASSED [0.1738s] [ 48%] 2025-09-07T07:13:02.8327862Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_median_cuda_float32 PASSED [0.0307s] [ 48%] 2025-09-07T07:13:02.8328009Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_norm_cuda_float32 PASSED [0.7536s] [ 48%] 2025-09-07T07:13:02.8328163Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_normalize_cuda_float32 PASSED [0.0537s] [ 48%] 2025-09-07T07:13:02.8328312Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_prod_cuda_float32 PASSED [0.1548s] [ 48%] 2025-09-07T07:13:02.8329548Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_scatter_cuda_float32 PASSED [0.0421s] [ 48%] 2025-09-07T07:13:02.8329704Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_select_cuda_float32 XFAIL [0.0071s] [ 48%] 2025-09-07T07:13:02.8329883Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_softmax_cuda_float32 PASSED [0.0382s] [ 48%] 2025-09-07T07:13:02.8330035Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_softmin_cuda_float32 PASSED [0.0392s] [ 48%] 2025-09-07T07:13:02.8330182Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_std_cuda_float32 PASSED [0.1095s] [ 48%] 2025-09-07T07:13:02.8330326Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_sum_cuda_float32 PASSED [0.1496s] [ 48%] 2025-09-07T07:13:02.8330472Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_masked_var_cuda_float32 PASSED [0.1064s] [ 48%] 2025-09-07T07:13:02.8330975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_matmul_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157790 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%] 2025-09-07T07:13:02.8331153Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_matrix_exp_cuda_float32 PASSED [0.0344s] [ 49%] 2025-09-07T07:13:02.8331297Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_binary_cuda_float32 PASSED [0.3961s] [ 49%] 2025-09-07T07:13:02.8331470Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_pool2d_with_indices_backward_cuda_float32 PASSED [2.2178s] [ 49%] 2025-09-07T07:13:02.8331631Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_reduction_no_dim_cuda_float32 PASSED [0.0053s] [ 49%] 2025-09-07T07:13:02.8331808Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_max_reduction_with_dim_cuda_float32 PASSED [0.0098s] [ 49%] 2025-09-07T07:13:02.8331952Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_maximum_cuda_float32 PASSED [0.2188s] [ 49%] 2025-09-07T07:13:02.8332092Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mean_cuda_float32 PASSED [0.0284s] [ 49%] 2025-09-07T07:13:02.8332233Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_median_cuda_float32 PASSED [0.0252s] [ 49%] 2025-09-07T07:13:02.8332399Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_meshgrid_list_of_tensors_cuda_float32 PASSED [0.0038s] [ 49%] 2025-09-07T07:13:02.8332565Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_meshgrid_variadic_tensors_cuda_float32 PASSED [0.0772s] [ 49%] 2025-09-07T07:13:02.8332712Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_binary_cuda_float32 PASSED [0.3914s] [ 49%] 2025-09-07T07:13:02.8332872Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_reduction_no_dim_cuda_float32 PASSED [0.0050s] [ 49%] 2025-09-07T07:13:02.8333035Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_min_reduction_with_dim_cuda_float32 PASSED [0.0098s] [ 49%] 2025-09-07T07:13:02.8334255Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_minimum_cuda_float32 PASSED [0.2185s] [ 49%] 2025-09-07T07:13:02.8334445Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mm_cuda_float32 PASSED [0.0091s] [ 49%] 2025-09-07T07:13:02.8334584Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mode_cuda_float32 PASSED [0.0424s] [ 50%] 2025-09-07T07:13:02.8334724Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_movedim_cuda_float32 XFAIL [0.0210s] [ 50%] 2025-09-07T07:13:02.8334862Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_msort_cuda_float32 PASSED [0.0057s] [ 50%] 2025-09-07T07:13:02.8334999Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mul_cuda_float32 PASSED [0.5907s] [ 50%] 2025-09-07T07:13:02.8335175Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_multinomial_cuda_float32 XFAIL [0.0131s] [ 50%] 2025-09-07T07:13:02.8335688Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mv_cuda_float32 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142631 for platform(s) linux, rocm, 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.) [ 50%] 2025-09-07T07:13:02.8335855Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_1_cuda_float32 PASSED [0.1881s] [ 50%] 2025-09-07T07:13:02.8336017Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_3_cuda_float32 PASSED [0.1902s] [ 50%] 2025-09-07T07:13:02.8336179Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_mvlgamma_mvlgamma_p_5_cuda_float32 PASSED [0.1937s] [ 50%] 2025-09-07T07:13:02.8336346Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nan_to_num_cuda_float32 PASSED [0.0783s] [ 50%] 2025-09-07T07:13:02.8336506Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanmean_cuda_float32 PASSED [0.0554s] [ 50%] 2025-09-07T07:13:02.8336651Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanmedian_cuda_float32 PASSED [0.0255s] [ 50%] 2025-09-07T07:13:02.8336798Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nanquantile_cuda_float32 PASSED [0.2508s] [ 50%] 2025-09-07T07:13:02.8336938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nansum_cuda_float32 PASSED [0.0435s] [ 50%] 2025-09-07T07:13:02.8337103Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_narrow_copy_cuda_float32 PASSED [0.0348s] [ 50%] 2025-09-07T07:13:02.8337244Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_narrow_cuda_float32 XFAIL [0.0134s] [ 51%] 2025-09-07T07:13:02.8337401Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_batch_norm_cuda_float32 XFAIL [0.0062s] [ 51%] 2025-09-07T07:13:02.8337566Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_dropout_backward_cuda_float32 PASSED [0.0230s] [ 51%] 2025-09-07T07:13:02.8337722Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_native_layer_norm_cuda_float32 PASSED [0.1073s] [ 51%] 2025-09-07T07:13:02.8338938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ne_cuda_float32 XFAIL [0.1715s] [ 51%] 2025-09-07T07:13:02.8339077Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_neg_cuda_float32 PASSED [0.1315s] [ 51%] 2025-09-07T07:13:02.8339224Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_empty_cuda_float32 PASSED [0.0073s] [ 51%] 2025-09-07T07:13:02.8339398Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_empty_strided_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 51%] 2025-09-07T07:13:02.8339544Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_full_cuda_float32 PASSED [0.0112s] [ 51%] 2025-09-07T07:13:02.8339687Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_ones_cuda_float32 PASSED [0.0108s] [ 51%] 2025-09-07T07:13:02.8339832Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_new_zeros_cuda_float32 PASSED [0.0109s] [ 51%] 2025-09-07T07:13:02.8339975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nextafter_cuda_float32 PASSED [0.3115s] [ 51%] 2025-09-07T07:13:02.8340154Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool1d_cuda_float32 PASSED [0.0123s] [ 51%] 2025-09-07T07:13:02.8340348Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool2d_cuda_float32 PASSED [0.0187s] [ 51%] 2025-09-07T07:13:02.8340525Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_avg_pool3d_cuda_float32 PASSED [0.0215s] [ 51%] 2025-09-07T07:13:02.8340700Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool1d_cuda_float32 PASSED [0.0179s] [ 52%] 2025-09-07T07:13:02.8340874Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool2d_cuda_float32 PASSED [0.0450s] [ 52%] 2025-09-07T07:13:02.8341049Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_adaptive_max_pool3d_cuda_float32 PASSED [0.0388s] [ 52%] 2025-09-07T07:13:02.8341217Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_alpha_dropout_cuda_float32 XFAIL [0.0135s] [ 52%] 2025-09-07T07:13:02.8341381Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool1d_cuda_float32 PASSED [0.0166s] [ 52%] 2025-09-07T07:13:02.8341561Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool2d_cuda_float32 PASSED [0.0171s] [ 52%] 2025-09-07T07:13:02.8341738Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_avg_pool3d_cuda_float32 PASSED [0.0185s] [ 52%] 2025-09-07T07:13:02.8341903Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_batch_norm_cuda_float32 PASSED [0.1837s] [ 52%] 2025-09-07T07:13:02.8342085Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_batch_norm_without_cudnn_cuda_float32 PASSED [0.1782s] [ 52%] 2025-09-07T07:13:02.8342248Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_bilinear_cuda_float32 PASSED [0.3654s] [ 52%] 2025-09-07T07:13:02.8343516Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_binary_cross_entropy_cuda_float32 PASSED [0.0300s] [ 52%] 2025-09-07T07:13:02.8343715Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_binary_cross_entropy_with_logits_cuda_float32 PASSED [0.0926s] [ 52%] 2025-09-07T07:13:02.8343874Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_celu_cuda_float32 PASSED [0.0795s] [ 52%] 2025-09-07T07:13:02.8344044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_channel_shuffle_cuda_float32 PASSED [0.0063s] [ 52%] 2025-09-07T07:13:02.8344205Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv1d_cuda_float32 PASSED [0.1147s] [ 52%] 2025-09-07T07:13:02.8344589Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_cuda_float32 MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback AI] Solver , workspace required: 2400, provided ptr: 0x7f3f51602c00 size: 1024 2025-09-07T07:13:02.8344771Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 2400, provided ptr: 0x7f3f51602c00 size: 1024 2025-09-07T07:13:02.8344958Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback AI] Solver , workspace required: 2400, provided ptr: 0x7f3f51603600 size: 1536 2025-09-07T07:13:02.8345132Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 2400, provided ptr: 0x7f3f51603600 size: 1536 2025-09-07T07:13:02.8345316Z MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback AI] Solver , workspace required: 2400, provided ptr: 0x7f3f51603600 size: 1024 2025-09-07T07:13:02.8345490Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 2400, provided ptr: 0x7f3f51603600 size: 1024 2025-09-07T07:13:02.8345530Z PASSED [1.0867s] [ 53%] 2025-09-07T07:13:02.8345730Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_no_bias_cuda_float32 PASSED [0.0057s] [ 53%] 2025-09-07T07:13:02.8345927Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32 PASSED [0.0089s] [ 53%] 2025-09-07T07:13:02.8346119Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_groups_with_bias_cuda_float32 PASSED [0.0088s] [ 53%] 2025-09-07T07:13:02.8346296Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_no_bias_cuda_float32 PASSED [0.0055s] [ 53%] 2025-09-07T07:13:02.8346484Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_no_bias_cuda_float32 PASSED [0.0056s] [ 53%] 2025-09-07T07:13:02.8346677Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_with_bias_cuda_float32 PASSED [0.0088s] [ 53%] 2025-09-07T07:13:02.8346877Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_stride_with_bias_cuda_float32 PASSED [0.0087s] [ 53%] 2025-09-07T07:13:02.8347097Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32 PASSED [0.1048s] [ 53%] 2025-09-07T07:13:02.8347301Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_strided_padding_dilation_with_bias_cuda_float32 PASSED [0.0101s] [ 53%] 2025-09-07T07:13:02.8348606Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv2d_with_bias_cuda_float32 PASSED [0.0084s] [ 53%] 2025-09-07T07:13:02.8348771Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv3d_cuda_float32 PASSED [0.1863s] [ 53%] 2025-09-07T07:13:02.8348972Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose1d_cuda_float32 PASSED [0.1775s] [ 53%] 2025-09-07T07:13:02.8349147Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose2d_cuda_float32 PASSED [0.1996s] [ 53%] 2025-09-07T07:13:02.8349496Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_conv_transpose3d_cuda_float32 MIOpen(HIP): Warning [IsEnoughWorkspace] [GetSolutionsFallback WTI] Solver , workspace required: 3840, provided ptr: 0x7f3f83c03600 size: 3328 2025-09-07T07:13:02.8349672Z MIOpen(HIP): Warning [IsEnoughWorkspace] [EvaluateInvokers] Solver , workspace required: 3840, provided ptr: 0x7f3f83c03600 size: 3328 2025-09-07T07:13:02.8349710Z PASSED [0.1865s] [ 53%] 2025-09-07T07:13:02.8349891Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cosine_embedding_loss_cuda_float32 PASSED [0.0563s] [ 54%] 2025-09-07T07:13:02.8350066Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cosine_similarity_cuda_float32 PASSED [0.0321s] [ 54%] 2025-09-07T07:13:02.8350235Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_cross_entropy_cuda_float32 PASSED [0.0719s] [ 54%] 2025-09-07T07:13:02.8350400Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_ctc_loss_cuda_float32 PASSED [0.2453s] [ 54%] 2025-09-07T07:13:02.8350562Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout2d_cuda_float32 XFAIL [0.0137s] [ 54%] 2025-09-07T07:13:02.8350723Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout3d_cuda_float32 XFAIL [0.0139s] [ 54%] 2025-09-07T07:13:02.8350881Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_dropout_cuda_float32 XFAIL [0.0134s] [ 54%] 2025-09-07T07:13:02.8351041Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_elu_cuda_float32 PASSED [0.0801s] [ 54%] 2025-09-07T07:13:02.8351224Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_bag_cuda_float32 XFAIL [0.0308s] [ 54%] 2025-09-07T07:13:02.8351386Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_cuda_float32 XFAIL [0.0201s] [ 54%] 2025-09-07T07:13:02.8351564Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_embedding_functorch_cuda_float32 PASSED [0.0171s] [ 54%] 2025-09-07T07:13:02.8351756Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_feature_alpha_dropout_with_train_cuda_float32 XFAIL [0.0087s] [ 54%] 2025-09-07T07:13:02.8351952Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_feature_alpha_dropout_without_train_cuda_float32 PASSED [0.0496s] [ 54%] 2025-09-07T07:13:02.8352129Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_fractional_max_pool2d_cuda_float32 XFAIL [0.0036s] [ 54%] 2025-09-07T07:13:02.8353407Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_fractional_max_pool3d_cuda_float32 XFAIL [0.0037s] [ 54%] 2025-09-07T07:13:02.8353600Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_gaussian_nll_loss_cuda_float32 XFAIL [0.0090s] [ 55%] 2025-09-07T07:13:02.8353759Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_gelu_cuda_float32 PASSED [0.0137s] [ 55%] 2025-09-07T07:13:02.8353915Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_glu_cuda_float32 PASSED [0.0553s] [ 55%] 2025-09-07T07:13:02.8354081Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_grid_sample_cuda_float32 PASSED [0.0516s] [ 55%] 2025-09-07T07:13:02.8354259Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_group_norm_cuda_float32 PASSED [0.0874s] [ 55%] 2025-09-07T07:13:02.8354471Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardshrink_cuda_float32 PASSED [0.0524s] [ 55%] 2025-09-07T07:13:02.8354638Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardsigmoid_cuda_float32 PASSED [0.0789s] [ 55%] 2025-09-07T07:13:02.8354801Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardswish_cuda_float32 PASSED [0.0072s] [ 55%] 2025-09-07T07:13:02.8354962Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hardtanh_cuda_float32 PASSED [0.0516s] [ 55%] 2025-09-07T07:13:02.8355139Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_hinge_embedding_loss_cuda_float32 PASSED [0.0674s] [ 55%] 2025-09-07T07:13:02.8355302Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_huber_loss_cuda_float32 PASSED [0.0281s] [ 55%] 2025-09-07T07:13:02.8355472Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_instance_norm_cuda_float32 PASSED [0.0039s] [ 55%] 2025-09-07T07:13:02.8355645Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_area_cuda_float32 PASSED [0.0315s] [ 55%] 2025-09-07T07:13:02.8355822Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_bicubic_cuda_float32 PASSED [0.0403s] [ 55%] 2025-09-07T07:13:02.8355998Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_bilinear_cuda_float32 PASSED [0.0391s] [ 56%] 2025-09-07T07:13:02.8356171Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_linear_cuda_float32 PASSED [0.0255s] [ 56%] 2025-09-07T07:13:02.8356361Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_nearest-exact_cuda_float32 PASSED [0.0335s] [ 56%] 2025-09-07T07:13:02.8356556Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_nearest_cuda_float32 PASSED [0.0309s] [ 56%] 2025-09-07T07:13:02.8356737Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_interpolate_trilinear_cuda_float32 PASSED [0.0336s] [ 56%] 2025-09-07T07:13:02.8356896Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_kl_div_cuda_float32 PASSED [0.0483s] [ 56%] 2025-09-07T07:13:02.8358144Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_l1_loss_cuda_float32 PASSED [0.0201s] [ 56%] 2025-09-07T07:13:02.8358310Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_layer_norm_cuda_float32 PASSED [0.0475s] [ 56%] 2025-09-07T07:13:02.8358476Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_leaky_relu_cuda_float32 PASSED [0.0220s] [ 56%] 2025-09-07T07:13:02.8358660Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_linear_cuda_float32 PASSED [0.0691s] [ 56%] 2025-09-07T07:13:02.8358855Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_local_response_norm_cuda_float32 PASSED [0.0208s] [ 56%] 2025-09-07T07:13:02.8359019Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_logsigmoid_cuda_float32 PASSED [0.0454s] [ 56%] 2025-09-07T07:13:02.8359195Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_margin_ranking_loss_cuda_float32 PASSED [0.1986s] [ 56%] 2025-09-07T07:13:02.8359357Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool1d_cuda_float32 PASSED [1.3943s] [ 56%] 2025-09-07T07:13:02.8359539Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool2d_cuda_float32 PASSED [2.2788s] [ 56%] 2025-09-07T07:13:02.8359703Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_pool3d_cuda_float32 PASSED [0.9326s] [ 57%] 2025-09-07T07:13:02.8359871Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool1d_cuda_float32 PASSED [0.4235s] [ 57%] 2025-09-07T07:13:02.8360042Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool1d_grad_cuda_float32 PASSED [0.0507s] [ 57%] 2025-09-07T07:13:02.8360209Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool2d_cuda_float32 PASSED [0.8291s] [ 57%] 2025-09-07T07:13:02.8360382Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool2d_grad_cuda_float32 PASSED [0.0966s] [ 57%] 2025-09-07T07:13:02.8360549Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool3d_cuda_float32 PASSED [0.5162s] [ 57%] 2025-09-07T07:13:02.8360724Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_max_unpool3d_grad_cuda_float32 PASSED [0.0551s] [ 57%] 2025-09-07T07:13:02.8360883Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mish_cuda_float32 PASSED [0.0788s] [ 57%] 2025-09-07T07:13:02.8361044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mse_loss_cuda_float32 PASSED [0.0164s] [ 57%] 2025-09-07T07:13:02.8361216Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_mse_loss_functorch_cuda_float32 PASSED [0.0238s] [ 57%] 2025-09-07T07:13:02.8361402Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multi_head_attention_forward_cuda_float32 XFAIL [1.4700s] [ 57%] 2025-09-07T07:13:02.8361575Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multi_margin_loss_cuda_float32 PASSED [0.5244s] [ 57%] 2025-09-07T07:13:02.8362854Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multilabel_margin_loss_cuda_float32 PASSED [0.0859s] [ 57%] 2025-09-07T07:13:02.8363046Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_multilabel_soft_margin_loss_cuda_float32 PASSED [0.0171s] [ 57%] 2025-09-07T07:13:02.8363207Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_nll_loss_cuda_float32 PASSED [0.2129s] [ 57%] 2025-09-07T07:13:02.8363370Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_normalize_cuda_float32 PASSED [0.0137s] [ 58%] 2025-09-07T07:13:02.8363527Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_one_hot_cuda_int64 XFAIL [0.0032s] [ 58%] 2025-09-07T07:13:02.8363695Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pad_circular_cuda_float32 PASSED [0.0125s] [ 58%] 2025-09-07T07:13:02.8363877Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pad_constant_cuda_float32 PASSED [0.0598s] [ 58%] 2025-09-07T07:13:02.8364041Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pad_reflect_cuda_float32 PASSED [0.0165s] [ 58%] 2025-09-07T07:13:02.8364224Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pad_replicate_cuda_float32 PASSED [0.0163s] [ 58%] 2025-09-07T07:13:02.8364447Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pad_replicate_negative_cuda_float32 PASSED [0.0102s] [ 58%] 2025-09-07T07:13:02.8364620Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pairwise_distance_cuda_float32 PASSED [0.0194s] [ 58%] 2025-09-07T07:13:02.8364803Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pdist_cuda_float32 PASSED [0.0136s] [ 58%] 2025-09-07T07:13:02.8364972Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pixel_shuffle_cuda_float32 PASSED [0.0073s] [ 58%] 2025-09-07T07:13:02.8365141Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_pixel_unshuffle_cuda_float32 PASSED [0.0072s] [ 58%] 2025-09-07T07:13:02.8365311Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_poisson_nll_loss_cuda_float32 PASSED [0.2330s] [ 58%] 2025-09-07T07:13:02.8365470Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_prelu_cuda_float32 PASSED [0.1992s] [ 58%] 2025-09-07T07:13:02.8365626Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_relu6_cuda_float32 PASSED [0.0453s] [ 58%] 2025-09-07T07:13:02.8365784Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_relu_cuda_float32 PASSED [0.0481s] [ 58%] 2025-09-07T07:13:02.8365944Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_rms_norm_cuda_float32 PASSED [0.0330s] [ 59%] 2025-09-07T07:13:02.8366104Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_rrelu_cuda_float32 XFAIL [0.0056s] [ 59%] 2025-09-07T07:13:02.8366291Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_scaled_dot_product_attention_cuda_float32 XFAIL [0.0361s] [ 59%] 2025-09-07T07:13:02.8367538Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_selu_cuda_float32 PASSED [0.0790s] [ 59%] 2025-09-07T07:13:02.8367712Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_silu_complex_cuda_complex64 PASSED [0.0958s] [ 59%] 2025-09-07T07:13:02.8367867Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_silu_cuda_float32 PASSED [0.0793s] [ 59%] 2025-09-07T07:13:02.8368037Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_smooth_l1_loss_cuda_float32 PASSED [0.0217s] [ 59%] 2025-09-07T07:13:02.8368235Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_soft_margin_loss_cuda_float32 PASSED [0.0236s] [ 59%] 2025-09-07T07:13:02.8368398Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softmin_cuda_float32 PASSED [0.0109s] [ 59%] 2025-09-07T07:13:02.8368571Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softmin_with_dtype_cuda_float32 PASSED [0.0112s] [ 59%] 2025-09-07T07:13:02.8368731Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softplus_cuda_float32 PASSED [0.0451s] [ 59%] 2025-09-07T07:13:02.8368896Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softshrink_cuda_float32 PASSED [0.0504s] [ 59%] 2025-09-07T07:13:02.8369057Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_softsign_cuda_float32 PASSED [0.0490s] [ 59%] 2025-09-07T07:13:02.8369243Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_tanhshrink_cuda_float32 PASSED [0.0466s] [ 59%] 2025-09-07T07:13:02.8369428Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_threshold_cuda_float32 PASSED [0.0866s] [ 59%] 2025-09-07T07:13:02.8369605Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_triplet_margin_loss_cuda_float32 PASSED [0.0647s] [ 60%] 2025-09-07T07:13:02.8369800Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_triplet_margin_with_distance_loss_cuda_float32 PASSED [0.0577s] [ 60%] 2025-09-07T07:13:02.8369959Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_unfold_cuda_float32 PASSED [0.1897s] [ 60%] 2025-09-07T07:13:02.8370147Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_upsample_bilinear_cuda_float32 PASSED [0.0104s] [ 60%] 2025-09-07T07:13:02.8370321Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nn_functional_upsample_nearest_cuda_float32 PASSED [0.0178s] [ 60%] 2025-09-07T07:13:02.8370467Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nonzero_cuda_float32 XFAIL [0.0035s] [ 60%] 2025-09-07T07:13:02.8370643Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_nonzero_static_cuda_float32 SKIPPED [0.0004s] (Only runs on cpu) [ 60%] 2025-09-07T07:13:02.8370785Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_cuda_float32 PASSED [0.0535s] [ 60%] 2025-09-07T07:13:02.8370930Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_fro_cuda_float32 PASSED [0.0061s] [ 60%] 2025-09-07T07:13:02.8372154Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_inf_cuda_float32 PASSED [0.0073s] [ 60%] 2025-09-07T07:13:02.8372302Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_norm_nuc_cuda_float32 PASSED [0.0104s] [ 60%] 2025-09-07T07:13:02.8372443Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_normal_cuda_float32 XFAIL [0.0040s] [ 60%] 2025-09-07T07:13:02.8372639Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_normal_in_place_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 60%] 2025-09-07T07:13:02.8372797Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_normal_number_mean_cuda_float32 XFAIL [0.0039s] [ 60%] 2025-09-07T07:13:02.8372938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ones_cuda_float32 PASSED [0.0024s] [ 60%] 2025-09-07T07:13:02.8373085Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ones_like_cuda_float32 PASSED [0.0107s] [ 61%] 2025-09-07T07:13:02.8373287Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cuda_float32 PASSED [0.0208s] [ 61%] 2025-09-07T07:13:02.8373475Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ops_aten_index_put_functorch_cuda_float32 PASSED [0.2408s] [ 61%] 2025-09-07T07:13:02.8373617Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ormqr_cuda_float32 PASSED [0.6880s] [ 61%] 2025-09-07T07:13:02.8373756Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_outer_cuda_float32 PASSED [0.0068s] [ 61%] 2025-09-07T07:13:02.8373903Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_pca_lowrank_cuda_float32 XFAIL [0.0062s] [ 61%] 2025-09-07T07:13:02.8374055Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_permute_copy_cuda_float32 PASSED [0.0790s] [ 61%] 2025-09-07T07:13:02.8374197Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_permute_cuda_float32 PASSED [0.0663s] [ 61%] 2025-09-07T07:13:02.8374342Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_pinverse_cuda_float32 PASSED [0.0201s] [ 61%] 2025-09-07T07:13:02.8374573Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polar_cuda_float32 PASSED [0.2074s] [ 61%] 2025-09-07T07:13:02.8374759Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polygamma_polygamma_n_0_cuda_float32 PASSED [0.1021s] [ 61%] 2025-09-07T07:13:02.8374925Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polygamma_polygamma_n_1_cuda_float32 PASSED [0.3913s] [ 61%] 2025-09-07T07:13:02.8375089Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polygamma_polygamma_n_2_cuda_float32 PASSED [0.3971s] [ 61%] 2025-09-07T07:13:02.8375252Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polygamma_polygamma_n_3_cuda_float32 PASSED [0.3966s] [ 61%] 2025-09-07T07:13:02.8375437Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_polygamma_polygamma_n_4_cuda_float32 PASSED [0.3967s] [ 61%] 2025-09-07T07:13:02.8376677Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_positive_cuda_float32 PASSED [0.0381s] [ 62%] 2025-09-07T07:13:02.8376820Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_pow_cuda_float32 PASSED [0.3239s] [ 62%] 2025-09-07T07:13:02.8376959Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_prod_cuda_float32 PASSED [0.0486s] [ 62%] 2025-09-07T07:13:02.8377095Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_put_cuda_float32 PASSED [0.2173s] [ 62%] 2025-09-07T07:13:02.8377231Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_qr_cuda_float32 PASSED [0.0728s] [ 62%] 2025-09-07T07:13:02.8377376Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_quantile_cuda_float32 PASSED [0.2294s] [ 62%] 2025-09-07T07:13:02.8377519Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_rad2deg_cuda_float32 PASSED [0.0723s] [ 62%] 2025-09-07T07:13:02.8377665Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_rand_like_cuda_float32 XFAIL [0.0039s] [ 62%] 2025-09-07T07:13:02.8377851Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_randint_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 62%] 2025-09-07T07:13:02.8378000Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_randint_like_cuda_float32 XFAIL [0.0036s] [ 62%] 2025-09-07T07:13:02.8378179Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_randn_cuda_float32 SKIPPED [0.0001s] (Test expects tensor input) [ 62%] 2025-09-07T07:13:02.8378324Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_randn_like_cuda_float32 XFAIL [0.0036s] [ 62%] 2025-09-07T07:13:02.8378464Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_ravel_cuda_float32 PASSED [0.0060s] [ 62%] 2025-09-07T07:13:02.8378626Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_real_cuda_float32 PASSED [0.0427s] [ 62%] 2025-09-07T07:13:02.8378776Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_reciprocal_cuda_float32 PASSED [0.0779s] [ 62%] 2025-09-07T07:13:02.8378920Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_remainder_cuda_float32 PASSED [0.3323s] [ 63%] 2025-09-07T07:13:02.8379062Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_renorm_cuda_float32 PASSED [0.0154s] [ 63%] 2025-09-07T07:13:02.8379204Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_repeat_cuda_float32 PASSED [0.0408s] [ 63%] 2025-09-07T07:13:02.8379362Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_repeat_interleave_cuda_float32 PASSED [0.0129s] [ 63%] 2025-09-07T07:13:02.8379510Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_reshape_as_cuda_float32 PASSED [0.0604s] [ 63%] 2025-09-07T07:13:02.8379674Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_reshape_cuda_float32 PASSED [0.0551s] [ 63%] 2025-09-07T07:13:02.8380892Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_resize__cuda_float32 XFAIL [0.0043s] [ 63%] 2025-09-07T07:13:02.8381058Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_resize_as__cuda_float32 XFAIL [0.0065s] [ 63%] 2025-09-07T07:13:02.8381209Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_resolve_conj_cuda_float32 PASSED [0.0047s] [ 63%] 2025-09-07T07:13:02.8381356Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_resolve_neg_cuda_float32 PASSED [0.0047s] [ 63%] 2025-09-07T07:13:02.8381493Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_roll_cuda_float32 PASSED [0.0245s] [ 63%] 2025-09-07T07:13:02.8381651Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_rot90_cuda_float32 PASSED [0.0492s] [ 63%] 2025-09-07T07:13:02.8381795Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_round_cuda_float32 PASSED [0.0977s] [ 63%] 2025-09-07T07:13:02.8381951Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_round_decimals_0_cuda_float32 PASSED [0.1082s] [ 63%] 2025-09-07T07:13:02.8382108Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_round_decimals_3_cuda_float32 PASSED [0.1076s] [ 63%] 2025-09-07T07:13:02.8382268Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_round_decimals_neg_3_cuda_float32 PASSED [0.1081s] [ 64%] 2025-09-07T07:13:02.8382408Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_rsqrt_cuda_float32 PASSED [0.0778s] [ 64%] 2025-09-07T07:13:02.8382546Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_rsub_cuda_float32 PASSED [0.2383s] [ 64%] 2025-09-07T07:13:02.8382698Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scalar_tensor_cuda_float32 PASSED [0.0025s] [ 64%] 2025-09-07T07:13:02.8382847Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_add_cuda_float32 PASSED [0.0628s] [ 64%] 2025-09-07T07:13:02.8382989Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_cuda_float32 PASSED [0.2117s] [ 64%] 2025-09-07T07:13:02.8383149Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_amax_cuda_float32 PASSED [0.1788s] [ 64%] 2025-09-07T07:13:02.8383308Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_amin_cuda_float32 PASSED [0.1772s] [ 64%] 2025-09-07T07:13:02.8383465Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_mean_cuda_float32 PASSED [0.2251s] [ 64%] 2025-09-07T07:13:02.8383624Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_prod_cuda_float32 PASSED [0.1760s] [ 64%] 2025-09-07T07:13:02.8383796Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_scatter_reduce_sum_cuda_float32 PASSED [0.1774s] [ 64%] 2025-09-07T07:13:02.8383951Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_searchsorted_cuda_float32 PASSED [0.5996s] [ 64%] 2025-09-07T07:13:02.8384092Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_select_cuda_float32 PASSED [0.0094s] [ 64%] 2025-09-07T07:13:02.8385393Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_select_scatter_cuda_float32 PASSED [0.0147s] [ 64%] 2025-09-07T07:13:02.8385530Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sgn_cuda_float32 PASSED [0.0711s] [ 64%] 2025-09-07T07:13:02.8385669Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_short_cuda_float32 XFAIL [0.0079s] [ 65%] 2025-09-07T07:13:02.8385845Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_short_functorch_no_channels_last_cuda_float32 PASSED [0.0071s] [ 65%] 2025-09-07T07:13:02.8386018Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sigmoid_cuda_float32 PASSED [0.1371s] [ 65%] 2025-09-07T07:13:02.8386175Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sign_cuda_float32 PASSED [0.0711s] [ 65%] 2025-09-07T07:13:02.8386355Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_bartlett_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8386533Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_blackman_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8386707Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_cosine_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8386910Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_exponential_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387089Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_gaussian_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387276Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_general_cosine_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387461Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_general_hamming_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387636Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_hamming_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387807Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_hann_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8387982Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_kaiser_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8388200Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signal_windows_nuttall_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 65%] 2025-09-07T07:13:02.8388347Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_signbit_cuda_float32 PASSED [0.0408s] [ 66%] 2025-09-07T07:13:02.8388484Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sin_cuda_float32 PASSED [0.0673s] [ 66%] 2025-09-07T07:13:02.8388623Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sinc_cuda_float32 PASSED [0.1019s] [ 66%] 2025-09-07T07:13:02.8388760Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sinh_cuda_float32 PASSED [0.0719s] [ 66%] 2025-09-07T07:13:02.8389992Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_slice_cuda_float32 PASSED [0.0073s] [ 66%] 2025-09-07T07:13:02.8390145Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_slice_scatter_cuda_float32 PASSED [0.0257s] [ 66%] 2025-09-07T07:13:02.8390311Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_softmax_cuda_float32 PASSED [0.0225s] [ 66%] 2025-09-07T07:13:02.8390470Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_softmax_with_dtype_cuda_float32 PASSED [0.0243s] [ 66%] 2025-09-07T07:13:02.8390609Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sort_cuda_float32 PASSED [0.0691s] [ 66%] 2025-09-07T07:13:02.8390788Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sparse_mm_reduce_cuda_float32 SKIPPED [0.0005s] (Only runs on cpu) [ 66%] 2025-09-07T07:13:02.8390949Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sparse_sampled_addmm_cuda_float32 XFAIL [0.0033s] [ 66%] 2025-09-07T07:13:02.8391102Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_airy_ai_cuda_float32 PASSED [0.6307s] [ 66%] 2025-09-07T07:13:02.8391282Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_j0_cuda_float32 PASSED [0.0444s] [ 66%] 2025-09-07T07:13:02.8391437Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_j1_cuda_float32 PASSED [0.0441s] [ 66%] 2025-09-07T07:13:02.8391608Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_y0_cuda_float32 PASSED [0.0436s] [ 67%] 2025-09-07T07:13:02.8391763Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_bessel_y1_cuda_float32 PASSED [0.0439s] [ 67%] 2025-09-07T07:13:02.8391938Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_t_cuda_float32 PASSED [0.3499s] [ 67%] 2025-09-07T07:13:02.8392128Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_u_cuda_float32 PASSED [0.5126s] [ 67%] 2025-09-07T07:13:02.8392302Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_v_cuda_float32 PASSED [0.4860s] [ 67%] 2025-09-07T07:13:02.8392477Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_chebyshev_polynomial_w_cuda_float32 PASSED [0.4797s] [ 67%] 2025-09-07T07:13:02.8392631Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_entr_cuda_float32 PASSED [0.0429s] [ 67%] 2025-09-07T07:13:02.8392784Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_erfcx_cuda_float32 PASSED [0.0441s] [ 67%] 2025-09-07T07:13:02.8392954Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_hermite_polynomial_h_cuda_float32 PASSED [1.2131s] [ 67%] 2025-09-07T07:13:02.8393125Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_hermite_polynomial_he_cuda_float32 PASSED [0.2784s] [ 67%] 2025-09-07T07:13:02.8393275Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i0e_cuda_float32 PASSED [0.0436s] [ 67%] 2025-09-07T07:13:02.8394560Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i1_cuda_float32 PASSED [0.0437s] [ 67%] 2025-09-07T07:13:02.8394712Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_i1e_cuda_float32 PASSED [0.0449s] [ 67%] 2025-09-07T07:13:02.8394885Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_laguerre_polynomial_l_cuda_float32 PASSED [0.2821s] [ 67%] 2025-09-07T07:13:02.8395056Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_legendre_polynomial_p_cuda_float32 PASSED [0.2820s] [ 67%] 2025-09-07T07:13:02.8395212Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_log_ndtr_cuda_float32 PASSED [0.5111s] [ 68%] 2025-09-07T07:13:02.8395380Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_i0_cuda_float32 PASSED [0.0440s] [ 68%] 2025-09-07T07:13:02.8395572Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_i1_cuda_float32 PASSED [0.0438s] [ 68%] 2025-09-07T07:13:02.8395739Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_k0_cuda_float32 PASSED [0.0437s] [ 68%] 2025-09-07T07:13:02.8395906Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_modified_bessel_k1_cuda_float32 PASSED [0.0436s] [ 68%] 2025-09-07T07:13:02.8396058Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_ndtr_cuda_float32 PASSED [0.0494s] [ 68%] 2025-09-07T07:13:02.8396210Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_ndtri_cuda_float32 PASSED [0.0441s] [ 68%] 2025-09-07T07:13:02.8396396Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_polygamma_special_polygamma_n_0_cuda_float32 PASSED [0.0635s] [ 68%] 2025-09-07T07:13:02.8396572Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_scaled_modified_bessel_k0_cuda_float32 PASSED [0.0450s] [ 68%] 2025-09-07T07:13:02.8396766Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_scaled_modified_bessel_k1_cuda_float32 PASSED [0.0448s] [ 68%] 2025-09-07T07:13:02.8396967Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_t_cuda_float32 PASSED [0.3622s] [ 68%] 2025-09-07T07:13:02.8397151Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_u_cuda_float32 PASSED [0.5271s] [ 68%] 2025-09-07T07:13:02.8397333Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_v_cuda_float32 PASSED [0.4834s] [ 68%] 2025-09-07T07:13:02.8397536Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_w_cuda_float32 PASSED [0.4757s] [ 68%] 2025-09-07T07:13:02.8397707Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_spherical_bessel_j0_cuda_float32 PASSED [0.0446s] [ 68%] 2025-09-07T07:13:02.8397863Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_xlog1py_cuda_float32 PASSED [0.2329s] [ 69%] 2025-09-07T07:13:02.8398014Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_special_zeta_cuda_float32 PASSED [0.4425s] [ 69%] 2025-09-07T07:13:02.8399240Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_cuda_float32 PASSED [0.0061s] [ 69%] 2025-09-07T07:13:02.8399395Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_list_args_cuda_float32 PASSED [0.0096s] [ 69%] 2025-09-07T07:13:02.8399559Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_with_sizes_copy_cuda_float32 PASSED [0.0123s] [ 69%] 2025-09-07T07:13:02.8399715Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_split_with_sizes_cuda_float32 PASSED [0.0116s] [ 69%] 2025-09-07T07:13:02.8399858Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sqrt_cuda_float32 PASSED [0.0716s] [ 69%] 2025-09-07T07:13:02.8400000Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_square_cuda_float32 PASSED [0.0804s] [ 69%] 2025-09-07T07:13:02.8400152Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_copy_cuda_float32 PASSED [0.0130s] [ 69%] 2025-09-07T07:13:02.8400293Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_cuda_float32 PASSED [0.0195s] [ 69%] 2025-09-07T07:13:02.8400450Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_squeeze_multiple_cuda_float32 PASSED [0.0158s] [ 69%] 2025-09-07T07:13:02.8400590Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_stack_cuda_float32 PASSED [0.0034s] [ 69%] 2025-09-07T07:13:02.8400729Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_cuda_float32 PASSED [0.0156s] [ 69%] 2025-09-07T07:13:02.8400893Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_mean_cuda_float32 PASSED [0.0225s] [ 69%] 2025-09-07T07:13:02.8401050Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_mean_unbiased_cuda_float32 PASSED [0.0064s] [ 69%] 2025-09-07T07:13:02.8401198Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_std_unbiased_cuda_float32 PASSED [0.0049s] [ 70%] 2025-09-07T07:13:02.8401337Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_stft_cuda_float32 PASSED [0.3456s] [ 70%] 2025-09-07T07:13:02.8401473Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sub_cuda_float32 PASSED [0.6112s] [ 70%] 2025-09-07T07:13:02.8401610Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sum_cuda_float32 PASSED [0.0266s] [ 70%] 2025-09-07T07:13:02.8401758Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_sum_to_size_cuda_float32 PASSED [0.0246s] [ 70%] 2025-09-07T07:13:02.8401912Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_svd_cuda_float32 PASSED [0.5399s] [ 70%] 2025-09-07T07:13:02.8402072Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_svd_lowrank_cuda_float32 XFAIL [0.0080s] [ 70%] 2025-09-07T07:13:02.8402212Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_t_copy_cuda_float32 PASSED [0.0091s] [ 70%] 2025-09-07T07:13:02.8403424Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_t_cuda_float32 PASSED [0.0103s] [ 70%] 2025-09-07T07:13:02.8403579Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_take_along_dim_cuda_float32 PASSED [0.0122s] [ 70%] 2025-09-07T07:13:02.8403736Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_take_cuda_float32 PASSED [0.0265s] [ 70%] 2025-09-07T07:13:02.8403876Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tan_cuda_float32 PASSED [0.0736s] [ 70%] 2025-09-07T07:13:02.8404014Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tanh_cuda_float32 PASSED [0.1011s] [ 70%] 2025-09-07T07:13:02.8404162Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tensor_split_cuda_float32 XFAIL [0.0032s] [ 70%] 2025-09-07T07:13:02.8404310Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tensordot_cuda_float32 PASSED [0.0621s] [ 70%] 2025-09-07T07:13:02.8404495Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tile_cuda_float32 PASSED [0.0554s] [ 71%] 2025-09-07T07:13:02.8404630Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_to_cuda_float32 PASSED [0.0266s] [ 71%] 2025-09-07T07:13:02.8404776Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_to_sparse_cuda_float32 XFAIL [0.0045s] [ 71%] 2025-09-07T07:13:02.8404917Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_topk_cuda_float32 PASSED [0.0286s] [ 71%] 2025-09-07T07:13:02.8405093Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch__scaled_mm_cuda_float8_e4m3fn SKIPPED [0.0001s] (Skipped!) [ 71%] 2025-09-07T07:13:02.8405371Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__efficient_attention_forward_cuda_float32 SKIPPED [0.0004s] (Efficient attention on ROCM doesn't support custom_mask_type==2) [ 71%] 2025-09-07T07:13:02.8405870Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__flash_attention_forward_cuda_float16 [W907 07:12:58.749620012 BatchedFallback.cpp:81] Warning: There is a performance drop because we have not yet implemented the batching rule for aten::_flash_attention_forward. Please file us an issue on GitHub so that we can prioritize its implementation. (function warnFallback) 2025-09-07T07:13:02.8406217Z [W907 07:12:58.749809348 BatchedFallback.cpp:81] Warning: There is a performance drop because we have not yet implemented the batching rule for aten::_flash_attention_forward. Please file us an issue on GitHub so that we can prioritize its implementation. (function warnFallback) 2025-09-07T07:13:02.8406258Z XFAIL [0.0051s] [ 71%] 2025-09-07T07:13:02.8406439Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_torch_ops_aten__safe_softmax_default_cuda_float32 PASSED [0.0145s] [ 71%] 2025-09-07T07:13:02.8406583Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trace_cuda_float32 PASSED [0.0052s] [ 71%] 2025-09-07T07:13:02.8406737Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_transpose_copy_cuda_float32 PASSED [0.0132s] [ 71%] 2025-09-07T07:13:02.8406885Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_transpose_cuda_float32 PASSED [0.0502s] [ 71%] 2025-09-07T07:13:02.8407030Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trapezoid_cuda_float32 PASSED [0.0234s] [ 71%] 2025-09-07T07:13:02.8408283Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trapz_cuda_float32 PASSED [0.0234s] [ 71%] 2025-09-07T07:13:02.8408469Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triangular_solve_cuda_float32 PASSED [0.0511s] [ 71%] 2025-09-07T07:13:02.8408606Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tril_cuda_float32 XFAIL [0.0030s] [ 71%] 2025-09-07T07:13:02.8408751Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_tril_indices_cuda_int64 PASSED [0.0030s] [ 72%] 2025-09-07T07:13:02.8408887Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triu_cuda_float32 XFAIL [0.0030s] [ 72%] 2025-09-07T07:13:02.8409054Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_triu_indices_cuda_int64 PASSED [0.0029s] [ 72%] 2025-09-07T07:13:02.8409202Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_true_divide_cuda_float32 PASSED [0.3340s] [ 72%] 2025-09-07T07:13:02.8409343Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_trunc_cuda_float32 PASSED [0.1328s] [ 72%] 2025-09-07T07:13:02.8409489Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unbind_copy_cuda_float32 XFAIL [0.0057s] [ 72%] 2025-09-07T07:13:02.8409629Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unbind_cuda_float32 PASSED [0.0245s] [ 72%] 2025-09-07T07:13:02.8409773Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unflatten_cuda_float32 PASSED [0.0142s] [ 72%] 2025-09-07T07:13:02.8409921Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unfold_copy_cuda_float32 PASSED [0.0301s] [ 72%] 2025-09-07T07:13:02.8410064Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unfold_cuda_float32 PASSED [0.0289s] [ 72%] 2025-09-07T07:13:02.8410204Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_uniform_cuda_float32 XFAIL [0.0055s] [ 72%] 2025-09-07T07:13:02.8410362Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unique_consecutive_cuda_float32 XFAIL [0.0032s] [ 72%] 2025-09-07T07:13:02.8410500Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unique_cuda_float32 XFAIL [0.0033s] [ 72%] 2025-09-07T07:13:02.8410649Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unravel_index_cuda_int64 PASSED [0.0278s] [ 72%] 2025-09-07T07:13:02.8410798Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsafe_chunk_cuda_float32 PASSED [0.0600s] [ 72%] 2025-09-07T07:13:02.8410948Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsafe_split_cuda_float32 PASSED [0.0062s] [ 73%] 2025-09-07T07:13:02.8411101Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsqueeze_copy_cuda_float32 PASSED [0.0145s] [ 73%] 2025-09-07T07:13:02.8411272Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_unsqueeze_cuda_float32 PASSED [0.0223s] [ 73%] 2025-09-07T07:13:02.8412486Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_cuda_float32 PASSED [0.0161s] [ 73%] 2025-09-07T07:13:02.8412635Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_mean_cuda_float32 PASSED [0.0235s] [ 73%] 2025-09-07T07:13:02.8412790Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_mean_unbiased_cuda_float32 PASSED [0.0062s] [ 73%] 2025-09-07T07:13:02.8412939Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_var_unbiased_cuda_float32 PASSED [0.0049s] [ 73%] 2025-09-07T07:13:02.8413077Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vdot_cuda_float32 PASSED [0.0102s] [ 73%] 2025-09-07T07:13:02.8413230Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_complex_cuda_float32 XFAIL [0.0036s] [ 73%] 2025-09-07T07:13:02.8413392Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_cuda_float32 PASSED [0.0617s] [ 73%] 2025-09-07T07:13:02.8413555Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_as_real_cuda_complex64 PASSED [0.0048s] [ 73%] 2025-09-07T07:13:02.8413699Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_copy_cuda_float32 PASSED [0.0190s] [ 73%] 2025-09-07T07:13:02.8413836Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_view_cuda_float32 PASSED [0.0563s] [ 73%] 2025-09-07T07:13:02.8413975Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vsplit_cuda_float32 PASSED [0.0107s] [ 73%] 2025-09-07T07:13:02.8414114Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_vstack_cuda_float32 PASSED [0.0054s] [ 73%] 2025-09-07T07:13:02.8414268Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_where_cuda_float32 PASSED [0.0700s] [ 74%] 2025-09-07T07:13:02.8414465Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_xlogy_cuda_float32 PASSED [0.5289s] [ 74%] 2025-09-07T07:13:02.8414604Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zero__cuda_float32 PASSED [0.0094s] [ 74%] 2025-09-07T07:13:02.8414741Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zeros_cuda_float32 PASSED [0.0025s] [ 74%] 2025-09-07T07:13:02.8414888Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_exhaustive_zeros_like_cuda_float32 PASSED [0.0106s] [ 74%] 2025-09-07T07:13:02.8415059Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cholesky_cuda_float32 PASSED [0.0050s] [ 74%] 2025-09-07T07:13:02.8415234Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cholesky_ex_cuda_float32 PASSED [0.0047s] [ 74%] 2025-09-07T07:13:02.8415400Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cond_cuda_float32 PASSED [0.0028s] [ 74%] 2025-09-07T07:13:02.8415566Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_cross_cuda_float32 PASSED [0.0029s] [ 74%] 2025-09-07T07:13:02.8416815Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_det_cuda_float32 PASSED [0.0060s] [ 74%] 2025-09-07T07:13:02.8416984Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_diagonal_cuda_float32 XFAIL [0.0028s] [ 74%] 2025-09-07T07:13:02.8417145Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eig_cuda_float32 PASSED [0.0052s] [ 74%] 2025-09-07T07:13:02.8417307Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigh_cuda_float32 PASSED [0.0051s] [ 74%] 2025-09-07T07:13:02.8417473Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigvals_cuda_float32 PASSED [0.0050s] [ 74%] 2025-09-07T07:13:02.8417667Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_eigvalsh_cuda_float32 PASSED [0.0049s] [ 74%] 2025-09-07T07:13:02.8417939Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_householder_product_cuda_float32 SKIPPED [0.0006s] (skipCUDAIfRocm: test doesn't currently work on the ROCm stack) [ 75%] 2025-09-07T07:13:02.8418100Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_inv_cuda_float32 PASSED [0.0049s] [ 75%] 2025-09-07T07:13:02.8418264Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_inv_ex_cuda_float32 PASSED [0.0050s] [ 75%] 2025-09-07T07:13:02.8418433Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_factor_cuda_float32 PASSED [0.0032s] [ 75%] 2025-09-07T07:13:02.8418628Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_factor_ex_cuda_float32 PASSED [0.0032s] [ 75%] 2025-09-07T07:13:02.8418812Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_ldl_solve_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 75%] 2025-09-07T07:13:02.8418994Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lstsq_cuda_float32 PASSED [0.0045s] [ 75%] 2025-09-07T07:13:02.8419173Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lstsq_grad_oriented_cuda_float32 PASSED [0.0040s] [ 75%] 2025-09-07T07:13:02.8419334Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_cuda_float32 PASSED [0.0064s] [ 75%] 2025-09-07T07:13:02.8419518Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_factor_cuda_float32 PASSED [0.0206s] [ 75%] 2025-09-07T07:13:02.8419691Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_factor_ex_cuda_float32 PASSED [0.0064s] [ 75%] 2025-09-07T07:13:02.8419856Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_lu_solve_cuda_float32 PASSED [0.0236s] [ 75%] 2025-09-07T07:13:02.8420044Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_norm_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 75%] 2025-09-07T07:13:02.8420216Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_power_cuda_float32 PASSED [0.0075s] [ 75%] 2025-09-07T07:13:02.8420387Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_cuda_float32 PASSED [0.0261s] [ 75%] 2025-09-07T07:13:02.8421651Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_hermitian_cuda_float32 PASSED [0.0049s] [ 76%] 2025-09-07T07:13:02.8421839Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_multi_dot_cuda_float32 SKIPPED [0.0001s] (Skipped!) [ 76%] 2025-09-07T07:13:02.8422004Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_norm_cuda_float32 XFAIL [0.0073s] [ 76%] 2025-09-07T07:13:02.8422194Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_norm_subgradients_at_zero_cuda_float32 XFAIL [0.0066s] [ 76%] 2025-09-07T07:13:02.8422357Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_cuda_float32 PASSED [0.0075s] [ 76%] 2025-09-07T07:13:02.8422530Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_hermitian_cuda_float32 PASSED [0.0049s] [ 76%] 2025-09-07T07:13:02.8422781Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_pinv_singular_cuda_float32 SKIPPED [0.0006s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 76%] 2025-09-07T07:13:02.8422960Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_qr_cuda_float32 PASSED [0.0127s] [ 76%] 2025-09-07T07:13:02.8423127Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_slogdet_cuda_float32 PASSED [0.0057s] [ 76%] 2025-09-07T07:13:02.8423289Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_cuda_float32 PASSED [0.0102s] [ 76%] 2025-09-07T07:13:02.8423456Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_ex_cuda_float32 PASSED [0.0101s] [ 76%] 2025-09-07T07:13:02.8423632Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_solve_triangular_cuda_float32 PASSED [0.0227s] [ 76%] 2025-09-07T07:13:02.8423794Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_svd_cuda_float32 PASSED [0.0617s] [ 76%] 2025-09-07T07:13:02.8423988Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_svdvals_cuda_float32 PASSED [0.0068s] [ 76%] 2025-09-07T07:13:02.8424170Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_tensorinv_cuda_float32 PASSED [0.0039s] [ 76%] 2025-09-07T07:13:02.8424339Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_tensorsolve_cuda_float32 PASSED [0.0027s] [ 77%] 2025-09-07T07:13:02.8424542Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vander_cuda_float32 XFAIL [0.0035s] [ 77%] 2025-09-07T07:13:02.8424705Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vecdot_cuda_float32 XFAIL [0.0050s] [ 77%] 2025-09-07T07:13:02.8424896Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_linalg_failure_1D_input_linalg_vector_norm_cuda_float32 XFAIL [0.0094s] [ 77%] 2025-09-07T07:13:02.8425045Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_multi_dot_failure_1D_input_cuda PASSED [0.0007s] [ 77%] 2025-09-07T07:13:02.8425184Z functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_vmap_with_anomaly_detection_cuda PASSED [0.0026s] [ 77%] 2025-09-07T07:13:02.8426384Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_add_cuda PASSED [0.0072s] [ 77%] 2025-09-07T07:13:02.8426517Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_binary_cross_entropy_cuda PASSED [0.0038s] [ 77%] 2025-09-07T07:13:02.8426635Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_diagonal_cuda PASSED [0.0026s] [ 77%] 2025-09-07T07:13:02.8426743Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_div_cuda PASSED [0.0123s] [ 77%] 2025-09-07T07:13:02.8426858Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_expand_cuda PASSED [0.0016s] [ 77%] 2025-09-07T07:13:02.8426970Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_index_cuda PASSED [0.0037s] [ 77%] 2025-09-07T07:13:02.8427097Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_inplace_manyview_cuda PASSED [0.0021s] [ 77%] 2025-09-07T07:13:02.8427222Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_inplace_view_cuda PASSED [0.0017s] [ 77%] 2025-09-07T07:13:02.8427334Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_lgamma_cuda PASSED [0.0027s] [ 78%] 2025-09-07T07:13:02.8427444Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log1p_cuda PASSED [0.0028s] [ 78%] 2025-09-07T07:13:02.8427550Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log_cuda PASSED [0.0027s] [ 78%] 2025-09-07T07:13:02.8427670Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_log_softmax_cuda PASSED [0.0032s] [ 78%] 2025-09-07T07:13:02.8427788Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_logsumexp_cuda PASSED [0.0033s] [ 78%] 2025-09-07T07:13:02.8427892Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_max_cuda PASSED [0.0019s] [ 78%] 2025-09-07T07:13:02.8428030Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_median_cuda PASSED [0.0019s] [ 78%] 2025-09-07T07:13:02.8428194Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_min_cuda PASSED [0.0018s] [ 78%] 2025-09-07T07:13:02.8428299Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_mul_cuda PASSED [0.0100s] [ 78%] 2025-09-07T07:13:02.8428414Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_permute_cuda PASSED [0.0014s] [ 78%] 2025-09-07T07:13:02.8428574Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_different_cuda PASSED [0.0014s] [ 78%] 2025-09-07T07:13:02.8428728Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_error_cuda PASSED [0.0012s] [ 78%] 2025-09-07T07:13:02.8428878Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend0_randomness_same_cuda PASSED [0.0011s] [ 78%] 2025-09-07T07:13:02.8430150Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_different_cuda PASSED [0.0049s] [ 78%] 2025-09-07T07:13:02.8430304Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_error_cuda PASSED [0.0008s] [ 78%] 2025-09-07T07:13:02.8430476Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend1_randomness_same_cuda PASSED [0.0007s] [ 79%] 2025-09-07T07:13:02.8430633Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_different_cuda PASSED [0.0007s] [ 79%] 2025-09-07T07:13:02.8430782Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_error_cuda PASSED [0.0008s] [ 79%] 2025-09-07T07:13:02.8430930Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_randomness_backend2_randomness_same_cuda PASSED [0.0007s] [ 79%] 2025-09-07T07:13:02.8431065Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_reshape_cuda PASSED [0.0015s] [ 79%] 2025-09-07T07:13:02.8431192Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend0_cuda PASSED [0.0053s] [ 79%] 2025-09-07T07:13:02.8431312Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend1_cuda PASSED [0.0046s] [ 79%] 2025-09-07T07:13:02.8431434Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sdpa_backend2_cuda PASSED [0.0027s] [ 79%] 2025-09-07T07:13:02.8431545Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_select_cuda PASSED [0.0033s] [ 79%] 2025-09-07T07:13:02.8431659Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sigmoid_cuda PASSED [0.0159s] [ 79%] 2025-09-07T07:13:02.8431768Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_slice_cuda PASSED [0.0033s] [ 79%] 2025-09-07T07:13:02.8431878Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_stack_cuda PASSED [0.0017s] [ 79%] 2025-09-07T07:13:02.8431985Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_sub_cuda PASSED [0.0064s] [ 79%] 2025-09-07T07:13:02.8432102Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_threshold_cuda PASSED [0.0015s] [ 79%] 2025-09-07T07:13:02.8432209Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_trace_cuda PASSED [0.0024s] [ 79%] 2025-09-07T07:13:02.8432335Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_unrelated_output_cuda PASSED [0.0007s] [ 80%] 2025-09-07T07:13:02.8432478Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_unrelated_output_multiple_grad_cuda PASSED [0.0007s] [ 80%] 2025-09-07T07:13:02.8432598Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_vmap_fallback_check XFAIL [0.0006s] [ 80%] 2025-09-07T07:13:02.8432722Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_vmap_fallback_check_ok PASSED [0.0005s] [ 80%] 2025-09-07T07:13:02.8433910Z functorch/test_vmap.py::TestVmapBatchedGradientCUDA::test_where_cuda PASSED [0.0007s] [ 80%] 2025-09-07T07:13:02.8434081Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_grad_and_value_cuda PASSED [0.0006s] [ 80%] 2025-09-07T07:13:02.8434257Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_grad_cuda PASSED [0.0005s] [ 80%] 2025-09-07T07:13:02.8434494Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jacfwd_cuda PASSED [0.0425s] [ 80%] 2025-09-07T07:13:02.8434650Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jacrev_cuda PASSED [0.0008s] [ 80%] 2025-09-07T07:13:02.8434802Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_jvp_cuda PASSED [0.0006s] [ 80%] 2025-09-07T07:13:02.8434954Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_vjp_cuda PASSED [0.0005s] [ 80%] 2025-09-07T07:13:02.8435107Z functorch/test_vmap.py::TestTransformFailureCUDA::test_fails_with_autograd_function_transform_vmap_cuda PASSED [0.0005s] [ 80%] 2025-09-07T07:13:02.8435290Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_first_cuda PASSED [0.0011s] [ 80%] 2025-09-07T07:13:02.8435446Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_last_cuda PASSED [0.0008s] [ 80%] 2025-09-07T07:13:02.8435622Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_different_batched_input_none_cuda PASSED [0.0008s] [ 80%] 2025-09-07T07:13:02.8435773Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_first_cuda PASSED [0.0007s] [ 81%] 2025-09-07T07:13:02.8435923Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_last_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8436073Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_error_batched_input_none_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8436243Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_first_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8436394Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_last_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8436542Z functorch/test_vmap.py::TestRandomnessCUDA::test_alpha_dropout_randomness_same_batched_input_none_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8436768Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_first_cuda PASSED [0.0013s] [ 81%] 2025-09-07T07:13:02.8436987Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_last_cuda PASSED [0.0013s] [ 81%] 2025-09-07T07:13:02.8437206Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_none_cuda PASSED [0.0012s] [ 81%] 2025-09-07T07:13:02.8437425Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_first_cuda PASSED [0.0012s] [ 81%] 2025-09-07T07:13:02.8438735Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_last_cuda PASSED [0.0012s] [ 81%] 2025-09-07T07:13:02.8438953Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_none_cuda PASSED [0.0012s] [ 81%] 2025-09-07T07:13:02.8439169Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8439385Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8439625Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_none_cuda PASSED [0.0006s] [ 81%] 2025-09-07T07:13:02.8439841Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_first_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8440052Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_last_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8440262Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_none_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8440472Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_first_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8440698Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_last_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8440923Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_none_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8441133Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8441341Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8441563Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_none_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8441775Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_first_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8441985Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_last_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8442192Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_none_cuda PASSED [0.0015s] [ 82%] 2025-09-07T07:13:02.8442402Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_first_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8442611Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_last_cuda PASSED [0.0006s] [ 82%] 2025-09-07T07:13:02.8442820Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_last_batched_probability_none_cuda PASSED [0.0015s] [ 82%] 2025-09-07T07:13:02.8443030Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8444318Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8444574Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_none_cuda PASSED [0.0015s] [ 83%] 2025-09-07T07:13:02.8444794Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_first_cuda PASSED [0.0009s] [ 83%] 2025-09-07T07:13:02.8445041Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_last_cuda PASSED [0.0009s] [ 83%] 2025-09-07T07:13:02.8445258Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_none_cuda PASSED [0.0009s] [ 83%] 2025-09-07T07:13:02.8445475Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_first_cuda PASSED [0.0010s] [ 83%] 2025-09-07T07:13:02.8445689Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_last_cuda PASSED [0.0009s] [ 83%] 2025-09-07T07:13:02.8445904Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_none_cuda PASSED [0.0010s] [ 83%] 2025-09-07T07:13:02.8446144Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8446379Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8446593Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_none_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8446803Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_first_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8447032Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_last_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8447243Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_none_cuda PASSED [0.0006s] [ 83%] 2025-09-07T07:13:02.8447455Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_first_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8447661Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_last_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8447868Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_none_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8448079Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8448288Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8448497Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_none_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8448706Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_first_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8450010Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_last_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8450237Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_none_cuda PASSED [0.0012s] [ 84%] 2025-09-07T07:13:02.8450447Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_first_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8450653Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_last_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8450859Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_none_cuda PASSED [0.0013s] [ 84%] 2025-09-07T07:13:02.8451066Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_first_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8451273Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_last_cuda PASSED [0.0006s] [ 84%] 2025-09-07T07:13:02.8451494Z functorch/test_vmap.py::TestRandomnessCUDA::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_none_cuda PASSED [0.0013s] [ 84%] 2025-09-07T07:13:02.8451648Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_0_out_dim_0_cuda PASSED [0.0015s] [ 85%] 2025-09-07T07:13:02.8451772Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_0_out_dim_1_cuda PASSED [0.0014s] [ 85%] 2025-09-07T07:13:02.8451893Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_0_out_dim_2_cuda PASSED [0.0018s] [ 85%] 2025-09-07T07:13:02.8452013Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_1_out_dim_0_cuda PASSED [0.0014s] [ 85%] 2025-09-07T07:13:02.8452149Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_1_out_dim_1_cuda PASSED [0.0015s] [ 85%] 2025-09-07T07:13:02.8452271Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_1_out_dim_2_cuda PASSED [0.0014s] [ 85%] 2025-09-07T07:13:02.8452389Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_2_out_dim_0_cuda PASSED [0.0015s] [ 85%] 2025-09-07T07:13:02.8452510Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_2_out_dim_1_cuda PASSED [0.0015s] [ 85%] 2025-09-07T07:13:02.8452628Z functorch/test_vmap.py::TestRandomnessCUDA::test_chunk_vmap_in_dim_2_out_dim_2_cuda PASSED [0.0015s] [ 85%] 2025-09-07T07:13:02.8452781Z functorch/test_vmap.py::TestRandomnessCUDA::test_dropout_randomness_different_batched_input_first_cuda PASSED [0.0009s] [ 85%] 2025-09-07T07:13:02.8452930Z functorch/test_vmap.py::TestRandomnessCUDA::test_dropout_randomness_different_batched_input_last_cuda PASSED [0.0008s] [ 85%] 2025-09-07T07:13:02.8453080Z functorch/test_vmap.py::TestRandomnessCUDA::test_dropout_randomness_different_batched_input_none_cuda PASSED [0.0008s] [ 85%] 2025-09-07T07:13:02.8453226Z functorch/test_vmap.py::TestRandomnessCUDA::test_dropout_randomness_error_batched_input_first_cuda PASSED [0.0006s] [ 85%] 2025-09-07T07:13:02.8454499Z functorch/test_vmap.py::TestRandomnessCUDA::test_dropout_randomness_error_batched_input_last_cuda PASSED [0.0005s] [ 85%] 2025-09-07T07:13:02.8454646Z 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functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_none_cuda PASSED [0.0005s] [ 97%] 2025-09-07T07:13:02.8498698Z functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_first_cuda PASSED [0.0005s] [ 97%] 2025-09-07T07:13:02.8498882Z functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_last_cuda PASSED [0.0005s] [ 97%] 2025-09-07T07:13:02.8499067Z functorch/test_vmap.py::TestRandomnessCUDA::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_none_cuda PASSED [0.0056s] [ 97%] 2025-09-07T07:13:02.8499223Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_different_use_generator_False_cuda PASSED [0.0013s] [ 97%] 2025-09-07T07:13:02.8499376Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_different_use_generator_True_cuda PASSED [0.0013s] [ 97%] 2025-09-07T07:13:02.8499522Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_error_use_generator_False_cuda PASSED [0.0007s] [ 97%] 2025-09-07T07:13:02.8499688Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_error_use_generator_True_cuda PASSED [0.0006s] [ 97%] 2025-09-07T07:13:02.8499831Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_same_use_generator_False_cuda PASSED [0.0010s] [ 98%] 2025-09-07T07:13:02.8499973Z functorch/test_vmap.py::TestRandomnessCUDA::test_randperm_randomness_same_use_generator_True_cuda PASSED [0.0010s] [ 98%] 2025-09-07T07:13:02.8500090Z functorch/test_vmap.py::TestRandomnessCUDA::test_unsupported_random_cuda PASSED [0.0007s] [ 98%] 2025-09-07T07:13:02.8500219Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_0_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8500345Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_1_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8501581Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_0_out_dim_2_cuda PASSED [0.0013s] [ 98%] 2025-09-07T07:13:02.8501730Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_0_cuda PASSED [0.0016s] [ 98%] 2025-09-07T07:13:02.8501854Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_1_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8501977Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_1_out_dim_2_cuda PASSED [0.0015s] [ 98%] 2025-09-07T07:13:02.8502118Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_0_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8502243Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_1_cuda PASSED [0.0015s] [ 98%] 2025-09-07T07:13:02.8502368Z functorch/test_vmap.py::TestRandomnessCUDA::test_vmap_chunksize_in_dim_2_out_dim_2_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8502495Z functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test__is_all_true_cuda PASSED [0.0014s] [ 98%] 2025-09-07T07:13:02.8502609Z functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test__is_any_true_cuda PASSED [0.0009s] [ 98%] 2025-09-07T07:13:02.8502718Z functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_check_tensor_cuda PASSED [0.0047s] [ 98%] 2025-09-07T07:13:02.8502833Z functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_vmap_fallback_check XFAIL [0.0006s] [ 99%] 2025-09-07T07:13:02.8502950Z functorch/test_vmap.py::TestVmapDeviceTypeCUDA::test_vmap_fallback_check_ok PASSED [0.0005s] [ 99%] 2025-09-07T07:13:02.8503072Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_cat_batching_rule_cuda PASSED [0.0656s] [ 99%] 2025-09-07T07:13:02.8503189Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_cuda PASSED [0.0191s] [ 99%] 2025-09-07T07:13:02.8503335Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_nt_and_batched_dense_cuda PASSED [0.0042s] [ 99%] 2025-09-07T07:13:02.8503481Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_binary_nt_and_unbatched_dense_cuda PASSED [0.0101s] [ 99%] 2025-09-07T07:13:02.8503603Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_unary_cuda PASSED [0.0094s] [ 99%] 2025-09-07T07:13:02.8503774Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_fallback_with_nt_and_batched_dense_with_nonzero_bdim_raises_cuda PASSED [0.0083s] [ 99%] 2025-09-07T07:13:02.8503902Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_multilevel_vmap_raises_cuda PASSED [0.0074s] [ 99%] 2025-09-07T07:13:02.8504031Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_acts_as_dense_in_vmap_cuda PASSED [0.0073s] [ 99%] 2025-09-07T07:13:02.8504163Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_with_nonzero_in_dim_raises_cuda PASSED [0.0063s] [ 99%] 2025-09-07T07:13:02.8505495Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_nt_with_nonzero_out_dim_raises_cuda PASSED [0.0054s] [ 99%] 2025-09-07T07:13:02.8505614Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_shape_call_cuda XFAIL [0.0006s] [ 99%] 2025-09-07T07:13:02.8505762Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_vmap_fallback_check XFAIL [0.0007s] [ 99%] 2025-09-07T07:13:02.8505882Z functorch/test_vmap.py::TestVmapNestedTensorCUDA::test_vmap_fallback_check_ok PASSED [0.0005s] [100%] 2025-09-07T07:13:02.8505885Z 2025-09-07T07:13:02.8506085Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/functorch.test_vmap/functorch.test_vmap-386d48e47c150c53.xml - 2025-09-07T07:13:02.8506174Z == 1243 passed, 69 skipped, 645 deselected, 179 xfailed in 132.56s (0:02:12) === 2025-09-07T07:13:02.8506389Z The following tests failed and then succeeded when run in a new process['test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCUDA::test_op_has_batch_rule_mv_cuda_float32'] 2025-09-07T07:13:02.8506392Z 2025-09-07T07:13:02.8506536Z FINISHED PRINTING LOG FILE of functorch/test_vmap 1/1 (test/test-reports/functorch.test_vmap_1.1_f001b2d0622d0c76_.log) 2025-09-07T07:13:02.8506538Z 2025-09-07T07:13:02.8506648Z Running functorch/test_vmap_registrations 1/1 ... [2025-09-07 07:13:02.714303] 2025-09-07T07:13:02.8506696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:13:02.8507006Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_vmap_registrations.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:13:02.714522] 2025-09-07T07:13:06.3875365Z 2025-09-07T07:13:06.3876355Z functorch/test_vmap_registrations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_registrations_1.1_1509291403bbfede_.log 2025-09-07T07:13:06.4462533Z Running 1721 items in this shard: test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[_test::cat], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[_test::get_first], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[_test::leaky_relu], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__and__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__and__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__iand__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__iand__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__ior__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__ior__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__ixor__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__ixor__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__or__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__or__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__xor__.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::__xor__.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_add_batch_dim], 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test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Char], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Double], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Float], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Half], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Long], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cast_Short], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_choose_qparams_per_tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_convolution.deprecated], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_convolution_double_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_convolution_mode], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_cufft_clear_plan_cache], 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test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_nnpack_available], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_pack_padded_sequence_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_pad_circular], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_pad_enum], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_pad_packed_sequence], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_register_a_batching_rule_for_composite_implicit_autograd_[aten::_propagate_xla_data], 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test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::positive], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::prelu], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::promote_types], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::qr.Q], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::qr], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::quantile.scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::quantile], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::ravel], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::real], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::refine_names], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::relu6], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::relu6_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rename], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rename_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::repeat_interleave.self_Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::repeat_interleave.self_int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::requires_grad_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::reshape], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::reshape_as], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::resolve_conj], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::resolve_neg], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::result_type.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::result_type.Scalar_Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::result_type.Scalar_Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::result_type.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::retain_grad], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::retains_grad], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rms_norm], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_relu.data], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_relu.input], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_relu_cell], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_tanh.data], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_tanh.input], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rnn_tanh_cell], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::row_stack], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rrelu], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::rrelu_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::scaled_dot_product_attention], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::selu], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::selu_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::set_.source_Tensor_storage_offset], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::set_data], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::silu_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::size.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::slogdet], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::slow_conv3d], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::smm], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::softmax.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_digamma], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_erf], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_erfc], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_erfinv], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_exp2], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_expit], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_expm1], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_gammainc], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_gammaincc], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_gammaln], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_i0], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_log1p], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_log_softmax], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_logit], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_logsumexp], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_multigammaln], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_ndtr], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_polygamma], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_psi], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_round], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_sinc], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_softmax], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_xlogy.other_scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_xlogy.self_scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::special_xlogy], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::split.sizes], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::square], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::square_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sspaddmm], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std.correction_names], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std.dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std.names_dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std_mean.correction_names], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std_mean.dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std_mean.names_dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::std_mean], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::stft.center], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::stft], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::stride.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::subtract.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::subtract.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::subtract_.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::subtract_.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sum_to_size], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::svd.U], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::svd], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::swapaxes], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::swapaxes_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::swapdims], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::swapdims_], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sym_numel], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sym_size.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sym_storage_offset], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::sym_stride.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::take_along_dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::tensor_split.indices], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::tensor_split.sections], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::tensor_split.tensor_indices_or_sections], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::tensordot], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::thnn_conv2d], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::tile], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to.device], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to.dtype], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to.dtype_layout], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to.other], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to_dense], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to_dense_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::to_mkldnn_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::trace_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::trapezoid.dx], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::trapezoid.x], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::trapz.dx], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::trapz.x], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::triplet_margin_loss], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::true_divide.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::true_divide.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::true_divide_.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::true_divide_.Tensor], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::type_as], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::unflatten.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::unflatten_dense_tensors], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::unsafe_chunk], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_bicubic2d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_bilinear2d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_linear1d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_nearest1d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_nearest2d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_nearest3d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::upsample_trilinear3d.vec], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::value_selecting_reduction_backward], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::vander], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var.correction_names], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var.dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var.names_dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var_mean.correction_names], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var_mean.dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var_mean.names_dim], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::var_mean], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::view_as], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::vsplit.array], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::vsplit.int], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::vstack], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::where.ScalarOther], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::where.ScalarSelf], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::where.Scalar], test/functorch/test_vmap_registrations.py::TestFunctorchDispatcher::test_unimplemented_batched_registrations_[aten::where] 2025-09-07T07:13:06.4793718Z 2025-09-07T07:13:06.4793888Z Running higher_order_ops/test_invoke_quant 1/1 ... [2025-09-07 07:13:06.389853] 2025-09-07T07:13:06.4794078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:13:06.4794550Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_invoke_quant.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:13:06.390076] 2025-09-07T07:13:20.2802437Z 2025-09-07T07:13:20.2803321Z higher_order_ops/test_invoke_quant 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_quant_1.1_75cf59e298c686b2_.log 2025-09-07T07:13:20.2805802Z Running 14 items in this shard: test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_pattern_matching, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_prologue, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_simple 2025-09-07T07:13:20.2807591Z 2025-09-07T07:13:20.2807700Z Running higher_order_ops/test_invoke_subgraph 1/1 ... [2025-09-07 07:13:20.280194] 2025-09-07T07:13:20.2807895Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:13:20.2816100Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_invoke_subgraph.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:13:20.280404] 2025-09-07T07:13:55.3609116Z 2025-09-07T07:13:55.3610738Z higher_order_ops/test_invoke_subgraph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_subgraph_1.1_7f4e6a7d83447067_.log 2025-09-07T07:13:55.3627893Z Running 68 items in this shard: test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_aot_function, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_multiple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_simple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_ac, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_ac_rng, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_ac_rng_cudagraphs, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_auto_functionalize, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_autograd_function, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_buffer_mutation_errors_under_training, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_buffer_mutation_works_under_no_grad, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_bwd_partitioning, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_complex, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_const_tensor, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dce, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dedupe, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_different_strides_in_backward, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_different_symint, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_differing_strides_for_grad_outs, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_div, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dropout_checks_joint_graph, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dropout_checks_joint_graph_inference, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dynamic, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_fail_with_direct_invoke_subgraph, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_fake_tensor_checking, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_gen_schema, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_gen_schema_with_buffer_mutation, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_input_aliasing, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_mutation, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_mutation_inference_mode, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_mutation_mutiple_times, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_mutation_mutiple_times_fake_tensor_cahche_hit, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_output_aliasing, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_kwargs_only, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_list, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_mod_attr_aliasing, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_module, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_module_forward, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_module_method, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_nonlocal_update, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_normalize_gm, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_output_output_aliasing, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_pending_unbacked, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_preserves_output_strides, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_preserves_strides, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_redundant_compile_region, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_return_none, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_return_none_from_fwd, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_return_size, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_sdpa, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_simple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_simple_module, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_symint_from_fwd_to_bwd, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_triton_kernel_native, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_tuple_of_tuple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_unbacked, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_unbacked_symbol, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_view_to_reshape, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportNonstrict::test_multiple_module, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportNonstrict::test_pending_unbacked, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportNonstrict::test_simple_func, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportNonstrict::test_simple_method, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportNonstrict::test_unbacked, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportStrict::test_multiple_module, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportStrict::test_pending_unbacked, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportStrict::test_simple_func, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportStrict::test_simple_method, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphExportStrict::test_unbacked, test/higher_order_ops/test_invoke_subgraph.py::NegativeTesting::test_graph_break 2025-09-07T07:13:55.3638119Z 2025-09-07T07:13:55.3638215Z Running higher_order_ops/test_with_effects 1/1 ... [2025-09-07 07:13:55.360832] 2025-09-07T07:13:55.3638397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:13:55.3638813Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_with_effects.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:13:55.361099] 2025-09-07T07:14:06.4486857Z 2025-09-07T07:14:06.4488110Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_4869e8018bc96dba_.log 2025-09-07T07:14:06.4494354Z Running 18 items in this shard: test/higher_order_ops/test_with_effects.py::TestWithEffects::test_alias_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager_requires_grad, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor_external_op_return_none, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_custom_op_with_subclasses, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_op_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_aliased_outputs, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_is_output, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_return, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_output_view_simple, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_buffer_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_input_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_register_effectful_custom_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_in_forward_and_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_only_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_torchbind_custom_op 2025-09-07T07:14:06.4499537Z 2025-09-07T07:14:06.4499732Z Running inductor/test_alignment 1/1 ... [2025-09-07 07:14:06.448486] 2025-09-07T07:14:06.4500071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:14:06.4506954Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_alignment.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:14:06.448777] 2025-09-07T07:14:19.3377390Z 2025-09-07T07:14:19.3378500Z inductor/test_alignment 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_alignment_1.1_be3211b00d62d923_.log 2025-09-07T07:14:19.3382351Z Running 12 items in this shard: test/inductor/test_alignment.py::GPUTests::test_Q4_K_dequantization_cuda, test/inductor/test_alignment.py::GPUTests::test_alignment_without_custom_op_cuda, test/inductor/test_alignment.py::GPUTests::test_incorrect_meta_for_custom_op_2d_cuda, test/inductor/test_alignment.py::GPUTests::test_no_align_for_custom_op_2d_cuda, test/inductor/test_alignment.py::GPUTests::test_no_align_for_custom_op_cuda, test/inductor/test_alignment.py::GPUTests::test_slice_cuda, test/inductor/test_alignment.py::GPUTests::test_slice_view_dtype_size_1024_cuda, test/inductor/test_alignment.py::GPUTests::test_slice_view_dtype_size_1048576_cuda, test/inductor/test_alignment.py::GPUTests::test_slice_view_dtype_size_128_cuda, test/inductor/test_alignment.py::GPUTests::test_unaligned_input_2d_cuda, test/inductor/test_alignment.py::GPUTests::test_unaligned_input_cuda, test/inductor/test_alignment.py::GPUTests::test_view_dtype_slice_cuda 2025-09-07T07:14:19.3385298Z 2025-09-07T07:14:19.3385819Z Running inductor/test_analysis 1/1 ... [2025-09-07 07:14:19.337724] 2025-09-07T07:14:19.3386186Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:14:19.3387023Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_analysis.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:14:19.337986] 2025-09-07T07:14:24.9641703Z 2025-09-07T07:14:24.9643018Z inductor/test_analysis 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_analysis_1.1_7310673bd129585e_.log 2025-09-07T07:14:24.9652983Z Running 28 items in this shard: test/inductor/test_analysis.py::TestUtils::test_tabulate2d, test/inductor/test_analysis.py::TestUtils::test_zip_dicts, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat0_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat0_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat1_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat1_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat2_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat2_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat3_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat3_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_helper_unit_cuda, test/inductor/test_analysis.py::TestAnalysisCUDA::test_combine_profiles_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_combine_profiles_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_diff_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_diff_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_diff_cuda_float64, test/inductor/test_analysis.py::TestAnalysisCUDA::test_noop_cuda, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat0_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat0_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat1_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat1_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat2_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat2_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat3_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_pointwise_bandwidth_maxat3_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_triton_has_metadata_maxat0_cuda_float16, test/inductor/test_analysis.py::TestAnalysisCUDA::test_triton_has_metadata_maxat0_cuda_float32, test/inductor/test_analysis.py::TestAnalysisCUDA::test_triton_has_metadata_maxat0_cuda_float64 2025-09-07T07:14:24.9659291Z 2025-09-07T07:14:24.9659461Z Running inductor/test_aot_inductor_arrayref 1/1 ... [2025-09-07 07:14:24.964045] 2025-09-07T07:14:24.9659767Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:14:24.9660434Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_aot_inductor_arrayref.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:14:24.964275] 2025-09-07T07:23:30.0856091Z 2025-09-07T07:23:30.0857571Z inductor/test_aot_inductor_arrayref 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_arrayref_1.1_7d885eb7387fcfc2_.log 2025-09-07T07:23:30.0933432Z Running 287 items in this shard: test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__int_mm_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_m_32_n_64_q_group_32_num_groups_1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_m_32_n_64_q_group_32_num_groups_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_m_32_n_64_q_group_64_num_groups_1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_m_32_n_64_q_group_64_num_groups_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_32_num_groups_1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_32_num_groups_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_64_num_groups_1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_64_num_groups_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_add_complex_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_addmm_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_addmm_multiple_dynamic_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aliased_buffer_reuse_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_amp_fallback_random_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aot_inductor_consts_cpp_build_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_constant_tensor_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_constant_tensor_name_collision_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_codegen_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_cpp_kernel_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_fp8_dtype_cpu_with_stack_allocation, 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test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_composed_dynamic_size_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_mismatched_branch_output_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_mismatched_branch_output_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_nested_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_non_tensor_predicates_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_non_tensor_predicates_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_share_predicte_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_symint_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_unbacked_symint_closure_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_unbacked_symint_closure_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_use_buffers_from_outer_scope_cpu_with_stack_allocation, 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test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_constant_folding_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_constant_folding_with_update_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_constant_original_fqn_and_dtype_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_constant_type_propagation_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_conv3d_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_conv_freezing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_convolution_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_copy_non_blocking_is_pinned_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_custom_op_in_subgraph_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_d2h_copy_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_deconv_freezing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dup_unbacked_sym_decl_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dup_unbacked_sym_decl_with_refinement_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_duplicate_constant_folding_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_duplicated_params_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_cat_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_scalar_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_smem_above_default_limit_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_embedding_bag_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_empty_cat_dtype_promotion_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_empty_constant_folding_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_empty_graph_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_extract_constants_map_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fake_tensor_device_validation_cpu_with_stack_allocation, 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test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_with_none_index_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_inf_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_input_codegen_with_sympy_expr_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_int_list_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_issue_140766_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_dynamic_dim_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_grid_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_mmaped_weights_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_weight_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_linear_dynamic_maxautotune_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_linear_freezing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_load_package_multiple_gpus_cpu_with_stack_allocation, 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test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_proxy_executor_squeeze_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_pytree_inputs_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quanatized_int8_linear_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quantized_linear_bias_none_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quantized_linear_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_repeat_interleave_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_repeat_output_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_repeated_calling_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_repeated_user_defined_triton_kernel_embed_kernel_binary_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_repeated_user_defined_triton_kernel_embed_kernel_binary_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_replicate_on_devices_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_return_constant_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_return_view_constant_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_reuse_kernel_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_reuse_kernel_dynamic_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_rocm_triton_autotuning_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_run_with_grad_enabled_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_complex_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_device_type_failed_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_dtype_failed_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_fp8_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_large_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_shape_failed_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_same_backing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scaled_dot_product_efficient_attention_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scatter_fallback_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scatter_reduce_fallback_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sdpa_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sdpa_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_seq_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_shifted_constraint_ranges_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_dynamic_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_embed_kernel_binary_False_max_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_embed_kernel_binary_False_max_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_embed_kernel_binary_True_max_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_embed_kernel_binary_True_max_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_multi_arch_embed_kernel_binary_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_split_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_size_from_multi_output_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_size_with_unbacked_add_and_mul_expr_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_size_with_unbacked_add_expr_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_size_with_unbacked_add_expr_transitive_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_small_constant_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_so_without_weight_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_stft_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_stride_with_unbacked_expr_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_subclasses_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sym_expr_indexing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sym_i64_input_codegen_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_symbool_item_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_symfloat_item_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_symint_item_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sympy_cpp_printer_min_max_minmax0_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sympy_cpp_printer_min_max_minmax1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_torchvision_transforms_functional_tensor_resize_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_autotuning_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_dynamic_launcher_grid_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_dynamic_launcher_grid_infer_from_tensor_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_bool_param_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_dynamic_grid_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_dynamic_shape_with_div_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_equal_to_1_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_equal_to_1_float_arg_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_equal_to_1_float_arg_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_extern_kernel_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_True_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_multi_output_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_on_device_tma_dynamic_False_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_on_device_tma_dynamic_False_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_on_device_tma_dynamic_True_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_on_device_tma_dynamic_True_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_reinterpret_view_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_reinterpret_view_mem_leak_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_sympy_expr_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_sympy_fn_like_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_1d_dynamic_False_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_1d_dynamic_False_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_1d_dynamic_True_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_1d_dynamic_True_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_False_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_False_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_True_tma_version_new_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_True_tma_version_old_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_False_autotuning_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_False_autotuning_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_weird_param_order_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_with_none_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_with_none_inputs_and_equal_to_1_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_mutated_autotuning_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_next_power_of_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_unbacked_equals_input_size_runtime_assertion_mark_unbacked_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_unbacked_equals_input_size_runtime_assertion_mark_unbacked_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_update_constant_buffer_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_update_constant_buffer_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_update_inactive_constant_buffer_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_update_user_managed_buffer_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_upper_bound_i64_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_using_model_name_for_files_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_view_outputs_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_weight_on_disk_legacy_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_nested_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_conv_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_conv_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_mixed_device_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_mixed_device_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_outer_buffers_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_outer_code_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_parameters_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_pytree_inputs_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_sym_expr_cond_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_sym_expr_cond_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_unbacked_symint_closure_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_unbacked_symint_closure_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_cudagraphs_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_no_triton_profiler_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_offset_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_profiler_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_unbacked_symbols_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_size_buffer_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_size_weight_cpu_with_stack_allocation 2025-09-07T07:23:30.0998796Z 2025-09-07T07:23:30.0998893Z Running inductor/test_b2b_gemm 1/1 ... [2025-09-07 07:23:30.085930] 2025-09-07T07:23:30.0999072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:23:30.0999464Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_b2b_gemm.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:23:30.086184] 2025-09-07T07:23:35.9315715Z 2025-09-07T07:23:35.9322215Z inductor/test_b2b_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_b2b_gemm_1.1_91f575f85b7bba16_.log 2025-09-07T07:23:35.9322631Z Running 0 items in this shard: 2025-09-07T07:23:35.9322730Z 2025-09-07T07:23:35.9323151Z Running inductor/test_compiled_optimizers 2/2 ... [2025-09-07 07:23:35.931400] 2025-09-07T07:23:35.9323375Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:23:35.9323891Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_compiled_optimizers.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:23:35.931721] 2025-09-07T07:32:28.4958513Z 2025-09-07T07:32:28.4959589Z inductor/test_compiled_optimizers 2/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compiled_optimizers_2.2_de47a3e71d45f6a7_.log 2025-09-07T07:32:28.5036716Z Running 353 items in this shard: test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_S429861, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_rho_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_initial_accumulator_value_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_lr_decay_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_amsgrad_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_amsgrad_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_amsgrad_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_tensor_lr_weight_decay_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_weight_decay_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamax_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_lambd_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_recompile_default, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_t0_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_t0_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_basic_shampoo, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_closure_graph_break, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_foreach_map_adam, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_get_value_on_static_address, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_decoupled_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_eps_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_decoupled_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_decoupled_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_maximize_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_etas_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_step_sizes_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_step_sizes_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_dampening_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_nesterov_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_recompile_foreach, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_recompile_single, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_ASGD_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_ASGD_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adadelta_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adagrad_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adagrad_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_AdamW_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_AdamW_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adam_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adam_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adamax_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Adamax_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Muon_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Muon_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_NAdam_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_NAdam_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_RAdam_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_RMSprop_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_Rprop_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_SGD_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_SGD_use_closure_True_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_SparseAdam_use_closure_False_cuda_float32, test/inductor/test_compiled_optimizers.py::CompiledOptimizerParityTestsCUDA::test_correctness_SparseAdam_use_closure_True_cuda_float32 2025-09-07T07:32:28.5096949Z 2025-09-07T07:32:28.5097057Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T07:32:28.5101892Z Uploading artifacts took 0.00 seconds 2025-09-07T07:32:28.5102131Z Running inductor/test_cuda_repro 1/1 ... [2025-09-07 07:32:28.496985] 2025-09-07T07:32:28.5102299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:32:28.5102684Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cuda_repro.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:32:28.497451] 2025-09-07T07:33:41.2083013Z 2025-09-07T07:33:41.2083676Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_c9445a6563c35fb3_.log 2025-09-07T07:33:41.2092440Z Running 78 items in this shard: test/inductor/test_cuda_repro.py::CudaReproTests::test_3d_tiling, test/inductor/test_cuda_repro.py::CudaReproTests::test_accuracy_issue1, test/inductor/test_cuda_repro.py::CudaReproTests::test_adaptive_avg_pool3d_issue_157248, test/inductor/test_cuda_repro.py::CudaReproTests::test_atomic_add_bfloat16, test/inductor/test_cuda_repro.py::CudaReproTests::test_atomic_add_bfloat16_config, test/inductor/test_cuda_repro.py::CudaReproTests::test_autotune_inplace_kernel, test/inductor/test_cuda_repro.py::CudaReproTests::test_backward_context, test/inductor/test_cuda_repro.py::CudaReproTests::test_bool_emulate_low_precision, test/inductor/test_cuda_repro.py::CudaReproTests::test_bucketize_dynamic_dense, test/inductor/test_cuda_repro.py::CudaReproTests::test_bucketize_epilogue, test/inductor/test_cuda_repro.py::CudaReproTests::test_cat_int8_one_kernel, test/inductor/test_cuda_repro.py::CudaReproTests::test_cpu_index, test/inductor/test_cuda_repro.py::CudaReproTests::test_deterministic_algorithms, test/inductor/test_cuda_repro.py::CudaReproTests::test_dont_inplace_disjoint_accesses, test/inductor/test_cuda_repro.py::CudaReproTests::test_dtype_factory_issue, test/inductor/test_cuda_repro.py::CudaReproTests::test_dynamic_persistent_reductions, test/inductor/test_cuda_repro.py::CudaReproTests::test_dynamic_shapes, test/inductor/test_cuda_repro.py::CudaReproTests::test_dynamic_to_static_cudagraphs, test/inductor/test_cuda_repro.py::CudaReproTests::test_effn_attn_bias_padding, test/inductor/test_cuda_repro.py::CudaReproTests::test_effn_attn_bias_padding_misaligned, test/inductor/test_cuda_repro.py::CudaReproTests::test_embedding_var_mean, test/inductor/test_cuda_repro.py::CudaReproTests::test_emulate_low_precision, test/inductor/test_cuda_repro.py::CudaReproTests::test_epilogue_fusion_with_view, test/inductor/test_cuda_repro.py::CudaReproTests::test_expanded_inputs_cudagraphs, test/inductor/test_cuda_repro.py::CudaReproTests::test_expanded_inputs_cudagraphs_no_size_asserts, test/inductor/test_cuda_repro.py::CudaReproTests::test_flash_attention_dynamic, test/inductor/test_cuda_repro.py::CudaReproTests::test_float64_constants, test/inductor/test_cuda_repro.py::CudaReproTests::test_float8_e8m0fnu, test/inductor/test_cuda_repro.py::CudaReproTests::test_full_copy, test/inductor/test_cuda_repro.py::CudaReproTests::test_index_add_fallback, test/inductor/test_cuda_repro.py::CudaReproTests::test_index_put_cudagraph, test/inductor/test_cuda_repro.py::CudaReproTests::test_index_put_inplace_cudagraph, test/inductor/test_cuda_repro.py::CudaReproTests::test_index_put_issue, test/inductor/test_cuda_repro.py::CudaReproTests::test_index_put_no_fallback_cudagraph, test/inductor/test_cuda_repro.py::CudaReproTests::test_indirect_indexing_dense_mask, test/inductor/test_cuda_repro.py::CudaReproTests::test_inductor_output_aliases_intermediate, test/inductor/test_cuda_repro.py::CudaReproTests::test_inplace_add_alpha_autotune, test/inductor/test_cuda_repro.py::CudaReproTests::test_inplace_buffer_autotune, test/inductor/test_cuda_repro.py::CudaReproTests::test_inplace_updates_cudagraphs, test/inductor/test_cuda_repro.py::CudaReproTests::test_input_channels_last, test/inductor/test_cuda_repro.py::CudaReproTests::test_int64_index_intermediate, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue100806, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue103461, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue103481, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue104759, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue97695_1input, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue97695_2input, test/inductor/test_cuda_repro.py::CudaReproTests::test_issue_103924, test/inductor/test_cuda_repro.py::CudaReproTests::test_libdevice_routing, test/inductor/test_cuda_repro.py::CudaReproTests::test_linear_cpu_input, test/inductor/test_cuda_repro.py::CudaReproTests::test_linear_with_zero_infeature_size, test/inductor/test_cuda_repro.py::CudaReproTests::test_lookup_seed_backward, test/inductor/test_cuda_repro.py::CudaReproTests::test_max_autotune_nograd, test/inductor/test_cuda_repro.py::CudaReproTests::test_memory_history_inductor, test/inductor/test_cuda_repro.py::CudaReproTests::test_multi_output_layout_fallback, test/inductor/test_cuda_repro.py::CudaReproTests::test_mutated_aligned_tensor, test/inductor/test_cuda_repro.py::CudaReproTests::test_negative_arange_dynamic_shapes, test/inductor/test_cuda_repro.py::CudaReproTests::test_no_device_idx_repro_cudagraphs, test/inductor/test_cuda_repro.py::CudaReproTests::test_non_commutative_scan_op, test/inductor/test_cuda_repro.py::CudaReproTests::test_non_contiguous_unaligned_input_indices, test/inductor/test_cuda_repro.py::CudaReproTests::test_not_initializing_wrong_device, test/inductor/test_cuda_repro.py::CudaReproTests::test_permute_fusion, test/inductor/test_cuda_repro.py::CudaReproTests::test_reflection_pad_loop_order, test/inductor/test_cuda_repro.py::CudaReproTests::test_repeated_masked_load, test/inductor/test_cuda_repro.py::CudaReproTests::test_scalar_triton_index, test/inductor/test_cuda_repro.py::CudaReproTests::test_scaled_dot_product_efficient_attention_backward, test/inductor/test_cuda_repro.py::CudaReproTests::test_scatter_index_not_wrapped, test/inductor/test_cuda_repro.py::CudaReproTests::test_selecsls42b_misaligned_address, test/inductor/test_cuda_repro.py::CudaReproTests::test_simplify_dims, test/inductor/test_cuda_repro.py::CudaReproTests::test_sort_stride_issue, test/inductor/test_cuda_repro.py::CudaReproTests::test_sorted_masks, test/inductor/test_cuda_repro.py::CudaReproTests::test_split_reduction_channels_last, test/inductor/test_cuda_repro.py::CudaReproTests::test_split_reduction_transposed, test/inductor/test_cuda_repro.py::CudaReproTests::test_triton_interpret, test/inductor/test_cuda_repro.py::CudaReproTests::test_uint_view_copy, test/inductor/test_cuda_repro.py::CudaReproTests::test_unspec_inputs_interop, test/inductor/test_cuda_repro.py::CudaReproTests::test_unused_cpu_input_cudagraphs, test/inductor/test_cuda_repro.py::CudaReproTests::test_xlnet_lm_stride_repro 2025-09-07T07:33:41.2105543Z 2025-09-07T07:33:41.2105639Z Running inductor/test_cudagraph_trees 1/1 ... [2025-09-07 07:33:41.208297] 2025-09-07T07:33:41.2105810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:33:41.2106198Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:33:41.208542] 2025-09-07T07:35:47.7547157Z 2025-09-07T07:35:47.7565200Z PRINTING LOG FILE of inductor/test_cudagraph_trees 1/1 (test/test-reports/inductor.test_cudagraph_trees_1.1_881ba388d3005d89_.log) 2025-09-07T07:35:47.7565958Z /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. 2025-09-07T07:35:47.7566968Z import pkg_resources 2025-09-07T07:35:47.7567220Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-18673de86ef00474.xml 2025-09-07T07:35:47.7567626Z ============================= test session starts ============================== 2025-09-07T07:35:47.7567880Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:35:47.7568082Z cachedir: .pytest_cache 2025-09-07T07:35:47.7568307Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:35:47.7568541Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:35:47.7568684Z configfile: pytest.ini 2025-09-07T07:35:47.7568934Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:35:47.7569196Z collecting ... collected 159 items 2025-09-07T07:35:47.7569359Z stepcurrent: Cannot find last run test, not skipping 2025-09-07T07:35:47.7590232Z Running 159 items in this shard: test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_accumulate_grad, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_accumulate_multiple_recordings, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_alias_of_parameter, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_output_checkpoint, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_static_parameter, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_storage_single_weakref, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliasing_static_ref, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_amp_cache_disabled, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_backward_gets_cached_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cache_hit_forward_miss_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cached_boxed_forward_device_index, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cached_forward_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_checkpoint_shared_output_storage_deallocation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_checkpointing_resets_persistent_refs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cleanup, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_compiled_autograd_static_input_params, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_constant_output, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_conv_benchmark, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cpp_wrapper, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes1, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes2, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_or_error, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_empty_cpu_tensor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_empty_storage, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_end_recording_early, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_error_on_dealloc_use, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_error_on_dealloc_use2, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_execution_into_recording, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_expanded_inputs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_due_to_cudagraph_managed_tensor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_warn_only_once, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_generation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_with_skipped_cudagraphed_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_frozen_fn, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_function_compiled_multiple_times, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_buffer_reuse, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_condition_op, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_only, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_op_and_dynamic_shapes, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar1, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar2, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar3, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar4, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_device_put, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_multiple, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_mutation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_tensor_symints, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_dynamoc_shapes, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_mutation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_no_split, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_dynamic_scalar_inputs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_dynamic_shapes, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_foreach_op, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_backward_not_called, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_with_skipped_cudagraphed_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_fused_scheduler_node, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_gc, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_item, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_log_message, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_multiple_devices_msg, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reduce_overhead_mode_effectiveness, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu_interleave, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency1, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_simple, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_cat_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_from_mutation_index, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_from_nested_indirect_indexing, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint_multi_output_layout, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_item, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_backend, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_graph_breaks, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_index_put, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_live_outputs_multiple_graphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_manager_per_device, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mark_step, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_meta_tensor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_child_node, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_custom_module, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_custom_module_buffer, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_parent_node, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module_buffers, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_param_inputs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multinomial, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_insert_removal_caching, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_on_inp_backend_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_on_inp_backend_inductor, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_reinplaced, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_no_rerecord_with_mark_static_address, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_not_fallback_to_eager_if_have_not_recompiling_too_many_times, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_output_alias, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_peristed_output_livenes, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_remove_hooks_on_cached_tensors, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rerecord_if_static_input_address_changed, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rng_non_trees, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rng_trees, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_run_simple, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_separate_recordings, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_side_stream_memory_allocation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_single_stream_use, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_cpp_wrapper, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_cudagraph_unsafe_ops, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached1, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached2, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_symbolic, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_sparsity, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_static_inputs_address_mutation_log, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_storage_access_error, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_constant_mutation, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_dies_between_checkpoint, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_no_longer_in_pool, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_no_cudagraphs, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_non_trees, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_trees, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_parameter, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unstable_ptr, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warmup_stream_sync, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warn_on_pending_backward, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warn_once_if_dynamic_shape_limit_reached, test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_workspace_allocation_error, test/inductor/test_cudagraph_trees.py::TestSAC::test_cpu_and_cuda_rng, test/inductor/test_cudagraph_trees.py::TestSAC::test_cudagraph_uneven_forward_backward, test/inductor/test_cudagraph_trees.py::TestSAC::test_cudagraphs_aot_eager_compat_equal, test/inductor/test_cudagraph_trees.py::TestSAC::test_cudagraphs_aot_eager_compat_equal_device_one, test/inductor/test_cudagraph_trees.py::TestSAC::test_graph_partition_cudagraphs_aot_eager_compat_equal, test/inductor/test_cudagraph_trees.py::TestSAC::test_multi_device, test/inductor/test_cudagraph_trees.py::TestSAC::test_retain_graph, test/inductor/test_cudagraph_trees.py::TestSAC::test_simple, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order0, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order1, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order2, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order3, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order4, test/inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order5 2025-09-07T07:35:47.7610913Z 2025-09-07T07:35:47.7611050Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_accumulate_grad PASSED [1.6257s] [ 0%] 2025-09-07T07:35:47.7611381Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_accumulate_multiple_recordings PASSED [2.6454s] [ 1%] 2025-09-07T07:35:47.7611733Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_alias_of_parameter PASSED [0.7779s] [ 1%] 2025-09-07T07:35:47.7612059Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_output_checkpoint PASSED [0.2814s] [ 2%] 2025-09-07T07:35:47.7612387Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_static_parameter PASSED [0.2766s] [ 3%] 2025-09-07T07:35:47.7612714Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliased_storage_single_weakref PASSED [0.6260s] [ 3%] 2025-09-07T07:35:47.7613040Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_aliasing_static_ref PASSED [1.0133s] [ 4%] 2025-09-07T07:35:47.7613352Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_amp_cache_disabled PASSED [1.8931s] [ 5%] 2025-09-07T07:35:47.7613677Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_backward_gets_cached_cudagraphs PASSED [1.1208s] [ 5%] 2025-09-07T07:35:47.7614016Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cache_hit_forward_miss_backward PASSED [1.2662s] [ 6%] 2025-09-07T07:35:47.7614481Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cached_boxed_forward_device_index SKIPPED [0.0002s] (requires multiple cuda devices) [ 6%] 2025-09-07T07:35:47.7614864Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cached_forward_backward PASSED [0.9588s] [ 7%] 2025-09-07T07:35:47.7615217Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_checkpoint_shared_output_storage_deallocation PASSED [0.1910s] [ 8%] 2025-09-07T07:35:47.7615672Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_checkpointing_resets_persistent_refs SKIPPED [0.1749s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 8%] 2025-09-07T07:35:47.7616074Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cleanup PASSED [0.5709s] [ 9%] 2025-09-07T07:35:47.7616400Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_compiled_autograd_static_input_params PASSED [0.7097s] [ 10%] 2025-09-07T07:35:47.7616731Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_constant_output PASSED [0.5248s] [ 10%] 2025-09-07T07:35:47.7617066Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_conv_benchmark PASSED [1.1572s] [ 11%] 2025-09-07T07:35:47.7617364Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cpp_wrapper PASSED [1.3004s] [ 11%] 2025-09-07T07:35:47.7617673Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes PASSED [1.2212s] [ 12%] 2025-09-07T07:35:47.7617995Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes1 PASSED [1.0502s] [ 13%] 2025-09-07T07:35:47.7618317Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_capture_sizes2 PASSED [1.0456s] [ 13%] 2025-09-07T07:35:47.7619033Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_cudagraph_or_error SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/162125 for platform(s) linux, rocm, 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.) [ 14%] 2025-09-07T07:35:47.7619753Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_backward PASSED [1.6872s] [ 15%] 2025-09-07T07:35:47.7620084Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup ('RERUN', {'yellow': True}) [0.1977s] [ 15%] 2025-09-07T07:35:47.7620429Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup ('RERUN', {'yellow': True}) [0.1939s] [ 15%] 2025-09-07T07:35:47.7620771Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup FAILED [0.1942s] [ 15%] 2025-09-07T07:35:47.7620935Z 2025-09-07T07:35:47.7621012Z ==================================== RERUNS ==================================== 2025-09-07T07:35:47.7621204Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:35:47.7621418Z Traceback (most recent call last): 2025-09-07T07:35:47.7621662Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:35:47.7621916Z self.assertEqual(COUNTER, 2) 2025-09-07T07:35:47.7622159Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:35:47.7622411Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:35:47.7622568Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:35:47.7622832Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:35:47.7623119Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:35:47.7623292Z AssertionError: Scalars are not equal! 2025-09-07T07:35:47.7623380Z 2025-09-07T07:35:47.7623435Z Expected 2 but got 3. 2025-09-07T07:35:47.7623545Z Absolute difference: 1 2025-09-07T07:35:47.7623667Z Relative difference: 0.5 2025-09-07T07:35:47.7623746Z 2025-09-07T07:35:47.7623826Z To execute this test, run the following from the base repo dir: 2025-09-07T07:35:47.7624095Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:35:47.7624282Z 2025-09-07T07:35:47.7624459Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:35:47.7624685Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:35:47.7624871Z Traceback (most recent call last): 2025-09-07T07:35:47.7625102Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:35:47.7625336Z self.assertEqual(COUNTER, 2) 2025-09-07T07:35:47.7625567Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:35:47.7625819Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:35:47.7625971Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:35:47.7629605Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:35:47.7629886Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:35:47.7630038Z AssertionError: Scalars are not equal! 2025-09-07T07:35:47.7630121Z 2025-09-07T07:35:47.7630160Z Expected 2 but got 3. 2025-09-07T07:35:47.7630257Z Absolute difference: 1 2025-09-07T07:35:47.7630361Z Relative difference: 0.5 2025-09-07T07:35:47.7630425Z 2025-09-07T07:35:47.7630498Z To execute this test, run the following from the base repo dir: 2025-09-07T07:35:47.7630738Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:35:47.7630908Z 2025-09-07T07:35:47.7630995Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:35:47.7631172Z =================================== FAILURES =================================== 2025-09-07T07:35:47.7631364Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:35:47.7631533Z Traceback (most recent call last): 2025-09-07T07:35:47.7631745Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:35:47.7631958Z self.assertEqual(COUNTER, 2) 2025-09-07T07:35:47.7632166Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:35:47.7632446Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:35:47.7632579Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:35:47.7632818Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:35:47.7633082Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:35:47.7633253Z AssertionError: Scalars are not equal! 2025-09-07T07:35:47.7633334Z 2025-09-07T07:35:47.7633376Z Expected 2 but got 3. 2025-09-07T07:35:47.7633472Z Absolute difference: 1 2025-09-07T07:35:47.7633573Z Relative difference: 0.5 2025-09-07T07:35:47.7633636Z 2025-09-07T07:35:47.7633709Z To execute this test, run the following from the base repo dir: 2025-09-07T07:35:47.7633955Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:35:47.7634128Z 2025-09-07T07:35:47.7634221Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:35:47.7637167Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-18673de86ef00474.xml - 2025-09-07T07:35:47.7637483Z =========================== short test summary info ============================ 2025-09-07T07:35:47.7637749Z FAILED [0.1942s] inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup - AssertionError: Scalars are not equal! 2025-09-07T07:35:47.7637945Z 2025-09-07T07:35:47.7637983Z Expected 2 but got 3. 2025-09-07T07:35:47.7638081Z Absolute difference: 1 2025-09-07T07:35:47.7638176Z Relative difference: 0.5 2025-09-07T07:35:47.7638240Z 2025-09-07T07:35:47.7638311Z To execute this test, run the following from the base repo dir: 2025-09-07T07:35:47.7638547Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:35:47.7638719Z 2025-09-07T07:35:47.7638802Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:35:47.7638978Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-09-07T07:35:47.7639143Z ============== 1 failed, 21 passed, 3 skipped, 2 rerun in 22.75s =============== 2025-09-07T07:35:47.7639283Z Got exit code 1 2025-09-07T07:35:47.7641122Z Retrying single test... 2025-09-07T07:35:47.7641666Z /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. 2025-09-07T07:35:47.7642157Z import pkg_resources 2025-09-07T07:35:47.7642394Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-43e36274d5cfd223.xml 2025-09-07T07:35:47.7642662Z ============================= test session starts ============================== 2025-09-07T07:35:47.7642867Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:35:47.7643049Z cachedir: .pytest_cache 2025-09-07T07:35:47.7643264Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:35:47.7643493Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:35:47.7643606Z configfile: pytest.ini 2025-09-07T07:35:47.7643849Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:35:47.7645357Z collecting ... collected 159 items / 158 deselected / 1 selected 2025-09-07T07:35:47.7645633Z stepcurrent: skipping 24 already run items. Running only test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup 2025-09-07T07:35:47.7645909Z Running 1 items in this shard 2025-09-07T07:35:47.7645980Z 2025-09-07T07:35:47.7646103Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup PASSED [0.3442s] [100%] 2025-09-07T07:35:47.7646257Z 2025-09-07T07:35:47.7646482Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-43e36274d5cfd223.xml - 2025-09-07T07:35:47.7646801Z ====================== 1 passed, 158 deselected in 0.36s ======================= 2025-09-07T07:35:47.7646963Z Got exit code 0 2025-09-07T07:35:47.7647102Z Test succeeeded in new process, continuing with the rest of the tests 2025-09-07T07:35:47.7647639Z /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. 2025-09-07T07:35:47.7648123Z import pkg_resources 2025-09-07T07:35:47.7649447Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-e18104ba3ad88a02.xml 2025-09-07T07:35:47.7649722Z ============================= test session starts ============================== 2025-09-07T07:35:47.7649924Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:35:47.7650105Z cachedir: .pytest_cache 2025-09-07T07:35:47.7650324Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:35:47.7650558Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:35:47.7650669Z configfile: pytest.ini 2025-09-07T07:35:47.7650883Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:35:47.7651150Z collecting ... collected 159 items / 25 deselected / 134 selected 2025-09-07T07:35:47.7651310Z stepcurrent: skipping 25 already run items. 2025-09-07T07:35:47.7651434Z Running 134 items in this shard 2025-09-07T07:35:47.7651505Z 2025-09-07T07:35:47.7653066Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_empty_cpu_tensor SKIPPED [0.0006s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156735 for platform(s) linux, rocm, 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.) [ 0%] 2025-09-07T07:35:47.7654124Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_empty_storage SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156755 for platform(s) linux, rocm, 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.) [ 1%] 2025-09-07T07:35:47.7655222Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_end_recording_early SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156778 for platform(s) linux, rocm, 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.) [ 2%] 2025-09-07T07:35:47.7656264Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_error_on_dealloc_use SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156801 for platform(s) linux, rocm, 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.) [ 2%] 2025-09-07T07:35:47.7657322Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_error_on_dealloc_use2 SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156808 for platform(s) linux, rocm, 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.) [ 3%] 2025-09-07T07:35:47.7658409Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_execution_into_recording SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156838 for platform(s) linux, rocm, 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.) [ 4%] 2025-09-07T07:35:47.7659452Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_expanded_inputs SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156886 for platform(s) linux, rocm, 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.) [ 5%] 2025-09-07T07:35:47.7660521Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/130749 for platform(s) linux, rocm, 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.) [ 5%] 2025-09-07T07:35:47.7661671Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_due_to_cudagraph_managed_tensor SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156922 for platform(s) linux, rocm, 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.) [ 6%] 2025-09-07T07:35:47.7662835Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_warn_only_once SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156954 for platform(s) linux, rocm, 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.) [ 7%] 2025-09-07T07:35:47.7665221Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156957 for platform(s) linux, rocm, 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.) [ 8%] 2025-09-07T07:35:47.7666305Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_cudagraphs SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156984 for platform(s) linux, rocm, 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.) [ 8%] 2025-09-07T07:35:47.7667479Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_inductor SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157035 for platform(s) linux, rocm, 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.) [ 9%] 2025-09-07T07:35:47.7668577Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_generation SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157058 for platform(s) linux, rocm, 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.) [ 10%] 2025-09-07T07:35:47.7669690Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_forward_with_skipped_cudagraphed_backward SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157086 for platform(s) linux, rocm, 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.) [ 11%] 2025-09-07T07:35:47.7670751Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_frozen_fn SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157112 for platform(s) linux, rocm, 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.) [ 11%] 2025-09-07T07:35:47.7671800Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_function_compiled_multiple_times SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157143 for platform(s) linux, rocm, 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.) [ 12%] 2025-09-07T07:35:47.7672855Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157173 for platform(s) linux, rocm, 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.) [ 13%] 2025-09-07T07:35:47.7673638Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_buffer_reuse W0907 07:34:23.503000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7674042Z W0907 07:34:23.504000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7675523Z W0907 07:34:23.505000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7675835Z W0907 07:34:23.506000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7676029Z PASSED [2.4402s] [ 14%] 2025-09-07T07:35:47.7676229Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_condition_op PASSED [1.7971s] [ 14%] 2025-09-07T07:35:47.7676532Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_only PASSED [1.2687s] [ 15%] 2025-09-07T07:35:47.7677239Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_op_and_dynamic_shapes SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157258 for platform(s) linux, rocm, 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.) [ 16%] 2025-09-07T07:35:47.7678309Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar1 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157280 for platform(s) linux, rocm, 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.) [ 17%] 2025-09-07T07:35:47.7679391Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar2 SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157312 for platform(s) linux, rocm, 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.) [ 17%] 2025-09-07T07:35:47.7680489Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar3 SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157339 for platform(s) linux, rocm, 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.) [ 18%] 2025-09-07T07:35:47.7681545Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar4 SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157350 for platform(s) linux, rocm, 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.) [ 19%] 2025-09-07T07:35:47.7682344Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_device_put W0907 07:34:27.616000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7683819Z W0907 07:34:27.617000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7684019Z PASSED [0.6205s] [ 20%] 2025-09-07T07:35:47.7684330Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_multiple W0907 07:34:28.237000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7684724Z PASSED [0.8891s] [ 20%] 2025-09-07T07:35:47.7685028Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_mutation W0907 07:34:29.124000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7685433Z W0907 07:34:29.125000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7685623Z PASSED [0.7259s] [ 21%] 2025-09-07T07:35:47.7685827Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_cpu_tensor_symints PASSED [2.1315s] [ 22%] 2025-09-07T07:35:47.7686136Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op PASSED [0.8774s] [ 23%] 2025-09-07T07:35:47.7686483Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_dynamoc_shapes PASSED [1.3800s] [ 23%] 2025-09-07T07:35:47.7686813Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_mutation PASSED [0.6536s] [ 24%] 2025-09-07T07:35:47.7688222Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_custom_op_no_split PASSED [1.3473s] [ 25%] 2025-09-07T07:35:47.7688651Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_dynamic_scalar_inputs W0907 07:34:36.317000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7689056Z W0907 07:34:36.317000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7689330Z W0907 07:34:37.016000 537224 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:35:47.7689629Z W0907 07:34:37.017000 537224 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:35:47.7689817Z PASSED [1.3107s] [ 26%] 2025-09-07T07:35:47.7690012Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_dynamic_shapes PASSED [1.0894s] [ 26%] 2025-09-07T07:35:47.7690313Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_foreach_op PASSED [0.5310s] [ 27%] 2025-09-07T07:35:47.7690749Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_backward W0907 07:34:39.213000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7691147Z W0907 07:34:39.215000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7692370Z PASSED [1.4268s] [ 28%] 2025-09-07T07:35:47.7692613Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_backward_not_called PASSED [0.7484s] [ 29%] 2025-09-07T07:35:47.7692977Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_forward_with_skipped_cudagraphed_backward PASSED [0.7153s] [ 29%] 2025-09-07T07:35:47.7693327Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_fused_scheduler_node PASSED [0.5060s] [ 30%] 2025-09-07T07:35:47.7693628Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_gc PASSED [0.7998s] [ 31%] 2025-09-07T07:35:47.7693907Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_item PASSED [0.6183s] [ 32%] 2025-09-07T07:35:47.7694196Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_log_message PASSED [0.7559s] [ 32%] 2025-09-07T07:35:47.7694623Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_multiple_devices_msg SKIPPED [0.0002s] (requires multiple cuda devices) [ 33%] 2025-09-07T07:35:47.7695125Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reduce_overhead_mode_effectiveness W0907 07:34:44.791000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7695553Z W0907 07:34:44.792000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7695744Z PASSED [0.7960s] [ 34%] 2025-09-07T07:35:47.7696998Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu PASSED [0.8096s] [ 35%] 2025-09-07T07:35:47.7697449Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu_interleave W0907 07:34:46.416000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7697784Z PASSED [0.9449s] [ 35%] 2025-09-07T07:35:47.7698009Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency PASSED [1.1786s] [ 36%] 2025-09-07T07:35:47.7698377Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency1 PASSED [1.1538s] [ 37%] 2025-09-07T07:35:47.7698843Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_simple W0907 07:34:49.684000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7699234Z W0907 07:34:49.685000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7699425Z PASSED [1.1409s] [ 38%] 2025-09-07T07:35:47.7699714Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint W0907 07:34:50.829000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7700099Z W0907 07:34:50.830000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7700371Z W0907 07:34:51.515000 537224 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:35:47.7701715Z W0907 07:34:51.516000 537224 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:35:47.7701905Z PASSED [1.9761s] [ 38%] 2025-09-07T07:35:47.7702108Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_cat_backward PASSED [1.7113s] [ 39%] 2025-09-07T07:35:47.7702442Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_from_mutation_index PASSED [1.6667s] [ 40%] 2025-09-07T07:35:47.7702819Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_symint_from_nested_indirect_indexing PASSED [1.0100s] [ 41%] 2025-09-07T07:35:47.7703263Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint W0907 07:34:57.217000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7703680Z W0907 07:34:57.218000 537224 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:35:47.7703874Z PASSED [0.9280s] [ 41%] 2025-09-07T07:35:47.7704097Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint_multi_output_layout PASSED [0.8457s] [ 42%] 2025-09-07T07:35:47.7704503Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_item PASSED [0.3457s] [ 43%] 2025-09-07T07:35:47.7704822Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero PASSED [0.4046s] [ 44%] 2025-09-07T07:35:47.7706233Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_backend PASSED [0.4767s] [ 44%] 2025-09-07T07:35:47.7706581Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_graph_breaks PASSED [0.9027s] [ 45%] 2025-09-07T07:35:47.7706888Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_index_put PASSED [0.5682s] [ 46%] 2025-09-07T07:35:47.7707171Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_live_outputs_multiple_graphs PASSED [0.9392s] [ 47%] 2025-09-07T07:35:47.7707563Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_manager_per_device SKIPPED [0.0002s] (requires multiple cuda devices) [ 47%] 2025-09-07T07:35:47.7707874Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mark_step PASSED [0.5249s] [ 48%] 2025-09-07T07:35:47.7708131Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_meta_tensor PASSED [0.5278s] [ 49%] 2025-09-07T07:35:47.7708410Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_child_node PASSED [0.7969s] [ 50%] 2025-09-07T07:35:47.7708706Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_custom_module PASSED [0.5755s] [ 50%] 2025-09-07T07:35:47.7709015Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_custom_module_buffer PASSED [0.7286s] [ 51%] 2025-09-07T07:35:47.7710369Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_parent_node PASSED [0.7327s] [ 52%] 2025-09-07T07:35:47.7710733Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module PASSED [0.7051s] [ 52%] 2025-09-07T07:35:47.7711095Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module_buffers PASSED [1.0743s] [ 53%] 2025-09-07T07:35:47.7711455Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_param_inputs PASSED [0.5851s] [ 54%] 2025-09-07T07:35:47.7711761Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multinomial PASSED [0.3869s] [ 55%] 2025-09-07T07:35:47.7712109Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_cudagraphs SKIPPED [0.0002s] (requires multiple cuda devices) [ 55%] 2025-09-07T07:35:47.7712537Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_inductor SKIPPED [0.0001s] (requires multiple cuda devices) [ 56%] 2025-09-07T07:35:47.7712906Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_multiple_insert_removal_caching PASSED [0.2907s] [ 57%] 2025-09-07T07:35:47.7713279Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_cudagraphs PASSED [0.3624s] [ 58%] 2025-09-07T07:35:47.7713662Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_inductor PASSED [0.5946s] [ 58%] 2025-09-07T07:35:47.7714076Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_cudagraphs PASSED [0.3660s] [ 59%] 2025-09-07T07:35:47.7715649Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_inductor PASSED [0.5948s] [ 60%] 2025-09-07T07:35:47.7716036Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_cudagraphs PASSED [0.3707s] [ 61%] 2025-09-07T07:35:47.7716435Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_inductor PASSED [0.6879s] [ 61%] 2025-09-07T07:35:47.7716825Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_cudagraphs PASSED [0.4095s] [ 62%] 2025-09-07T07:35:47.7717212Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_inductor PASSED [0.6034s] [ 63%] 2025-09-07T07:35:47.7717566Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_on_inp_backend_cudagraphs PASSED [0.4368s] [ 64%] 2025-09-07T07:35:47.7717886Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_on_inp_backend_inductor PASSED [0.7492s] [ 64%] 2025-09-07T07:35:47.7718186Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_mutation_reinplaced PASSED [0.6586s] [ 65%] 2025-09-07T07:35:47.7718493Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_no_rerecord_with_mark_static_address PASSED [0.8315s] [ 66%] 2025-09-07T07:35:47.7718848Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_not_fallback_to_eager_if_have_not_recompiling_too_many_times PASSED [0.6078s] [ 67%] 2025-09-07T07:35:47.7719178Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_output_alias PASSED [0.2951s] [ 67%] 2025-09-07T07:35:47.7720523Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_peristed_output_livenes PASSED [0.5217s] [ 68%] 2025-09-07T07:35:47.7720828Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_remove_hooks_on_cached_tensors PASSED [0.6440s] [ 69%] 2025-09-07T07:35:47.7721150Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rerecord_if_static_input_address_changed PASSED [0.7159s] [ 70%] 2025-09-07T07:35:47.7721454Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rng_non_trees PASSED [0.3694s] [ 70%] 2025-09-07T07:35:47.7721716Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_rng_trees PASSED [0.4117s] [ 71%] 2025-09-07T07:35:47.7721970Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_run_simple PASSED [1.2273s] [ 72%] 2025-09-07T07:35:47.7722266Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_separate_recordings PASSED [1.2394s] [ 73%] 2025-09-07T07:35:47.7722560Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_side_stream_memory_allocation PASSED [0.3646s] [ 73%] 2025-09-07T07:35:47.7722849Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_single_stream_use PASSED [0.9222s] [ 74%] 2025-09-07T07:35:47.7723128Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_cpp_wrapper PASSED [1.2596s] [ 75%] 2025-09-07T07:35:47.7724574Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_cudagraph_unsafe_ops PASSED [0.6612s] [ 76%] 2025-09-07T07:35:47.7724891Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached1 PASSED [1.4181s] [ 76%] 2025-09-07T07:35:47.7725216Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached2 PASSED [6.3173s] [ 77%] 2025-09-07T07:35:47.7725549Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_skip_symbolic PASSED [1.2162s] [ 78%] 2025-09-07T07:35:47.7725812Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_sparsity PASSED [0.5019s] [ 79%] 2025-09-07T07:35:47.7726100Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_static_inputs_address_mutation_log PASSED [0.7574s] [ 79%] 2025-09-07T07:35:47.7726420Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_storage_access_error PASSED [0.3811s] [ 80%] 2025-09-07T07:35:47.7726708Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_constant_mutation PASSED [0.7370s] [ 81%] 2025-09-07T07:35:47.7727009Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_dies_between_checkpoint PASSED [0.4019s] [ 82%] 2025-09-07T07:35:47.7727309Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_tensor_no_longer_in_pool PASSED [0.4464s] [ 82%] 2025-09-07T07:35:47.7727638Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_no_cudagraphs PASSED [0.6035s] [ 83%] 2025-09-07T07:35:47.7729052Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_non_trees PASSED [0.6560s] [ 84%] 2025-09-07T07:35:47.7729369Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_input_trees PASSED [0.6906s] [ 85%] 2025-09-07T07:35:47.7729670Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unaligned_static_parameter PASSED [0.3742s] [ 85%] 2025-09-07T07:35:47.7729957Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_unstable_ptr PASSED [0.4594s] [ 86%] 2025-09-07T07:35:47.7730227Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warmup_stream_sync PASSED [0.5806s] [ 87%] 2025-09-07T07:35:47.7730510Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warn_on_pending_backward PASSED [0.6094s] [ 88%] 2025-09-07T07:35:47.7730824Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_warn_once_if_dynamic_shape_limit_reached PASSED [1.3110s] [ 88%] 2025-09-07T07:35:47.7731228Z inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_workspace_allocation_error SKIPPED [0.3350s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 89%] 2025-09-07T07:35:47.7731575Z inductor/test_cudagraph_trees.py::TestSAC::test_cpu_and_cuda_rng PASSED [0.1066s] [ 90%] 2025-09-07T07:35:47.7731835Z inductor/test_cudagraph_trees.py::TestSAC::test_cudagraph_uneven_forward_backward PASSED [0.0147s] [ 91%] 2025-09-07T07:35:47.7732115Z inductor/test_cudagraph_trees.py::TestSAC::test_cudagraphs_aot_eager_compat_equal PASSED [0.5962s] [ 91%] 2025-09-07T07:35:47.7732451Z inductor/test_cudagraph_trees.py::TestSAC::test_cudagraphs_aot_eager_compat_equal_device_one SKIPPED [0.0002s] (requires multiple cuda devices) [ 92%] 2025-09-07T07:35:47.7733862Z inductor/test_cudagraph_trees.py::TestSAC::test_graph_partition_cudagraphs_aot_eager_compat_equal PASSED [0.6500s] [ 93%] 2025-09-07T07:35:47.7734186Z inductor/test_cudagraph_trees.py::TestSAC::test_multi_device SKIPPED [0.0002s] (requires multiple cuda devices) [ 94%] 2025-09-07T07:35:47.7734589Z inductor/test_cudagraph_trees.py::TestSAC::test_retain_graph PASSED [0.0803s] [ 94%] 2025-09-07T07:35:47.7734814Z inductor/test_cudagraph_trees.py::TestSAC::test_simple PASSED [0.1399s] [ 95%] 2025-09-07T07:35:47.7735056Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order0 PASSED [0.0767s] [ 96%] 2025-09-07T07:35:47.7735325Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order1 PASSED [0.0684s] [ 97%] 2025-09-07T07:35:47.7735592Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order2 PASSED [0.0654s] [ 97%] 2025-09-07T07:35:47.7735857Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order3 PASSED [0.0650s] [ 98%] 2025-09-07T07:35:47.7736122Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order4 PASSED [0.0652s] [ 99%] 2025-09-07T07:35:47.7736387Z inductor/test_cudagraph_trees.py::TestSAC::test_uneven_forward_backward_order5 PASSED [0.0663s] [100%] 2025-09-07T07:35:47.7736568Z 2025-09-07T07:35:47.7736802Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees/inductor.test_cudagraph_trees-e18104ba3ad88a02.xml - 2025-09-07T07:35:47.7738186Z ========== 104 passed, 30 skipped, 25 deselected in 83.25s (0:01:23) =========== 2025-09-07T07:35:47.7738484Z The following tests failed and then succeeded when run in a new process['test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_dynamic_warmup'] 2025-09-07T07:35:47.7738734Z 2025-09-07T07:35:47.7738905Z FINISHED PRINTING LOG FILE of inductor/test_cudagraph_trees 1/1 (test/test-reports/inductor.test_cudagraph_trees_1.1_881ba388d3005d89_.log) 2025-09-07T07:35:47.7739111Z 2025-09-07T07:35:47.7739222Z Running inductor/test_cudagraph_trees_expandable_segments 1/1 ... [2025-09-07 07:35:47.754953] 2025-09-07T07:35:47.7739435Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:35:47.7739854Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudagraph_trees_expandable_segments.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:35:47.755254] 2025-09-07T07:37:39.8150173Z 2025-09-07T07:37:39.8151430Z PRINTING LOG FILE of inductor/test_cudagraph_trees_expandable_segments 1/1 (test/test-reports/inductor.test_cudagraph_trees_expandable_segments_1.1_d0d7d40bec642a0f_.log) 2025-09-07T07:37:39.8153497Z /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. 2025-09-07T07:37:39.8164566Z import pkg_resources 2025-09-07T07:37:39.8164939Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to ./.pytorch-disabled-tests.json 2025-09-07T07:37:39.8165516Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-4e83c20e2e448444.xml 2025-09-07T07:37:39.8165949Z ============================= test session starts ============================== 2025-09-07T07:37:39.8166219Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:37:39.8166450Z cachedir: .pytest_cache 2025-09-07T07:37:39.8166708Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:37:39.8166984Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:37:39.8167120Z configfile: pytest.ini 2025-09-07T07:37:39.8167384Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:37:39.8167663Z collecting ... collected 145 items 2025-09-07T07:37:39.8168192Z stepcurrent: Cannot find last run test, not skipping 2025-09-07T07:37:39.8191716Z Running 145 items in this shard: test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_accumulate_grad, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_accumulate_multiple_recordings, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_alias_of_parameter, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_output_checkpoint, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_static_parameter, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_storage_single_weakref, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliasing_static_ref, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_amp_cache_disabled, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_backward_gets_cached_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cache_hit_forward_miss_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cached_boxed_forward_device_index, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cached_forward_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_checkpoint_shared_output_storage_deallocation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_checkpointing_resets_persistent_refs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cleanup, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_compiled_autograd_static_input_params, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_constant_output, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_conv_benchmark, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cpp_wrapper, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes1, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes2, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_or_error, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_empty_cpu_tensor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_empty_storage, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_end_recording_early, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_error_on_dealloc_use, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_error_on_dealloc_use2, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_execution_into_recording, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_expanded_inputs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_due_to_cudagraph_managed_tensor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_warn_only_once, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_generation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_with_skipped_cudagraphed_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_frozen_fn, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_function_compiled_multiple_times, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_buffer_reuse, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_condition_op, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_only, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_op_and_dynamic_shapes, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar1, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar2, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar3, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar4, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_device_put, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_multiple, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_mutation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_tensor_symints, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_dynamoc_shapes, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_mutation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_no_split, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_dynamic_scalar_inputs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_dynamic_shapes, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_foreach_op, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_backward_not_called, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_with_skipped_cudagraphed_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_fused_scheduler_node, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_gc, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_item, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_log_message, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_multiple_devices_msg, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reduce_overhead_mode_effectiveness, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu_interleave, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency1, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_simple, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_cat_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_from_mutation_index, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_from_nested_indirect_indexing, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint_multi_output_layout, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_item, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_backend, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_graph_breaks, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_index_put, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_live_outputs_multiple_graphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_manager_per_device, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mark_step, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_meta_tensor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_child_node, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_custom_module, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_custom_module_buffer, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_parent_node, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module_buffers, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_param_inputs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multinomial, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_insert_removal_caching, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_on_inp_backend_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_on_inp_backend_inductor, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_reinplaced, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_no_rerecord_with_mark_static_address, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_not_fallback_to_eager_if_have_not_recompiling_too_many_times, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_output_alias, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_peristed_output_livenes, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_remove_hooks_on_cached_tensors, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rerecord_if_static_input_address_changed, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rng_non_trees, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rng_trees, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_run_simple, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_separate_recordings, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_side_stream_memory_allocation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_single_stream_use, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_cpp_wrapper, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_cudagraph_unsafe_ops, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached1, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached2, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_symbolic, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_sparsity, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_static_inputs_address_mutation_log, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_storage_access_error, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_constant_mutation, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_dies_between_checkpoint, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_no_longer_in_pool, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_no_cudagraphs, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_non_trees, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_trees, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_parameter, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unstable_ptr, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warmup_stream_sync, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warn_on_pending_backward, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warn_once_if_dynamic_shape_limit_reached, test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_workspace_allocation_error 2025-09-07T07:37:39.8214116Z 2025-09-07T07:37:39.8214321Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_accumulate_grad <- test/inductor/test_cudagraph_trees.py PASSED [1.6969s] [ 0%] 2025-09-07T07:37:39.8214835Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_accumulate_multiple_recordings <- test/inductor/test_cudagraph_trees.py PASSED [2.9564s] [ 1%] 2025-09-07T07:37:39.8215328Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_alias_of_parameter <- test/inductor/test_cudagraph_trees.py PASSED [0.7809s] [ 2%] 2025-09-07T07:37:39.8215775Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_output_checkpoint <- test/inductor/test_cudagraph_trees.py PASSED [0.3206s] [ 2%] 2025-09-07T07:37:39.8216242Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_static_parameter <- test/inductor/test_cudagraph_trees.py PASSED [0.3097s] [ 3%] 2025-09-07T07:37:39.8216697Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliased_storage_single_weakref <- test/inductor/test_cudagraph_trees.py PASSED [0.7064s] [ 4%] 2025-09-07T07:37:39.8217175Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_aliasing_static_ref <- test/inductor/test_cudagraph_trees.py PASSED [1.0528s] [ 4%] 2025-09-07T07:37:39.8217614Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_amp_cache_disabled <- test/inductor/test_cudagraph_trees.py PASSED [2.0260s] [ 5%] 2025-09-07T07:37:39.8218063Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_backward_gets_cached_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [1.0559s] [ 6%] 2025-09-07T07:37:39.8218525Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cache_hit_forward_miss_backward <- test/inductor/test_cudagraph_trees.py PASSED [1.2991s] [ 6%] 2025-09-07T07:37:39.8219040Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cached_boxed_forward_device_index <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0003s] (requires multiple cuda devices) [ 7%] 2025-09-07T07:37:39.8219549Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cached_forward_backward <- test/inductor/test_cudagraph_trees.py PASSED [1.0327s] [ 8%] 2025-09-07T07:37:39.8220032Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_checkpoint_shared_output_storage_deallocation <- test/inductor/test_cudagraph_trees.py PASSED [0.2501s] [ 8%] 2025-09-07T07:37:39.8220610Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_checkpointing_resets_persistent_refs <- test/inductor/test_cudagraph_trees.py SKIPPED [0.2236s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 9%] 2025-09-07T07:37:39.8221140Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cleanup <- test/inductor/test_cudagraph_trees.py PASSED [0.5738s] [ 10%] 2025-09-07T07:37:39.8221589Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_compiled_autograd_static_input_params <- test/inductor/test_cudagraph_trees.py PASSED [0.7295s] [ 11%] 2025-09-07T07:37:39.8222075Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_constant_output <- test/inductor/test_cudagraph_trees.py PASSED [0.5743s] [ 11%] 2025-09-07T07:37:39.8222505Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_conv_benchmark <- test/inductor/test_cudagraph_trees.py PASSED [0.7753s] [ 12%] 2025-09-07T07:37:39.8222928Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cpp_wrapper <- test/inductor/test_cudagraph_trees.py PASSED [1.2066s] [ 13%] 2025-09-07T07:37:39.8229341Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes <- test/inductor/test_cudagraph_trees.py PASSED [1.1556s] [ 13%] 2025-09-07T07:37:39.8229799Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes1 <- test/inductor/test_cudagraph_trees.py PASSED [1.0436s] [ 14%] 2025-09-07T07:37:39.8230249Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_capture_sizes2 <- test/inductor/test_cudagraph_trees.py PASSED [1.0477s] [ 15%] 2025-09-07T07:37:39.8231144Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_cudagraph_or_error <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/162125 for platform(s) linux, rocm, 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.) [ 15%] 2025-09-07T07:37:39.8231981Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_backward <- test/inductor/test_cudagraph_trees.py PASSED [1.6609s] [ 16%] 2025-09-07T07:37:39.8232459Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup <- test/inductor/test_cudagraph_trees.py ('RERUN', {'yellow': True}) [0.2478s] [ 17%] 2025-09-07T07:37:39.8232937Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup <- test/inductor/test_cudagraph_trees.py ('RERUN', {'yellow': True}) [0.2463s] [ 17%] 2025-09-07T07:37:39.8233387Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup <- test/inductor/test_cudagraph_trees.py FAILED [0.2453s] [ 17%] 2025-09-07T07:37:39.8233616Z 2025-09-07T07:37:39.8233672Z ==================================== RERUNS ==================================== 2025-09-07T07:37:39.8233843Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:37:39.8234032Z Traceback (most recent call last): 2025-09-07T07:37:39.8234249Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:37:39.8234541Z self.assertEqual(COUNTER, 2) 2025-09-07T07:37:39.8234757Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:37:39.8234987Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:37:39.8235125Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:37:39.8235360Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:37:39.8235619Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:37:39.8235769Z AssertionError: Scalars are not equal! 2025-09-07T07:37:39.8235854Z 2025-09-07T07:37:39.8235894Z Expected 2 but got 3. 2025-09-07T07:37:39.8235990Z Absolute difference: 1 2025-09-07T07:37:39.8236089Z Relative difference: 0.5 2025-09-07T07:37:39.8236155Z 2025-09-07T07:37:39.8236225Z To execute this test, run the following from the base repo dir: 2025-09-07T07:37:39.8236467Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:37:39.8236635Z 2025-09-07T07:37:39.8236721Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:37:39.8236956Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:37:39.8237113Z Traceback (most recent call last): 2025-09-07T07:37:39.8237319Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:37:39.8237524Z self.assertEqual(COUNTER, 2) 2025-09-07T07:37:39.8237728Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:37:39.8237954Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:37:39.8238083Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:37:39.8238314Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:37:39.8238570Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:37:39.8238716Z AssertionError: Scalars are not equal! 2025-09-07T07:37:39.8238815Z 2025-09-07T07:37:39.8238853Z Expected 2 but got 3. 2025-09-07T07:37:39.8238948Z Absolute difference: 1 2025-09-07T07:37:39.8239044Z Relative difference: 0.5 2025-09-07T07:37:39.8239108Z 2025-09-07T07:37:39.8239177Z To execute this test, run the following from the base repo dir: 2025-09-07T07:37:39.8239409Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:37:39.8239600Z 2025-09-07T07:37:39.8239683Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:37:39.8239848Z =================================== FAILURES =================================== 2025-09-07T07:37:39.8240011Z ____________________ CudaGraphTreeTests.test_dynamic_warmup ____________________ 2025-09-07T07:37:39.8240165Z Traceback (most recent call last): 2025-09-07T07:37:39.8240387Z File "/var/lib/jenkins/pytorch/test/inductor/test_cudagraph_trees.py", line 1898, in test_dynamic_warmup 2025-09-07T07:37:39.8240597Z self.assertEqual(COUNTER, 2) 2025-09-07T07:37:39.8240809Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual 2025-09-07T07:37:39.8241039Z return super().assertEqual(x, y, *args, **kwargs) 2025-09-07T07:37:39.8241176Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-09-07T07:37:39.8241413Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4179, in assertEqual 2025-09-07T07:37:39.8241674Z raise error_metas.pop()[0].to_error( # type: ignore[index] 2025-09-07T07:37:39.8241824Z AssertionError: Scalars are not equal! 2025-09-07T07:37:39.8241905Z 2025-09-07T07:37:39.8241946Z Expected 2 but got 3. 2025-09-07T07:37:39.8242048Z Absolute difference: 1 2025-09-07T07:37:39.8242147Z Relative difference: 0.5 2025-09-07T07:37:39.8242210Z 2025-09-07T07:37:39.8242285Z To execute this test, run the following from the base repo dir: 2025-09-07T07:37:39.8242520Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:37:39.8242689Z 2025-09-07T07:37:39.8242773Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:37:39.8243171Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-4e83c20e2e448444.xml - 2025-09-07T07:37:39.8243541Z =========================== short test summary info ============================ 2025-09-07T07:37:39.8243823Z FAILED [0.2453s] inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup - AssertionError: Scalars are not equal! 2025-09-07T07:37:39.8244044Z 2025-09-07T07:37:39.8244084Z Expected 2 but got 3. 2025-09-07T07:37:39.8244180Z Absolute difference: 1 2025-09-07T07:37:39.8244278Z Relative difference: 0.5 2025-09-07T07:37:39.8244342Z 2025-09-07T07:37:39.8244483Z To execute this test, run the following from the base repo dir: 2025-09-07T07:37:39.8244739Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_cudagraph_trees.py CudaGraphTreeTests.test_dynamic_warmup 2025-09-07T07:37:39.8244909Z 2025-09-07T07:37:39.8244991Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-09-07T07:37:39.8245170Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-09-07T07:37:39.8245336Z ============== 1 failed, 21 passed, 3 skipped, 2 rerun in 23.27s =============== 2025-09-07T07:37:39.8245474Z Got exit code 1 2025-09-07T07:37:39.8245569Z Retrying single test... 2025-09-07T07:37:39.8246052Z /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. 2025-09-07T07:37:39.8246536Z import pkg_resources 2025-09-07T07:37:39.8246775Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to ./.pytorch-disabled-tests.json 2025-09-07T07:37:39.8247198Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-d97c65d6c7e3d1ee.xml 2025-09-07T07:37:39.8247523Z ============================= test session starts ============================== 2025-09-07T07:37:39.8247747Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:37:39.8247930Z cachedir: .pytest_cache 2025-09-07T07:37:39.8248146Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:37:39.8248380Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:37:39.8248495Z configfile: pytest.ini 2025-09-07T07:37:39.8248731Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:37:39.8248998Z collecting ... collected 145 items / 144 deselected / 1 selected 2025-09-07T07:37:39.8249302Z stepcurrent: skipping 24 already run items. Running only test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup 2025-09-07T07:37:39.8249574Z Running 1 items in this shard 2025-09-07T07:37:39.8249646Z 2025-09-07T07:37:39.8249846Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup <- test/inductor/test_cudagraph_trees.py PASSED [0.3627s] [100%] 2025-09-07T07:37:39.8251564Z 2025-09-07T07:37:39.8251850Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-d97c65d6c7e3d1ee.xml - 2025-09-07T07:37:39.8252224Z ====================== 1 passed, 144 deselected in 0.54s ======================= 2025-09-07T07:37:39.8252359Z Got exit code 0 2025-09-07T07:37:39.8252493Z Test succeeeded in new process, continuing with the rest of the tests 2025-09-07T07:37:39.8253028Z /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. 2025-09-07T07:37:39.8253509Z import pkg_resources 2025-09-07T07:37:39.8253725Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to ./.pytorch-disabled-tests.json 2025-09-07T07:37:39.8254143Z Test results will be stored in test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-cb4c598f5c78568f.xml 2025-09-07T07:37:39.8254526Z ============================= test session starts ============================== 2025-09-07T07:37:39.8254724Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-09-07T07:37:39.8254940Z cachedir: .pytest_cache 2025-09-07T07:37:39.8256379Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-09-07T07:37:39.8256608Z rootdir: /var/lib/jenkins/pytorch 2025-09-07T07:37:39.8256718Z configfile: pytest.ini 2025-09-07T07:37:39.8256933Z plugins: hypothesis-5.35.1, subtests-0.13.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-09-07T07:37:39.8257195Z collecting ... collected 145 items / 25 deselected / 120 selected 2025-09-07T07:37:39.8257349Z stepcurrent: skipping 25 already run items. 2025-09-07T07:37:39.8257473Z Running 120 items in this shard 2025-09-07T07:37:39.8257542Z 2025-09-07T07:37:39.8258130Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_empty_cpu_tensor <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156735 for platform(s) linux, rocm, 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.) [ 0%] 2025-09-07T07:37:39.8259330Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_empty_storage <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156755 for platform(s) linux, rocm, 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.) [ 1%] 2025-09-07T07:37:39.8260537Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_end_recording_early <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156778 for platform(s) linux, rocm, 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.) [ 2%] 2025-09-07T07:37:39.8262819Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_error_on_dealloc_use <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156801 for platform(s) linux, rocm, 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.) [ 3%] 2025-09-07T07:37:39.8263999Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_error_on_dealloc_use2 <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156808 for platform(s) linux, rocm, 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.) [ 4%] 2025-09-07T07:37:39.8265257Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_execution_into_recording <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156838 for platform(s) linux, rocm, 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.) [ 5%] 2025-09-07T07:37:39.8266457Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_expanded_inputs <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156886 for platform(s) linux, rocm, 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.) [ 5%] 2025-09-07T07:37:39.8267711Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/130749 for platform(s) linux, rocm, 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.) [ 6%] 2025-09-07T07:37:39.8268995Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_due_to_cudagraph_managed_tensor <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156922 for platform(s) linux, rocm, 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.) [ 7%] 2025-09-07T07:37:39.8270311Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times_warn_only_once <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156954 for platform(s) linux, rocm, 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.) [ 8%] 2025-09-07T07:37:39.8271569Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156957 for platform(s) linux, rocm, 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.) [ 9%] 2025-09-07T07:37:39.8272777Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/156984 for platform(s) linux, rocm, 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.) [ 10%] 2025-09-07T07:37:39.8274024Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_backward_not_called_backend_inductor <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157035 for platform(s) linux, rocm, 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.) [ 10%] 2025-09-07T07:37:39.8276430Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_generation <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157058 for platform(s) linux, rocm, 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.) [ 11%] 2025-09-07T07:37:39.8277669Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_forward_with_skipped_cudagraphed_backward <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157086 for platform(s) linux, rocm, 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.) [ 12%] 2025-09-07T07:37:39.8278859Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_frozen_fn <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157112 for platform(s) linux, rocm, 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.) [ 13%] 2025-09-07T07:37:39.8280037Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_function_compiled_multiple_times <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157143 for platform(s) linux, rocm, 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.) [ 14%] 2025-09-07T07:37:39.8281246Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157173 for platform(s) linux, rocm, 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.) [ 15%] 2025-09-07T07:37:39.8282202Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_buffer_reuse <- test/inductor/test_cudagraph_trees.py W0907 07:36:31.688000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8282675Z W0907 07:36:31.689000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8282944Z W0907 07:36:31.691000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8283209Z W0907 07:36:31.691000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8283396Z PASSED [2.0516s] [ 15%] 2025-09-07T07:37:39.8283670Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_condition_op <- test/inductor/test_cudagraph_trees.py PASSED [1.8143s] [ 16%] 2025-09-07T07:37:39.8284121Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_only <- test/inductor/test_cudagraph_trees.py PASSED [1.1233s] [ 17%] 2025-09-07T07:37:39.8285026Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_op_and_dynamic_shapes <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157258 for platform(s) linux, rocm, 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.) [ 18%] 2025-09-07T07:37:39.8287391Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar1 <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157280 for platform(s) linux, rocm, 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.) [ 19%] 2025-09-07T07:37:39.8288621Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar2 <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157312 for platform(s) linux, rocm, 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.) [ 20%] 2025-09-07T07:37:39.8289814Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar3 <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157339 for platform(s) linux, rocm, 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.) [ 20%] 2025-09-07T07:37:39.8291042Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar4 <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0003s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157350 for platform(s) linux, rocm, 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.) [ 21%] 2025-09-07T07:37:39.8292002Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_device_put <- test/inductor/test_cudagraph_trees.py W0907 07:36:35.591000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8292486Z W0907 07:36:35.591000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8292695Z PASSED [0.5278s] [ 22%] 2025-09-07T07:37:39.8293080Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_multiple <- test/inductor/test_cudagraph_trees.py W0907 07:36:36.124000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8293474Z PASSED [0.7886s] [ 23%] 2025-09-07T07:37:39.8293851Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_scalar_mutation <- test/inductor/test_cudagraph_trees.py W0907 07:36:36.909000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8295447Z W0907 07:36:36.910000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8295647Z PASSED [0.6411s] [ 24%] 2025-09-07T07:37:39.8295925Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_cpu_tensor_symints <- test/inductor/test_cudagraph_trees.py PASSED [1.9775s] [ 25%] 2025-09-07T07:37:39.8296384Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op <- test/inductor/test_cudagraph_trees.py PASSED [0.8145s] [ 25%] 2025-09-07T07:37:39.8296850Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_dynamoc_shapes <- test/inductor/test_cudagraph_trees.py PASSED [1.2986s] [ 26%] 2025-09-07T07:37:39.8297324Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_mutation <- test/inductor/test_cudagraph_trees.py PASSED [0.5755s] [ 27%] 2025-09-07T07:37:39.8297792Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_custom_op_no_split <- test/inductor/test_cudagraph_trees.py PASSED [1.2343s] [ 28%] 2025-09-07T07:37:39.8298374Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_dynamic_scalar_inputs <- test/inductor/test_cudagraph_trees.py W0907 07:36:43.524000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8298887Z W0907 07:36:43.525000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8299153Z W0907 07:36:44.119000 583709 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:37:39.8299417Z W0907 07:36:44.120000 583709 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:37:39.8300604Z PASSED [1.1272s] [ 29%] 2025-09-07T07:37:39.8300882Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_dynamic_shapes <- test/inductor/test_cudagraph_trees.py PASSED [1.1329s] [ 30%] 2025-09-07T07:37:39.8301334Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_foreach_op <- test/inductor/test_cudagraph_trees.py PASSED [0.4337s] [ 30%] 2025-09-07T07:37:39.8301898Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_backward <- test/inductor/test_cudagraph_trees.py W0907 07:36:46.181000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8302398Z W0907 07:36:46.183000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8302584Z PASSED [1.3409s] [ 31%] 2025-09-07T07:37:39.8302871Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_backward_not_called <- test/inductor/test_cudagraph_trees.py PASSED [0.6625s] [ 32%] 2025-09-07T07:37:39.8303402Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_forward_with_skipped_cudagraphed_backward <- test/inductor/test_cudagraph_trees.py PASSED [0.5944s] [ 33%] 2025-09-07T07:37:39.8303919Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_fused_scheduler_node <- test/inductor/test_cudagraph_trees.py PASSED [0.4395s] [ 34%] 2025-09-07T07:37:39.8304452Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_gc <- test/inductor/test_cudagraph_trees.py PASSED [0.6497s] [ 35%] 2025-09-07T07:37:39.8304882Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_item <- test/inductor/test_cudagraph_trees.py PASSED [0.5408s] [ 35%] 2025-09-07T07:37:39.8306344Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_log_message <- test/inductor/test_cudagraph_trees.py PASSED [0.6506s] [ 36%] 2025-09-07T07:37:39.8306852Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_multiple_devices_msg <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0001s] (requires multiple cuda devices) [ 37%] 2025-09-07T07:37:39.8307578Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reduce_overhead_mode_effectiveness <- test/inductor/test_cudagraph_trees.py W0907 07:36:51.068000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8308077Z W0907 07:36:51.069000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8308264Z PASSED [0.6765s] [ 38%] 2025-09-07T07:37:39.8308541Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu <- test/inductor/test_cudagraph_trees.py PASSED [0.6874s] [ 39%] 2025-09-07T07:37:39.8309134Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_cpu_and_gpu_interleave <- test/inductor/test_cudagraph_trees.py W0907 07:36:52.453000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8309540Z PASSED [0.8318s] [ 40%] 2025-09-07T07:37:39.8309837Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency <- test/inductor/test_cudagraph_trees.py PASSED [1.0644s] [ 40%] 2025-09-07T07:37:39.8310381Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_reorder_custom_op_with_no_dependency1 <- test/inductor/test_cudagraph_trees.py PASSED [1.0064s] [ 41%] 2025-09-07T07:37:39.8312818Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_simple <- test/inductor/test_cudagraph_trees.py W0907 07:36:55.341000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8313301Z W0907 07:36:55.341000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8313491Z PASSED [0.8424s] [ 42%] 2025-09-07T07:37:39.8313860Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint <- test/inductor/test_cudagraph_trees.py W0907 07:36:56.184000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8314357Z W0907 07:36:56.185000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8314695Z W0907 07:36:56.796000 583709 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:37:39.8314958Z W0907 07:36:56.797000 583709 site-packages/torch/_inductor/utils.py:2298] [0/1] DeviceCopy in input program 2025-09-07T07:37:39.8315171Z PASSED [1.6605s] [ 43%] 2025-09-07T07:37:39.8315450Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_cat_backward <- test/inductor/test_cudagraph_trees.py PASSED [1.3774s] [ 44%] 2025-09-07T07:37:39.8315933Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_from_mutation_index <- test/inductor/test_cudagraph_trees.py PASSED [1.5191s] [ 45%] 2025-09-07T07:37:39.8316452Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_symint_from_nested_indirect_indexing <- test/inductor/test_cudagraph_trees.py PASSED [0.9288s] [ 45%] 2025-09-07T07:37:39.8318098Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint <- test/inductor/test_cudagraph_trees.py W0907 07:37:01.701000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8318571Z W0907 07:37:01.702000 583709 site-packages/torch/_inductor/utils.py:2298] [0/0] DeviceCopy in input program 2025-09-07T07:37:39.8318758Z PASSED [0.8460s] [ 46%] 2025-09-07T07:37:39.8319055Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_graph_partition_unbacked_symint_multi_output_layout <- test/inductor/test_cudagraph_trees.py PASSED [0.7098s] [ 47%] 2025-09-07T07:37:39.8319546Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_item <- test/inductor/test_cudagraph_trees.py PASSED [0.2728s] [ 48%] 2025-09-07T07:37:39.8320019Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero <- test/inductor/test_cudagraph_trees.py PASSED [0.3207s] [ 49%] 2025-09-07T07:37:39.8320501Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_backend <- test/inductor/test_cudagraph_trees.py PASSED [0.2608s] [ 50%] 2025-09-07T07:37:39.8320999Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_incompatible_cudagraph_ops_nonzero_graph_breaks <- test/inductor/test_cudagraph_trees.py PASSED [0.8157s] [ 50%] 2025-09-07T07:37:39.8321457Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_index_put <- test/inductor/test_cudagraph_trees.py PASSED [0.4885s] [ 51%] 2025-09-07T07:37:39.8321887Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_live_outputs_multiple_graphs <- test/inductor/test_cudagraph_trees.py PASSED [0.8385s] [ 52%] 2025-09-07T07:37:39.8322398Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_manager_per_device <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (requires multiple cuda devices) [ 53%] 2025-09-07T07:37:39.8323823Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mark_step <- test/inductor/test_cudagraph_trees.py PASSED [0.4658s] [ 54%] 2025-09-07T07:37:39.8324237Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_meta_tensor <- test/inductor/test_cudagraph_trees.py PASSED [0.4545s] [ 55%] 2025-09-07T07:37:39.8324717Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_child_node <- test/inductor/test_cudagraph_trees.py PASSED [0.7099s] [ 55%] 2025-09-07T07:37:39.8325165Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_custom_module <- test/inductor/test_cudagraph_trees.py PASSED [0.4966s] [ 56%] 2025-09-07T07:37:39.8325655Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_custom_module_buffer <- test/inductor/test_cudagraph_trees.py PASSED [0.6608s] [ 57%] 2025-09-07T07:37:39.8326115Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_parent_node <- test/inductor/test_cudagraph_trees.py PASSED [0.6047s] [ 58%] 2025-09-07T07:37:39.8326599Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module <- test/inductor/test_cudagraph_trees.py PASSED [0.5437s] [ 59%] 2025-09-07T07:37:39.8327101Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_builtin_module_buffers <- test/inductor/test_cudagraph_trees.py PASSED [0.9822s] [ 60%] 2025-09-07T07:37:39.8327620Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multi_dispatch_single_compile_param_inputs <- test/inductor/test_cudagraph_trees.py PASSED [0.5052s] [ 60%] 2025-09-07T07:37:39.8328075Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multinomial <- test/inductor/test_cudagraph_trees.py PASSED [0.3043s] [ 61%] 2025-09-07T07:37:39.8329523Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0002s] (requires multiple cuda devices) [ 62%] 2025-09-07T07:37:39.8330099Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_devices_msg_backend_inductor <- test/inductor/test_cudagraph_trees.py SKIPPED [0.0001s] (requires multiple cuda devices) [ 63%] 2025-09-07T07:37:39.8330614Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_multiple_insert_removal_caching <- test/inductor/test_cudagraph_trees.py PASSED [0.2289s] [ 64%] 2025-09-07T07:37:39.8331111Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.3046s] [ 65%] 2025-09-07T07:37:39.8331638Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_backend_inductor <- test/inductor/test_cudagraph_trees.py PASSED [0.5207s] [ 65%] 2025-09-07T07:37:39.8332175Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.3011s] [ 66%] 2025-09-07T07:37:39.8332724Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensor_warn_only_once_backend_inductor <- test/inductor/test_cudagraph_trees.py PASSED [0.5212s] [ 67%] 2025-09-07T07:37:39.8333254Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.3059s] [ 68%] 2025-09-07T07:37:39.8333794Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_backend_inductor <- test/inductor/test_cudagraph_trees.py PASSED [0.5381s] [ 69%] 2025-09-07T07:37:39.8334319Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.2942s] [ 70%] 2025-09-07T07:37:39.8334930Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_cudagraph_managed_tensors_config_backend_inductor <- test/inductor/test_cudagraph_trees.py PASSED [0.5195s] [ 70%] 2025-09-07T07:37:39.8336381Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_on_inp_backend_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.2830s] [ 71%] 2025-09-07T07:37:39.8336850Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_on_inp_backend_inductor <- test/inductor/test_cudagraph_trees.py PASSED [0.6540s] [ 72%] 2025-09-07T07:37:39.8337327Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_mutation_reinplaced <- test/inductor/test_cudagraph_trees.py PASSED [0.4816s] [ 73%] 2025-09-07T07:37:39.8337780Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_no_rerecord_with_mark_static_address <- test/inductor/test_cudagraph_trees.py PASSED [0.7309s] [ 74%] 2025-09-07T07:37:39.8338296Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_not_fallback_to_eager_if_have_not_recompiling_too_many_times <- test/inductor/test_cudagraph_trees.py PASSED [0.5205s] [ 75%] 2025-09-07T07:37:39.8338764Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_output_alias <- test/inductor/test_cudagraph_trees.py PASSED [0.2395s] [ 75%] 2025-09-07T07:37:39.8339210Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_peristed_output_livenes <- test/inductor/test_cudagraph_trees.py PASSED [0.4467s] [ 76%] 2025-09-07T07:37:39.8339660Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_remove_hooks_on_cached_tensors <- test/inductor/test_cudagraph_trees.py PASSED [0.4515s] [ 77%] 2025-09-07T07:37:39.8340128Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rerecord_if_static_input_address_changed <- test/inductor/test_cudagraph_trees.py PASSED [0.5221s] [ 78%] 2025-09-07T07:37:39.8340580Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rng_non_trees <- test/inductor/test_cudagraph_trees.py PASSED [0.2699s] [ 79%] 2025-09-07T07:37:39.8341912Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_rng_trees <- test/inductor/test_cudagraph_trees.py PASSED [0.2729s] [ 80%] 2025-09-07T07:37:39.8342327Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_run_simple <- test/inductor/test_cudagraph_trees.py PASSED [0.9538s] [ 80%] 2025-09-07T07:37:39.8342752Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_separate_recordings <- test/inductor/test_cudagraph_trees.py PASSED [0.9717s] [ 81%] 2025-09-07T07:37:39.8343199Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_side_stream_memory_allocation <- test/inductor/test_cudagraph_trees.py PASSED [0.2268s] [ 82%] 2025-09-07T07:37:39.8343641Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_single_stream_use <- test/inductor/test_cudagraph_trees.py PASSED [0.6396s] [ 83%] 2025-09-07T07:37:39.8344064Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_cpp_wrapper <- test/inductor/test_cudagraph_trees.py PASSED [1.0250s] [ 84%] 2025-09-07T07:37:39.8344549Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_cudagraph_unsafe_ops <- test/inductor/test_cudagraph_trees.py PASSED [0.4383s] [ 85%] 2025-09-07T07:37:39.8345032Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached1 <- test/inductor/test_cudagraph_trees.py PASSED [1.0549s] [ 85%] 2025-09-07T07:37:39.8345500Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_if_dynamic_shape_limit_reached2 <- test/inductor/test_cudagraph_trees.py PASSED [5.1985s] [ 86%] 2025-09-07T07:37:39.8345942Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_skip_symbolic <- test/inductor/test_cudagraph_trees.py PASSED [0.8530s] [ 87%] 2025-09-07T07:37:39.8346353Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_sparsity <- test/inductor/test_cudagraph_trees.py PASSED [0.3084s] [ 88%] 2025-09-07T07:37:39.8347788Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_static_inputs_address_mutation_log <- test/inductor/test_cudagraph_trees.py PASSED [0.5381s] [ 89%] 2025-09-07T07:37:39.8348269Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_storage_access_error <- test/inductor/test_cudagraph_trees.py PASSED [0.2391s] [ 90%] 2025-09-07T07:37:39.8348708Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_constant_mutation <- test/inductor/test_cudagraph_trees.py PASSED [0.4819s] [ 90%] 2025-09-07T07:37:39.8349161Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_dies_between_checkpoint <- test/inductor/test_cudagraph_trees.py PASSED [0.2517s] [ 91%] 2025-09-07T07:37:39.8349626Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_tensor_no_longer_in_pool <- test/inductor/test_cudagraph_trees.py PASSED [0.2561s] [ 92%] 2025-09-07T07:37:39.8350097Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_no_cudagraphs <- test/inductor/test_cudagraph_trees.py PASSED [0.4253s] [ 93%] 2025-09-07T07:37:39.8350566Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_non_trees <- test/inductor/test_cudagraph_trees.py PASSED [0.4272s] [ 94%] 2025-09-07T07:37:39.8351022Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_input_trees <- test/inductor/test_cudagraph_trees.py PASSED [0.4287s] [ 95%] 2025-09-07T07:37:39.8351471Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unaligned_static_parameter <- test/inductor/test_cudagraph_trees.py PASSED [0.2541s] [ 95%] 2025-09-07T07:37:39.8351902Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_unstable_ptr <- test/inductor/test_cudagraph_trees.py PASSED [0.3303s] [ 96%] 2025-09-07T07:37:39.8352320Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warmup_stream_sync <- test/inductor/test_cudagraph_trees.py PASSED [0.4442s] [ 97%] 2025-09-07T07:37:39.8353700Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warn_on_pending_backward <- test/inductor/test_cudagraph_trees.py PASSED [0.4717s] [ 98%] 2025-09-07T07:37:39.8354158Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_warn_once_if_dynamic_shape_limit_reached <- test/inductor/test_cudagraph_trees.py PASSED [1.1325s] [ 99%] 2025-09-07T07:37:39.8354802Z inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_workspace_allocation_error <- test/inductor/test_cudagraph_trees.py SKIPPED [0.2393s] (skipIfRocm: test doesn't currently work on the ROCm stack) [100%] 2025-09-07T07:37:39.8355129Z 2025-09-07T07:37:39.8355410Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_cudagraph_trees_expandable_segments/inductor.test_cudagraph_trees_expandable_segments-cb4c598f5c78568f.xml - 2025-09-07T07:37:39.8355798Z =========== 92 passed, 28 skipped, 25 deselected in 67.68s (0:01:07) =========== 2025-09-07T07:37:39.8356149Z The following tests failed and then succeeded when run in a new process['test/inductor/test_cudagraph_trees_expandable_segments.py::CudaGraphTreeTests::test_dynamic_warmup'] 2025-09-07T07:37:39.8356395Z 2025-09-07T07:37:39.8356624Z FINISHED PRINTING LOG FILE of inductor/test_cudagraph_trees_expandable_segments 1/1 (test/test-reports/inductor.test_cudagraph_trees_expandable_segments_1.1_d0d7d40bec642a0f_.log) 2025-09-07T07:37:39.8356881Z 2025-09-07T07:37:39.8356964Z Running inductor/test_dependencies 1/1 ... [2025-09-07 07:37:39.815274] 2025-09-07T07:37:39.8357124Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:37:39.8357503Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_dependencies.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:37:39.815527] 2025-09-07T07:37:45.3413524Z 2025-09-07T07:37:45.3415279Z inductor/test_dependencies 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_dependencies_1.1_21832e3bd3447ccb_.log 2025-09-07T07:37:45.3418384Z Running 5 items in this shard: test/inductor/test_dependencies.py::TestDependencies::test_bucketize_dependencies_no_sorter, test/inductor/test_dependencies.py::TestDependencies::test_bucketize_dependencies_sorter, test/inductor/test_dependencies.py::TestDependencies::test_get_offset, test/inductor/test_dependencies.py::TestDependencies::test_normalize_with_stride_order_equal, test/inductor/test_dependencies.py::TestDependencies::test_normalize_with_stride_order_unequal 2025-09-07T07:37:45.3420365Z 2025-09-07T07:37:45.3420621Z Running inductor/test_device_assert 1/1 ... [2025-09-07 07:37:45.341070] 2025-09-07T07:37:45.3421079Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:37:45.3423993Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_device_assert.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:37:45.341334] 2025-09-07T07:37:53.3728048Z 2025-09-07T07:37:53.3729341Z inductor/test_device_assert 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_device_assert_1.1_9ecddad5037533cf_.log 2025-09-07T07:37:53.3730741Z Running 4 items in this shard: test/inductor/test_device_assert.py::TestTorchDeviceAssertTrigger::test_assert_fusion, test/inductor/test_device_assert.py::TestTorchDeviceAssertTrigger::test_assert_should_not_throw, test/inductor/test_device_assert.py::TestTorchDeviceAssertTrigger::test_assert_should_throw, test/inductor/test_device_assert.py::TestTorchDeviceAssertTrigger::test_run_assert_triton 2025-09-07T07:37:53.3731440Z 2025-09-07T07:37:53.3731559Z Running inductor/test_distributed_patterns 1/1 ... [2025-09-07 07:37:53.372715] 2025-09-07T07:37:53.3731741Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:37:53.3732159Z 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'] ... [2025-09-07 07:37:53.372985] 2025-09-07T07:38:08.3171332Z 2025-09-07T07:38:08.3172917Z 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_3d10a07d13f541a5_.log 2025-09-07T07:38:08.3182212Z Running 20 items in this shard: test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_aot_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_nested_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_aot, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_multi_layers, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return3, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return4, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter2 2025-09-07T07:38:08.3186202Z 2025-09-07T07:38:08.3186326Z Running inductor/test_efficient_conv_bn_eval 1/1 ... [2025-09-07 07:38:08.317147] 2025-09-07T07:38:08.3186559Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:38:08.3187096Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_efficient_conv_bn_eval.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:08.317395] 2025-09-07T07:38:14.6446752Z 2025-09-07T07:38:14.6449037Z inductor/test_efficient_conv_bn_eval 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_efficient_conv_bn_eval_1.1_f1e7293016027026_.log 2025-09-07T07:38:14.6450651Z Running 2 items in this shard: test/inductor/test_efficient_conv_bn_eval.py::EfficientConvBNEvalCpuTests::test_basic_cpu, test/inductor/test_efficient_conv_bn_eval.py::EfficientConvBNEvalGpuTests::test_basic_cuda 2025-09-07T07:38:14.6451509Z 2025-09-07T07:38:14.6451780Z Running inductor/test_external_callables 1/1 ... [2025-09-07 07:38:14.644671] 2025-09-07T07:38:14.6452267Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:38:14.6453400Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_external_callables.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:14.644995] 2025-09-07T07:38:28.9438604Z 2025-09-07T07:38:28.9444158Z inductor/test_external_callables 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_external_callables_1.1_aaeb4c5805d5f0fa_.log 2025-09-07T07:38:28.9445051Z Running 3 items in this shard: test/inductor/test_external_callables.py::TestInductorExternalCallable::test_matmul_cpu, test/inductor/test_external_callables.py::TestInductorExternalCallable::test_matmul_cuda, test/inductor/test_external_callables.py::TestInductorExternalCallable::test_matmul_dup 2025-09-07T07:38:28.9445500Z 2025-09-07T07:38:28.9445604Z Running inductor/test_flex_attention 2/4 ... [2025-09-07 07:38:28.944058] 2025-09-07T07:38:28.9445769Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:38:28.9446155Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:28.944322] 2025-09-07T07:48:08.8103149Z 2025-09-07T07:48:08.8113669Z inductor/test_flex_attention 2/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_2.4_a0521354722a43b3_.log 2025-09-07T07:48:08.8148831Z Running 179 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_causal_mask_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod4_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod5_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_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_automatic_dynamic_score_mod7_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_mod0_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cuda_float16, 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_mod0_BLOCK_SIZE_256_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_mod1_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_128_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_256_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE_256_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE_256_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_dynamic_score_mask_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_dynamic_score_mask_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_dynamic_score_mask_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod5_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_cant_lower_error_message_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_captured_score_mod_aot_eager_gradcheck_score_mod_name__head_offset_mode_aot_eager_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_cpu_error_message_return_lse_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_debug_flag_disables_internal_compilation_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_dependent_causal_bidirectional_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_document_masking_edge_case_mode_aot_eager_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_document_masking_edge_case_mode_eager_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_dynamic_shapes_bug_dynamic_batch_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_eager_backward_strides_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order0_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order1_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order2_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order3_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_eager_permute_order4_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order3_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order4_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_free_symbol_dynamic_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_fully_masked_out_rows_0_check_compile_False_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_function_composition_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_function_composition_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims0_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims0_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_logsumexp_correctness_score_mod0_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_logsumexp_correctness_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_logsumexp_only_return_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_lse_masked_output_backend_flex_decode_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_max_autotune_with_captured_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_mixed_device_error_message_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_mixed_dtypes_fails_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_modular_indexing_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_new_empty_mask_mod_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_njt_causal_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_njt_causal_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod0_head_dims1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod1_head_dims0_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod1_head_dims0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod1_head_dims1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod2_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod3_head_dims0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod3_head_dims1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod3_head_dims1_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod4_head_dims0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod4_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod6_head_dims1_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_padded_dense_causal_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_selective_ac_ops_to_save0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_seq_masking_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_skip_odd_keys_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s0_k_s3_v_s3_do_s0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s0_k_s3_v_s3_do_s1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s0_k_s3_v_s3_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s0_v_s0_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s2_v_s2_do_s0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_tma_with_customer_kernel_options_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_triton_template_warp_specialization_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_validate_small_embedding_size_error_message_cuda, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod0_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_mask_attributes_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_size_changes_BLOCK_SIZE_256_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_size_changes_BLOCK_SIZE_64_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_forward_pass_with_none_q_indices_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_getitem_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_init_mismatched_full_q_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_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_comparison_vs_sdpa_with_learnable_bias_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_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:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_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_head_specific_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_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: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:bfloat16_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: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:float16_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_indirect_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_learnable_bias_global_compiled_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_learnable_bias_global_compiled_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:bfloat16_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:float16_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:277_headdim:16_dtype:float16_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_default_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: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:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:256_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:256_headdim:16_dtype:float32_mode_default_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_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:277_headdim:16_dtype:bfloat16_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:277_headdim:16_dtype:float32_cuda 2025-09-07T07:48:08.8179054Z 2025-09-07T07:48:08.8179139Z Running inductor/test_fp8 1/1 ... [2025-09-07 07:48:08.810405] 2025-09-07T07:48:08.8179307Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:48:08.8179691Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_fp8.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:48:08.810647] 2025-09-07T07:51:58.5348849Z 2025-09-07T07:51:58.5349663Z inductor/test_fp8 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fp8_1.1_a4819d23cd502a16_.log 2025-09-07T07:51:58.5392398Z Running 240 items in this shard: test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,1,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,1,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,512_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_1,10,512_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e4m3fn_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,1,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,1,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,512_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_1,10,512_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_along_with_fp8_quant_float8_e5m2_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,1,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,1,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,512_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_1,10,512_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e4m3fn_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,1,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,1,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,15_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,512_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_1,10,512_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_amax_fp8_quant_float8_e5m2_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_bad_cast, test/inductor/test_fp8.py::TestFP8Types::test_eager_fallback_bfloat16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_eager_fallback_bfloat16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_eager_fallback_float16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_eager_fallback_float16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_benchmark_float8_e4m3fn_shape_4,2048,4096_keepdim_False, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_benchmark_float8_e4m3fn_shape_4,2048,4096_keepdim_True, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_benchmark_float8_e5m2_shape_4,2048,4096_keepdim_False, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_benchmark_float8_e5m2_shape_4,2048,4096_keepdim_True, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_float8_e4m3fn_amax_keep_dim_False_shape_1,1,15_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_float8_e4m3fn_amax_keep_dim_False_shape_1,1,15_device_cuda, 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test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_float8_e5m2_amax_keep_dim_True_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_layernorm_fp8_quant_float8_e5m2_amax_keep_dim_True_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e4m3fn_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e4m3fn_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e4m3fn_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e4m3fn_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e5m2_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e5m2_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e5m2_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_bfloat16_float8_e5m2_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e4m3fn_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e4m3fn_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e4m3fn_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e4m3fn_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e5m2_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e5m2_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e5m2_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float16_float8_e5m2_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e4m3fn_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e4m3fn_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e4m3fn_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e4m3fn_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e5m2_shape_16,16,16_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e5m2_shape_16,16,16_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e5m2_shape_4,2048,4096_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_to_fp8_saturated_float32_float8_e5m2_shape_4,2048,4096_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_bfloat16_shape_15,3,13_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_bfloat16_shape_15,3,13_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_bfloat16_shape_4,2048,4096_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_bfloat16_shape_4,2048,4096_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float16_shape_15,3,13_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float16_shape_15,3,13_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float16_shape_4,2048,4096_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float16_shape_4,2048,4096_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float32_shape_15,3,13_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float32_shape_15,3,13_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float32_shape_4,2048,4096_dst_types0_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_valid_cast_float32_shape_4,2048,4096_dst_types0_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_xblock_for_small_numel_float8_e4m3fn_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_xblock_for_small_numel_float8_e4m3fn_device_cuda, test/inductor/test_fp8.py::TestFP8Types::test_xblock_for_small_numel_float8_e5m2_device_cpu, test/inductor/test_fp8.py::TestFP8Types::test_xblock_for_small_numel_float8_e5m2_device_cuda, test/inductor/test_fp8.py::TestFP8Lowering::test_mx_fp8_max_autotune, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1024_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_1_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_257_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_33_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_acceptable_input_dims_M_3_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_1024,1024,512_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_1024,1024,512_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_1024,1024,512_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_1024,1024,512_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,16,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,16,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,16,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,16,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,32,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,32,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,32,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_shape_16,32,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_tma_template_shape_1024,1024,512_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_tma_template_shape_1024,1024,512_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_tma_template_shape_16,32,32_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_rowwise_scaling_tma_template_shape_16,32,32_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1024_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_1_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_257_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_33_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_1024_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_1024_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_16_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_16_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_32_N_16_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_acceptable_input_dims_M_3_K_32_N_2048_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_1024,1024,512_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_1024,1024,512_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_1024,1024,512_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_1024,1024,512_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,16,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,16,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,16,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,16,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,32,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,32,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,32,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_bfloat16_shape_16,32,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_1024,1024,512_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_1024,1024,512_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_1024,1024,512_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_1024,1024,512_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,16,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,16,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,16,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,16,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,32,32_has_bias_False_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,32,32_has_bias_False_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,32,32_has_bias_True_use_fast_accum_False_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_float32_shape_16,32,32_has_bias_True_use_fast_accum_True_persistent_matmul_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_bfloat16_shape_1024,1024,512_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_bfloat16_shape_1024,1024,512_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_bfloat16_shape_16,32,32_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_bfloat16_shape_16,32,32_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_float32_shape_1024,1024,512_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_float32_shape_1024,1024,512_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_float32_shape_16,32,32_use_fast_accum_False, test/inductor/test_fp8.py::TestFP8Lowering::test_tensorwise_scaling_tma_template_float32_shape_16,32,32_use_fast_accum_True, test/inductor/test_fp8.py::TestFP8Lowering::test_unacceptable_input_dims, test/inductor/test_fp8.py::TestFP8Lowering::test_unacceptable_scale_dims_rowwise_scaling 2025-09-07T07:51:58.5433636Z 2025-09-07T07:51:58.5433729Z Running inductor/test_minifier_isolate 1/1 ... [2025-09-07 07:51:58.535245] 2025-09-07T07:51:58.5433904Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:51:58.5434351Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_minifier_isolate.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:51:58.535488] 2025-09-07T07:52:04.0296101Z 2025-09-07T07:52:04.0296959Z 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_5d3e110f18aae806_.log 2025-09-07T07:52:04.0297774Z 2025-09-07T07:52:04.0299984Z Running inductor/test_op_completeness 1/1 ... [2025-09-07 07:52:04.029682] 2025-09-07T07:52:04.0300267Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:52:04.0303791Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_op_completeness.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:52:04.029978] 2025-09-07T07:52:06.5246677Z 2025-09-07T07:52:06.5247647Z inductor/test_op_completeness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_op_completeness_1.1_931ad435967e63ba_.log 2025-09-07T07:52:06.5253776Z Running 5 items in this shard: test/inductor/test_op_completeness.py::TestOpCompleteness::test_cpp_overrides, test/inductor/test_op_completeness.py::TestOpCompleteness::test_cpp_vec_overrides, test/inductor/test_op_completeness.py::TestOpCompleteness::test_halide_overrides, test/inductor/test_op_completeness.py::TestOpCompleteness::test_metal_overrides, test/inductor/test_op_completeness.py::TestOpCompleteness::test_triton_overrides 2025-09-07T07:52:06.5254716Z 2025-09-07T07:52:06.5254804Z Running inductor/test_op_dtype_prop 1/1 ... [2025-09-07 07:52:06.524497] 2025-09-07T07:52:06.5254971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:52:06.5255357Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_op_dtype_prop.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:52:06.524711] 2025-09-07T07:57:37.2833446Z 2025-09-07T07:57:37.2835412Z inductor/test_op_dtype_prop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_op_dtype_prop_1.1_22745a6a423fd6bd_.log 2025-09-07T07:57:37.2925924Z Running 567 items in this shard: test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_any_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_assoc_scan_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_binary_math_mixed_precision_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_codegen_upcast_to_fp32_upcast_to_fp32_False_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_codegen_upcast_to_fp32_upcast_to_fp32_True_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_constant_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_downcast_div_mod_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_abs_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_abs_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_abs_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_abs_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acos_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acos_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acos_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acos_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acosh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acosh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acosh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_acosh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asin_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asin_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asin_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asin_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asinh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asinh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asinh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_asinh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan2_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan2_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan2_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan2_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atan_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atanh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atanh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atanh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_atanh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_ceil_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_ceil_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_ceil_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_ceil_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_copysign_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_copysign_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_copysign_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_copysign_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cos_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cos_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cos_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cos_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cosh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cosh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cosh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_cosh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erf_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erf_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erf_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erf_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfc_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfc_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfc_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfc_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfinv_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfinv_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfinv_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_erfinv_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp2_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp2_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp2_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp2_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_exp_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_expm1_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_expm1_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_expm1_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_expm1_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_floor_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_floor_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_floor_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_floor_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_fmod_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_fmod_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_fmod_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_fmod_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_hypot_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_hypot_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_hypot_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_hypot_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isinf_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isinf_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isinf_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isinf_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isnan_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isnan_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isnan_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_isnan_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_lgamma_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_lgamma_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_lgamma_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_lgamma_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log10_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log10_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log10_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log10_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log1p_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log1p_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log1p_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log1p_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log2_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log2_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log2_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log2_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_log_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_nextafter_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_nextafter_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_nextafter_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_nextafter_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_pow_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_pow_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_pow_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_pow_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_round_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_round_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_round_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_round_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_rsqrt_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_rsqrt_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_rsqrt_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_rsqrt_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sigmoid_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sigmoid_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sigmoid_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sigmoid_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sin_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sin_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sin_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sin_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sinh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sinh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sinh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sinh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sqrt_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sqrt_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sqrt_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_sqrt_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tan_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tan_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tan_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tan_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tanh_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tanh_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tanh_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_tanh_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_trunc_load_upcast_to_fp32_False_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_trunc_load_upcast_to_fp32_False_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_trunc_load_upcast_to_fp32_True_bfloat16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_dtype_aware_codegen_op_name_trunc_load_upcast_to_fp32_True_float16_cuda, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_abs_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_abs_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_abs_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_abs_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_abs_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acos_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acos_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acos_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acos_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acos_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acosh_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acosh_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acosh_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acosh_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_acosh_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_add_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_add_cuda_float32, 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test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sin_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sin_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sin_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sin_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sinh_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sinh_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sinh_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sinh_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sinh_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sqrt_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sqrt_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sqrt_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sqrt_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sqrt_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_square_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_square_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_square_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_square_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_square_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sub_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sub_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sub_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_sub_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tan_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tan_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tan_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tan_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tan_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tanh_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tanh_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tanh_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tanh_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_tanh_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_true_divide_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_true_divide_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_true_divide_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_true_divide_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_true_divide_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_trunc_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_trunc_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_trunc_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_trunc_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_where_cuda_bool, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_where_cuda_float32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_where_cuda_float64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_where_cuda_int32, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_where_cuda_int64, test/inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_support_cuda 2025-09-07T07:57:37.3000695Z 2025-09-07T07:57:37.3000797Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T07:57:37.3000974Z Uploading artifacts took 0.00 seconds 2025-09-07T07:57:37.3001167Z Running inductor/test_torchinductor_codegen_dynamic_shapes 4/4 ... [2025-09-07 07:57:37.284050] 2025-09-07T07:57:37.3001361Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T07:57:37.3001784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:57:37.284308] 2025-09-07T08:05:47.5505729Z 2025-09-07T08:05:47.5506800Z inductor/test_torchinductor_codegen_dynamic_shapes 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_4.4_94d049ce8245016e_.log 2025-09-07T08:05:47.5598146Z Running 430 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__dyn_quant_matmul_4bit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__dyn_quant_pack_4bit_weight_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool_errors_with_long_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_const_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_addmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_dtype_device_layout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_override_registration_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_support_str_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_to_float_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_batch_norm_2d_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_add_autotune_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int16_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int32_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int64_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_uint8_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_nd_tiling_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_copied_in_graph_with_different_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_single_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clone_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_computed_buffer_inlining_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_config_option_dont_assume_alignment_cudagraphs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_2d_strides_nonpositive_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_backward_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_shape_check_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cummin_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_fixed_layout_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_unbacked_symints_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_multi_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dense_mask_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dist_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_precision_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_prim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_softmax_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_bfloat16_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_bfloat16_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_bfloat16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_bfloat16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_sparse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_erfc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expand_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_no_mutated_tensors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fft_real_input_real_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fill2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float_index_expression_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float_index_expression_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float_repr_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_boolean_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_like_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_truncation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fuse_large_params_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fusing_write_into_disjoint_read_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_generate_rand_fp8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_argmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_constant_tensor1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_pad_dynamic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_refcount_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_scalar_inputs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_device_assert_masked_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_nested_indirect_indexing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_deterministic_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_remainder_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inductor_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inductor_layout_optimization_input_mutations_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_insignificant_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isinf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_kernel_names_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_kwargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_block_sizes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_strided_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_tensor_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear_mixed_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linspace2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log_softmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_low_memory_max_pool_dilation_1_dim_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_low_memory_max_pool_dilation_2_dim_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_low_memory_max_pool_dilation_2_dim_3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d6_dilation_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mm_views_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mul_softmax_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_any_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mutations_loop_fusion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nan_sort_stable_True_descending_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_needs_contiguous_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_neg_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nll_loss_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_norm_constant_overflow_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_single_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_view_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_y0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_u_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_w_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_hermite_polynomial_h_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i1e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_logit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_sinc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_spherical_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_zeta_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_distribution_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_with_dtype_and_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_no_ops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_slice1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_interleave_Tensor_decomp_int64_nd_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_resize_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_roi_align_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_unaligned_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_single_elem_indirect_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_size_asserts_for_multi_output_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_mutation1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_mutation2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_one_kernel_loop_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tanh_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor_index_put_slice_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transpose_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transposed_propagates_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_bfloat16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unsqueeze_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bicubic2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_mean_tile_reduction_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_var_novec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_detach_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_weight_norm_bwd_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_where_with_logical_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test__dyn_quant_pack_4bit_weight_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test__unsafe_masked_index_put_accumulate_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_add_complex10_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_add_complex3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_add_complex4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_add_complex7_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_add_complex9_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_aoti_eager_cache_hit_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_aoti_eager_dtype_device_layout_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_aoti_eager_support_out_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_arange1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_arange3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_arange4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool2d1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool2d4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool2d_backward4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool2d_backward_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool3d_backward2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bfloat16_to_int16_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bitwise_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bmm1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bmm2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bucketize_int_int16_int32_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bucketize_int_int16_int8_dynamic_shapes_cuda, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_builtins_round_int_ndigits_zero_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_cat_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_cat_extern_kernel_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_cat_single_empty_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_cat_unbacked_2d_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_cat_unbacked_legacy_empty_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_check_stack_no_cycles_dynamic_shapes_cuda, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_resize_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_reuse_buffers_with_aliasing_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scalar_cpu_tensor_arg_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scalar_input_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scaled_dot_product_efficient_attention_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scatter2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scatter3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scatter_add2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_scatter_add3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_False_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sgn_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sgn_extremal_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_shape_prop_torch_ones_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_silu_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_single_elem_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_single_elem_indirect_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_slice2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_slice_mutation2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_softmax_backward_data_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sort_bool_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sort_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sort_stable_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_cumprod_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_failed_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_with_integer_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_squeeze2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sum1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sum4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tensor_index_slice_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tmp_not_defined_issue2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tmp_not_defined_issue3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_torch_device_split_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_transpose_add_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_transpose_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_triu_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_uint4x2_mixed_mm_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_unbacked_floordiv_simplify_errors_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_unspec_inputs_int16_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_unspec_inputs_uint8_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_upsample_nearest2d_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_var_correction_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_var_mean_tile_reduction_False_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_view_on_aliased_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_view_uint8_through_differing_bitwidths_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_views4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_weight_norm_bwd_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_where_with_logical_op_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_zero_dim_reductions_dynamic_shapes_cuda 2025-09-07T08:05:47.5676031Z 2025-09-07T08:05:47.5676178Z Running inductor/test_torchinductor_opinfo 2/9 ... [2025-09-07 08:05:47.551029] 2025-09-07T08:05:47.5676365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:05:47.5676765Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '--shard-id=2', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:05:47.551238] 2025-09-07T08:14:41.5184972Z 2025-09-07T08:14:41.5192712Z inductor/test_torchinductor_opinfo 2/9 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_2.9_4e354336955a494e_.log 2025-09-07T08:14:41.5264819Z Running 407 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___getitem___cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___radd___cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rand___cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmod___cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmod___cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rpow___cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rpow___cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rsub___cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__chunk_cat_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__segment_reduce_lengths_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__segment_reduce_offsets_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__unsafe_masked_index_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__unsafe_masked_index_put_accumulate_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__unsafe_masked_index_put_accumulate_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acosh_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acosh_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_add_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addcdiv_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addcmul_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addmm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addr_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_alias_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amax_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amin_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_aminmax_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_any_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_any_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_any_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argwhere_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argwhere_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argwhere_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_partial_views_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_partial_views_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_scatter_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_scatter_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_scatter_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_asin_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_asin_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_asinh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atan2_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atan_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atanh_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atanh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_2d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_baddbmm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bfloat16_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bitwise_and_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_block_diag_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bool_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_broadcast_shapes_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_broadcast_to_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_broadcast_to_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_broadcast_to_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_byte_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cartesian_prod_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chalf_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_char_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chunk_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_min_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_min_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clone_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_column_stack_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_column_stack_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_complex_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_conj_physical_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_constant_pad_nd_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_corrcoef_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_corrcoef_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cos_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cos_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cosh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_count_nonzero_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_count_nonzero_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cov_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cross_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummax_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummax_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummin_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumprod_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumprod_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumulative_trapezoid_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diag_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diff_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_digamma_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_digamma_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_div_trunc_rounding_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_dsplit_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_dstack_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_like_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_permuted_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_strided_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eq_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eq_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_equal_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_equal_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erf_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfc_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfinv_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expm1_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eye_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eye_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft2_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft2_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfftn_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft2_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft2_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfftn_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfft_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfft_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfftn_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfftn_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_rfftn_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_flip_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_flipud_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_float_power_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fmin_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fmin_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_geometric_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_grid_sampler_2d_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_half_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_heaviside_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_histc_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_add_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_add_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_fill_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_put_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_amin_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_select_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_inner_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isclose_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isin_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isnan_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isnan_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isneginf_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isposinf_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isposinf_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isreal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_2inputs_2outputs_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_2inputs_2outputs_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_return_by_ref_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_return_by_ref_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_return_by_ref_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_kron_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_le_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_lgamma_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_cross_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_diagonal_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_diagonal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_householder_product_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_matrix_rank_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_multi_dot_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_solve_triangular_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linspace_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log2_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log_softmax_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logical_xor_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logit_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_tensor_overload_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logsumexp_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logsumexp_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_long_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mT_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_amax_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_amin_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_argmin_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_argmin_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumprod_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumprod_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumprod_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_log_softmax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_norm_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_prod_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_select_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_softmax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_std_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_sum_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_matrix_exp_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mean_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_median_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_binary_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_with_dim_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_minimum_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_minimum_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_minimum_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_movedim_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_msort_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_msort_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_5_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nan_to_num_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nan_to_num_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nansum_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nansum_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_narrow_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_narrow_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_native_dropout_backward_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ne_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_empty_strided_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_empty_strided_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_full_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_avg_pool3d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_max_pool1d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_alpha_dropout_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_binary_cross_entropy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv2d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv_transpose3d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_cosine_embedding_loss_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_dropout2d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_dropout3d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_fractional_max_pool2d_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_gaussian_nll_loss_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_gelu_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardswish_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardtanh_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hinge_embedding_loss_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_area_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_linear_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_nearest-exact_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_nearest_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_nearest_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_l1_loss_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_l1_loss_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_pool2d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_unpool1d_grad_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_unpool2d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_mse_loss_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_multilabel_soft_margin_loss_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_circular_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_circular_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_replicate_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pairwise_distance_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_relu_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softmin_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softplus_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softsign_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softsign_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softsign_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_loss_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_upsample_nearest_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_normal_number_mean_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ones_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ones_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ones_like_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ones_like_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ormqr_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_copy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polar_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_1_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_3_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_3_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_3_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_4_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_positive_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pow_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_qr_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rad2deg_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_randint_like_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_randint_like_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_randn_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ravel_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_real_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reciprocal_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_remainder_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_renorm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_interleave_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_interleave_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reshape_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize__cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize__cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize_as__cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_neg_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_neg_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_neg_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_roll_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rsub_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scalar_tensor_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_amax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_prod_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_prod_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_sum_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_scatter_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_short_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sigmoid_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sigmoid_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_bartlett_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_gaussian_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_hamming_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_hann_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_nuttall_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signbit_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sinc_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sinc_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_slice_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sort_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_j0_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_j0_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_y0_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_y1_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_v_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_w_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_erfcx_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_erfcx_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_hermite_polynomial_he_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_hermite_polynomial_he_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_hermite_polynomial_he_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i0e_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i1_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i1e_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_laguerre_polynomial_l_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_legendre_polynomial_p_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_legendre_polynomial_p_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_i0_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_i1_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_i1_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_k0_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_k1_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_ndtri_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_ndtri_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_scaled_modified_bessel_k0_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_v_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_spherical_bessel_j0_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_zeta_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_list_args_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_with_sizes_copy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_with_sizes_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sqrt_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sqrt_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_square_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_copy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_stack_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_stft_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_take_along_dim_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_take_along_dim_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_take_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tanh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tanh_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_sparse_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_topk_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_torch_ops_aten__efficient_attention_forward_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_trace_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_trapz_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_triu_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_true_divide_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_true_divide_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unbind_copy_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_uniform_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_consecutive_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unravel_index_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_var_unbiased_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_vsplit_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_where_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_xlogy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zero__cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zeros_like_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zeros_like_cuda_uint8 2025-09-07T08:14:41.5327600Z 2025-09-07T08:14:41.5327705Z Running inductor/test_torchinductor_opinfo 8/9 ... [2025-09-07 08:14:41.519217] 2025-09-07T08:14:41.5327881Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:14:41.5328273Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '--shard-id=8', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:14:41.519435] 2025-09-07T08:21:56.4098601Z 2025-09-07T08:21:56.4100423Z inductor/test_torchinductor_opinfo 8/9 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_8.9_33f47d6446d3ba9e_.log 2025-09-07T08:21:56.4169085Z Running 397 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_H_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_H_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_H_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_T_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___radd___cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rdiv___cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmod___cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmul___cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmul___cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rsub___cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rsub___cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__batch_norm_with_update_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__chunk_cat_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__native_batch_norm_legit_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__upsample_bilinear2d_aa_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acosh_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addcmul_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addcmul_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addmm_decomposed_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addmv_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addr_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_alias_copy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_alias_copy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_alias_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_all_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_all_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amin_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_aminmax_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_aminmax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_angle_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_arange_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_partial_views_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atanh_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atanh_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_1d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_1d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_1d_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_3d_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bernoulli_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bitwise_left_shift_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bitwise_or_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bitwise_right_shift_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bool_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_broadcast_tensors_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bucketize_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bucketize_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bucketize_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_byte_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cartesian_prod_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cartesian_prod_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cartesian_prod_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cauchy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cdouble_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cdouble_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cfloat_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cfloat_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cfloat_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chalf_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cholesky_solve_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_max_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_max_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_column_stack_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_column_stack_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_combinations_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_conj_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_contiguous_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_contiguous_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_copysign_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cosh_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_count_nonzero_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cross_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummin_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumprod_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumsum_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cumulative_trapezoid_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_deg2rad_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diag_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagflat_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_scatter_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_div_floor_rounding_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_dot_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_double_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_dstack_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_einsum_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_like_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eq_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erf_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erf_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erf_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfc_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfc_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfinv_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfinv_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp2_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_as_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exponential_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft2_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifft2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifft2_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifftn_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft2_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft2_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfftn_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfftn_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfft2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfft2_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_irfft_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_rfft2_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_rfft2_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_rfft2_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_rfftn_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_float_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_floor_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_floor_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_floor_divide_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fmax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_frac_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_like_cuda_uint32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_like_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gcd_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hash_tensor_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_heaviside_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hstack_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hypot_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_add_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_fill_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_amax_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_amax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_mean_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_prod_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_select_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_select_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_inner_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isfinite_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isinf_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isinf_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isnan_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isnan_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isreal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_4inputs_with_extra_args_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_4inputs_with_extra_args_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_return_by_ref_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_kron_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_kthvalue_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_lcm_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_cholesky_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_cross_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_diagonal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_ldl_factor_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_ldl_solve_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_lu_factor_ex_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_matrix_norm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_matrix_power_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_norm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_tensorsolve_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_vector_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linspace_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log10_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log10_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log1p_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_log1p_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logaddexp2_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logaddexp_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logit_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logit_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_tensor_overload_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_tensor_overload_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_tensor_overload_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_tensor_overload_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_long_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_long_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_lt_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumprod_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumprod_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_log_softmax_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_logsumexp_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_median_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_prod_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_matmul_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_binary_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_pool2d_with_indices_backward_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_reduction_no_dim_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_reduction_with_dim_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_maximum_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mean_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_meshgrid_list_of_tensors_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_no_dim_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_with_dim_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_with_dim_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mm_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_msort_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mv_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_1_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_1_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_1_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_3_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nansum_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_narrow_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_native_batch_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_native_batch_norm_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_native_layer_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_neg_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_empty_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_full_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_full_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_zeros_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_max_pool1d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_max_pool2d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_batch_norm_without_cudnn_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_bilinear_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_binary_cross_entropy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_celu_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_channel_shuffle_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv_transpose1d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_cosine_embedding_loss_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_ctc_loss_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_ctc_loss_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_dropout_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_grid_sample_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardshrink_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardshrink_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardtanh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardtanh_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hinge_embedding_loss_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_huber_loss_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_linear_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_layer_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_layer_norm_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_linear_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_logsigmoid_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_margin_ranking_loss_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_pool1d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_pool2d_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_unpool1d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_unpool3d_grad_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_multi_head_attention_forward_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_constant_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_replicate_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_replicate_negative_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pixel_shuffle_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pixel_unshuffle_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pixel_unshuffle_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_rms_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_scaled_dot_product_attention_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_scaled_dot_product_attention_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_soft_margin_loss_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softplus_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_softshrink_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_threshold_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nonzero_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nonzero_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nonzero_static_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_normal_number_mean_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ones_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_outer_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_outer_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_outer_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pca_lowrank_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_copy_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pinverse_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_1_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_3_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_positive_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pow_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pow_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_pow_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_prod_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rad2deg_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ravel_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_real_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_real_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reciprocal_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reciprocal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize__cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize_as__cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_conj_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_conj_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rot90_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_round_decimals_0_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rsub_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scalar_tensor_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_add_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_mean_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_sum_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_sum_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_reduce_sum_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_scatter_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_short_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sigmoid_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_nuttall_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signbit_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sinc_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sinc_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_slice_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_slice_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_slice_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_slice_scatter_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_softmax_with_dtype_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_softmax_with_dtype_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_airy_ai_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_u_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_u_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_v_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_w_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_hermite_polynomial_he_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_hermite_polynomial_he_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i1_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i1_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_laguerre_polynomial_l_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_legendre_polynomial_p_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_legendre_polynomial_p_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_ndtr_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_ndtr_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_scaled_modified_bessel_k1_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_v_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_v_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_w_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_xlog1py_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_list_args_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_with_sizes_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sqrt_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_copy_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_multiple_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_multiple_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_multiple_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_stack_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_stack_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_std_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sub_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sum_to_size_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sum_to_size_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_t_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tan_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tensor_split_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tensor_split_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_sparse_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_sparse_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_transpose_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_trapz_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tril_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_triu_indices_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_true_divide_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unbind_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unflatten_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unflatten_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_uniform_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_consecutive_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_consecutive_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsafe_chunk_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsafe_chunk_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_copy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_var_mean_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_as_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_copy_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_vsplit_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_vsplit_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_vstack_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_where_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_where_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_where_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_xlogy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zeros_like_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zeros_like_cuda_float64 2025-09-07T08:21:56.4230460Z 2025-09-07T08:21:56.4235392Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T08:21:56.4235588Z Uploading artifacts took 0.00 seconds 2025-09-07T08:21:56.4235751Z Running optim/test_lrscheduler 1/1 ... [2025-09-07 08:21:56.410430] 2025-09-07T08:21:56.4235907Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:21:56.4236288Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_lrscheduler.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:56.410692] 2025-09-07T08:21:58.1222582Z 2025-09-07T08:21:58.1223807Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_f4144794f2b3dd9c_.log 2025-09-07T08:21:58.1224918Z 2025-09-07T08:21:58.1225882Z Running test_appending_byte_serializer 1/1 ... [2025-09-07 08:21:58.122072] 2025-09-07T08:21:58.1226561Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:21:58.1227217Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_appending_byte_serializer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:58.122353] 2025-09-07T08:22:00.1412070Z 2025-09-07T08:22:00.1413194Z test_appending_byte_serializer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_appending_byte_serializer_1.1_7d0a309aa96c42be_.log 2025-09-07T08:22:00.1415286Z Running 3 items in this shard: test/test_appending_byte_serializer.py::TestAppendingByteSerializer::test_checksum, test/test_appending_byte_serializer.py::TestAppendingByteSerializer::test_write_and_read_class, test/test_appending_byte_serializer.py::TestAppendingByteSerializer::test_write_and_read_int 2025-09-07T08:22:00.1416909Z 2025-09-07T08:22:00.1417101Z Running test_autograd 1/1 ... [2025-09-07 08:22:00.141172] 2025-09-07T08:22:00.1417503Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:22:00.1418511Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autograd.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:00.141426] 2025-09-07T08:22:40.4522867Z 2025-09-07T08:22:40.4524160Z test_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_1.1_78a9abbe00a28d28_.log 2025-09-07T08:22:40.4599111Z Running 651 items in this shard: test/test_autograd.py::TestAutograd::test_access_saved_tensor_twice_without_recomputation_works, test/test_autograd.py::TestAutograd::test_accumulate_grad, test/test_autograd.py::TestAutograd::test_accumulate_grad_posthooks_can_observe_tensor_prehook, test/test_autograd.py::TestAutograd::test_accumulate_grad_posthooks_should_not_execute, test/test_autograd.py::TestAutograd::test_accumulate_grad_tensor_reference, test/test_autograd.py::TestAutograd::test_accumulate_grad_with_zero_numel_grad, test/test_autograd.py::TestAutograd::test_anomaly_assign_parent_cleanup, test/test_autograd.py::TestAutograd::test_anomaly_detect_nan, test/test_autograd.py::TestAutograd::test_anomaly_grad_warnings, test/test_autograd.py::TestAutograd::test_anomaly_mode_no_check_nan, test/test_autograd.py::TestAutograd::test_attribute_deletion, test/test_autograd.py::TestAutograd::test_autograd_inplace_view_of_view, test/test_autograd.py::TestAutograd::test_autograd_inplace_views_creation_meta, test/test_autograd.py::TestAutograd::test_autograd_inplace_views_cross_dtype, test/test_autograd.py::TestAutograd::test_autograd_multiple_views_python, test/test_autograd.py::TestAutograd::test_autograd_node_isinstance, test/test_autograd.py::TestAutograd::test_autograd_print_tensor, test/test_autograd.py::TestAutograd::test_autograd_python_custom_function_inplace, test/test_autograd.py::TestAutograd::test_autograd_simple_views_python, test/test_autograd.py::TestAutograd::test_autograd_views_codegen, test/test_autograd.py::TestAutograd::test_backward, test/test_autograd.py::TestAutograd::test_backward_badcalls, test/test_autograd.py::TestAutograd::test_backward_copy, test/test_autograd.py::TestAutograd::test_backward_create_graph_warns, test/test_autograd.py::TestAutograd::test_backward_hook_relative_ordering, test/test_autograd.py::TestAutograd::test_backward_no_grad, test/test_autograd.py::TestAutograd::test_backward_to_node, test/test_autograd.py::TestAutograd::test_backward_twice_retained_graph_with_saved_values, test/test_autograd.py::TestAutograd::test_backward_twice_retained_graph_without_saved_values, test/test_autograd.py::TestAutograd::test_backward_twice_with_saved_values, test/test_autograd.py::TestAutograd::test_backward_twice_without_saved_values, test/test_autograd.py::TestAutograd::test_backward_with_inputs, test/test_autograd.py::TestAutograd::test_backward_with_nonleaf_inputs, test/test_autograd.py::TestAutograd::test_backward_with_scalar_input, test/test_autograd.py::TestAutograd::test_calculate_shape_util, test/test_autograd.py::TestAutograd::test_callback_adds_callback, test/test_autograd.py::TestAutograd::test_callback_propagates_errors_from_device_thread, test/test_autograd.py::TestAutograd::test_cant_create_saved_tensors, test/test_autograd.py::TestAutograd::test_checkpoint_detects_non_determinism, test/test_autograd.py::TestAutograd::test_checkpoint_sequential_warns_if_use_reentrant_not_passed_explcitly, test/test_autograd.py::TestAutograd::test_checkpoint_valid_reset_on_error, test/test_autograd.py::TestAutograd::test_checkpoint_warns_if_use_reentrant_not_passed_explcitly, test/test_autograd.py::TestAutograd::test_checkpointing, test/test_autograd.py::TestAutograd::test_checkpointing_non_reentrant_autocast_cpu, test/test_autograd.py::TestAutograd::test_checkpointing_non_reentrant_autocast_gpu, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_arbitrary_input_output, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_correct_grad, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_custom_function_works, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_dataparallel, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_detached_tensor_use_reentrant_False, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_detached_tensor_use_reentrant_True, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_input_requires_grad_False, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_input_requires_grad_True, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_memory_savings, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_parameter_used_in_an_out, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_saved_object_identity, test/test_autograd.py::TestAutograd::test_checkpointing_without_reentrant_with_context_fn, test/test_autograd.py::TestAutograd::test_copy_slices_graph_task_updates, test/test_autograd.py::TestAutograd::test_create_graph_and_full_backward_hook_cycle, test/test_autograd.py::TestAutograd::test_current_graph_task_execution_order, test/test_autograd.py::TestAutograd::test_current_graph_task_id, test/test_autograd.py::TestAutograd::test_current_node, test/test_autograd.py::TestAutograd::test_custom_autograd_ac_early_stop, test/test_autograd.py::TestAutograd::test_custom_autograd_no_early_free, test/test_autograd.py::TestAutograd::test_custom_autograd_repeated_grad_grad, test/test_autograd.py::TestAutograd::test_custom_function_cycle, test/test_autograd.py::TestAutograd::test_custom_function_error, test/test_autograd.py::TestAutograd::test_custom_function_exception, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_forward_is_no_op, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_inplace_checks, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_non_differentiable, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_non_tensor_before_tensor_args, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_view_checks, test/test_autograd.py::TestAutograd::test_custom_function_forward_mode_wrong_formula, test/test_autograd.py::TestAutograd::test_custom_function_inplace_on_non_default_view, test/test_autograd.py::TestAutograd::test_custom_function_inplace_on_view_of_leaf, test/test_autograd.py::TestAutograd::test_custom_function_local_inplace, test/test_autograd.py::TestAutograd::test_custom_function_mark_dirty_not_differentiable, test/test_autograd.py::TestAutograd::test_custom_function_mark_output_view_of_intermediate, test/test_autograd.py::TestAutograd::test_custom_function_no_tensors, test/test_autograd.py::TestAutograd::test_custom_function_non_tensor_inputs_outputs, test/test_autograd.py::TestAutograd::test_custom_function_preserve_torch_function_when_return_as_is, test/test_autograd.py::TestAutograd::test_custom_function_return_view_in_nograd, test/test_autograd.py::TestAutograd::test_custom_function_save_for_forward, test/test_autograd.py::TestAutograd::test_custom_function_saved_tensors, test/test_autograd.py::TestAutograd::test_custom_function_setup_context_multi_input, test/test_autograd.py::TestAutograd::test_custom_function_setup_context_multi_output, test/test_autograd.py::TestAutograd::test_custom_function_setup_context_simple, test/test_autograd.py::TestAutograd::test_custom_function_vmap_defaults, test/test_autograd.py::TestAutograd::test_deep_reentrant, test/test_autograd.py::TestAutograd::test_default_saved_tensors_hooks_double_backward, test/test_autograd.py::TestAutograd::test_dep_nograd, test/test_autograd.py::TestAutograd::test_dependent_backward, test/test_autograd.py::TestAutograd::test_detach, test/test_autograd.py::TestAutograd::test_detach_base, test/test_autograd.py::TestAutograd::test_detach_then_inplace_raises_in_autograd, test/test_autograd.py::TestAutograd::test_diagonal_expanded_v, test/test_autograd.py::TestAutograd::test_dir, test/test_autograd.py::TestAutograd::test_disabling_saved_tensor_hooks, test/test_autograd.py::TestAutograd::test_disabling_saved_tensor_hooks_nested, test/test_autograd.py::TestAutograd::test_dont_materialize_grads, test/test_autograd.py::TestAutograd::test_duplicate_backward_root, test/test_autograd.py::TestAutograd::test_enable_grad_decorator_no_paren, test/test_autograd.py::TestAutograd::test_first_grad_fn_access_in_no_grad_mode, test/test_autograd.py::TestAutograd::test_free_deep_graph, test/test_autograd.py::TestAutograd::test_free_deep_graph_complicated, test/test_autograd.py::TestAutograd::test_free_deep_graph_pyfunction, test/test_autograd.py::TestAutograd::test_full_backward_hook_double_backward, test/test_autograd.py::TestAutograd::test_function, test/test_autograd.py::TestAutograd::test_function_returns_input, test/test_autograd.py::TestAutograd::test_function_returns_undefined_tensor, test/test_autograd.py::TestAutograd::test_gc_in_destructor, test/test_autograd.py::TestAutograd::test_grad, test/test_autograd.py::TestAutograd::test_grad_badcalls, test/test_autograd.py::TestAutograd::test_grad_batched_grad, test/test_autograd.py::TestAutograd::test_grad_empty_inputs, test/test_autograd.py::TestAutograd::test_grad_fn_attr_bindings, test/test_autograd.py::TestAutograd::test_grad_fn_badcalls, test/test_autograd.py::TestAutograd::test_grad_fn_input_metadata, test/test_autograd.py::TestAutograd::test_grad_fn_prehooks, test/test_autograd.py::TestAutograd::test_grad_fn_prehooks_multiple_outputs, test/test_autograd.py::TestAutograd::test_grad_fn_prehooks_remove_hooks, test/test_autograd.py::TestAutograd::test_grad_materialize_grads, test/test_autograd.py::TestAutograd::test_grad_mode_class_decoration, test/test_autograd.py::TestAutograd::test_grad_mode_restored_reentrant, test/test_autograd.py::TestAutograd::test_grad_nonleaf, test/test_autograd.py::TestAutograd::test_grad_nonleaf_many_outputs, test/test_autograd.py::TestAutograd::test_grad_nonleaf_register_hook, test/test_autograd.py::TestAutograd::test_grad_to_node, test/test_autograd.py::TestAutograd::test_grad_to_node_inplace, test/test_autograd.py::TestAutograd::test_grad_to_node_materialize, test/test_autograd.py::TestAutograd::test_grad_to_node_multi, test/test_autograd.py::TestAutograd::test_grad_to_node_set, test/test_autograd.py::TestAutograd::test_grad_unreachable, test/test_autograd.py::TestAutograd::test_grad_unreachable_discovery, test/test_autograd.py::TestAutograd::test_gradcheck_backward_mul_by_grad_output, test/test_autograd.py::TestAutograd::test_gradcheck_check_batched_grad, 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test/test_autograd.py::TestAutograd::test_gradient_edge_graph_ownership, test/test_autograd.py::TestAutograd::test_gradient_edge_output, test/test_autograd.py::TestAutograd::test_graph_save_on_cpu, test/test_autograd.py::TestAutograd::test_graph_save_on_cpu_cuda, test/test_autograd.py::TestAutograd::test_hessian_vector, test/test_autograd.py::TestAutograd::test_hook_closure_cycle_use_custom_function_False_use_tensor_hook_False, test/test_autograd.py::TestAutograd::test_hook_closure_cycle_use_custom_function_False_use_tensor_hook_True, test/test_autograd.py::TestAutograd::test_hook_closure_cycle_use_custom_function_True_use_tensor_hook_False, test/test_autograd.py::TestAutograd::test_hook_closure_cycle_use_custom_function_True_use_tensor_hook_True, test/test_autograd.py::TestAutograd::test_hook_edge_case_when_called_with_grad, test/test_autograd.py::TestAutograd::test_hook_none, test/test_autograd.py::TestAutograd::test_hook_with_no_name, test/test_autograd.py::TestAutograd::test_hooks, 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test/test_autograd.py::TestAutograd::test_saved_variable_saved_original_inplace_detach, test/test_autograd.py::TestAutograd::test_saved_variable_version_counter, test/test_autograd.py::TestAutograd::test_saved_variables_deprecated, test/test_autograd.py::TestAutograd::test_saving_variable_to_disk, test/test_autograd.py::TestAutograd::test_scalar_grad_mixed_device, test/test_autograd.py::TestAutograd::test_select_expanded_v, test/test_autograd.py::TestAutograd::test_select_sum, test/test_autograd.py::TestAutograd::test_set_data_preserve_pyobj, test/test_autograd.py::TestAutograd::test_set_data_self_requires_grad, test/test_autograd.py::TestAutograd::test_set_data_tensorimpl_type, test/test_autograd.py::TestAutograd::test_set_grad_coroutines, test/test_autograd.py::TestAutograd::test_set_grad_coroutines_benign_exceptions, test/test_autograd.py::TestAutograd::test_set_grad_coroutines_critical_exceptions, test/test_autograd.py::TestAutograd::test_set_grad_coroutines_exit, 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test/test_autograd.py::TestAutograd::test_sparse_gather_both_scalar, test/test_autograd.py::TestAutograd::test_sparse_gather_dim0, test/test_autograd.py::TestAutograd::test_sparse_gather_dim1, test/test_autograd.py::TestAutograd::test_sparse_gather_dim_neg, test/test_autograd.py::TestAutograd::test_sparse_gather_ind_scalar, test/test_autograd.py::TestAutograd::test_sparse_gather_x_scalar, test/test_autograd.py::TestAutograd::test_sparse_mm_backward, test/test_autograd.py::TestAutograd::test_tensor_grad_warnings, test/test_autograd.py::TestAutograd::test_tensor_hooks_inplace, test/test_autograd.py::TestAutograd::test_tensor_hooks_inplace_multiple_outputs, test/test_autograd.py::TestAutograd::test_tensor_hooks_inplace_over_view, test/test_autograd.py::TestAutograd::test_thread_shutdown, test/test_autograd.py::TestAutograd::test_to_sparse_backward, test/test_autograd.py::TestAutograd::test_too_many_grads, test/test_autograd.py::TestAutograd::test_type_conversions, test/test_autograd.py::TestAutograd::test_unpack_hooks_exec_count, test/test_autograd.py::TestAutograd::test_unrelated_inputs, test/test_autograd.py::TestAutograd::test_unsafe_set_version_counter, test/test_autograd.py::TestAutograd::test_unused_output, test/test_autograd.py::TestAutograd::test_var_mean_differentiable, test/test_autograd.py::TestAutograd::test_variable_traverse, test/test_autograd.py::TestAutograd::test_version_counter, test/test_autograd.py::TestAutograd::test_view_func_replay, test/test_autograd.py::TestAutograd::test_view_func_replay_with_modified_state, test/test_autograd.py::TestAutograd::test_view_replay_enabled, test/test_autograd.py::TestAutograd::test_volatile_deprecated, test/test_autograd.py::TestAutograd::test_will_engine_execute_node, test/test_autograd.py::TestAutograd::test_wrapped_number_saved_tensors_hooks, test/test_autograd.py::TestAutogradForwardModeBatchedGrad::test_inplace_on_view_not_same_layout, test/test_autograd.py::TestAutogradForwardModeBatchedGrad::test_inplace_on_view_same_layout, test/test_autograd.py::TestAutogradForwardModeBatchedGrad::test_metadata_check_for_storage_numel_skipped, test/test_autograd.py::TestAutogradForwardModeBatchedGrad::test_out_of_place_basic, test/test_autograd.py::TestAutogradForwardModeBatchedGrad::test_out_of_place_not_same_layout, test/test_autograd.py::TestAutogradForwardMode::test_advanced_packing_unpacking, test/test_autograd.py::TestAutogradForwardMode::test_backward_graph_destruction, test/test_autograd.py::TestAutogradForwardMode::test_basic_packing_unpacking, test/test_autograd.py::TestAutogradForwardMode::test_codegen_ignores_undefined_outputs, test/test_autograd.py::TestAutogradForwardMode::test_create_new_zeros_with_same_meta, test/test_autograd.py::TestAutogradForwardMode::test_default_level, test/test_autograd.py::TestAutogradForwardMode::test_detach_view_tracking, test/test_autograd.py::TestAutogradForwardMode::test_forward_level_cleanup, test/test_autograd.py::TestAutogradForwardMode::test_fwd_grad_enabled, test/test_autograd.py::TestAutogradForwardMode::test_grad_cleanup, test/test_autograd.py::TestAutogradForwardMode::test_make_dual_forbid_integral_dtype, test/test_autograd.py::TestAutogradForwardMode::test_make_dual_inference_tensor_in_inference_mode, test/test_autograd.py::TestAutogradForwardMode::test_make_dual_torch_dispatch, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_check_conj, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_checks_ignores_size_zero, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_checks_storage_numel, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_ignore_storage_offset_for_zero_numel_tensor, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_when_primal_has_conj_bit, test/test_autograd.py::TestAutogradForwardMode::test_metadata_check_when_primal_has_neg_bit, test/test_autograd.py::TestAutogradForwardMode::test_nested_level, test/test_autograd.py::TestAutogradForwardMode::test_non_differentiable, test/test_autograd.py::TestAutogradForwardMode::test_out_variant, test/test_autograd.py::TestAutogradForwardMode::test_print, test/test_autograd.py::TestAutogradForwardMode::test_set_fw_grad_having_own_fw_grad_at_same_level, test/test_autograd.py::TestAutogradForwardMode::test_set_fwd_grad_enabled, test/test_autograd.py::TestAutogradForwardMode::test_size_check, test/test_autograd.py::TestAutogradForwardMode::test_view_inplace_always_creates_a_view, test/test_autograd.py::TestAutogradForwardMode::test_view_inplace_differentiable_views, test/test_autograd.py::TestAutogradForwardMode::test_view_inplace_non_differentiable_views, test/test_autograd.py::TestAllowMutationOnSaved::test_backward_out_of_context, 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test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_multi_output_safe_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_multi_output_unsafe_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_multiple_outputs_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_non_contig_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_of_multiple_output_view_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_of_view_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_python_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_then_no_grad_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_undefined_grad_output_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inputbuffer_add_multidevice_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_min_max_median_backprops_to_all_values_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_mv_grad_stride_0_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_non_differentiable_ops_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_parameter_resize_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_pin_memory_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_pow_real_negative_base_complex_exponent_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_profiler_emit_itt_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_profiler_emit_nvtx_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_pyscalar_conversions_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_reentrant_parent_error_on_cpu_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_requires_grad_factory_cuda_float32, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_requires_grad_factory_cuda_float64, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_resize_version_bump_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_rnn_backward_to_input_but_not_parameters_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_scatter_index_reduce_amin_amax_backprops_to_all_values_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_scatter_index_reduce_prod_gradgrad_error_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_float16, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_float32, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_float64, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_int16, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_int32, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_int64, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_set_requires_grad_only_for_floats_cuda_int8, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_simple_reentrant_cross_device_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_sparse_backward_cuda_complex128, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_sparse_backward_cuda_float64, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_sparse_ctor_getter_backward_cuda_complex128, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_sparse_ctor_getter_backward_cuda_float64, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_sparse_mask_autograd_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_strided_leaf_grad_layout_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_to_r_to_c_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_unused_output_device_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_warning_in_backward_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_where_functional_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_where_scalar_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_zero_dim_param_mixed_device_grad_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_autograd_composite_implicit_and_dispatch_registration_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_autograd_multiple_dispatch_registrations_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_backward_single_threaded_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_backward_tls_stash_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_foward_mode_AD_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_is_retain_graph_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_per_dispatch_key_input_saving_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_set_sequence_nr_cuda, test/test_autograd.py::TestAutogradMultipleDispatchCUDA::test_view_copy_cuda 2025-09-07T08:22:40.4671238Z 2025-09-07T08:22:40.4671341Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-09-07 08:22:40.452687] 2025-09-07T08:22:40.6161452Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions 2025-09-07T08:22:42.9751741Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T08:22:42.9767796Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:22:42.9778131Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:24:11.9636113Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ done 2025-09-07T08:24:11.9708751Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp312-cp312-linux_x86_64.whl size=13035760 sha256=f735a7cf59becce8eb196bb3120119736f0001366071333c7d1885e825de1407 2025-09-07T08:24:11.9709555Z Stored in directory: /tmp/pip-ephem-wheel-cache-gsclw2cw/wheels/fc/3b/ed/ea7c0cea7dc731e18eb485707930be2a5fbc6e49db88cadb75 2025-09-07T08:24:11.9725652Z Successfully built torch_test_cpp_extension 2025-09-07T08:24:12.0570436Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:24:12.1745768Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:24:12.3677656Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T08:24:13.2909533Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:24:13.2928602Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T08:24:13.2933409Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:24:14.4849442Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ done 2025-09-07T08:24:14.4852872Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp312-cp312-linux_x86_64.whl size=2972 sha256=e1667884600553bc861ec2da97b72c49cf4a2e478a551812dc74ba2b79de4bec 2025-09-07T08:24:14.4853795Z Stored in directory: /tmp/pip-ephem-wheel-cache-yspr3bzx/wheels/0f/c6/d1/a47ade092c4854ee96ffa8750c0df779c8a501cdb658bb0982 2025-09-07T08:24:14.4870714Z Successfully built no_python_abi_suffix_test 2025-09-07T08:24:14.5693226Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T08:24:14.5776308Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T08:24:14.7393976Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension 2025-09-07T08:24:16.0086681Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:24:16.0110245Z [?25hBuilding wheels for collected packages: python_agnostic 2025-09-07T08:24:16.0116403Z  DEPRECATION: Building 'python_agnostic' 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 'python_agnostic'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:24:24.5796822Z  Building wheel for python_agnostic (setup.py) ... [?25l- \ | done 2025-09-07T08:24:24.5803771Z [?25h Created wheel for python_agnostic: filename=python_agnostic-0.0-cp39-abi3-linux_x86_64.whl size=21265 sha256=f61586108fca898c030d1117849e755166c4a37cbc04111a00d95afb7a0958ba 2025-09-07T08:24:24.5804897Z Stored in directory: /tmp/pip-ephem-wheel-cache-gbdqs711/wheels/a4/0f/99/61a8b395951d01575fff503a83d3590b992d7a3cd2208af383 2025-09-07T08:24:24.5816988Z Successfully built python_agnostic 2025-09-07T08:24:24.7593226Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension 2025-09-07T08:24:26.0242021Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:24:26.0262775Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from libtorch_agnostic==0.0) (2.9.0a0+git93fb23d) 2025-09-07T08:24:26.0268430Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (3.19.1) 2025-09-07T08:24:26.0269815Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (4.15.0) 2025-09-07T08:24:26.0276610Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (80.9.0) 2025-09-07T08:24:26.0282155Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (1.13.3) 2025-09-07T08:24:26.0282573Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (2.8.8) 2025-09-07T08:24:26.0282968Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (3.1.6) 2025-09-07T08:24:26.0283368Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (2025.7.0) 2025-09-07T08:24:26.0331964Z 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->libtorch_agnostic==0.0) (1.3.0) 2025-09-07T08:24:26.0344188Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->libtorch_agnostic==0.0) (3.0.2) 2025-09-07T08:24:26.0379164Z Building wheels for collected packages: libtorch_agnostic 2025-09-07T08:24:26.0384692Z  DEPRECATION: Building 'libtorch_agnostic' 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 'libtorch_agnostic'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:24:28.3694789Z  Building wheel for libtorch_agnostic (setup.py) ... [?25l- \ | done 2025-09-07T08:24:28.3695855Z [?25h Created wheel for libtorch_agnostic: filename=libtorch_agnostic-0.0-cp39-abi3-linux_x86_64.whl size=32553 sha256=7ee93b3e15ed498e4329f8ef443d5d9e39e3f9a506ed2f0f5b8160fab430f5e4 2025-09-07T08:24:28.3709391Z Stored in directory: /tmp/pip-ephem-wheel-cache-4xl_0s2d/wheels/51/70/8e/57220688bace24fcb90e7b7262d0def97fbfabece1c3854bc7 2025-09-07T08:24:28.3709748Z Successfully built libtorch_agnostic 2025-09-07T08:24:28.4469117Z Installing collected packages: libtorch_agnostic 2025-09-07T08:24:28.4561418Z Successfully installed libtorch_agnostic-0.0 2025-09-07T08:24:28.4838325Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:24:28.4842385Z 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'] ... [2025-09-07 08:24:28.483752] 2025-09-07T08:24:30.6584804Z 2025-09-07T08:24:30.6586552Z 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_4252cb3f8eeb0c72_.log 2025-09-07T08:24:30.6598041Z Running 21 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2025-09-07T08:24:30.6600440Z 2025-09-07T08:24:30.6600531Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-09-07 08:24:30.658249] 2025-09-07T08:24:30.8188050Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions 2025-09-07T08:24:32.8805959Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T08:24:32.8826205Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:24:32.8839233Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:26:22.1886130Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ done 2025-09-07T08:26:22.1957097Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp312-cp312-linux_x86_64.whl size=13035534 sha256=46c8a7e72568ed4636bde22865b30de1b2e75afff18cb8913d24ddce108f41b9 2025-09-07T08:26:22.1957575Z Stored in directory: /tmp/pip-ephem-wheel-cache-h26w686v/wheels/fc/3b/ed/ea7c0cea7dc731e18eb485707930be2a5fbc6e49db88cadb75 2025-09-07T08:26:22.1972830Z Successfully built torch_test_cpp_extension 2025-09-07T08:26:22.2841941Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:26:22.4126084Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:26:22.5995375Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T08:26:23.5749174Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:26:23.5771845Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T08:26:23.5780343Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:26:24.6225880Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ done 2025-09-07T08:26:24.6230253Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp312-cp312-linux_x86_64.whl size=2972 sha256=3410ea230eb4b8217313686bf1ebe7a6bee5b277b2c1881b78ad9e9584a9501b 2025-09-07T08:26:24.6231039Z Stored in directory: /tmp/pip-ephem-wheel-cache-j6uysphr/wheels/0f/c6/d1/a47ade092c4854ee96ffa8750c0df779c8a501cdb658bb0982 2025-09-07T08:26:24.6249496Z Successfully built no_python_abi_suffix_test 2025-09-07T08:26:24.7100605Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T08:26:24.7175706Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T08:26:24.8911089Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension 2025-09-07T08:26:26.1064820Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:26:26.1087483Z [?25hBuilding wheels for collected packages: python_agnostic 2025-09-07T08:26:26.1092090Z  DEPRECATION: Building 'python_agnostic' 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 'python_agnostic'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:26:27.6632762Z  Building wheel for python_agnostic (setup.py) ... [?25l- \ done 2025-09-07T08:26:27.6637884Z [?25h Created wheel for python_agnostic: filename=python_agnostic-0.0-cp39-abi3-linux_x86_64.whl size=21265 sha256=7a3d72ddbc5fcc1594786888cd160c53f162d5b8aab26ac4eb79855310939816 2025-09-07T08:26:27.6638583Z Stored in directory: /tmp/pip-ephem-wheel-cache-xty_og51/wheels/a4/0f/99/61a8b395951d01575fff503a83d3590b992d7a3cd2208af383 2025-09-07T08:26:27.6654681Z Successfully built python_agnostic 2025-09-07T08:26:27.8339268Z Processing /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension 2025-09-07T08:26:29.2004890Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:26:29.2028388Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from libtorch_agnostic==0.0) (2.9.0a0+git93fb23d) 2025-09-07T08:26:29.2035187Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (3.19.1) 2025-09-07T08:26:29.2035704Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (4.15.0) 2025-09-07T08:26:29.2041958Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (80.9.0) 2025-09-07T08:26:29.2043458Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (1.13.3) 2025-09-07T08:26:29.2046064Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (2.8.8) 2025-09-07T08:26:29.2046585Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (3.1.6) 2025-09-07T08:26:29.2048145Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->libtorch_agnostic==0.0) (2025.7.0) 2025-09-07T08:26:29.2096249Z 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->libtorch_agnostic==0.0) (1.3.0) 2025-09-07T08:26:29.2109621Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->libtorch_agnostic==0.0) (3.0.2) 2025-09-07T08:26:29.2143574Z Building wheels for collected packages: libtorch_agnostic 2025-09-07T08:26:29.2147550Z  DEPRECATION: Building 'libtorch_agnostic' 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 'libtorch_agnostic'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:26:30.8640401Z  Building wheel for libtorch_agnostic (setup.py) ... [?25l- \ done 2025-09-07T08:26:30.8646051Z [?25h Created wheel for libtorch_agnostic: filename=libtorch_agnostic-0.0-cp39-abi3-linux_x86_64.whl size=32553 sha256=955ac30ed6b7f6cf316a880cbe0784559161a0e3b3c6f618e9f8d063674f419e 2025-09-07T08:26:30.8646625Z Stored in directory: /tmp/pip-ephem-wheel-cache-ygw2dkzq/wheels/51/70/8e/57220688bace24fcb90e7b7262d0def97fbfabece1c3854bc7 2025-09-07T08:26:30.8665593Z Successfully built libtorch_agnostic 2025-09-07T08:26:30.9435259Z Installing collected packages: libtorch_agnostic 2025-09-07T08:26:30.9519186Z Successfully installed libtorch_agnostic-0.0 2025-09-07T08:26:30.9801472Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:26:30.9804810Z 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'] ... [2025-09-07 08:26:30.980125] 2025-09-07T08:26:33.1972569Z 2025-09-07T08:26:33.1973582Z 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_56cbc9fca66f87d6_.log 2025-09-07T08:26:33.1976850Z Running 21 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2025-09-07T08:26:33.1979429Z 2025-09-07T08:26:33.1979524Z Running test_cpp_extensions_jit 1/1 ... [2025-09-07 08:26:33.197311] 2025-09-07T08:26:33.1984750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:26:33.1985260Z 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'] ... [2025-09-07 08:26:33.197554] 2025-09-07T08:29:52.4495713Z 2025-09-07T08:29:52.4496337Z 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_3cb57bf64539a976_.log 2025-09-07T08:29:52.4501231Z Running 34 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_aoti_torch_call_dispatcher, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_non_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_pluggable_allocator_include, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_xpu, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_archlists, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2025-09-07T08:29:52.4505975Z 2025-09-07T08:29:52.4506071Z Running test_cuda_expandable_segments 1/1 ... [2025-09-07 08:29:52.449535] 2025-09-07T08:29:52.4506309Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:29:52.4511706Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_expandable_segments.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:29:52.449770] 2025-09-07T08:29:55.2711213Z 2025-09-07T08:29:55.2712364Z test_cuda_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_expandable_segments_1.1_5cae927a1c661996_.log 2025-09-07T08:29:55.2712826Z 2025-09-07T08:29:55.2714879Z Running test_decomp 3/12 ... [2025-09-07 08:29:55.271097] 2025-09-07T08:29:55.2716177Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:29:55.2716956Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_decomp.py', '--shard-id=3', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:29:55.271420] 2025-09-07T08:40:36.0640735Z 2025-09-07T08:40:36.0641900Z test_decomp 3/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_3.12_ae8609555ae8130b_.log 2025-09-07T08:40:36.0729520Z Running 751 items in this shard: test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___getitem___cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rand___cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmul___cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmul___cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___ror___cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive___ror___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___ror___cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rsub___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive__chunk_cat_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive__chunk_cat_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive__segment_reduce_lengths_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_put_accumulate_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acosh_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acosh_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_addcmul_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_all_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_all_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_allclose_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_amax_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_aminmax_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_aminmax_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_angle_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_angle_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_arange_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argmax_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argsort_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argwhere_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_partial_views_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_partial_views_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_scatter_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_scatter_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atan2_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atan_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atleast_1d_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atleast_1d_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atleast_2d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atleast_3d_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_baddbmm_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bernoulli_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bfloat16_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bitwise_not_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bitwise_not_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bitwise_right_shift_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bool_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_broadcast_tensors_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_broadcast_tensors_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cdouble_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cdouble_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ceil_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cfloat_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_chalf_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_char_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_char_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_max_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_min_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_combinations_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_conj_physical_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_copysign_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_corrcoef_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cos_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cosh_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_count_nonzero_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cross_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cumprod_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cumsum_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cumsum_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cumsum_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cumulative_trapezoid_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diag_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diag_embed_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diagflat_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diagonal_scatter_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diagonal_scatter_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diff_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_diff_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_digamma_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_div_floor_rounding_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_div_no_rounding_mode_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_div_no_rounding_mode_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_double_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_dstack_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_einsum_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_empty_like_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_empty_strided_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_eq_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_equal_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_equal_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_exp_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_eye_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_fft2_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_fft2_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_fftn_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_fftshift_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_fftshift_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_hfft2_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_hfft2_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_hfft2_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_hfftn_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ifft2_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ifft2_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ifftshift_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ifftshift_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ifftshift_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ihfft2_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_ihfft_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_irfft2_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_irfft_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_irfftn_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_irfftn_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_irfftn_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fft_rfft_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fill_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_flatten_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_flatten_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_flatten_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_flatten_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fliplr_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fliplr_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_flipud_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_float_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_float_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_float_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_float_power_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_floor_divide_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_floor_divide_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_floor_divide_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_floor_divide_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fmax_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fmax_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_fmin_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_frexp_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_full_like_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_full_like_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_full_like_cuda_uint16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_gather_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ge_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ge_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_grid_sampler_3d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_gt_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_gt_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_heaviside_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_heaviside_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_hsplit_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_hsplit_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_hypot_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_put_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_reduce_amax_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_reduce_mean_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_reduce_mean_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_reduce_prod_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_index_select_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_int_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isclose_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isfinite_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isinf_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isnan_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isposinf_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isposinf_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_isreal_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_item_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_jiterator_2inputs_2outputs_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_jiterator_2inputs_2outputs_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_jiterator_unary_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_jiterator_unary_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_jiterator_unary_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_kron_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_kron_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_kthvalue_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ldexp_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ldexp_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_le_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_cholesky_ex_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_cross_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_cross_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_det_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_diagonal_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_diagonal_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_eig_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_ldl_factor_ex_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_ldl_solve_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_lstsq_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_lu_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_lu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_lu_solve_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_matrix_norm_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_matrix_power_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_norm_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_norm_subgradients_at_zero_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_svdvals_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_tensorsolve_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_vecdot_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linalg_vector_norm_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linspace_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_linspace_tensor_overload_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_log10_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_log_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_log_normal_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_log_softmax_with_dtype_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logaddexp2_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logcumsumexp_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logcumsumexp_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logical_not_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logical_or_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_tensor_overload_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logspace_tensor_overload_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_logsumexp_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_long_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_lt_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_lt_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_lu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mT_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mT_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_argmin_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumprod_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumsum_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_fill_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_fill_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_fill_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_logsumexp_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_scatter_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_scatter_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_select_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_matmul_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_matrix_exp_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_reduction_with_dim_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_maximum_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mean_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_list_of_tensors_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_list_of_tensors_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_reduction_no_dim_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_reduction_with_dim_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_minimum_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mm_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mode_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mode_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mode_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_movedim_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_movedim_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_movedim_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_msort_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mul_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_multinomial_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mvlgamma_mvlgamma_p_3_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mvlgamma_mvlgamma_p_5_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nanmedian_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nansum_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nansum_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nansum_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_narrow_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_narrow_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_narrow_copy_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ne_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ne_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_neg_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_empty_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_empty_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_empty_strided_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_empty_strided_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_empty_strided_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_full_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_ones_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_ones_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_zeros_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_new_zeros_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_adaptive_avg_pool2d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_alpha_dropout_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_avg_pool3d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv1d_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv2d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv2d_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv_transpose1d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv_transpose3d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_ctc_loss_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_embedding_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_fractional_max_pool2d_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_gaussian_nll_loss_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_hardshrink_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_hardsigmoid_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_hardsigmoid_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_hardswish_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_hardtanh_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_instance_norm_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_trilinear_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_local_response_norm_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_logsigmoid_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_logsigmoid_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_unpool1d_grad_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_unpool2d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_unpool3d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_unpool3d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_mse_loss_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_multi_head_attention_forward_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_multi_head_attention_forward_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_nll_loss_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_circular_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_replicate_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_replicate_negative_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_replicate_negative_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pixel_shuffle_cuda_bool, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softsign_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softsign_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_upsample_nearest_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nonzero_static_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nonzero_static_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_norm_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_norm_fro_cuda_complex64, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_i1e_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_log_ndtr_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_i1_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_k1_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtr_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtr_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_scaled_modified_bessel_k1_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_u_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_v_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_spherical_bessel_j0_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_xlog1py_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_list_args_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_list_args_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sqrt_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sqrt_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_copy_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_multiple_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_stack_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_mean_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_unbiased_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_unbiased_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sum_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sum_to_size_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_copy_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_copy_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_copy_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tensor_split_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tile_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_torch_ops_aten__safe_softmax_default_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trace_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trace_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trace_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_transpose_copy_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trapezoid_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tril_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_triu_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_triu_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unbind_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unbind_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unflatten_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unfold_copy_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_uniform_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unique_consecutive_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unique_consecutive_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unique_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_chunk_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_chunk_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_split_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_mean_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vdot_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_as_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_copy_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_copy_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vsplit_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vsplit_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vstack_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vstack_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_where_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_where_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_xlogy_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_xlogy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_zero__cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_zeros_like_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick__chunk_cat_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick__softmax_backward_data_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick__unsafe_masked_index_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick__unsafe_masked_index_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick__unsafe_masked_index_put_accumulate_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick__unsafe_masked_index_put_accumulate_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_abs_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_add_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_add_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_addmv_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_addmv_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_addr_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_alias_copy_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_alias_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_amin_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_aminmax_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_any_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_arange_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_arange_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_arange_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_as_strided_copy_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_asin_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_asin_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_asinh_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_atan2_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_atan2_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_atan_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_atan_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_atan_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_atan_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_baddbmm_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_bernoulli_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_bitwise_left_shift_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_bitwise_not_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_bitwise_right_shift_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_bitwise_xor_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_bucketize_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_cat_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_cat_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_cat_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_cat_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_clone_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_clone_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_conj_physical_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_constant_pad_nd_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_constant_pad_nd_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_bernoulli_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_index_fill_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_linalg_cross_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_mvlgamma_mvlgamma_p_5_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_nansum_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_select_scatter_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_transpose_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_count_nonzero_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_count_nonzero_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_count_nonzero_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_cumprod_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_cumprod_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_cumprod_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_cumsum_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_deg2rad_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_diag_embed_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_scatter_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_dist_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_div_no_rounding_mode_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_div_trunc_rounding_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_dot_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_empty_like_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_eq_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_eq_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_eq_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_erf_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_erf_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_erfc_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_erfinv_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_exp_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_expm1_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fft2_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fft_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fft_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fftn_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_hfft2_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_hfftn_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_hfftn_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft2_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ihfft_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ihfftn_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ihfftn_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_irfft_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_irfftn_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_rfft_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_rfft_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_rfft_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_rfftn_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_floor_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_floor_divide_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_fmin_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_fmin_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_fmin_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_frexp_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_gt_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_heaviside_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_heaviside_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_i0_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_i0_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_i0_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_index_fill_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_index_select_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_index_select_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_isin_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_isneginf_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_item_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_diagonal_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_diagonal_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_vector_norm_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_vector_norm_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_tensor_overload_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_tensor_overload_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_log10_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_log10_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_log1p_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_log2_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_log_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_log_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_log_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_logaddexp2_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_logical_and_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_logical_and_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_logical_not_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_logical_not_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_logical_or_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_logical_or_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_logical_or_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_logit_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_masked_fill_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_list_of_tensors_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_mul_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_mul_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_mul_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_mv_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_3_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_3_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_narrow_copy_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_native_dropout_backward_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ne_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_new_empty_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_new_empty_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_new_empty_strided_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_new_empty_strided_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_new_full_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_new_zeros_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_new_zeros_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_new_zeros_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_nextafter_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_hardshrink_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_leaky_relu_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_mse_loss_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_pad_constant_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_pad_constant_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_relu_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_relu_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_silu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_unfold_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_norm_fro_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_norm_inf_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_normal_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_normal_in_place_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_ones_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ones_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_ones_like_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ones_like_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_ones_like_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_permute_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_pow_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_pow_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_prod_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_rad2deg_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_rad2deg_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_rad2deg_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_rad2deg_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_reciprocal_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_reciprocal_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_remainder_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_repeat_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_repeat_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_rot90_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_rsqrt_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_rsqrt_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_sigmoid_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_sigmoid_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_sigmoid_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_sigmoid_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_signbit_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_sin_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_sin_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_sinc_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_slice_scatter_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_special_erfcx_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1e_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1e_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1e_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_special_log_ndtr_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_special_log_ndtr_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_split_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_split_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_split_list_args_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_split_list_args_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_copy_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_multiple_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_std_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_std_mean_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_std_mean_unbiased_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_sub_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_sum_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_t_copy_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_take_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_tan_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_tanh_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_tanh_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_trace_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_transpose_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_tril_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_tril_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_triu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_triu_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_triu_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_trunc_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_unbind_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_unfold_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_unfold_copy_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_uniform_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_unsafe_split_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_copy_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_var_unbiased_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_vdot_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_view_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_view_copy_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_view_copy_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_xlogy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_rnn_decomp_module_nn_GRU_eval_mode_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_rnn_decomp_module_nn_GRU_train_mode_cuda_float64, test/test_decomp.py::DecompOneOffTestsCUDA::test_sdpa_nn_functional_scaled_dot_product_attention_cuda_float16 2025-09-07T08:40:36.0805071Z 2025-09-07T08:40:36.0805149Z Running test_decomp 9/12 ... [2025-09-07 08:40:36.064484] 2025-09-07T08:40:36.0805337Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:40:36.0805698Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_decomp.py', '--shard-id=9', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:40:36.064699] 2025-09-07T08:51:55.1146676Z 2025-09-07T08:51:55.1148363Z test_decomp 9/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_9.12_3d88276fe0eb9736_.log 2025-09-07T08:51:55.1237268Z Running 753 items in this shard: test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_T_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_T_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___radd___cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rand___cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rdiv___cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rdiv___cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rdiv___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rdiv___cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmod___cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rsub___cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rsub___cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive__chunk_cat_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive__chunk_cat_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acos_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acosh_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acosh_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_addcmul_cuda_float16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_smooth_l1_loss_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softmin_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softmin_with_dtype_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softmin_with_dtype_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_triplet_margin_loss_cuda_complex64, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_1_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_2_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_2_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_2_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_positive_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_positive_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_positive_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_pow_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_prod_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_prod_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_put_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rad2deg_cuda_bool, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_resize_as__cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_resolve_neg_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_resolve_neg_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_roll_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_roll_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rot90_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rot90_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rot90_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rsqrt_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scalar_tensor_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_amin_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_amin_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_mean_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_prod_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_searchsorted_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_searchsorted_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_scatter_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sgn_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sgn_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_short_cuda_bool, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_k0_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtr_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_scaled_modified_bessel_k0_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_t_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_t_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_u_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_xlog1py_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_xlog1py_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_list_args_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_list_args_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_copy_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sqrt_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_square_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_multiple_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_multiple_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_stack_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_stack_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_stack_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_unbiased_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sum_to_size_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_svd_lowrank_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_svd_lowrank_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tan_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tanh_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tensor_split_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tensordot_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tile_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_tile_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_to_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_to_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_to_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_to_sparse_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_transpose_cuda_float16, 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test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_nn_functional_max_unpool3d_grad_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_norm_fro_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_rot90_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_special_log_ndtr_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_core_backward_unsqueeze_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_cosh_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_cumsum_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_diag_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_scatter_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_diagonal_scatter_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_digamma_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_dist_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_div_trunc_rounding_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_div_trunc_rounding_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_empty_like_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_exp2_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_exp_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_expm1_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_expm1_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fft_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fftn_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_fftn_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_hfft2_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_fft_hfftn_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft2_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft2_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifft_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifftn_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifftn_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ifftn_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_ihfft_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_fft_irfft2_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_fft_irfft2_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_fft_rfftn_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_flip_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_flip_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_floor_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_fmax_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_fmax_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_gcd_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_gcd_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_ge_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_geometric_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_geometric_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_grid_sampler_2d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_heaviside_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_heaviside_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_index_copy_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_index_fill_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_isinf_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_isnan_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_isnan_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_isneginf_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_isposinf_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_le_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_lgamma_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_diagonal_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_vector_norm_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_tensor_overload_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_log1p_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_log_softmax_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_log_softmax_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_logical_and_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_logical_not_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_logical_not_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_logical_xor_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_logit_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_logit_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_tensor_overload_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_logsumexp_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_logsumexp_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_masked_fill_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_variadic_tensors_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_mul_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_mv_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_1_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_5_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_5_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_nan_to_num_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_nan_to_num_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_nansum_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nansum_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_narrow_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_ne_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_ne_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_neg_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_new_full_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_new_ones_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_new_ones_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_new_ones_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_new_zeros_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_binary_cross_entropy_with_logits_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_hardtanh_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_max_unpool2d_grad_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_max_unpool3d_grad_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_mish_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_mish_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_pad_constant_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_relu6_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_relu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_relu_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_unfold_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_norm_fro_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_norm_fro_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_normal_in_place_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_ones_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_ones_like_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_permute_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_pow_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_pow_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_prod_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_randn_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_randn_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_reciprocal_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_remainder_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_rot90_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_round_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_round_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_round_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_round_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_round_decimals_0_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_round_decimals_0_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_select_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_select_scatter_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_select_scatter_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_sgn_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_signbit_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_sinc_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_sinh_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_special_entr_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_special_entr_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_special_i0e_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_special_i0e_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_special_xlog1py_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_special_zeta_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_split_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_sqrt_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_sqrt_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_sqrt_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_copy_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_multiple_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_sub_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_sub_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_sum_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_t_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_t_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_take_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_take_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_tan_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_tanh_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_transpose_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_tril_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_tril_indices_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_triu_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_unbind_copy_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_unbind_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_unbind_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_unbind_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_unfold_copy_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_unsafe_split_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_copy_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_var_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_var_mean_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_var_mean_unbiased_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_var_unbiased_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_view_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_where_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_zero__cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_rnn_decomp_module_nn_LSTM_train_mode_cuda_float64, test/test_decomp.py::DecompOneOffTestsCUDA::test_amp_batch_norm_backward_cuda, test/test_decomp.py::DecompOneOffTestsCUDA::test_sdpa_nn_functional_scaled_dot_product_attention_cuda_bfloat16, test/test_decomp.py::DecompOneOffTestsCUDA::test_sdpa_nn_functional_scaled_dot_product_attention_cuda_float64 2025-09-07T08:51:55.1312505Z 2025-09-07T08:51:55.1312611Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T08:51:55.1312795Z Uploading artifacts took 0.00 seconds 2025-09-07T08:51:55.1312936Z Running test_fx 2/3 ... [2025-09-07 08:51:55.115193] 2025-09-07T08:51:55.1313076Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:51:55.1318572Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx.py', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:51:55.115406] 2025-09-07T08:54:46.4869584Z 2025-09-07T08:54:46.4870402Z test_fx 2/3 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_2.3_28277d6e3840bc2e_.log 2025-09-07T08:54:46.4928137Z Running 426 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_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, 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::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_impure_kwargs, 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_simple, 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_unused_placeholder, test/test_fx.py::TestConstFold::test_retain_node_meta, 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_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, 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_true, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_function, 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::TestPassManager::test_pass_manager_bad_checks, 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_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_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::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, 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_replace_with_duplicated_outputs, 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_results, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, 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_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_tracer, 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_disallow_override, 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_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, 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_interpreter, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, 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_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_shape_prop_layout, 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_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_return_namedtuple, 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_pre_pep585, 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_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, 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_pool2d_with_indices, 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_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, 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_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, 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_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, 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_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_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, 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_silu, 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::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmatmul___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__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_addbmm_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_decomposed_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_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_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_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_atanh_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_broadcast_shapes_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_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_conj_physical_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_cumprod_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_diagonal_copy_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_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_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_erfc_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_fft_fft_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_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_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_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_gather_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_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_index_copy_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_amin_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_isin_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_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_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_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_lstsq_grad_oriented_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_norm_subgradients_at_zero_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_solve_triangular_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_log1p_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_logaddexp_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_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_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_masked_argmax_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_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_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_scatter_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_mean_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_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_nanmedian_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_native_dropout_backward_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_zeros_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_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_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_bilinear_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_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_cosine_embedding_loss_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_elu_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_gelu_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_hardshrink_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_leaky_relu_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_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_multi_margin_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_pairwise_distance_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_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_softmin_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_tanhshrink_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_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_inf_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_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_polar_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_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_quantile_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_ravel_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_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_round_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_scalar_tensor_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_prod_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_scatter_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_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_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_softmax_with_dtype_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_w_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_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_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_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_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_unbiased_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_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_tensordot_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_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_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_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_unflatten_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_var_mean_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_vstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_zero__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_detection_fasterrcnn_mobilenet_v3_large_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, 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_b5, 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_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_3, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, 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_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet152, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_64x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, 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_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2 2025-09-07T08:54:46.4977913Z 2025-09-07T08:54:46.4978017Z Running test_mkl_verbose 1/1 ... [2025-09-07 08:54:46.488014] 2025-09-07T08:54:46.4978207Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:54:46.4978636Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkl_verbose.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:54:46.488333] 2025-09-07T08:54:50.7140281Z 2025-09-07T08:54:50.7142024Z test_mkl_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkl_verbose_1.1_c00044cd9ce1fc57_.log 2025-09-07T08:54:50.7143693Z Running 2 items in this shard: test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_off, test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_on 2025-09-07T08:54:50.7144753Z 2025-09-07T08:54:50.7145027Z Running test_mkldnn_fusion 1/1 ... [2025-09-07 08:54:50.713792] 2025-09-07T08:54:50.7145663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:54:50.7146778Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_fusion.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:54:50.714028] 2025-09-07T08:56:03.6790458Z 2025-09-07T08:56:03.6791574Z test_mkldnn_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_fusion_1.1_d1b80f5c3817e6e0_.log 2025-09-07T08:56:03.6794804Z Running 8 items in this shard: test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_transpose_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_nnc, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_single_conv, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_unsupported_conv 2025-09-07T08:56:03.6797690Z 2025-09-07T08:56:03.6805435Z Running test_monitor 1/1 ... [2025-09-07 08:56:03.678867] 2025-09-07T08:56:03.6806273Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:56:03.6806752Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_monitor.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:56:03.679086] 2025-09-07T08:56:05.6987191Z 2025-09-07T08:56:05.6991817Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_81b5b232c6f34ada_.log 2025-09-07T08:56:05.6993725Z Running 6 items in this shard: test/test_monitor.py::TestMonitor::test_event_handler, test/test_monitor.py::TestMonitor::test_fixed_count_stat, test/test_monitor.py::TestMonitor::test_interval_stat, test/test_monitor.py::TestMonitor::test_log_event, test/test_monitor.py::TestMonitor::test_wait_counter, test/test_monitor.py::TestMonitorTensorboard::test_event_handler 2025-09-07T08:56:05.6995415Z 2025-09-07T08:56:05.6995638Z Running test_multiprocessing 1/1 ... [2025-09-07 08:56:05.698457] 2025-09-07T08:56:05.6996083Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:56:05.6997158Z 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'] ... [2025-09-07 08:56:05.698744] 2025-09-07T08:57:01.4747956Z 2025-09-07T08:57:01.4748993Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_eb4481fbdbd51d1e_.log 2025-09-07T08:57:01.4757785Z Running 42 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_rebuild_cuda_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2025-09-07T08:57:01.4764338Z 2025-09-07T08:57:01.4764605Z Running test_multiprocessing_spawn 1/1 ... [2025-09-07 08:57:01.474613] 2025-09-07T08:57:01.4770059Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:57:01.4770567Z 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'] ... [2025-09-07 08:57:01.474889] 2025-09-07T08:59:00.2252934Z 2025-09-07T08:59:00.2253990Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_5a86469db3d09eb2_.log 2025-09-07T08:59:00.2260001Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2025-09-07T08:59:00.2265093Z 2025-09-07T08:59:00.2265199Z Running test_nestedtensor 2/2 ... [2025-09-07 08:59:00.225178] 2025-09-07T08:59:00.2265406Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T08:59:00.2265899Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nestedtensor.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:59:00.225466] 2025-09-07T09:06:37.0185961Z 2025-09-07T09:06:37.0187402Z test_nestedtensor 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nestedtensor_2.2_487c696fce5bee67_.log 2025-09-07T09:06:37.0321361Z Running 809 items in this shard: test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_default_nested_tensor, test/test_nestedtensor.py::TestNestedTensor::test_dim, test/test_nestedtensor.py::TestNestedTensor::test_fill_, 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test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_jagged_requires_grad_True_contiguous_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_jagged_requires_grad_True_contiguous_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_strided_requires_grad_False_contiguous_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_strided_requires_grad_False_contiguous_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_strided_requires_grad_False_contiguous_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_strided_requires_grad_False_contiguous_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_as_nested_tensor_from_tensor_dim_4_layout_strided_requires_grad_True_contiguous_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_binary_pointwise_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_binary_pointwise_transposed_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_broadcast_shapes_on_in_graph_constructed_njt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_compile_preserves_metadata_cache_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_compile_with_dynamic_max_seq_len_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_compile_with_dynamic_min_seq_len_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_composite_op_with_custom_mode_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_device_dtype_transfer_updates_offsets_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_device_dtype_transfer_updates_offsets_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_dropout_inference_mode_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_dummy_mha_with_nt_use_legacy_api_False_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_dummy_mha_with_nt_use_legacy_api_True_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_flex_attention_converts_stacked_seq_indices_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_flex_attention_noncontig_with_holes_True_cross_attention_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_is_same_size_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_as_nested_tensor_components_require_grad_False_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_as_nested_tensor_components_require_grad_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_as_nested_tensor_components_require_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_as_nested_tensor_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_as_nested_tensor_components_require_grad_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_False_components_require_grad_False_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_False_components_require_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_True_components_require_grad_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_nested_tensor_requires_grad_True_components_require_grad_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_layout_construction_with_pinned_memory_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_mean_keepdim_True_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_mean_keepdim_True_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_sum_keepdim_False_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_sum_keepdim_False_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_sum_keepdim_True_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_op_different_output_shape_dim_sum_keepdim_True_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_padded_dense_conversion_kernels_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_padded_dense_conversion_kernels_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_view_from_values_offsets_requires_grad_False_values_is_view_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_view_from_values_offsets_requires_grad_False_values_is_view_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_view_from_values_offsets_requires_grad_False_values_is_view_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_view_from_values_offsets_requires_grad_True_values_is_view_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_jagged_view_from_values_offsets_requires_grad_True_values_is_view_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_layer_norm_operate_on_batch_dim_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_layer_norm_reduce_ragged_idx_1_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_layer_norm_reduce_ragged_idx_1_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_layer_norm_with_lengths_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_layout_under_torch_dispatch_mode_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_shape_empty_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_shape_randn_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_empty_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_ones_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_rand_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_randint_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_randn_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_like_value_zeros_like_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_linear_backward_memory_usage_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_nested_tensor_activation_checkpoint_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_nested_tensor_from_jagged_fx_trace_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_nested_tensor_from_jagged_pass_min_max_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_njt_cat_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_noncontiguous_pointwise_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_noncontiguous_to_noncontig_transposed_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_noncontiguous_to_noncontig_transposed_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_noncontiguous_to_noncontig_with_holes_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_op_dim_reduce_batch_only_different_output_shape_mean_keepdim_False_requires_grad_False_components_require_grad_False_cuda_float32, 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test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_backwards_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_compile_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_cuda_bfloat16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_flop_counter_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_with_constant_sequence_length_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_with_constant_sequence_length_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sdpa_with_packed_in_proj_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_serialization_noncontig_transposed_weights_only_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_serialization_noncontig_with_holes_weights_only_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_reduce_ragged_idx_1_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_reduce_ragged_idx_1_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_reduce_ragged_idx_1_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_requires_grad_False_components_require_grad_False_log_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_requires_grad_False_components_require_grad_True_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_requires_grad_True_components_require_grad_True_log_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_requires_grad_True_components_require_grad_True_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_transpose_non_ragged_dim_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_transpose_non_ragged_dim_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_transpose_non_ragged_dim_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_with_lengths_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_with_lengths_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_dim_with_lengths_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_False_log_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_False_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_False_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_True_log_softmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_split_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_tensor_attributes_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_threshold_backward_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_copy_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_dtype_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_3_requires_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_3_requires_grad_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_3_requires_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_3_requires_grad_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_compile_nt_dim_4_requires_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_2_requires_grad_False_cuda_bool, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_2_requires_grad_False_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_2_requires_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_2_requires_grad_True_cuda_bool, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_False_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_False_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_False_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_3_requires_grad_True_cuda_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_4_requires_grad_False_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_4_requires_grad_True_cuda_bool, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_4_requires_grad_True_cuda_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_to_padded_tensor_nt_dim_4_requires_grad_True_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unary_pointwise_transposed_inputs_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_backward_cuda_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_lengths_ragged_idx_0_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_lengths_ragged_idx_2_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_lengths_ragged_idx_3_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_lengths_ragged_idx_equals_2_bad_dim_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unbind_transpose_ragged_idx_2_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_unsafe_view_cuda, test/test_nestedtensor.py::TestNestedTensorSubclassCUDA::test_views_inherit_ragged_dim_cuda, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward___rsub___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_acosh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_add_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_angle_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_asin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_asinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_atan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_atanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_bfloat16_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_cdouble_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_ceil_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_chunk_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_clamp_max_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_conj_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_copysign_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_cos_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_div_floor_rounding_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_erf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_expm1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_float_power_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_floor_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_fmod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_hypot_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_lgamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_linalg_vector_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_log1p_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_amin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_select_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_masked_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_max_reduction_with_dim_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_maximum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_mul_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nan_to_num_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nanmean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nansum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_neg_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_celu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_hardsigmoid_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_linear_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_mish_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_prelu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_rrelu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_selu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_softshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_nn_functional_softsign_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_polar_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_positive_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_pow_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_reciprocal_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_round_decimals_0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_round_decimals_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_rsqrt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_select_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_sin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_sinc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_sinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_special_entr_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_special_i0e_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_special_i1e_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_special_ndtr_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_squeeze_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_true_divide_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_trunc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_unsqueeze_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_var_unbiased_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_where_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_backward_xlogy_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward___radd___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward___rdiv___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward___rmod___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_angle_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_asin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_asinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_atan2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_atan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_cdouble_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_cfloat_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_chunk_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_clamp_max_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_conj_physical_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_deg2rad_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_digamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_div_floor_rounding_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_double_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_erf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_erfc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_exp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_expm1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_fmin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_ldexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_linalg_vector_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_log10_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_log1p_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_log_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_logaddexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_logit_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_masked_logsumexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_masked_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_masked_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_masked_std_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_max_binary_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_min_binary_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nan_to_num_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nanmean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_narrow_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_neg_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_celu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_hardsigmoid_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_hardtanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_relu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_softshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_nn_functional_softsign_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_polygamma_polygamma_n_0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_polygamma_polygamma_n_4_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_pow_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_rad2deg_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_real_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_reciprocal_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_remainder_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_round_decimals_0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_round_decimals_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_rsqrt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_rsub_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_select_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sgn_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sigmoid_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_special_entr_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_special_i1e_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_split_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sqrt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_square_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_squeeze_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_std_unbiased_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sub_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_tan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_tanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_true_divide_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_trunc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_unsqueeze_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_var_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_var_unbiased_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_backward_where_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward___rmod___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward___rmul___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward___rpow___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward___rsub___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_abs_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_acos_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_acosh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_add_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_all_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_amin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_angle_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_any_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_argmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_argmin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_atan2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_atan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_atanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_bfloat16_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_bmm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_bool_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_cdouble_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_ceil_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_cfloat_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_chalf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_chunk_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_clamp_min_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_clone_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_complex_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_conj_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_conj_physical_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_count_nonzero_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_div_no_rounding_mode_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_eq_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_exp2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_exp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_float_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_fmin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_fmod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_frexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_ge_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_gt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_hash_tensor_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_heaviside_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_hypot_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_i0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_igamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_int_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_isclose_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_isfinite_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_isnan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_ldexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_le_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_lgamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_log10_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_log1p_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_log2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_log_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_logical_and_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_logit_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_long_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_lt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_masked_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_masked_logsumexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_masked_select_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_masked_std_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_masked_var_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_max_binary_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_maximum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_min_reduction_with_dim_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_minimum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_mul_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nan_to_num_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nanmean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_ne_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_neg_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_elu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_embedding_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_hardshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_prelu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_relu6_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_relu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_rms_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_selu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_softplus_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_softshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_softsign_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_nn_functional_threshold_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_polar_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_polygamma_polygamma_n_2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_polygamma_polygamma_n_4_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_positive_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_pow_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_real_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_remainder_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_round_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_round_decimals_neg_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_select_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sign_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sinc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_bessel_y0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_chebyshev_polynomial_u_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_chebyshev_polynomial_v_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_erfcx_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_i1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_i1e_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_laguerre_polynomial_l_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_modified_bessel_i1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_modified_bessel_k0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_modified_bessel_k1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_ndtr_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_ndtri_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_scaled_modified_bessel_k0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_shifted_chebyshev_polynomial_t_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_special_spherical_bessel_j0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_split_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sqrt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_square_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_squeeze_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_std_unbiased_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sub_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_tanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_to_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_var_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_compile_forward_where_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward___radd___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward___rdiv___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward___rmod___cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_acosh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_all_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_amax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_amin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_angle_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_any_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_asin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_asinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_atan2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_atanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_bool_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_byte_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_cfloat_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_char_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_clamp_min_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_conj_physical_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_cosh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_deg2rad_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_digamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_double_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_eq_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_erf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_erfc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_erfinv_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_exp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_expm1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_fill_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_floor_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_fmax_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_fmin_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_fmod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_frexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_ge_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_gt_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_half_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_heaviside_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_i0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_int_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_isfinite_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_isinf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_isnan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_isneginf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_isposinf_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_jiterator_binary_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_jiterator_unary_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_ldexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_le_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_lgamma_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_log10_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_log1p_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_log2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_log_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_logaddexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_logical_or_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_masked_logsumexp_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_masked_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_masked_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_masked_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_masked_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_max_reduction_with_dim_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_mean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_mul_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nan_to_num_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nanmean_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_narrow_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_ne_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_celu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_elu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_hardshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_linear_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_relu6_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_rms_norm_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_rrelu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_selu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_silu_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_softplus_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_softshrink_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_nn_functional_threshold_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_polar_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_polygamma_polygamma_n_0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_polygamma_polygamma_n_2_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_positive_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_prod_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_real_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_reciprocal_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_remainder_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_round_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_round_decimals_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_round_decimals_neg_3_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_short_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_signbit_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_sinc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_sinh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_airy_ai_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_bessel_j1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_bessel_y0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_chebyshev_polynomial_t_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_chebyshev_polynomial_u_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_chebyshev_polynomial_w_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_hermite_polynomial_h_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_hermite_polynomial_he_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_i1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_modified_bessel_k0_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_modified_bessel_k1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_ndtr_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_ndtri_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_scaled_modified_bessel_k1_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_shifted_chebyshev_polynomial_t_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_shifted_chebyshev_polynomial_w_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_xlog1py_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_special_zeta_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_split_with_sizes_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_sum_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_tan_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_tanh_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_true_divide_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_trunc_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_var_unbiased_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_forward_where_cuda_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCUDA::test_nested_tensor_non_contiguous_mutation_cuda 2025-09-07T09:06:37.0443475Z 2025-09-07T09:06:37.0448482Z Running test_ops 4/4 ... [2025-09-07 09:06:37.019542] 2025-09-07T09:06:37.0448646Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:06:37.0449005Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ops.py', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:06:37.019763] 2025-09-07T09:15:51.1095116Z 2025-09-07T09:15:51.1096228Z test_ops 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_4.4_e446afbe9d7e77b7_.log 2025-09-07T09:15:51.2047235Z Running 8260 items in this shard: test/test_ops.py::TestSelfKwarg::test_self_kwargs, test/test_ops.py::TestCommonCUDA::test_compare_cpu_H_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu___rmatmul___cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu___rsub___cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__native_batch_norm_legit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs__conversions_cfloat_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs__conversions_complex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs__conversions_float_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs__conversions_long_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs__conversions_polar_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_alias_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_as_strided_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_as_strided_partial_views_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_atleast_2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_diagonal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_diagonal_scatter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_dsplit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_exponential_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_flip_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_fmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_lerp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_linalg_diagonal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_linspace_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_log_normal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_logaddexp2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_new_zeros_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_alpha_dropout_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_huber_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_relu6_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_nn_functional_triplet_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_select_scatter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_t_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_t_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_transpose_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_true_divide_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_var_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_view_as_complex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_view_as_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__refs_vstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__segment_reduce_lengths_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu__unsafe_masked_index_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_addmm_decomposed_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_as_strided_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_atleast_1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_atleast_3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_bitwise_right_shift_cuda_int64, test/test_ops.py::TestCommonCUDA::test_compare_cpu_block_diag_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_bool_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_cauchy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_cholesky_inverse_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_complex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_copysign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_cross_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_dist_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_double_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_einsum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_empty_strided_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_fft_ifftshift_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_flip_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_grid_sampler_2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_grid_sampler_3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_index_fill_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_index_reduce_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_index_reduce_prod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_isin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_ldexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_inv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_inv_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_ldl_factor_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_lu_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linalg_vector_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_linspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_log_softmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_logcumsumexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_masked_normalize_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_masked_select_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_median_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_min_reduction_no_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_multinomial_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_narrow_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_native_batch_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_new_full_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_avg_pool1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_channel_shuffle_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_conv1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_conv2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_cosine_similarity_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_ctc_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_dropout3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_interpolate_trilinear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_linear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_local_response_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_max_unpool1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_max_unpool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_max_unpool3d_grad_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_pad_constant_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_soft_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_softmin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nn_functional_upsample_bilinear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nonzero_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_nonzero_static_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_norm_inf_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_norm_nuc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_normal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_normal_number_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_ones_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_pca_lowrank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_polar_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_repeat_interleave_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_reshape_as_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_reshape_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_resize_as__cuda_float32, 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test/test_ops.py::TestCommonCUDA::test_compare_cpu_take_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_triangular_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_unflatten_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_unfold_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_unsqueeze_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_var_mean_unbiased_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_view_as_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_view_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_xlogy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_zero__cuda_float32, test/test_ops.py::TestCommonCUDA::test_compare_cpu_zeros_cuda_float32, test/test_ops.py::TestCommonCUDA::test_complex_half_reference_testing_as_strided_partial_views_cuda_complex32, 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test/test_ops.py::TestCommonCUDA::test_dtypes_index_select_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_inner_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_isposinf_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_item_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_jiterator_2inputs_2outputs_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_jiterator_4inputs_with_extra_args_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_le_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_cholesky_ex_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_diagonal_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_eigh_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_ldl_factor_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_ldl_solve_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_lstsq_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_norm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_linalg_pinv_cuda, 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test/test_ops.py::TestCommonCUDA::test_dtypes_mH_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_masked_amax_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_masked_argmax_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_masked_argmin_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_masked_normalize_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_masked_select_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_matmul_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_max_pool2d_with_indices_backward_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_maximum_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_median_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_meshgrid_list_of_tensors_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_mode_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_mul_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_multinomial_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_mv_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_mvlgamma_mvlgamma_p_3_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nan_to_num_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nanquantile_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_narrow_copy_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_ne_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_neg_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_new_empty_strided_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_adaptive_avg_pool2d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_adaptive_max_pool1d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_celu_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_conv_transpose1d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_embedding_bag_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_group_norm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_hardshrink_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_layer_norm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_max_pool3d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_max_unpool1d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_max_unpool3d_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_multi_margin_loss_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_normalize_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_one_hot_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_pixel_shuffle_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_pixel_unshuffle_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_relu_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_rms_norm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_scaled_dot_product_attention_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_selu_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_soft_margin_loss_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_softmin_with_dtype_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nn_functional_upsample_nearest_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nonzero_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_nonzero_static_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_norm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_norm_fro_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_normal_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_normal_in_place_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_outer_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_polygamma_polygamma_n_0_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_polygamma_polygamma_n_2_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_polygamma_polygamma_n_3_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_positive_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_pow_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_prod_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_randint_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_randn_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_renorm_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_reshape_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_resize_as__cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_round_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_round_decimals_0_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_rsqrt_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_scatter_reduce_mean_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_select_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_sign_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_signal_windows_blackman_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_signal_windows_cosine_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_signal_windows_exponential_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_signal_windows_gaussian_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_signbit_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_slice_scatter_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_softmax_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_sparse_mm_reduce_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_bessel_y0_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_chebyshev_polynomial_t_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_chebyshev_polynomial_v_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_hermite_polynomial_he_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_ndtri_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_special_spherical_bessel_j0_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_split_with_sizes_copy_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_squeeze_copy_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_squeeze_multiple_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_stack_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_std_mean_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_std_unbiased_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_sum_to_size_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_svd_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_t_copy_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_take_along_dim_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_tan_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_tile_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_torch_ops_aten__safe_softmax_default_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_trace_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_trapezoid_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_triu_indices_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_unbind_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_unsafe_chunk_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_var_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_var_mean_unbiased_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_var_unbiased_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_view_copy_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_view_cuda, test/test_ops.py::TestCommonCUDA::test_dtypes_zeros_cuda, test/test_ops.py::TestCommonCUDA::test_errors_T_cuda, test/test_ops.py::TestCommonCUDA::test_errors___rand___cuda, test/test_ops.py::TestCommonCUDA::test_errors___rmod___cuda, test/test_ops.py::TestCommonCUDA::test_errors__chunk_cat_cuda, test/test_ops.py::TestCommonCUDA::test_errors_as_strided_scatter_cuda, test/test_ops.py::TestCommonCUDA::test_errors_bitwise_and_cuda, test/test_ops.py::TestCommonCUDA::test_errors_cat_cuda, test/test_ops.py::TestCommonCUDA::test_errors_cauchy_cuda, test/test_ops.py::TestCommonCUDA::test_errors_clamp_max_cuda, test/test_ops.py::TestCommonCUDA::test_errors_diff_cuda, test/test_ops.py::TestCommonCUDA::test_errors_div_floor_rounding_cuda, test/test_ops.py::TestCommonCUDA::test_errors_div_trunc_rounding_cuda, test/test_ops.py::TestCommonCUDA::test_errors_dsplit_cuda, test/test_ops.py::TestCommonCUDA::test_errors_fft_fftn_cuda, test/test_ops.py::TestCommonCUDA::test_errors_fft_hfft2_cuda, test/test_ops.py::TestCommonCUDA::test_errors_fft_ifft_cuda, test/test_ops.py::TestCommonCUDA::test_errors_fmax_cuda, test/test_ops.py::TestCommonCUDA::test_errors_gt_cuda, test/test_ops.py::TestCommonCUDA::test_errors_heaviside_cuda, test/test_ops.py::TestCommonCUDA::test_errors_jiterator_binary_return_by_ref_cuda, test/test_ops.py::TestCommonCUDA::test_errors_linalg_diagonal_cuda, test/test_ops.py::TestCommonCUDA::test_errors_linalg_lstsq_grad_oriented_cuda, test/test_ops.py::TestCommonCUDA::test_errors_log_normal_cuda, test/test_ops.py::TestCommonCUDA::test_errors_logaddexp_cuda, test/test_ops.py::TestCommonCUDA::test_errors_lt_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_adaptive_max_pool2d_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_avg_pool3d_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_conv1d_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_conv3d_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_max_pool3d_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_soft_margin_loss_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_softshrink_cuda, test/test_ops.py::TestCommonCUDA::test_errors_nn_functional_triplet_margin_loss_cuda, test/test_ops.py::TestCommonCUDA::test_errors_normal_in_place_cuda, test/test_ops.py::TestCommonCUDA::test_errors_polar_cuda, test/test_ops.py::TestCommonCUDA::test_errors_signal_windows_blackman_cuda, test/test_ops.py::TestCommonCUDA::test_errors_signal_windows_exponential_cuda, test/test_ops.py::TestCommonCUDA::test_errors_signal_windows_general_hamming_cuda, test/test_ops.py::TestCommonCUDA::test_errors_sparse_randn_like_layout2_cuda, test/test_ops.py::TestCommonCUDA::test_errors_sparse_sum_layout2_cuda, test/test_ops.py::TestCommonCUDA::test_errors_sparse_sum_layout3_cuda, test/test_ops.py::TestCommonCUDA::test_errors_sparse_sum_layout4_cuda, test/test_ops.py::TestCommonCUDA::test_errors_sparse_zeros_like_layout3_cuda, test/test_ops.py::TestCommonCUDA::test_errors_special_chebyshev_polynomial_v_cuda, test/test_ops.py::TestCommonCUDA::test_errors_special_chebyshev_polynomial_w_cuda, test/test_ops.py::TestCommonCUDA::test_errors_special_laguerre_polynomial_l_cuda, test/test_ops.py::TestCommonCUDA::test_errors_special_shifted_chebyshev_polynomial_u_cuda, test/test_ops.py::TestCommonCUDA::test_errors_special_shifted_chebyshev_polynomial_v_cuda, test/test_ops.py::TestCommonCUDA::test_errors_triu_cuda, test/test_ops.py::TestCommonCUDA::test_errors_vstack_cuda, test/test_ops.py::TestCommonCUDA::test_errors_where_cuda, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch__chunk_cat_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_acosh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_addmm_decomposed_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_amin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_asin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_complex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_copysign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_cosh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_cummax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_cummin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_cumsum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_deg2rad_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_div_no_rounding_mode_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_div_trunc_rounding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_dstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_erfinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_exp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_expand_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fft_fft2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fft_hfftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fft_ifftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fft_ihfft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fft_irfft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_float_power_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_floor_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_fmin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_frac_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_full_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_gt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_hash_tensor_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_hstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_igamma_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_index_add_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_index_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_index_reduce_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_index_reduce_prod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_index_select_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_isposinf_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_eig_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_lu_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_matrix_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_pinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_slogdet_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_linalg_svd_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_log1p_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_log2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_log_softmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_logcumsumexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_logical_not_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_logical_xor_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_max_reduction_no_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_maximum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_minimum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_mm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_nanmean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_native_batch_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_nn_functional_avg_pool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_nn_functional_avg_pool3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_nn_functional_softplus_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_norm_inf_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_ormqr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_4_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_pow_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_reciprocal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_rsqrt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_sign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_signbit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_sort_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_sparse_sampled_addmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_entr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_i1e_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_log_ndtr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_i0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_ndtri_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_stack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_svd_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_topk_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_true_divide_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_trunc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_var_cuda_float32, test/test_ops.py::TestCommonCUDA::test_meta_consistency_out_dtype_mismatch_zeros_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_H_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices___rand___cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices___rmod___cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices___rsub___cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices__segment_reduce_lengths_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices__upsample_bilinear2d_aa_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_acosh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_add_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_addcmul_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_addmm_decomposed_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_addmv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_all_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_angle_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_argmin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_argsort_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_argwhere_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_as_strided_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_as_strided_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_as_strided_scatter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_as_strided_scatter_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_asin_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_asinh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_asinh_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_atan_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_baddbmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_bincount_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_bitwise_left_shift_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_bool_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_broadcast_shapes_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cat_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cat_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cdouble_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cholesky_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_chunk_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_clamp_max_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_column_stack_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_combinations_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_contiguous_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_contiguous_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_copysign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_copysign_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cos_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cov_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cross_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cumsum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cumulative_trapezoid_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_cumulative_trapezoid_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_diag_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_diagflat_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_diagonal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_diff_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_digamma_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_dist_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_div_floor_rounding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_div_floor_rounding_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_dstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_empty_permuted_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_exponential_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_eye_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_fft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_ifft_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_irfft2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_irfft2_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_irfft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_irfftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fft_rfftn_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fill_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_flatten_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_flip_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_flip_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fliplr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fliplr_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_float_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_floor_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_floor_divide_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_fmin_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_gather_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_geometric_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_grid_sampler_3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_hash_tensor_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_hsplit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_hypot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_igammac_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_put_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_reduce_amin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_reduce_prod_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_index_select_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_int_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_isclose_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_isfinite_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_item_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_jiterator_binary_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_kron_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_kron_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_kthvalue_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_lcm_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_ldexp_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_le_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_lerp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_lgamma_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_cond_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_cross_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_householder_product_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_inv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_inv_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_lu_factor_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_lu_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_solve_triangular_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_svd_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_svdvals_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linalg_tensorinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_linspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log10_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log10_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log1p_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_log_normal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logaddexp2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logaddexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logical_and_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logical_and_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logical_not_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logical_or_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_logsumexp_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_long_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_mH_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_mT_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_argmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_fill_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_logaddexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_logsumexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_prod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_std_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_masked_sum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_matrix_exp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_max_binary_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_max_pool2d_with_indices_backward_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_max_reduction_with_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_median_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_meshgrid_list_of_tensors_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_min_reduction_with_dim_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_msort_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_mul_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_mul_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nan_to_num_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_narrow_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_ne_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_neg_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_new_empty_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_new_empty_strided_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_new_zeros_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_adaptive_avg_pool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_adaptive_max_pool1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_binary_cross_entropy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_celu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_channel_shuffle_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_cosine_embedding_loss_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_dropout2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_embedding_bag_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_gaussian_nll_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_group_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_hardshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_hardsigmoid_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_hardswish_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_hardtanh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_hardtanh_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_huber_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_interpolate_bilinear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_max_unpool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_max_unpool3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_multi_head_attention_forward_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_multi_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_normalize_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_one_hot_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pad_circular_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pad_circular_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pad_constant_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pad_reflect_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pad_replicate_negative_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pixel_shuffle_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_pixel_unshuffle_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_relu6_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_relu_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_softmin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_triplet_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_triplet_margin_loss_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_nn_functional_triplet_margin_with_distance_loss_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_norm_nuc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_normal_number_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_ones_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_pca_lowrank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_permute_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_permute_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_polygamma_polygamma_n_0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_prod_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_rad2deg_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_randint_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_randint_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_ravel_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_reciprocal_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_renorm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_repeat_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_resize__cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_resolve_conj_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_scatter_add_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_scatter_reduce_amin_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_scatter_reduce_sum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_searchsorted_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_select_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_select_scatter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_signal_windows_blackman_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_signal_windows_cosine_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_signal_windows_general_hamming_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_sinh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_sinh_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_bessel_j1_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_chebyshev_polynomial_u_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_chebyshev_polynomial_w_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_hermite_polynomial_h_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_i0e_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_i0e_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_log_ndtr_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_modified_bessel_i1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_modified_bessel_k0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_modified_bessel_k0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_polygamma_special_polygamma_n_0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_scaled_modified_bessel_k1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_scaled_modified_bessel_k1_cuda_int64, 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test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifft2_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifft_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifftn_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifftshift_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ifftshift_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_ihfft_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_irfftn_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_irfftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fft_rfftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_flip_cuda_complex64, 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test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_gather_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_gcd_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_geometric_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_geometric_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_geqrf_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_geqrf_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_grid_sampler_3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_gt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_hash_tensor_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_igamma_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_fill_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_fill_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_put_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_put_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_reduce_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_index_reduce_mean_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_isfinite_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_isfinite_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_isnan_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_isposinf_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_isreal_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_item_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_jiterator_4inputs_with_extra_args_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_kthvalue_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_le_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_le_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_lerp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_cond_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_cross_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_eig_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_eigvals_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_ldl_factor_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_ldl_factor_ex_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_lu_factor_ex_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_lu_solve_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_multi_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_pinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_pinv_hermitian_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_pinv_singular_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_slogdet_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_slogdet_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_solve_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_solve_triangular_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_svd_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_tensorinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_vander_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linalg_vecdot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_linspace_tensor_overload_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log10_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log1p_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log_normal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log_softmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_log_softmax_with_dtype_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logaddexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logical_and_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logical_not_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logspace_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logspace_tensor_overload_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_logsumexp_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_lu_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_lu_unpack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mH_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mT_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_amin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_cumprod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_logaddexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_prod_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_softmin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_masked_var_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_max_reduction_no_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_max_reduction_with_dim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_meshgrid_list_of_tensors_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_meshgrid_variadic_tensors_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_min_binary_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mul_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mvlgamma_mvlgamma_p_3_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nansum_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_ne_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_neg_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_new_empty_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nextafter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_binary_cross_entropy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_channel_shuffle_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_conv3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_cosine_embedding_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_elu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_glu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_hardtanh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_interpolate_bilinear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_l1_loss_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_l1_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_linear_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_mse_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_multi_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_normalize_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_normalize_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pad_reflect_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pad_reflect_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pad_replicate_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pairwise_distance_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pairwise_distance_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pdist_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pixel_shuffle_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_pixel_unshuffle_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_relu6_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_rms_norm_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_selu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_threshold_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_threshold_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_triplet_margin_loss_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nn_functional_upsample_nearest_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_nonzero_static_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_norm_nuc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_normal_in_place_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_normal_number_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_ones_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_pca_lowrank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_pinverse_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_polygamma_polygamma_n_0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_polygamma_polygamma_n_1_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_polygamma_polygamma_n_2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_polygamma_polygamma_n_2_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_polygamma_polygamma_n_4_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_pow_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_prod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_prod_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_qr_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_qr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_rand_like_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_randint_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_ravel_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_reciprocal_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_reciprocal_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_renorm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_resize__cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_resize_as__cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_resolve_neg_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_rot90_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_rot90_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_rsqrt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_rsub_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_scatter_reduce_amin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_scatter_reduce_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_scatter_reduce_sum_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_select_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sigmoid_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sign_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_signal_windows_exponential_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_signal_windows_hamming_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sin_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sinh_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_softmax_with_dtype_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_softmax_with_dtype_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sparse_sampled_addmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_bessel_j0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_bessel_j1_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_bessel_y0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_chebyshev_polynomial_w_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_hermite_polynomial_h_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_hermite_polynomial_h_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_i0e_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_i1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_i1_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_legendre_polynomial_p_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_modified_bessel_i0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_modified_bessel_i0_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_modified_bessel_k1_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_shifted_chebyshev_polynomial_w_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_xlog1py_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_xlog1py_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_special_zeta_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_split_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_split_list_args_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sqrt_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sqrt_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_squeeze_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_squeeze_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_squeeze_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_squeeze_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_std_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_sum_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_svd_lowrank_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_t_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_take_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_tan_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_tensor_split_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_to_sparse_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_transpose_copy_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_transpose_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_trapz_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_trapz_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_tril_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_tril_indices_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_triu_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_true_divide_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unbind_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unbind_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unfold_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unique_consecutive_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unique_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsafe_chunk_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsafe_chunk_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsafe_split_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsafe_split_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsqueeze_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsqueeze_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_unsqueeze_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_view_copy_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_view_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_view_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_vsplit_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_where_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_zero__cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_zeros_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_zeros_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_zeros_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_zeros_like_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_addbmm_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_argwhere_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_argwhere_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_broadcast_tensors_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_broadcast_to_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_broadcast_to_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_broadcast_to_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_cat_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_clone_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_clone_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_item_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_linalg_cross_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_linalg_tensorsolve_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_linalg_tensorsolve_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_meshgrid_variadic_tensors_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_nn_functional_conv_transpose2d_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_nn_functional_l1_loss_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_nn_functional_one_hot_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_nn_functional_pairwise_distance_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_nn_functional_pairwise_distance_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_ravel_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_ravel_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_repeat_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_roll_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_signal_windows_bartlett_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_signal_windows_gaussian_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_signal_windows_hann_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_signal_windows_kaiser_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_signal_windows_nuttall_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_squeeze_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_squeeze_multiple_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_tile_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_tril_indices_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_unbind_copy_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_unbind_cuda_int64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_view_copy_cuda_float64, test/test_ops.py::TestCommonCUDA::test_numpy_ref_where_cuda_complex128, test/test_ops.py::TestCommonCUDA::test_numpy_ref_where_cuda_float64, 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test/test_ops.py::TestCommonCUDA::test_out__refs_as_strided_partial_views_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_asin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_atleast_1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_bitwise_not_cuda_int64, test/test_ops.py::TestCommonCUDA::test_out__refs_bitwise_or_cuda_int64, test/test_ops.py::TestCommonCUDA::test_out__refs_clamp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_clamp_min_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_clone_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_conj_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_conj_physical_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_cumsum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_diagonal_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_div_floor_rounding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_dsplit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_erfc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_exp2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_exp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fft_fft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fft_fftshift_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fft_hfft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fft_ihfft_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fft_rfft2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fliplr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_fmod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_ge_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_igammac_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_index_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_isclose_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_isfinite_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_le_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_lerp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_linalg_cross_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_linalg_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_log10_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_log2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logaddexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logical_and_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logical_or_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logical_xor_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logspace_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_logsumexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_masked_fill_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_maximum_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_movedim_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_ne_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_new_empty_strided_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_new_zeros_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_nn_functional_relu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_nn_functional_threshold_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_normal__in_place_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_positive_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_reciprocal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_renorm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_round_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_rsqrt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_sgn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_sign_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_i1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_log_ndtr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_multigammaln_mvlgamma_p_1_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_ndtr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_special_softmax_with_dtype_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_sqrt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_squeeze_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_std_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_tanh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_tril_indices_cuda_int64, test/test_ops.py::TestCommonCUDA::test_out__refs_true_divide_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_vdot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_view_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__refs_view_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__segment_reduce_lengths_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out__upsample_bilinear2d_aa_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_acos_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_add_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_addbmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_addcdiv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_addmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_all_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_aminmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_argmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_argsort_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_as_strided_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_asin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_bitwise_left_shift_cuda_int64, test/test_ops.py::TestCommonCUDA::test_out_broadcast_shapes_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_bucketize_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_cholesky_solve_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_conj_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_cov_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_cummax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_cumprod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_cumulative_trapezoid_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_diagonal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_diagonal_scatter_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_digamma_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_div_floor_rounding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_equal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_erfinv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_exp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_expand_as_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_exponential_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_fft_fftshift_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_floor_divide_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_frac_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_frexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_full_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_full_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_ge_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_gt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_hsplit_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_i0_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_igamma_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_imag_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_index_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_index_reduce_amin_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_index_select_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_inner_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_integral_dtype__refs_sum_cuda_int16, test/test_ops.py::TestCommonCUDA::test_out_isinf_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_ldexp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_lerp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_cond_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_eig_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_eigh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_inv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_inv_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_ldl_factor_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_lstsq_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_linalg_lstsq_grad_oriented_cuda_float32, 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test/test_ops.py::TestCommonCUDA::test_out_matrix_exp_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_mm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nanmean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nanquantile_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_new_empty_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_new_full_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_new_ones_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_new_zeros_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_adaptive_avg_pool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_adaptive_max_pool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_adaptive_max_pool3d_cuda_float32, 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test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_nn_functional_gelu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_nn_functional_normalize_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_normal_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_normal_number_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_ormqr_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_permute_copy_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_polygamma_polygamma_n_2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_polygamma_polygamma_n_3_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_reciprocal_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_renorm_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_any_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_as_strided_copy_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_atleast_2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_baddbmm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_bmm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_cauchy_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_chalf_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_clamp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_conj_physical_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_count_nonzero_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_cross_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fft_ihfftn_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fft_irfft_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fft_irfftn_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fft_rfft2_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fliplr_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_float_power_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_fmin_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_full_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_grid_sampler_2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_gt_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_half_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_index_add_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_index_put_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_index_reduce_amax_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_index_reduce_prod_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_isinf_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_isnan_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_kron_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_ldexp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_le_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_det_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_eig_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_inv_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_ldl_factor_ex_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_lu_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_lu_factor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_lu_solve_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_matrix_rank_hermitian_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_pinv_hermitian_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_slogdet_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_cow_input_linalg_solve_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_ifftshift_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_ihfft_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_irfft2_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_irfftn_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_rfft_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fft_rfftn_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fill_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_floor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_fmod_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_ge_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_geqrf_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_index_add_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_index_fill_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_index_reduce_mean_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_index_select_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_int_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_isposinf_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_jiterator_2inputs_2outputs_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_jiterator_unary_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_le_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_lerp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_cholesky_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_cross_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_ldl_factor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_lstsq_grad_oriented_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_lu_factor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_lu_solve_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_matrix_power_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_matrix_rank_hermitian_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_multi_dot_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_pinv_hermitian_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linalg_svdvals_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_linspace_tensor_overload_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_log1p_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_log2_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_logical_not_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_argmin_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_fill_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_log_softmax_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_logsumexp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_normalize_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_masked_softmin_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_matmul_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_matrix_exp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_max_pool2d_with_indices_backward_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_max_reduction_no_dim_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_maximum_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_mean_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_min_binary_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_mv_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nanmedian_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_new_empty_strided_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_adaptive_max_pool3d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_avg_pool1d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_avg_pool2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_conv1d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_dropout3d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_embedding_bag_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_embedding_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_gelu_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_group_norm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_hinge_embedding_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_interpolate_area_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_interpolate_nearest_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_l1_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_max_pool2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_max_unpool2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_mish_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_multi_head_attention_forward_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_multi_margin_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_pairwise_distance_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_relu_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_softmin_with_dtype_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_nn_functional_upsample_nearest_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_outer_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_pca_lowrank_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_polygamma_polygamma_n_2_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_pow_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_rand_like_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_renorm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_reshape_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_resize_as__cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_round_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_round_decimals_neg_3_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_scalar_tensor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_scatter_reduce_amin_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_scatter_reduce_prod_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_scatter_reduce_sum_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_signal_windows_bartlett_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_signal_windows_cosine_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_signal_windows_hamming_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_sparse_mm_reduce_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_hermite_polynomial_h_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_i1e_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_log_ndtr_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_ndtri_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_scaled_modified_bessel_k1_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_spherical_bessel_j0_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_special_xlog1py_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_std_mean_unbiased_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_svd_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_tanh_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_transpose_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_trapz_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_unsafe_split_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_vstack_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_xlogy_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_view_replay_zeros_cuda_float32, test/test_ops.py::TestMathBitsCUDA::test_conj_view_T_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view___getitem___cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view___rdiv___cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view___rpow___cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs__conversions_byte_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs__conversions_cfloat_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs__conversions_int_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_addcmul_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_any_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_as_strided_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_as_strided_partial_views_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_asin_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_cat_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_cumprod_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_div_no_rounding_mode_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_empty_like_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_equal_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_exp2_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_exp_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_expand_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_expm1_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fft_hfftn_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fft_ifft2_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fft_ifftshift_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fft_irfft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fft_irfftn_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fill_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_flatten_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_fliplr_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_index_select_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_isclose_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_isfinite_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_isnan_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_linspace_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_linspace_tensor_overload_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_logical_not_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_logsumexp_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_mean_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_meshgrid_list_of_tensors_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_narrow_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_ne_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_new_full_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_new_ones_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_nn_functional_pixel_unshuffle_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_norm_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_pow_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_prod_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_renorm_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_rsub_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_softmax_with_dtype_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_special_softmax_with_dtype_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_sqrt_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_square_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_stack_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_std_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_std_mean_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_sum_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_sum_to_size_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_tensor_split_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_triu_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_unflatten_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_view_as_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_vstack_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__refs_where_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__unsafe_masked_index_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view__unsafe_masked_index_put_accumulate_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_acosh_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_addcmul_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_addmm_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_as_strided_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_as_strided_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_as_strided_partial_views_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_as_strided_scatter_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_baddbmm_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_block_diag_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_broadcast_to_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cartesian_prod_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cdouble_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cholesky_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cholesky_inverse_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_combinations_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_contiguous_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cos_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_cumprod_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_diag_embed_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_diagonal_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_diagonal_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_dist_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_dot_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_empty_permuted_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_exp_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_expand_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fft_fft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fft_hfft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fft_ifft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fft_ifftshift_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fft_irfft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_fill_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_flipud_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_float_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_geqrf_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_gradient_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_hstack_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_index_add_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_index_copy_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_isclose_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_isfinite_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_item_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_jiterator_binary_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_det_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_householder_product_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_lu_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_lu_factor_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_lu_factor_ex_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_matrix_rank_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_pinv_singular_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_slogdet_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linalg_vector_norm_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_linspace_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_log10_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_lu_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_masked_mean_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_masked_prod_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_masked_sum_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_masked_var_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_matrix_exp_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_movedim_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nansum_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_narrow_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_new_full_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_new_zeros_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_conv1d_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_conv3d_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_conv_transpose2d_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_feature_alpha_dropout_without_train_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_l1_loss_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_normalize_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_pad_replicate_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_silu_complex_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nn_functional_softsign_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_nonzero_static_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_norm_nuc_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_normal_in_place_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_ones_like_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_outer_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_pca_lowrank_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_permute_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_qr_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_real_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_resize__cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_resolve_neg_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_rot90_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_rsqrt_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_scalar_tensor_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_scatter_add_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_select_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_sgn_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_short_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_softmax_with_dtype_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_split_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_square_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_std_unbiased_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_stft_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_svd_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_t_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_tanh_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_tensordot_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_to_sparse_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_unbind_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_uniform_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_unsafe_split_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_var_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_vsplit_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_vstack_cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_conj_view_zero__cuda_complex64, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view___rdiv___cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view___rmatmul___cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs__conversions_int_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_addcmul_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_allclose_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_any_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_as_strided_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_as_strided_partial_views_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_atanh_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_cumprod_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_diagonal_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_div_no_rounding_mode_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_empty_like_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_fft_fftshift_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_fft_hfft_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_fft_irfft_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_fft_irfftn_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_hsplit_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_index_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_istft_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_item_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_lerp_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_linalg_svd_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_linalg_vecdot_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_linspace_tensor_overload_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_logical_not_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_logspace_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_masked_fill_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_meshgrid_list_of_tensors_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_movedim_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_mul_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_neg_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_new_empty_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_nn_functional_l1_loss_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_nn_functional_log_softmax_with_dtype_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_nn_functional_tanhshrink_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_normal__in_place_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_ones_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_positive_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_repeat_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_rot90_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_rsqrt_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_special_log_softmax_with_dtype_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_special_softmax_with_dtype_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_square_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_squeeze_multiple_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_std_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_sum_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_t_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_transpose_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_tril_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_triu_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_vdot_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_vsplit_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__refs_where_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view__unsafe_masked_index_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_addbmm_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_addmm_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_addmv_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_allclose_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_any_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_atan_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_atleast_2d_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_atleast_3d_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_block_diag_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_bool_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_cdouble_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_cholesky_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_clone_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_combinations_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_cos_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_diagonal_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_double_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_empty_like_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_empty_permuted_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_eq_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_exp_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_expand_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_eye_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_fft2_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_fft_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_fftn_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_hfft_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_ifftn_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_irfft2_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fft_irfftn_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_fill_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_gather_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_geqrf_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_hstack_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_index_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_isfinite_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_lerp_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_cross_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_eig_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_ldl_factor_ex_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_lu_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_lu_factor_ex_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_solve_triangular_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_svdvals_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_vecdot_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linalg_vector_norm_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_linspace_tensor_overload_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_log2_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_logical_not_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_logical_xor_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_lu_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_lu_unpack_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_masked_logsumexp_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_masked_scatter_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_matmul_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_mean_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_mul_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_mv_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nansum_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_narrow_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_neg_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_new_full_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_new_ones_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_conv_transpose3d_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_l1_loss_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_pad_constant_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_pad_reflect_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_silu_complex_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_softmin_with_dtype_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_nn_functional_triplet_margin_with_distance_loss_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_outer_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_qr_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_roll_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_rsqrt_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_sqrt_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_square_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_std_mean_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_take_along_dim_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_take_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_tanh_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_tensordot_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_tile_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_transpose_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_tril_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_triu_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_uniform_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_unsafe_chunk_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_unsqueeze_copy_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_var_mean_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_view_as_real_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_conj_view_vsplit_cuda_complex128, test/test_ops.py::TestMathBitsCUDA::test_neg_view_H_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view___rdiv___cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view___rmul___cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_T_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs__conversions_char_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs__conversions_half_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs__conversions_polar_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_atan_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_cauchy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_ceil_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_clamp_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_clamp_max_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_column_stack_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_conj_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_conj_physical_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_equal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_erf_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_erfc_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fft_fft_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fft_hfft2_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fft_hfftn_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fft_ifft_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fft_ihfft_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fill_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fliplr_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_floor_divide_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fmax_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fmin_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_fmod_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_frac_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_ge_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_hsplit_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_linalg_diagonal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_linspace_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_log1p_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_log_normal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_logaddexp_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_logical_and_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_logical_not_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_logspace_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_lt_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_maximum_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nan_to_num_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_narrow_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_ne_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_new_zeros_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nextafter_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_celu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_channel_shuffle_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_hardshrink_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_leaky_relu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_margin_ranking_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_pdist_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_prelu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_relu6_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_relu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_softmax_with_dtype_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_nn_functional_threshold_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_norm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_prod_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_roll_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_rsqrt_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_sinh_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_special_erfcx_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_special_multigammaln_mvlgamma_p_1_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_special_ndtr_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_square_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_std_mean_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_sub_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_tan_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_transpose_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_unbind_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_view_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__refs_zeros_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__segment_reduce_offsets_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view__upsample_bilinear2d_aa_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_addcdiv_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_any_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_as_strided_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_asinh_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_atan2_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_bernoulli_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_block_diag_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_bmm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_bool_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_broadcast_to_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_cat_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_cdouble_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_cfloat_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_chalf_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_cholesky_inverse_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_clamp_min_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_conj_physical_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_constant_pad_nd_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_contiguous_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_diag_embed_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_diagflat_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_diff_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_div_trunc_rounding_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_dot_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_eq_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_equal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_erfc_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_erfinv_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_expand_as_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_fft_fftn_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_fft_irfftn_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_fill_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_fmin_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_fmod_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_full_like_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_gather_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_igammac_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_index_reduce_amin_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_index_select_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_int_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_isnan_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_isreal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_cross_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_eigh_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_lu_factor_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_matrix_norm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_pinv_singular_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_qr_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_linalg_vector_norm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_log10_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_logsumexp_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_lt_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_lu_unpack_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_amin_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_log_softmax_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_mean_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_median_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_norm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_sum_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_masked_var_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_msort_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_mv_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_narrow_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_new_empty_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_new_empty_strided_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_adaptive_avg_pool2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_adaptive_avg_pool3d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_adaptive_max_pool2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_celu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_conv2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_conv_transpose2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_conv_transpose3d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_dropout2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_dropout3d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_feature_alpha_dropout_with_train_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_gaussian_nll_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_group_norm_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_hardtanh_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_hinge_embedding_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_huber_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_interpolate_bicubic_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_interpolate_linear_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_margin_ranking_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_max_pool1d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_max_unpool2d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_max_unpool3d_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_max_unpool3d_grad_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_multilabel_margin_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_nll_loss_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_relu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_selu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_softsign_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_nn_functional_unfold_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_norm_fro_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_normal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_outer_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_pinverse_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_polygamma_polygamma_n_1_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_polygamma_polygamma_n_4_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_randn_like_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_ravel_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_reciprocal_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_remainder_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_repeat_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_repeat_interleave_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_rot90_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_scalar_tensor_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_scatter_reduce_amin_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_searchsorted_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_signal_windows_cosine_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_signal_windows_general_hamming_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_signal_windows_hamming_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_signbit_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_softmax_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_sort_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_sparse_mm_reduce_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_airy_ai_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_chebyshev_polynomial_w_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_entr_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_hermite_polynomial_h_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_i1_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_legendre_polynomial_p_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_log_ndtr_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_shifted_chebyshev_polynomial_t_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_special_spherical_bessel_j0_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_split_with_sizes_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_sqrt_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_square_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_std_mean_unbiased_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_svd_lowrank_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_t_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_t_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_tan_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_tanh_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_to_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_to_sparse_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_transpose_copy_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_transpose_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_triangular_solve_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_triu_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_trunc_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_unfold_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_unique_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_var_mean_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_var_unbiased_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_vdot_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_view_as_complex_cuda_float64, test/test_ops.py::TestMathBitsCUDA::test_neg_view_xlogy_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_fake___rxor___cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_add_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_allclose_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_any_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_arange_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_as_strided_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_asin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_T_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast___radd___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast___rmul___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast___rsub___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast__segment_reduce_lengths_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast__unsafe_masked_index_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_acosh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_addbmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_addmm_decomposed_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_aminmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_angle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_argmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_as_strided_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_as_strided_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_atan2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_atanh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_atleast_1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_atleast_2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_baddbmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_bernoulli_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_bitwise_and_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cdist_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cdouble_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cholesky_inverse_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_chunk_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_conj_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_corrcoef_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cos_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cummax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cumsum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_cumulative_trapezoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_deg2rad_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_diagonal_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_digamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_div_floor_rounding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_div_trunc_rounding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_empty_permuted_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_exp2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fft_ifft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fft_irfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fft_rfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fft_rfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_flip_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_flipud_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_floor_divide_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_fmod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_full_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_gather_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_gcd_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_geometric_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_grid_sampler_3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_hash_tensor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_histc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_hsplit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_igamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_imag_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_index_fill_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_index_put_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_index_reduce_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_isclose_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_isnan_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_isposinf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_istft_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_jiterator_binary_return_by_ref_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_cholesky_ex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_cond_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_ldl_factor_ex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_lstsq_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_lu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_multi_dot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_norm_subgradients_at_zero_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_pinv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_pinv_singular_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_solve_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_svd_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_svdvals_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_tensorinv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_linalg_vector_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_log1p_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_masked_logsumexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_masked_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_max_pool2d_with_indices_backward_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_mode_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_movedim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_msort_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_multinomial_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_native_dropout_backward_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_new_empty_strided_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_new_zeros_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nextafter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_adaptive_avg_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_adaptive_max_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_bilinear_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_binary_cross_entropy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_conv2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_conv_transpose1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_conv_transpose2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_cosine_similarity_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_fractional_max_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_hardshrink_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_huber_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_interpolate_linear_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_interpolate_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_mse_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_multi_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_pixel_shuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_pixel_unshuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_prelu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_normal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_ones_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_outer_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_pca_lowrank_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_permute_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_polar_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_polygamma_polygamma_n_1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_positive_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_rand_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_randint_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_randn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_resize__cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_roll_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_round_decimals_0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_round_decimals_neg_3_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_rsub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_scalar_tensor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_scatter_reduce_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_sigmoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_signal_windows_exponential_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_signbit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_sin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_sinc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_softmax_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_chebyshev_polynomial_t_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_hermite_polynomial_he_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_i1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_log_ndtr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_modified_bessel_i1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_split_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_square_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_std_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_std_mean_unbiased_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_sub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_svd_lowrank_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_t_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_take_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_tanh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_tensor_split_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_topk_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_trace_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_triangular_solve_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_tril_indices_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_true_divide_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_trunc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_unfold_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_unsafe_split_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_var_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_view_as_complex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_xlogy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_autocast_zeros_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_baddbmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_bernoulli_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_bitwise_xor_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_bool_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_byte_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_char_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_clone_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_combinations_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_conj_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_copysign_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_corrcoef_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_cos_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_cross_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp___radd___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp___rmatmul___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp___rmul___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp___rsub___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp__unsafe_masked_index_put_accumulate_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_acos_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_addcdiv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_addcmul_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_as_strided_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_atan2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_atleast_1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_atleast_3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_bernoulli_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_bfloat16_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_bmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_chunk_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_clamp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_clamp_min_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_corrcoef_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_cov_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_cross_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_cumulative_trapezoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_diagonal_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_digamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_dot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_dsplit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_erfc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_expand_as_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fft_fft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fft_ihfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fft_ihfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fft_irfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fft_rfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_flip_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_float_power_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_floor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_fmod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_frac_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_gather_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_grid_sampler_2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_i0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_index_put_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_index_reduce_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_index_reduce_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_kron_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_eigh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_lu_factor_ex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_matrix_power_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_norm_subgradients_at_zero_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_pinv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_pinv_singular_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_qr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_svd_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_vander_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_linalg_vecdot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_logaddexp2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_logit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_masked_cumprod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_masked_select_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_masked_softmin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_matrix_exp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_max_reduction_with_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_mode_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_movedim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_mv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nanmean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nansum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_native_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_neg_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_ctc_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_elu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_embedding_bag_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_embedding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_fractional_max_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_group_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_instance_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_interpolate_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_local_response_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_max_unpool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_max_unpool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_normalize_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_pairwise_distance_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_pixel_unshuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_poisson_nll_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_relu6_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_smooth_l1_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_soft_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_tanhshrink_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_nn_functional_upsample_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_norm_inf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_normal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_ormqr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_polygamma_polygamma_n_2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_pow_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_ravel_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_reshape_as_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_round_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_round_decimals_0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_round_decimals_3_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_select_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_sgn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_sigmoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_slice_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_slice_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_special_i1e_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_special_log_ndtr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_split_with_sizes_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_squeeze_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_squeeze_multiple_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_sub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_t_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_t_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_tile_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_trapz_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_triangular_solve_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_trunc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_unbind_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_unsqueeze_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_unsqueeze_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_amp_view_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp___rmatmul___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp___rmod___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp__batch_norm_with_update_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp__native_batch_norm_legit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_add_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_addbmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_addcmul_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_amin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_as_strided_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_as_strided_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_as_strided_partial_views_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_atanh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_broadcast_to_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_cat_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_chalf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_cholesky_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_cholesky_solve_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_chunk_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_clone_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_constant_pad_nd_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_corrcoef_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_cov_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_cumulative_trapezoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_diag_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_diff_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_digamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_div_no_rounding_mode_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_dstack_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_einsum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_erfinv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_expand_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fft_hfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fft_ihfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fft_ihfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fft_rfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_flatten_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fliplr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_fmin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_frexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_gather_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_i0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_index_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_index_fill_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_index_select_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_inner_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_kron_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_kthvalue_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_cond_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_cross_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_diagonal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_lstsq_grad_oriented_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_pinv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_pinv_hermitian_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_qr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_linalg_slogdet_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_fill_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_masked_var_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_max_reduction_no_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_min_binary_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nanmedian_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_narrow_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_native_batch_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_native_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_avg_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_binary_cross_entropy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_channel_shuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_cosine_similarity_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_cross_entropy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_dropout2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_elu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_gaussian_nll_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_gelu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_hinge_embedding_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_kl_div_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_leaky_relu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_pool1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_unpool1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_unpool1d_grad_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_unpool2d_grad_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_max_unpool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_pad_reflect_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_silu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_softmin_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_nn_functional_softsign_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_normal_number_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_put_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_ravel_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_rot90_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_round_decimals_neg_3_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_rsqrt_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_scatter_reduce_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_scatter_reduce_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_sort_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_special_entr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_special_i1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_special_log_ndtr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_split_list_args_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_split_with_sizes_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_square_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_squeeze_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_sub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_take_along_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_take_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_tensordot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_tile_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_to_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_triu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_unsqueeze_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_var_mean_unbiased_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_view_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_vsplit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_where_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_crossref_backward_no_amp_xlogy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_cumsum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_diag_embed_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_diagonal_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_div_floor_rounding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_einsum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_empty_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_empty_strided_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_expand_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_expm1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_eye_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_fft_fftshift_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_fft_hfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_fft_hfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_fft_ifftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_fft_rfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_float_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_float_power_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_floor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_frexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_full_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_gather_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_gcd_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_grid_sampler_2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_igamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_imag_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_fake_int_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_istft_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_fake_item_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_jiterator_2inputs_2outputs_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_kron_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_lgamma_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_cond_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_det_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_eigh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_eigvals_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_ldl_factor_ex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_lstsq_grad_oriented_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_lu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_lu_factor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_matrix_power_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_pinv_singular_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_solve_ex_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_svd_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_vander_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_linalg_vector_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_logcumsumexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_logical_and_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_logical_or_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_logical_xor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_argmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_logaddexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_median_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_select_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_softmin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_masked_var_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_matmul_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_max_reduction_no_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_meshgrid_list_of_tensors_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_mul_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nan_to_num_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_narrow_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_native_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_neg_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_new_ones_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_adaptive_avg_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_alpha_dropout_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_bilinear_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_conv1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_conv2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_dropout3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_embedding_bag_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_gelu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_group_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_interpolate_bicubic_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_kl_div_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_local_response_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_logsigmoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_max_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_max_unpool1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_pad_constant_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_pad_replicate_negative_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_relu6_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_relu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_smooth_l1_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_softsign_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_threshold_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_upsample_bilinear_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nn_functional_upsample_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_nonzero_static_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_norm_fro_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_ones_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_ormqr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_permute_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_polar_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_polygamma_polygamma_n_1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_put_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_randn_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_reciprocal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_resize__cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_resolve_conj_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_round_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_round_decimals_3_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_rsub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_scatter_reduce_amin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_scatter_reduce_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_sgn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_short_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_signal_windows_blackman_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_signal_windows_kaiser_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_sparse_mm_reduce_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_sparse_sampled_addmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_bessel_y1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_chebyshev_polynomial_w_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_i0e_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_i1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_i1e_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_legendre_polynomial_p_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_log_ndtr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_modified_bessel_i0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_special_zeta_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_std_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_sub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_svd_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_svd_lowrank_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_t_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_topk_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_torch__scaled_mm_cuda_float8_e4m3fn, test/test_ops.py::TestFakeTensorCUDA::test_fake_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_true_divide_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_unbind_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_unbind_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_unfold_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_uniform_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_unravel_index_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_fake_unsqueeze_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_fake_zeros_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops___getitem___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops___rdiv___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops___rsub___cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops___rxor___cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_add_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_addcdiv_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_alias_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_amax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_arange_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_argmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_as_strided_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_as_strided_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_atan2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_atleast_2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_atleast_3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_baddbmm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_bitwise_or_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_broadcast_tensors_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_cartesian_prod_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_cauchy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_chalf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_char_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_conj_physical_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_cos_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_deg2rad_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_diagflat_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_div_floor_rounding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_div_trunc_rounding_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_dsplit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_einsum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_exp2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_expand_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_expand_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_expm1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fft_hfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fft_ifft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fft_irfft2_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fft_irfft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fft_rfftn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_fliplr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_flipud_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_gather_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_gcd_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_geometric_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_heaviside_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_histc_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_hypot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_i0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_index_add_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_index_fill_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_index_reduce_amin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_isnan_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_isneginf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_isposinf_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_isreal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_istft_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_jiterator_binary_return_by_ref_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_jiterator_unary_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_kthvalue_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_lcm_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_ldexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_le_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linalg_diagonal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linalg_eigh_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linalg_matrix_power_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linalg_matrix_rank_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linalg_vector_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_linspace_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_log1p_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_logaddexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_logit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_logsumexp_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_lt_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_lu_solve_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_lu_unpack_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_mT_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_argmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_fill_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_log_softmax_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_mean_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_normalize_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_select_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_masked_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_matmul_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_maximum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_min_reduction_no_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nan_to_num_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_narrow_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_native_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_adaptive_avg_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_adaptive_avg_pool3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_celu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_conv2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_conv_transpose1d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_dropout3d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_fractional_max_pool2d_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_glu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_hardswish_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_instance_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_interpolate_area_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_interpolate_linear_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_interpolate_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_kl_div_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_layer_norm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_mse_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_normalize_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pad_constant_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pad_replicate_negative_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pairwise_distance_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pixel_shuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_pixel_unshuffle_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_poisson_nll_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_silu_complex_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_silu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_soft_margin_loss_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_softmin_with_dtype_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_softplus_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_softshrink_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_nn_functional_upsample_nearest_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_norm_fro_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_normal_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_outer_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_polygamma_polygamma_n_1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_polygamma_polygamma_n_4_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_rand_like_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_renorm_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_repeat_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_reshape_as_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_round_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_rsub_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_scalar_tensor_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_scatter_add_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_scatter_reduce_amin_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_select_scatter_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_sgn_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_sigmoid_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_sign_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signal_windows_blackman_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signal_windows_cosine_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signal_windows_general_hamming_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signal_windows_hann_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signal_windows_kaiser_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_signbit_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_bessel_j1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_chebyshev_polynomial_u_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_entr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_i1e_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_laguerre_polynomial_l_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_modified_bessel_k0_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_ndtr_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_ndtri_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_scaled_modified_bessel_k1_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_shifted_chebyshev_polynomial_t_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_split_with_sizes_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_stack_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_std_mean_unbiased_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_stft_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_sum_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_sum_to_size_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_tensordot_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_tile_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_to_sparse_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_trapz_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_tril_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_triu_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_unflatten_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_unsqueeze_copy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_view_as_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_pointwise_ops_xlogy_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_arange_cuda_float16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_arange_cuda_int8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_linspace_tensor_overload_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_linspace_tensor_overload_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_linspace_tensor_overload_cuda_int32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_cuda_bfloat16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_cuda_int16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_cuda_uint8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_tensor_overload_cuda_bfloat16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_tensor_overload_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_logspace_tensor_overload_cuda_int32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_ones_cuda_complex128, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_ones_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_ones_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout__refs_ones_cuda_int16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_arange_cuda_float16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_arange_cuda_int32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_arange_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_full_cuda_int16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_linspace_cuda_complex128, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_linspace_tensor_overload_cuda_bfloat16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_linspace_tensor_overload_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_linspace_tensor_overload_cuda_uint8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_cuda_int16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_cuda_int8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_tensor_overload_cuda_bfloat16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_tensor_overload_cuda_complex128, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_tensor_overload_cuda_float64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_logspace_tensor_overload_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_float32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_int16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_int32, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_int64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_int8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_ones_cuda_uint8, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_zeros_cuda_bfloat16, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_zeros_cuda_complex64, test/test_ops.py::TestFakeTensorCUDA::test_strided_layout_zeros_cuda_int8, test/test_ops.py::TestTagsCUDA::test_tags_H_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags___getitem___cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags___rdiv___cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__chunk_cat_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_T_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs__conversions_chalf_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs__conversions_half_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs__conversions_long_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_abs_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_acosh_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_amin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_as_strided_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_asin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_atan2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_bitwise_xor_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags__refs_broadcast_shapes_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_bucketize_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_cauchy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_clamp_min_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_conj_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_deg2rad_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_diagonal_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_div_no_rounding_mode_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_dot_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_erfc_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_expand_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_expand_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_fft2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_fftshift_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_hfft2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_hfft_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_ifft2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_ifft_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_irfftn_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fft_rfft2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_float_power_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_floor_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_floor_divide_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_fmin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_gcd_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags__refs_ge_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_hstack_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_index_select_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_isfinite_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_isposinf_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_item_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_le_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_linspace_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_log1p_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_log_softmax_with_dtype_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_logical_xor_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_logspace_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_logspace_tensor_overload_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_minimum_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_narrow_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_ne_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_channel_shuffle_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_elu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_gelu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_hardtanh_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_leaky_relu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_softmin_with_dtype_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_softplus_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_nn_functional_tanhshrink_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_norm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_normal__in_place_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_normal_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_ones_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_permute_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_rad2deg_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_randn_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_renorm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_signbit_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_sin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_softmax_with_dtype_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_i0e_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_i1_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_i1e_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_logit_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_spherical_bessel_j0_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_special_zeta_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_squeeze_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_sub_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_t_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_tan_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_tril_indices_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags__refs_true_divide_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_unbind_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_unfold_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_var_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_view_as_complex_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_view_as_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_view_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__refs_vstack_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags__unsafe_masked_index_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_alias_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_angle_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_atan_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_atleast_2d_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_baddbmm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_bitwise_and_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags_bitwise_left_shift_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags_bitwise_or_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags_bmm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_broadcast_shapes_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_broadcast_to_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_cdouble_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_clamp_min_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_complex_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_contiguous_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_deg2rad_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_diagonal_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_diagonal_scatter_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_einsum_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_expand_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_exponential_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_fft_fft_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_fft_ifft2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_fft_ifftn_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_fft_ifftshift_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_fft_ihfft_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_flipud_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_float_power_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_floor_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_frac_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_geqrf_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_grid_sampler_3d_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_histc_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_hypot_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_igammac_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_index_reduce_amin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_isclose_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_isinf_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_isnan_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_isneginf_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_lerp_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_det_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_eig_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_ldl_solve_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_lstsq_grad_oriented_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_lu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_lu_factor_ex_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_lu_solve_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_pinv_hermitian_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_slogdet_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_svdvals_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_linalg_tensorsolve_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_log10_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_logical_and_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_logical_or_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_logical_xor_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_long_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_lt_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_lu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_lu_solve_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_amin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_cumprod_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_cumsum_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_fill_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_select_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_softmin_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_sum_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_masked_var_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_matmul_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_max_pool2d_with_indices_backward_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_max_reduction_with_dim_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_min_reduction_no_dim_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_min_reduction_with_dim_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_mode_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_msort_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_narrow_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_neg_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_new_empty_strided_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_new_zeros_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_bilinear_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_conv1d_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_glu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_layer_norm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_local_response_norm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_mish_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_mse_loss_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_pad_circular_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_pairwise_distance_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_poisson_nll_loss_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_relu6_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_softmin_with_dtype_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_nn_functional_softsign_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_ormqr_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_permute_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_polygamma_polygamma_n_1_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_polygamma_polygamma_n_2_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_renorm_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_repeat_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_repeat_interleave_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_reshape_as_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_reshape_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_resize_as__cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_roll_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_round_decimals_0_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_sign_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_signal_windows_blackman_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_signal_windows_exponential_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_signal_windows_general_hamming_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_signbit_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_slice_scatter_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_sparse_mm_reduce_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_airy_ai_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_hermite_polynomial_he_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_modified_bessel_k0_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_modified_bessel_k1_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_ndtr_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_ndtri_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_special_spherical_bessel_j0_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_split_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_split_with_sizes_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_split_with_sizes_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_squeeze_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_squeeze_multiple_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_sub_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_tensordot_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_tile_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_to_sparse_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_topk_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_torch_ops_aten__flash_attention_forward_cuda_float16, test/test_ops.py::TestTagsCUDA::test_tags_transpose_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_transpose_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_trapezoid_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_tril_indices_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags_triu_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_triu_indices_cuda_int64, test/test_ops.py::TestTagsCUDA::test_tags_true_divide_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_unflatten_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_unfold_copy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_uniform_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_var_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_var_mean_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_vdot_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_view_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_where_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_xlogy_cuda_float32, test/test_ops.py::TestTagsCUDA::test_tags_zeros_like_cuda_float32 2025-09-07T09:15:51.2951721Z 2025-09-07T09:15:51.2951833Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T09:15:51.2952025Z Uploading artifacts took 0.00 seconds 2025-09-07T09:15:51.2952188Z Running test_quantization 5/5 ... [2025-09-07 09:15:51.115883] 2025-09-07T09:15:51.2952355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:15:51.2952852Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:15:51.116122] 2025-09-07T09:22:41.4811392Z 2025-09-07T09:22:41.4816041Z test_quantization 5/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_5.5_d0e87acf2e2c203a_.log 2025-09-07T09:22:41.4851418Z Running 283 items in this shard: test/test_quantization.py::TestQuantizedOps::test_avg_pool2d, test/test_quantization.py::TestQuantizedOps::test_empty_batch, test/test_quantization.py::TestQuantizedOps::test_group_norm, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qcelu, test/test_quantization.py::TestQuantizedOps::test_qgelu, test/test_quantization.py::TestQuantizedOps::test_qmatmul, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qtanh, test/test_quantization.py::TestQNNPackOps::test_mean, test/test_quantization.py::TestQNNPackOps::test_qnnpack_maxpool2d, test/test_quantization.py::TestQuantizedLinear::test_qlinear, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_cudnn, test/test_quantization.py::TestQuantizedLinear::test_qlinear_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_relu_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv1d_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_fp8, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_linear_prepack_fp16_numerics, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_legacy, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv1d_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_control, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quantize_per_channel_affine_scale_dtypes, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_channel_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_numerical_consistency_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_numerical_consistency_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_cuda_quantization_does_not_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_fp16_saturate_op, test/test_quantization.py::TestQuantizedTensor::test_pickle_checkpoint_qtensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_creation, test/test_quantization.py::TestQuantizedTensor::test_qtensor_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_put_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_quant_dequant, test/test_quantization.py::TestQuantizedTensor::test_qtensor_sub_byte_aligned_cols, test/test_quantization.py::TestQuantizedTensor::test_qtensor_view, test/test_quantization.py::TestFakeQuantize::test_fq_serializable_per_channel, test/test_quantization.py::TestObserver::test_observer_scriptable, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm3d_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_channel_shuffle, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_api, test/test_quantization.py::TestStaticQuantizedModule::test_group_norm, test/test_quantization.py::TestStaticQuantizedModule::test_instance_norm, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_linear_relu, test/test_quantization.py::TestStaticQuantizedModule::test_pool_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv3d, test/test_quantization.py::TestDynamicQuantizedModule::test_gru_api, test/test_quantization.py::TestReferenceQuantizedModule::test_linear_decomposed_weight_custom_qmin_qmax, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_correct_numel, test/test_quantization.py::TestDistributed::test_fake_quant_preserves_buffers, test/test_quantization.py::TestDistributed::test_qat_data_parallel, test/test_quantization.py::TestFusedObsFakeQuantModule::test_compare_fused_obs_fq_oss_module, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_bag_qat_config, test/test_quantization.py::TestBackendConfig::test_backend_config_to_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_extra_inputs_getter, test/test_quantization.py::TestBackendConfig::test_backend_op_config_to_dict, test/test_quantization.py::TestBackendConfig::test_dtype_config_from_dict, test/test_quantization.py::TestBackendConfig::test_dtype_config_to_dict, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_complex_args, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_default_kwargs, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_simple, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_tensor, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_fails_early, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_custom_module_class, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_dequant_stub, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_mha_batch_first_attr_is_copied_in_prepare, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_save_load_state_dict, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_quantized_rnn_cell, test/test_quantization.py::TestQuantizeEagerQAT::test_add_scalar_uses_input_qparams, test/test_quantization.py::TestQuantizeEagerQAT::test_defused_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerQAT::test_mul_scalar_uses_input_qparams, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_folded_vs_unfolded, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_leaky_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_workflow, test/test_quantization.py::TestFuseEager::test_fuse_function_customization, test/test_quantization.py::TestModelNumericsEager::test_fake_quant_true_quant_compare, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_partial, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_submodule_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v2, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_convrelu, test/test_quantization.py::TestFuseFx::test_fuse_custom_pattern, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_linear_bn_leaky_relu_not_fused_by_default, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_per_channel_quant, test/test_quantization.py::TestQuantizeFx::test_assert_on_size_after_quant_layer, test/test_quantization.py::TestQuantizeFx::test_change_backend_config_for_fixed_qparam_ops, test/test_quantization.py::TestQuantizeFx::test_conv_linear_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_relu_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_relu_reference, test/test_quantization.py::TestQuantizeFx::test_custom_module_class, test/test_quantization.py::TestQuantizeFx::test_default_quant_after_none_qconfig, test/test_quantization.py::TestQuantizeFx::test_keep_original_weights, test/test_quantization.py::TestQuantizeFx::test_linear_qint8_activation, test/test_quantization.py::TestQuantizeFx::test_mixed_dtypes, test/test_quantization.py::TestQuantizeFx::test_no_obs_between_unmatched_node_and_copy_node, test/test_quantization.py::TestQuantizeFx::test_non_traceable_module, test/test_quantization.py::TestQuantizeFx::test_packed_weight_fused_op, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_classes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_class, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_prepare_device_affinity, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_func, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_method, test/test_quantization.py::TestQuantizeFx::test_qconfig_function, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_precedence, test/test_quantization.py::TestQuantizeFx::test_ref_linear_module, test/test_quantization.py::TestQuantizeFx::test_repeat_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qat_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_view_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_add_relu, test/test_quantization.py::TestQuantizeFxOps::test_clamp, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeFxOps::test_div, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_gelu_reference, test/test_quantization.py::TestQuantizeFxOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeFxOps::test_getitem, test/test_quantization.py::TestQuantizeFxOps::test_hardswish, test/test_quantization.py::TestQuantizeFxOps::test_multiple_qconfigs_for_single_value, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestQuantizeFxOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeFxOps::test_rnn_cell, test/test_quantization.py::TestQuantizeFxOps::test_sub, test/test_quantization.py::TestQuantizeFxOps::test_sum, test/test_quantization.py::TestQuantizeFxModels::test_qat_embeddingbag_linear, test/test_quantization.py::TestQuantizeFxModels::test_static_gpu_convert_basic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_writer_replace_consecutive_submodules, test/test_quantization.py::TestDuplicateDQPass::test_no_add_quant_duplicate_dq, test/test_quantization.py::TestDuplicateDQPass::test_no_need_for_duplicate_dq, test/test_quantization.py::TestNumericDebugger::test_copy_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_extract_results_from_loggers, test/test_quantization.py::TestQuantizePT2E::test_conv_padding_bn_relu, test/test_quantization.py::TestQuantizePT2E::test_dont_fold_other_constant, test/test_quantization.py::TestQuantizePT2E::test_embedding_quantizer, test/test_quantization.py::TestQuantizePT2E::test_model_is_exported, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_bn_device_cuda, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_dropout_inplace, test/test_quantization.py::TestQuantizePT2E::test_observer_callback, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e4m3fn, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec_transitivity_case_2, test/test_quantization.py::TestQuantizePT2E::test_simple_quantizer, test/test_quantization.py::TestQuantizePT2E::test_wo_annotate_conv_output_quantizer, test/test_quantization.py::TestQuantizePT2EAffineQuantization::test_dynamic_per_tok_act_per_group_weights, test/test_quantization.py::TestPT2ERepresentation::test_add_relu, test/test_quantization.py::TestPT2ERepresentation::test_dynamic_linear, test/test_quantization.py::TestPT2ERepresentation::test_maxpool2d, test/test_quantization.py::TestXNNPACKQuantizer::test_add_and_inplace_add, test/test_quantization.py::TestXNNPACKQuantizer::test_conv1d, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_with_conv, test/test_quantization.py::TestXNNPACKQuantizer::test_obs_sharing_ops, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_name_with_underscores, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_attention_block, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_serials_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case1, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_type, test/test_quantization.py::TestFXGraphMatcher::test_nodes_with_equal_types_get_matched, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod_multi, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_fun, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_weights, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_logging_inputs, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_mul_add_cat_stack_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_unsupported_op_copy_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_deduplication, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_compare_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_shadow, test/test_quantization.py::TestFxModelReportObserver::test_observer_after_relu, test/test_quantization.py::TestFxModelReportClass::test_generate_report, test/test_quantization.py::TestFxModelReportClass::test_qconfig_mapping_generation, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_get_modules_and_features, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_convert, test/test_quantization.py::TestSerialization::test_conv2d, test/test_quantization.py::TestSerialization::test_conv2d_graph_v2, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v2, test/test_quantization.py::TestSerialization::test_per_channel_observer, test/test_quantization.py::TestQuantizeJit::test_conv, test/test_quantization.py::TestQuantizeJit::test_nested, test/test_quantization.py::TestQuantizeJitPasses::test_conv_trace, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_no_fusion, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_shared_classtype, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial_nobias, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface_unshare_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_for_function, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_interface_with_fork, test/test_quantization.py::TestQuantizeJitPasses::test_module_list, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize, test/test_quantization.py::TestQuantizeJitPasses::test_skip_dequant_constant_prop, test/test_quantization.py::TestQuantizeJitOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_alpha, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_with_if, test/test_quantization.py::TestQuantizeDynamicJitOps::test_embedding_bag_padding_idx_error, test/test_quantization.py::TestDeprecatedJitQuantized::test_erase_class_tensor_shapes, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_cell_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_observer, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_functional_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantized_dynamic_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_linear, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_quantized, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_conv_fused, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_conv_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_no_import_nn_intrinsic_quantized_dynamic, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_pattern_utils, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_prepare, test/test_quantization.py::TestFloat8DtypeCUDA::test_cast_round_trip_soak_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_cast_round_trip_subnormals_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_finfo_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_to_string_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_to_string_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_type_promotion_fails_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_type_promotion_fails_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_type_promotion_fails_cuda_float8_e8m0fnu 2025-09-07T09:22:41.4886788Z 2025-09-07T09:22:41.4886867Z Running test_subclass 1/1 ... [2025-09-07 09:22:41.481606] 2025-09-07T09:22:41.4887018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:22:41.4887377Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_subclass.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:22:41.481832] 2025-09-07T09:22:43.9016890Z 2025-09-07T09:22:43.9017629Z test_subclass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_subclass_1.1_1afc0b9b3c3e49d0_.log 2025-09-07T09:22:43.9045105Z Running 100 items in this shard: test/test_subclass.py::TestSubclass::test_deepcopy_base_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_base_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_diag_tensor_below_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_diag_tensor_below_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_logging_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_logging_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_non_wrapper_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_non_wrapper_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_sparse_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_sparse_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_wrapper_with_custom_sizes_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_wrapper_with_custom_sizes_as_param_True, test/test_subclass.py::TestSubclass::test_deepcopy_wrapper_with_custom_strides_as_param_False, test/test_subclass.py::TestSubclass::test_deepcopy_wrapper_with_custom_strides_as_param_True, test/test_subclass.py::TestSubclass::test_lazy_module_base_tensor, test/test_subclass.py::TestSubclass::test_lazy_module_diag_tensor_below, test/test_subclass.py::TestSubclass::test_lazy_module_logging_tensor, test/test_subclass.py::TestSubclass::test_lazy_module_non_wrapper_tensor, test/test_subclass.py::TestSubclass::test_lazy_module_sparse_tensor, test/test_subclass.py::TestSubclass::test_lazy_module_wrapper_with_custom_sizes, test/test_subclass.py::TestSubclass::test_lazy_module_wrapper_with_custom_strides, test/test_subclass.py::TestSubclass::test_module_optimization_base_tensor, test/test_subclass.py::TestSubclass::test_module_optimization_diag_tensor_below, test/test_subclass.py::TestSubclass::test_module_optimization_logging_tensor, test/test_subclass.py::TestSubclass::test_module_optimization_non_wrapper_tensor, test/test_subclass.py::TestSubclass::test_module_optimization_sparse_tensor, test/test_subclass.py::TestSubclass::test_module_optimization_wrapper_with_custom_sizes, test/test_subclass.py::TestSubclass::test_module_optimization_wrapper_with_custom_strides, test/test_subclass.py::TestSubclass::test_non_rewrapping_torch_dispatch_subclass_as_parameter_throws_for_detach, test/test_subclass.py::TestSubclass::test_param_invariants_base_tensor_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_base_tensor_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_diag_tensor_below_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_diag_tensor_below_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_logging_tensor_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_logging_tensor_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_non_wrapper_tensor_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_non_wrapper_tensor_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_sparse_tensor_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_sparse_tensor_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_wrapper_with_custom_sizes_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_wrapper_with_custom_sizes_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_param_invariants_wrapper_with_custom_strides_tensor_requires_grad_False, test/test_subclass.py::TestSubclass::test_param_invariants_wrapper_with_custom_strides_tensor_requires_grad_True, test/test_subclass.py::TestSubclass::test_parametrization_base_tensor_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_base_tensor_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_diag_tensor_below_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_diag_tensor_below_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_logging_tensor_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_logging_tensor_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_non_wrapper_tensor_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_non_wrapper_tensor_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_sparse_tensor_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_sparse_tensor_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_wrapper_with_custom_sizes_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_wrapper_with_custom_sizes_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_parametrization_wrapper_with_custom_strides_leave_parametrized_False, test/test_subclass.py::TestSubclass::test_parametrization_wrapper_with_custom_strides_leave_parametrized_True, test/test_subclass.py::TestSubclass::test_repr_base_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_repr_base_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_repr_diag_tensor_below_as_param_False, test/test_subclass.py::TestSubclass::test_repr_diag_tensor_below_as_param_True, test/test_subclass.py::TestSubclass::test_repr_logging_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_repr_logging_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_repr_non_wrapper_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_repr_non_wrapper_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_repr_sparse_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_repr_sparse_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_repr_wrapper_with_custom_sizes_as_param_False, test/test_subclass.py::TestSubclass::test_repr_wrapper_with_custom_sizes_as_param_True, test/test_subclass.py::TestSubclass::test_repr_wrapper_with_custom_strides_as_param_False, test/test_subclass.py::TestSubclass::test_repr_wrapper_with_custom_strides_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_base_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_base_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_diag_tensor_below_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_diag_tensor_below_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_logging_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_logging_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_non_wrapper_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_non_wrapper_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_sparse_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_sparse_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_wrapper_with_custom_sizes_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_wrapper_with_custom_sizes_as_param_True, test/test_subclass.py::TestSubclass::test_serialization_wrapper_with_custom_strides_as_param_False, test/test_subclass.py::TestSubclass::test_serialization_wrapper_with_custom_strides_as_param_True, test/test_subclass.py::TestSubclass::test_tensor_subclass_storage_data_accesses_throw, test/test_subclass.py::TestSubclass::test_type_propagation_base_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_base_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_diag_tensor_below_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_diag_tensor_below_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_logging_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_logging_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_non_wrapper_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_non_wrapper_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_sparse_tensor_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_sparse_tensor_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_wrapper_with_custom_sizes_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_wrapper_with_custom_sizes_as_param_True, test/test_subclass.py::TestSubclass::test_type_propagation_wrapper_with_custom_strides_as_param_False, test/test_subclass.py::TestSubclass::test_type_propagation_wrapper_with_custom_strides_as_param_True 2025-09-07T09:22:43.9057656Z 2025-09-07T09:22:43.9057770Z Running test_tensor_creation_ops 1/1 ... [2025-09-07 09:22:43.901682] 2025-09-07T09:22:43.9057931Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:22:43.9058308Z 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'] ... [2025-09-07 09:22:43.901896] 2025-09-07T09:24:39.7990127Z 2025-09-07T09:24:39.7991262Z 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_0c0e59f35a97ef0a_.log 2025-09-07T09:24:39.8069037Z Running 531 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_misaligned_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_multi_batch_cuda_float32, 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_size1_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_stack_cross_devices_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_trailing_dim_cuda_float32, 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_bounds_checking_cuda, 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 2025-09-07T09:24:39.8130987Z 2025-09-07T09:24:39.8136266Z Running test_utils 1/1 ... [2025-09-07 09:24:39.799124] 2025-09-07T09:24:39.8136485Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:24:39.8136847Z 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'] ... [2025-09-07 09:24:39.799341] 2025-09-07T09:25:08.4141303Z 2025-09-07T09:25:08.4146243Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_205c585c9c28e596_.log 2025-09-07T09:25:08.4855357Z Running 6012 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, test/test_utils.py::TestHipify::test_import_hipify, test/test_utils.py::TestHipifyTrie::test_add_and_search_trie, test/test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie, test/test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_quote_escape, test/test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex, test/test_utils.py::TestAssert::test_assert_scriptable, test/test_utils.py::TestAssert::test_assert_true, test/test_utils.py::TestStandaloneCPPJIT::test_load_standalone, test/test_utils.py::TestRenderUtils::test_basic, test/test_utils.py::TestDeviceUtilsCUDA::test_basic_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_decorator_cuda, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__segment_reduce_offsets_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__segment_reduce_offsets_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__segment_reduce_offsets_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__softmax_backward_data_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__softmax_backward_data_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__softmax_backward_data_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__softmax_backward_data_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__unsafe_masked_index_put_accumulate_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__upsample_bilinear2d_aa_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__upsample_bilinear2d_aa_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__upsample_bilinear2d_aa_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__upsample_bilinear2d_aa_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_abs_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acos_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_acosh_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_add_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addbmm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcdiv_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addcmul_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmm_decomposed_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addmv_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_addr_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_alias_copy_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_all_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_allclose_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amax_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_amin_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_aminmax_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_angle_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_any_cuda_bfloat16, 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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, 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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, 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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_cumulative_trapezoid_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_deg2rad_cuda_uint8, 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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, 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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, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_3d_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, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cholesky_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cholesky_ex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cholesky_ex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cholesky_ex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cholesky_ex_cuda_float64, 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, 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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, 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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 2025-09-07T09:25:08.5535162Z 2025-09-07T09:25:08.5535271Z Running torch_np/numpy_tests/core/test_dlpack 1/1 ... [2025-09-07 09:25:08.418121] 2025-09-07T09:25:08.5535457Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:08.5535858Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dlpack.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:08.418333] 2025-09-07T09:25:10.6373290Z 2025-09-07T09:25:10.6374284Z torch_np/numpy_tests/core/test_dlpack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_7b0c530f6030629d_.log 2025-09-07T09:25:10.6389304Z Running 53 items in this shard: test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_destructor_exception, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_device, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_stream, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_torch, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_10, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_11, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_12, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_13, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_14, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_15, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_16, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_17, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_18, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_19, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_20, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_21, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_22, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_23, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_24, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_25, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_26, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_27, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_28, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_29, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_30, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_31, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_32, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_ndim0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_non_contiguous, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_readonly, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_to_torch 2025-09-07T09:25:10.6405436Z 2025-09-07T09:25:10.6405553Z Running torch_np/numpy_tests/core/test_dtype 1/1 ... [2025-09-07 09:25:10.637192] 2025-09-07T09:25:10.6405766Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:10.6406233Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dtype.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:10.637433] 2025-09-07T09:25:12.9567870Z 2025-09-07T09:25:12.9568974Z torch_np/numpy_tests/core/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dtype_1.1_3b849fb9e604fd9d_.log 2025-09-07T09:25:12.9601325Z Running 102 items in this shard: test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_equivalent_dtype_hashing, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_invalid_types, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Bool, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Bytes0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex128, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Datetime64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float128, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int8, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Object0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Str0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Timedelta64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt8, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Uint32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Uint64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Void0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation1, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation2, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation3, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_equality, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t1, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t2, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t3, test/torch_np/numpy_tests/core/test_dtype.py::TestDtypeAttributeDeletion::test_dtype_non_writable_attributes_deletion, test/torch_np/numpy_tests/core/test_dtype.py::TestDtypeAttributeDeletion::test_dtype_writable_attributes_deletion, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t0, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t1, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t2, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t3, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t4, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_DType11, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_bool__10, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_complex128_4, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_complex64_3, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float16_0, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float32_1, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float64_2, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int16_7, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int32_8, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int64_9, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int8_6, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint8_5, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_complex64_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_float16_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_float32_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_other_4294967295_expected1_expected_weak1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_other_65535_expected0_expected_weak0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other0_expected0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other1_expected1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other2_expected2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other3_expected3, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other4_expected4, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other5_expected5, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other6_expected6, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes0_expected0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes1_expected1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes2_expected2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes3_expected3, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes4_expected4, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes5_expected5, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes6_expected6, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes7_expected7, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes8_expected8, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes9_expected9, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_18446744073709551616, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_200, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_4294967296, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_9223372036854775808, test/torch_np/numpy_tests/core/test_dtype.py::TestMisc::test_dtypes_are_true, test/torch_np/numpy_tests/core/test_dtype.py::TestMisc::test_keyword_argument, test/torch_np/numpy_tests/core/test_dtype.py::TestFromDTypeAttribute::test_recursion, test/torch_np/numpy_tests/core/test_dtype.py::TestFromDTypeAttribute::test_simple, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_?, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_B, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_D, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_F, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_b, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_d, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_e, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_f, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_h, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_i, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_l, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_3 2025-09-07T09:25:12.9615119Z 2025-09-07T09:25:12.9615221Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2025-09-07 09:25:12.956801] 2025-09-07T09:25:12.9615400Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:12.9615796Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:12.957036] 2025-09-07T09:25:19.1384813Z 2025-09-07T09:25:19.1397947Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_d6a961e0afa02b08_.log 2025-09-07T09:25:19.1407345Z Running 50 items in this shard: test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_broadcasting_dot_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_collapse, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_combined_views_mapping, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_complex, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_B, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_D, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_F, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_b, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_d, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_e, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_f, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_h, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_i, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_l, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_edge_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_all_contig_non_contig_output, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_broadcast, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_errors, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_failed_on_p9_and_s390x, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixed_collapsingbug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixedstridebug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_misc, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat128, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_uint8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_views, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_expand, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_hadamard_like_products, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_index_transformations, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_inner_product, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_out_is_res, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_output_order, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_random_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_small_boolean_arrays, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_subscript_range, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_edge_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_long_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_memory_contraints, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_internal_trace, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_invalid, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_spaces, test/torch_np/numpy_tests/core/test_einsum.py::TestMisc::test_overlap 2025-09-07T09:25:19.1414530Z 2025-09-07T09:25:19.1414662Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2025-09-07 09:25:19.138511] 2025-09-07T09:25:19.1414906Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:19.1415415Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_getlimits.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:19.138729] 2025-09-07T09:25:21.2574301Z 2025-09-07T09:25:21.2575983Z torch_np/numpy_tests/core/test_getlimits 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_0663806367e2985a_.log 2025-09-07T09:25:21.2579630Z Running 17 items in this shard: test/torch_np/numpy_tests/core/test_getlimits.py::TestPythonFloat::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestHalf::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestSingle::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestDouble::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic_missing, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T0, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T1, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T2, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T3, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_finfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_iinfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_instances, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_known_types, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_plausible_finfo, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_subnormal_warning 2025-09-07T09:25:21.2582829Z 2025-09-07T09:25:21.2583005Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2025-09-07 09:25:21.257315] 2025-09-07T09:25:21.2583334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:21.2584784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:21.257575] 2025-09-07T09:25:23.5816122Z 2025-09-07T09:25:23.5819549Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_8b07c4b51185a12a_.log 2025-09-07T09:25:23.5840759Z Running 67 items in this shard: test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_assignment_value_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_list, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_onedim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_twodim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_shape_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broaderrors_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broken_sequence_not_nd_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index_2, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_fancy_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_tuple_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_everything_returns_views, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_array_to_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_negative_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_weird_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_memory_order, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_none_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_nontuple_ndindex, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reverse_strides_and_subspace_bufferinit, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reversed_strides_result_allocation, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_same_kind_index_casting, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_scalar_array_bool, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_bool_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_int_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_slicing_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_small_regressions, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_fancy_indices_special_case, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_not_possible, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_out_of_bounds, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_tuple_subclass, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_uncontiguous_subspace_assignment, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index0, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index1, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index2, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_subspace, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_index_is_larger, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepend_not_one, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepending_ones, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_simple_broadcasting_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFancyIndexingCast::test_boolean_index_cast_assign, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_1d, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_boolean, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_multidim, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_sequence_multiplication, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_reduce_axis_float_index, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_slicing, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_bool_as_int_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_fast_path, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_weirdness, test/torch_np/numpy_tests/core/test_indexing.py::TestArrayToIndexDeprecation::test_array_to_index_error, test/torch_np/numpy_tests/core/test_indexing.py::TestNonIntegerArrayLike::test_basic, test/torch_np/numpy_tests/core/test_indexing.py::TestMultipleEllipsisError::test_basic 2025-09-07T09:25:23.5849992Z 2025-09-07T09:25:23.5850100Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2025-09-07 09:25:23.581644] 2025-09-07T09:25:23.5850284Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:23.5850683Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:23.581862] 2025-09-07T09:25:27.5590563Z 2025-09-07T09:25:27.5592072Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_1c31f5c810ed855c_.log 2025-09-07T09:25:27.5635023Z Running 273 items in this shard: test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_copies, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_negative_resize, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_repeats, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_reshape_from_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_zeroresize, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_choose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_clip, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_compress, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_count_nonzero, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_cumproduct, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_diagonal, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_accuracy, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype3, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype4, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype5, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype6, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype7, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-10, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-9, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_mean, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_prod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ptp, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ravel, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_repeat, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_reshape, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_py_consistency, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_searchsorted, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_size, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_squeeze, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_std, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_sum, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_swapaxes, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_take, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_trace, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_transpose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_var, test/torch_np/numpy_tests/core/test_numeric.py::TestIsscalar::test_isscalar, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_logical, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_all_any, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_and_or_xor, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_not_abs, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_double, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_default, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_divide_err, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_errobj, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_set, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_warnings, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_values, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_promote_types_endian, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_result_type, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_tesult_type_2, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_5, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_9, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_empty_result, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_failed_itemsetting, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_lengths, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_too_few_items, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_types, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_values, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_?, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_B, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_b, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_h, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_i, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_l, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_list, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_axis_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_keepdims, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_twodim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean_edgecase, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_large_neg_int64, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_neg_width_boundaries, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_sufficient_width, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base3, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base_range, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equiv, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_none_compares_elementwise, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_array_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_func_takes_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_array, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_non_contig, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_property, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_scalar_nan_propagation_arr0_amin0_amax0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin2_amax2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax1, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax_0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_outint32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_memory_overlap, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_transposed, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_noncontig_inplace, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_D, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_F, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_e, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_?, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_B, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_b, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_d, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_f, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_h, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_i, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_l, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting_unsafe, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_inout, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_nonnative, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_03, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_04, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_05, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_06, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_07, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_08, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_09, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_10, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_11, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_12, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_equalnan, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_not_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_min_int, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_all_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_none_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_non_finite_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_scalar_return, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof1, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof2, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_out_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_for_reference_leak, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_full, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_ones, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_zeros, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_empty_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_filled_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_ones_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_zeros_like, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_zero_size, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_numpy_doc_examples, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_object, test/torch_np/numpy_tests/core/test_numeric.py::TestDtypePositional::test_dtype_positional, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_2D, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_list, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_0, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_1, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_2, test/torch_np/numpy_tests/core/test_numeric.py::TestStringFunction::test_set_string_function, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll1d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll2d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_exceptions, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_results, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_errors, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_multiples, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_new_position, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_to_end, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_preserve_order, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x2, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_3x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting_shapes, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_uint8_int32_mixed_dtypes, test/torch_np/numpy_tests/core/test_numeric.py::TestOuterMisc::test_outer_out_param, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_scalar_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_single_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_C_and_F_simul, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_non_array_input, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_require_each, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_unknown_requirement, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_error_kwargs, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_in_args, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_single_arg, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_number_of_arguments, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_shape_mismatch_error_message, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension_einsum, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimensional 2025-09-07T09:25:27.5665380Z 2025-09-07T09:25:27.5665494Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2025-09-07 09:25:27.559292] 2025-09-07T09:25:27.5665692Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:27.5666102Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:27.559544] 2025-09-07T09:25:29.8283959Z 2025-09-07T09:25:29.8285468Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_a9266a3ed3dc8ebc_.log 2025-09-07T09:25:29.8292461Z Running 34 items in this shard: test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_both_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_nondtype_nonscalartype, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_same, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_sibling_class, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass_backwards, test/torch_np/numpy_tests/core/test_numerictypes.py::TestBitName::test_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestDocStrings::test_platform_dependent_aliases, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t9, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_unique, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t9 2025-09-07T09:25:29.8298492Z 2025-09-07T09:25:29.8298631Z Running torch_np/numpy_tests/core/test_scalar_ctors 1/1 ... [2025-09-07 09:25:29.828238] 2025-09-07T09:25:29.8298831Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:29.8304480Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_ctors.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:29.828414] 2025-09-07T09:25:32.0463798Z 2025-09-07T09:25:32.0465840Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_c212ad3aca1ca85d_.log 2025-09-07T09:25:32.0482231Z Running 65 items in this shard: test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_bool, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating_overflow, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_intp, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_uint64_from_negative, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t23 2025-09-07T09:25:32.0493121Z 2025-09-07T09:25:32.0493242Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2025-09-07 09:25:32.046334] 2025-09-07T09:25:32.0498440Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:32.0498871Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:32.046553] 2025-09-07T09:25:34.0145124Z 2025-09-07T09:25:34.0146069Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_809e475aa2737e26_.log 2025-09-07T09:25:34.0157080Z Running 77 items in this shard: test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_against_known_values, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype0_frac_vals0_exp_vals0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype1_frac_vals1_exp_vals1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype2_frac_vals2_exp_vals2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_non_numeric_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_?, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_D, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_F, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_bit_count, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype6, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype7 2025-09-07T09:25:34.0167523Z 2025-09-07T09:25:34.0167647Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-09-07 09:25:34.014526] 2025-09-07T09:25:34.0173161Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:34.0173608Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:34.014787] 2025-09-07T09:25:35.9834016Z 2025-09-07T09:25:35.9835330Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_2c14597e8891a997_.log 2025-09-07T09:25:35.9837139Z Running 3 items in this shard: test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_gh_15395, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init2 2025-09-07T09:25:35.9838199Z 2025-09-07T09:25:35.9838485Z Running torch_np/numpy_tests/core/test_scalarmath 1/1 ... [2025-09-07 09:25:35.983143] 2025-09-07T09:25:35.9839830Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:35.9840887Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarmath.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:35.983373] 2025-09-07T09:25:43.7629299Z 2025-09-07T09:25:43.7632151Z torch_np/numpy_tests/core/test_scalarmath 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarmath_1.1_451b7bf99763a799_.log 2025-09-07T09:25:43.7672576Z Running 186 items in this shard: test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_leak, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_type_add, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_type_create, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBaseMath::test_blocked, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBaseMath::test_lower_align, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_integers_to_negative_integer_power, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_large_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_mixed_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_modular_power, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_small_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_d, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_e, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_f, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_exact, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_roundoff, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_modulus_basic, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_branches, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_signed_zeros, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_zero_division, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_iinfo_long_values_1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_iinfo_long_values_2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_int_from_long, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_int_raise_behaviour, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_numpy_scalar_relational_operators, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_numpy_scalar_relational_operators_2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_scalar_comparison_to_none, test/torch_np/numpy_tests/core/test_scalarmath.py::TestRepr::test_float_repr, test/torch_np/numpy_tests/core/test_scalarmath.py::TestMultiply::test_no_seq_repeat_basic_array_like, test/torch_np/numpy_tests/core/test_scalarmath.py::TestMultiply::test_seq_repeat, test/torch_np/numpy_tests/core/test_scalarmath.py::TestNegative::test_exceptions, test/torch_np/numpy_tests/core/test_scalarmath.py::TestNegative::test_result, test/torch_np/numpy_tests/core/test_scalarmath.py::TestSubtract::test_exceptions, test/torch_np/numpy_tests/core/test_scalarmath.py::TestSubtract::test_result, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype4, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype4, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_B_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_B_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_b_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_b_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_h_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_h_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_i_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_i_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_l_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_l_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_complex_hashes_type_code_D, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_complex_hashes_type_code_F, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_D, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_F, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_d, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_e, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_f, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_B, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_b, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_h, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_i, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_l, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_B_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_B_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_unsigned_integer_overflow_dtype_B, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____add_____rop_____radd___op8_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____add_____rop_____radd___op8_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____eq_____rop_____eq___op2_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____eq_____rop_____eq___op2_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ge_____rop_____le___op5_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ge_____rop_____le___op5_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____gt_____rop_____lt___op4_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____gt_____rop_____lt___op4_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____le_____rop_____ge___op1_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____le_____rop_____ge___op1_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____lt_____rop_____gt___op0_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____lt_____rop_____gt___op0_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mod_____rop_____rmod___op9_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mod_____rop_____rmod___op9_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mul_____rop_____rmul___op10_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mul_____rop_____rmul___op10_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ne_____rop_____ne___op3_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ne_____rop_____ne___op3_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____pow_____rop_____rpow___op11_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____pow_____rop_____rpow___op11_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____sub_____rop_____rsub___op12_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____sub_____rop_____rsub___op12_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____truediv_____rop_____rtruediv___op7_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____truediv_____rop_____rtruediv___op7_cmp_False_sctype1 2025-09-07T09:25:43.7707535Z 2025-09-07T09:25:43.7707647Z Running torch_np/numpy_tests/core/test_shape_base 1/1 ... [2025-09-07 09:25:43.763155] 2025-09-07T09:25:43.7707892Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:43.7708315Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_shape_base.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:43.763396] 2025-09-07T09:25:46.1830710Z 2025-09-07T09:25:46.1832361Z torch_np/numpy_tests/core/test_shape_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_shape_base_1.1_dc0e3f4d5c2dfd12_.log 2025-09-07T09:25:46.1856469Z Running 119 items in this shard: test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_r1array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_r2array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_casting_and_dtype, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_casting_and_dtype_type_error, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_empty_input, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_generator, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_non_iterable, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_2D_array2, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_casting_and_dtype, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_casting_and_dtype_type_error, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_empty_input, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_generator, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_non_iterable, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_bad_out_shape, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_concatenate, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_concatenate_axis_None, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_exceptions, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_large_concatenate_axis_None, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_operator_concat, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_simple, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_returns_copy, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_3d, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_complicated, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_memory_order, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_mixed_1d_and_2d, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_simple_column_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_simple_row_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_total_size_estimate, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_column_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_multiple_rows, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_row_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_mismatched_shape, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_different_ndims, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_different_ndims_depths, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_empty_lists, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_invalid_nesting, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_nested, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_no_lists, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_returns_copy, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_tuple 2025-09-07T09:25:46.1877047Z 2025-09-07T09:25:46.1877151Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2025-09-07 09:25:46.183320] 2025-09-07T09:25:46.1877646Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:46.1878045Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:46.183592] 2025-09-07T09:25:49.6554192Z 2025-09-07T09:25:49.6555568Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_a03e1c022b5a57a7_.log 2025-09-07T09:25:49.6567699Z Running 8 items in this shard: test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_axes_keyword, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_equal_to_original, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_inverse, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_uneven_dims, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestRFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestIRFFTN::test_not_last_axis_success 2025-09-07T09:25:49.6569208Z 2025-09-07T09:25:49.6569337Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2025-09-07 09:25:49.655388] 2025-09-07T09:25:49.6569557Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:25:49.6570041Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:25:49.655599] 2025-09-07T09:26:15.3165721Z 2025-09-07T09:26:15.3167564Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_fb06fba468f5ffe5_.log 2025-09-07T09:26:15.3182758Z Running 79 items in this shard: test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTShift::test_fft_n, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_all_1d_norm_preserving, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_hfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_identity, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_backward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_forward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_ortho, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ihfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_ifft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_rfft 2025-09-07T09:26:15.3192615Z 2025-09-07T09:26:15.3192712Z Running torch_np/test_ndarray_methods 1/1 ... [2025-09-07 09:26:15.316566] 2025-09-07T09:26:15.3192890Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:15.3193282Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ndarray_methods.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:26:15.316817] 2025-09-07T09:26:18.6887338Z 2025-09-07T09:26:18.6887912Z torch_np/test_ndarray_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ndarray_methods_1.1_8645e20cf2281686_.log 2025-09-07T09:26:18.6935015Z Running 342 items in this shard: test/torch_np/test_ndarray_methods.py::TestIndexing::test_indexing_simple, test/torch_np/test_ndarray_methods.py::TestIndexing::test_setitem, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_function, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_method, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_function, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_method, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_function, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_method, test/torch_np/test_ndarray_methods.py::TestNonzero::test_array_method, 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test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size77_axis77_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size77_axis77_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size7_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size7_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size8_axis8_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size8_axis8_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size9_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size9_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_vs_ndarray_arr_method_argmax_np_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_vs_ndarray_arr_method_argmin_np_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_vs_ndarray_positional_arr_method_argmax_np_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_vs_ndarray_positional_arr_method_argmin_np_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_output_shape_method_argmax, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_output_shape_method_argmin, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_ret_is_out_ndim_0_method_argmax, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_ret_is_out_ndim_0_method_argmin, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_ret_is_out_ndim_1_method_argmax, 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test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data42, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data43, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data44, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data45, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data46, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data47, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data48, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data49, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data5, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data50, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data51, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data52, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data53, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data54, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data55, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data56, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data57, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data58, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data59, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data6, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data60, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data61, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data62, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data63, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data64, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data65, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data66, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data67, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data68, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data69, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data7, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data70, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data71, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data72, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data73, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data8, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data9, test/torch_np/test_ndarray_methods.py::TestArgmax::test_maximum_signed_integers, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data0, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data1, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data10, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data11, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data12, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data13, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data14, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data15, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data16, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data17, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data18, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data19, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data2, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data20, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data21, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data22, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data23, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data24, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data25, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data26, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data27, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data28, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data29, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data3, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data30, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data31, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data32, 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test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data45, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data46, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data47, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data48, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data49, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data5, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data50, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data51, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data52, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data53, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data54, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data55, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data56, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data57, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data58, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data59, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data6, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data60, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data61, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data62, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data63, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data64, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data65, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data66, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data67, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data68, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data69, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data7, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data70, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data71, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data72, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data73, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data8, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data9, test/torch_np/test_ndarray_methods.py::TestArgmin::test_minimum_signed_integers, test/torch_np/test_ndarray_methods.py::TestAmax::test_basic, test/torch_np/test_ndarray_methods.py::TestAmin::test_basic, test/torch_np/test_ndarray_methods.py::TestContains::test_contains, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_fn, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_ivar, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_method, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_name, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_plain, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_rvar, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_1d, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_2d 2025-09-07T09:26:18.6981280Z 2025-09-07T09:26:18.6981359Z Running torch_np/test_random 1/1 ... [2025-09-07 09:26:18.689128] 2025-09-07T09:26:18.6981514Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:18.6981891Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_random.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:26:18.689391] 2025-09-07T09:26:20.8583618Z 2025-09-07T09:26:20.8585623Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_7052d17e91e8a59e_.log 2025-09-07T09:26:20.8597189Z Running 41 items in this shard: test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_sample, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_True, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_False, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_True, test/torch_np/test_random.py::TestNumpyGlobal::test_numpy_global 2025-09-07T09:26:20.8611632Z 2025-09-07T09:26:20.8611729Z Running torch_np/test_reductions 1/1 ... [2025-09-07 09:26:20.858145] 2025-09-07T09:26:20.8611922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:20.8612363Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:26:20.858384] 2025-09-07T09:26:24.3811543Z 2025-09-07T09:26:24.3814130Z torch_np/test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_reductions_1.1_ceddbaedf7193768_.log 2025-09-07T09:26:24.3956258Z Running 966 items in this shard: test/torch_np/test_reductions.py::TestFlatnonzero::test_basic, test/torch_np/test_reductions.py::TestAny::test_basic, test/torch_np/test_reductions.py::TestAny::test_method_vs_function, test/torch_np/test_reductions.py::TestAny::test_nd, test/torch_np/test_reductions.py::TestAll::test_basic, test/torch_np/test_reductions.py::TestAll::test_method_vs_function, test/torch_np/test_reductions.py::TestAll::test_nd, test/torch_np/test_reductions.py::TestMean::test_mean, test/torch_np/test_reductions.py::TestMean::test_mean_float16, test/torch_np/test_reductions.py::TestMean::test_mean_values, test/torch_np/test_reductions.py::TestMean::test_mean_where, test/torch_np/test_reductions.py::TestSum::test_sum, test/torch_np/test_reductions.py::TestSum::test_sum_boolean, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_2, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_warnings, test/torch_np/test_reductions.py::TestSum::test_sum_initial, test/torch_np/test_reductions.py::TestSum::test_sum_stability, test/torch_np/test_reductions.py::TestSum::test_sum_where, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis8_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-1_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_-2_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis_0_func6, 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test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func9, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func1 2025-09-07T09:26:24.4082270Z 2025-09-07T09:26:24.4082362Z Running torch_np/test_ufuncs_basic 1/1 ... [2025-09-07 09:26:24.381978] 2025-09-07T09:26:24.4082525Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:24.4082902Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ufuncs_basic.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 09:26:24.382192] 2025-09-07T09:26:26.9025278Z 2025-09-07T09:26:26.9026754Z torch_np/test_ufuncs_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ufuncs_basic_1.1_4b67978a59fb5fb7_.log 2025-09-07T09:26:26.9099370Z Running 371 items in this shard: test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_broadcast_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_float32, 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test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc2_out_dtype_float32, 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test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype_and_out 2025-09-07T09:26:26.9151100Z 2025-09-07T09:26:28.2741377Z /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. 2025-09-07T09:26:28.2742605Z import pkg_resources 2025-09-07T09:26:29.0377454Z Running test batch 'tests to run' cost 9704.9 seconds 2025-09-07T09:26:29.8713470Z 2025-09-07T09:26:29.8714621Z real 161m47.406s 2025-09-07T09:26:29.8714876Z user 1521m42.162s 2025-09-07T09:26:29.8715015Z sys 62m41.204s 2025-09-07T09:26:29.8715205Z + assert_git_not_dirty 2025-09-07T09:26:29.8721776Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]] 2025-09-07T09:26:29.8721998Z + test_aten 2025-09-07T09:26:29.8722150Z + echo 'Running ATen tests with pytorch lib' 2025-09-07T09:26:29.8722352Z Running ATen tests with pytorch lib 2025-09-07T09:26:29.8722535Z + [[ -n '' ]] 2025-09-07T09:26:29.8722694Z + echo 'Running test with the build folder' 2025-09-07T09:26:29.8722897Z Running test with the build folder 2025-09-07T09:26:29.8723090Z + TEST_BASE_DIR=build/bin 2025-09-07T09:26:29.8724198Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10_hip.so build/bin 2025-09-07T09:26:29.8731563Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2_nvrtc.so build/bin 2025-09-07T09:26:29.8741888Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2025-09-07T09:26:29.8748318Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2025-09-07T09:26:29.8754937Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_hip.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so build/bin 2025-09-07T09:26:29.8761069Z + ls build/bin 2025-09-07T09:26:29.8776710Z BackoffTest 2025-09-07T09:26:29.8776920Z CMakeFiles 2025-09-07T09:26:29.8777041Z CTestTestfile.cmake 2025-09-07T09:26:29.8777211Z CppSignature_test 2025-09-07T09:26:29.8777330Z Dict_test 2025-09-07T09:26:29.8777451Z Dimname_test 2025-09-07T09:26:29.8777593Z FileStoreTest 2025-09-07T09:26:29.8777710Z HashStoreTest 2025-09-07T09:26:29.8777815Z IListRef_test 2025-09-07T09:26:29.8777917Z KernelFunction_test 2025-09-07T09:26:29.8778032Z List_test 2025-09-07T09:26:29.8778129Z MaybeOwned_test 2025-09-07T09:26:29.8778234Z NamedTensor_test 2025-09-07T09:26:29.8784252Z ProcessGroupGlooTest 2025-09-07T09:26:29.8784361Z StorageUtils_test 2025-09-07T09:26:29.8784553Z TCPStoreTest 2025-09-07T09:26:29.8784644Z apply_utils_test 2025-09-07T09:26:29.8784757Z atest 2025-09-07T09:26:29.8784848Z backend_fallback_test 2025-09-07T09:26:29.8784948Z basic 2025-09-07T09:26:29.8785033Z broadcast_test 2025-09-07T09:26:29.8785138Z c10_AllocatorConfig_test 2025-09-07T09:26:29.8785286Z c10_ArrayRef_test 2025-09-07T09:26:29.8785383Z c10_Bitset_test 2025-09-07T09:26:29.8785491Z c10_CompileTimeFunctionPointer_test 2025-09-07T09:26:29.8785624Z c10_ConstexprCrc_test 2025-09-07T09:26:29.8785733Z c10_DeadlockDetection_test 2025-09-07T09:26:29.8785846Z c10_DeviceGuard_test 2025-09-07T09:26:29.8785946Z c10_Device_test 2025-09-07T09:26:29.8786045Z c10_DispatchKeySet_test 2025-09-07T09:26:29.8786153Z c10_Enumerate_test 2025-09-07T09:26:29.8786245Z c10_Half_test 2025-09-07T09:26:29.8788364Z c10_InlineDeviceGuard_test 2025-09-07T09:26:29.8788485Z c10_InlineStreamGuard_test 2025-09-07T09:26:29.8788597Z c10_IntrusiveList_test 2025-09-07T09:26:29.8788696Z c10_LeftRight_test 2025-09-07T09:26:29.8788796Z c10_Metaprogramming_test 2025-09-07T09:26:29.8788903Z c10_NetworkFlow_test 2025-09-07T09:26:29.8789001Z c10_Scalar_test 2025-09-07T09:26:29.8789094Z c10_Semaphore_test 2025-09-07T09:26:29.8789190Z c10_SizesAndStrides_test 2025-09-07T09:26:29.8789304Z c10_StreamGuard_test 2025-09-07T09:26:29.8789400Z c10_SymInt_test 2025-09-07T09:26:29.8790863Z c10_Synchronized_test 2025-09-07T09:26:29.8790964Z c10_ThreadLocal_test 2025-09-07T09:26:29.8791064Z c10_TypeIndex_test 2025-09-07T09:26:29.8791157Z c10_TypeList_test 2025-09-07T09:26:29.8791249Z c10_TypeTraits_test 2025-09-07T09:26:29.8791345Z c10_accumulate_test 2025-09-07T09:26:29.8791440Z c10_bfloat16_test 2025-09-07T09:26:29.8791533Z c10_bit_cast_test 2025-09-07T09:26:29.8791625Z c10_complex_math_test 2025-09-07T09:26:29.8791721Z c10_complex_test 2025-09-07T09:26:29.8791811Z c10_cow_test 2025-09-07T09:26:29.8793104Z c10_error_test 2025-09-07T09:26:29.8793196Z c10_exception_test 2025-09-07T09:26:29.8793287Z c10_flags_test 2025-09-07T09:26:29.8793378Z c10_generic_math_test 2025-09-07T09:26:29.8793489Z c10_hip_HIPAssertionsTest_1_var_test 2025-09-07T09:26:29.8793626Z c10_hip_HIPAssertionsTest_catches_stream 2025-09-07T09:26:29.8794205Z c10_hip_HIPAssertionsTest_catches_thread_and_block_and_device 2025-09-07T09:26:29.8794359Z c10_hip_HIPAssertionsTest_from_2_processes 2025-09-07T09:26:29.8794627Z c10_hip_HIPAssertionsTest_multiple_writes_from_blocks_and_threads 2025-09-07T09:26:29.8794832Z c10_hip_HIPAssertionsTest_multiple_writes_from_multiple_blocks 2025-09-07T09:26:29.8795001Z c10_hip_HIPAssertionsTest_multiple_writes_from_same_block 2025-09-07T09:26:29.8796280Z c10_hip_HIPTest 2025-09-07T09:26:29.8796404Z c10_intrusive_ptr_benchmark 2025-09-07T09:26:29.8796509Z c10_intrusive_ptr_test 2025-09-07T09:26:29.8796601Z c10_irange_test 2025-09-07T09:26:29.8796684Z c10_lazy_test 2025-09-07T09:26:29.8796766Z c10_logging_test 2025-09-07T09:26:29.8796850Z c10_optional_test 2025-09-07T09:26:29.8796945Z c10_ordered_preserving_dict_test 2025-09-07T09:26:29.8797051Z c10_registry_test 2025-09-07T09:26:29.8797137Z c10_small_vector_test 2025-09-07T09:26:29.8797225Z c10_ssize_test 2025-09-07T09:26:29.8798418Z c10_string_util_test 2025-09-07T09:26:29.8798509Z c10_string_view_test 2025-09-07T09:26:29.8798600Z c10_tempfile_test 2025-09-07T09:26:29.8798683Z c10_typeid_test 2025-09-07T09:26:29.8798768Z cmake_install.cmake 2025-09-07T09:26:29.8798857Z cpu_allocator_test 2025-09-07T09:26:29.8798943Z cpu_generator_test 2025-09-07T09:26:29.8799036Z cpu_profiling_allocator_test 2025-09-07T09:26:29.8799136Z cpu_rng_test 2025-09-07T09:26:29.8799218Z dlconvertor_test 2025-09-07T09:26:29.8799302Z example_allreduce 2025-09-07T09:26:29.8801036Z extension_backend_test 2025-09-07T09:26:29.8801133Z half_test 2025-09-07T09:26:29.8801217Z hip_apply_test 2025-09-07T09:26:29.8801322Z hip_complex_math_test 2025-09-07T09:26:29.8801412Z hip_complex_test 2025-09-07T09:26:29.8801508Z hip_distributions_test 2025-09-07T09:26:29.8801604Z hip_dlconvertor_test 2025-09-07T09:26:29.8801694Z hip_generator_test 2025-09-07T09:26:29.8801779Z hip_half_test 2025-09-07T09:26:29.8801865Z hip_integer_divider_test 2025-09-07T09:26:29.8803434Z hip_optional_test 2025-09-07T09:26:29.8803538Z hip_packedtensoraccessor_test 2025-09-07T09:26:29.8803644Z hip_vectorized_test 2025-09-07T09:26:29.8803739Z inline_container_test 2025-09-07T09:26:29.8803828Z ivalue_test 2025-09-07T09:26:29.8803917Z kernel_function_legacy_test 2025-09-07T09:26:29.8804021Z kernel_function_test 2025-09-07T09:26:29.8804114Z kernel_lambda_legacy_test 2025-09-07T09:26:29.8804211Z kernel_lambda_test 2025-09-07T09:26:29.8804298Z kernel_stackbased_test 2025-09-07T09:26:29.8804479Z lazy_tensor_test 2025-09-07T09:26:29.8805759Z legacy_vmap_test 2025-09-07T09:26:29.8805859Z libc10.so 2025-09-07T09:26:29.8805943Z libc10_hip.so 2025-09-07T09:26:29.8806026Z libcaffe2_nvrtc.so 2025-09-07T09:26:29.8806110Z 'libmkldnn*' 2025-09-07T09:26:29.8806189Z 'libnccl*' 2025-09-07T09:26:29.8806264Z libtorch.so 2025-09-07T09:26:29.8806342Z libtorch_cpu.so 2025-09-07T09:26:29.8806430Z libtorch_global_deps.so 2025-09-07T09:26:29.8806525Z libtorch_hip.so 2025-09-07T09:26:29.8806610Z libtorch_python.so 2025-09-07T09:26:29.8807853Z libtorchbind_test.so 2025-09-07T09:26:29.8807961Z make_boxed_from_unboxed_functor_test 2025-09-07T09:26:29.8808074Z math_kernel_test 2025-09-07T09:26:29.8808159Z memory_format_test 2025-09-07T09:26:29.8808248Z memory_overlapping_test 2025-09-07T09:26:29.8808347Z mobile_memory_cleanup 2025-09-07T09:26:29.8808436Z native_test 2025-09-07T09:26:29.8808515Z op_allowlist_test 2025-09-07T09:26:29.8808603Z op_registration_test 2025-09-07T09:26:29.8808694Z operator_name_test 2025-09-07T09:26:29.8808779Z operators_test 2025-09-07T09:26:29.8809866Z packedtensoraccessor_test 2025-09-07T09:26:29.8809968Z parallel_benchmark 2025-09-07T09:26:29.8810053Z pow_test 2025-09-07T09:26:29.8810129Z protoc 2025-09-07T09:26:29.8810206Z protoc-3.13.0.0 2025-09-07T09:26:29.8810292Z quantized_test 2025-09-07T09:26:29.8810374Z reduce_ops_test 2025-09-07T09:26:29.8810461Z reportMemoryUsage_test 2025-09-07T09:26:29.8810554Z scalar_tensor_test 2025-09-07T09:26:29.8810638Z scalar_test 2025-09-07T09:26:29.8810719Z static_runtime_bench 2025-09-07T09:26:29.8811851Z static_runtime_test 2025-09-07T09:26:29.8811946Z stride_properties_test 2025-09-07T09:26:29.8812038Z tensor_iterator_test 2025-09-07T09:26:29.8812125Z test_api 2025-09-07T09:26:29.8812224Z test_cpp_rpc 2025-09-07T09:26:29.8812306Z test_dist_autograd 2025-09-07T09:26:29.8812388Z test_jit 2025-09-07T09:26:29.8812462Z test_lazy 2025-09-07T09:26:29.8812537Z test_nativert 2025-09-07T09:26:29.8812619Z test_parallel 2025-09-07T09:26:29.8812721Z thread_init_test 2025-09-07T09:26:29.8813808Z torch_shm_manager 2025-09-07T09:26:29.8813895Z type_ptr_test 2025-09-07T09:26:29.8813975Z type_test 2025-09-07T09:26:29.8814054Z undefined_tensor_test 2025-09-07T09:26:29.8814150Z vec_test_all_types_AVX2 2025-09-07T09:26:29.8814249Z vec_test_all_types_AVX512 2025-09-07T09:26:29.8814351Z vec_test_all_types_DEFAULT 2025-09-07T09:26:29.8814553Z verify_api_visibility 2025-09-07T09:26:29.8814642Z weakref_test 2025-09-07T09:26:29.8814754Z wrapdim_test 2025-09-07T09:26:29.8814834Z xla_tensor_test 2025-09-07T09:26:29.8815962Z + aten/tools/run_tests.sh build/bin 2025-09-07T09:26:29.8816079Z + set -e 2025-09-07T09:26:29.8816169Z ++ dirname aten/tools/run_tests.sh 2025-09-07T09:26:29.8817477Z + VALGRIND_SUP=/var/lib/jenkins/pytorch/aten/tools/valgrind.sup 2025-09-07T09:26:29.8817643Z + export CPP_TESTS_DIR=build/bin 2025-09-07T09:26:29.8817754Z + CPP_TESTS_DIR=build/bin 2025-09-07T09:26:29.8817858Z + VALGRIND=OFF 2025-09-07T09:26:29.8818455Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2025-09-07T09:26:31.2131634Z /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. 2025-09-07T09:26:31.2132241Z import pkg_resources 2025-09-07T09:26:31.5345550Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-09-07T09:26:31.5407072Z Found test times from artifacts 2025-09-07T09:26:31.5687608Z Found test times from artifacts 2025-09-07T09:26:31.5694070Z Running all tests 2025-09-07T09:26:31.5701780Z Running parallel tests on 1 processes 2025-09-07T09:26:31.5702169Z Name: tests to run (est. time: 0.0min) 2025-09-07T09:26:31.5702382Z Serial tests (19): 2025-09-07T09:26:31.5702509Z cpp/Dict_test 1/1 2025-09-07T09:26:31.5702647Z cpp/Dimname_test 1/1 2025-09-07T09:26:31.5702789Z cpp/NamedTensor_test 1/1 2025-09-07T09:26:31.5702937Z cpp/apply_utils_test 1/1 2025-09-07T09:26:31.5703066Z cpp/atest 1/1 2025-09-07T09:26:31.5703187Z cpp/basic 1/1 2025-09-07T09:26:31.5703312Z cpp/broadcast_test 1/1 2025-09-07T09:26:31.5703523Z cpp/cpu_generator_test 1/1 2025-09-07T09:26:31.5703673Z cpp/dlconvertor_test 1/1 2025-09-07T09:26:31.5703808Z cpp/extension_backend_test 1/1 2025-09-07T09:26:31.5703941Z cpp/lazy_tensor_test 1/1 2025-09-07T09:26:31.5704071Z cpp/legacy_vmap_test 1/1 2025-09-07T09:26:31.5704204Z cpp/native_test 1/1 2025-09-07T09:26:31.5704328Z cpp/operators_test 1/1 2025-09-07T09:26:31.5704542Z cpp/scalar_tensor_test 1/1 2025-09-07T09:26:31.5709831Z cpp/scalar_test 1/1 2025-09-07T09:26:31.5709970Z cpp/tensor_iterator_test 1/1 2025-09-07T09:26:31.5710105Z cpp/undefined_tensor_test 1/1 2025-09-07T09:26:31.5710229Z cpp/wrapdim_test 1/1 2025-09-07T09:26:31.5710342Z Parallel tests (0): 2025-09-07T09:26:31.5710464Z Name: excluded (est. time: 0.0min) 2025-09-07T09:26:31.5710588Z Serial tests (0): 2025-09-07T09:26:31.5710691Z Parallel tests (0): 2025-09-07T09:26:31.5711153Z Running cpp/Dict_test 1/1 ... [2025-09-07 09:26:31.569635] 2025-09-07T09:26:31.5711319Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:31.5711851Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/Dict_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0c9d6870d0ef04db.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:31.569908] 2025-09-07T09:26:31.9852212Z 2025-09-07T09:26:31.9853027Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_9409edb9f5fe7f29_.log 2025-09-07T09:26:31.9853608Z 2025-09-07T09:26:31.9853899Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T09:26:31.9854617Z Uploading artifacts took 0.00 seconds 2025-09-07T09:26:31.9855026Z Running cpp/Dimname_test 1/1 ... [2025-09-07 09:26:31.984816] 2025-09-07T09:26:31.9858678Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:31.9859798Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/Dimname_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-ea989dd5797f855c.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:31.985368] 2025-09-07T09:26:32.4018507Z 2025-09-07T09:26:32.4019293Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_4415c598140a774d_.log 2025-09-07T09:26:32.4019904Z 2025-09-07T09:26:32.4020133Z Running cpp/NamedTensor_test 1/1 ... [2025-09-07 09:26:32.401374] 2025-09-07T09:26:32.4027165Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:32.4028071Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/NamedTensor_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8314d132664fc74e.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:32.401612] 2025-09-07T09:26:32.8672968Z 2025-09-07T09:26:32.8673856Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_e5bcc18791efd7ad_.log 2025-09-07T09:26:32.8674711Z 2025-09-07T09:26:32.8674937Z Running cpp/apply_utils_test 1/1 ... [2025-09-07 09:26:32.866865] 2025-09-07T09:26:32.8675374Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:32.8676569Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/apply_utils_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a5c19e6ec63bc5fe.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:32.867179] 2025-09-07T09:26:33.2831094Z 2025-09-07T09:26:33.2831608Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_3200c590142d19d2_.log 2025-09-07T09:26:33.2831965Z 2025-09-07T09:26:33.2832079Z Running cpp/atest 1/1 ... [2025-09-07 09:26:33.282719] 2025-09-07T09:26:33.2837660Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:33.2838316Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/atest', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b1b3e750b809de53.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:33.282868] 2025-09-07T09:26:33.6984734Z 2025-09-07T09:26:33.6985809Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_b12f0bc6e1b9245b_.log 2025-09-07T09:26:33.6986206Z 2025-09-07T09:26:33.6986314Z Running cpp/basic 1/1 ... [2025-09-07 09:26:33.698063] 2025-09-07T09:26:33.6986565Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:33.6987267Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/basic', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-fae4542040d40faa.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:33.698408] 2025-09-07T09:26:34.1140794Z 2025-09-07T09:26:34.1141698Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_6b5f5d3a20ed2979_.log 2025-09-07T09:26:34.1142071Z 2025-09-07T09:26:34.1142296Z Running cpp/broadcast_test 1/1 ... [2025-09-07 09:26:34.113595] 2025-09-07T09:26:34.1142585Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:34.1150169Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/broadcast_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c2b042698f9c3390.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:34.113757] 2025-09-07T09:26:34.5297184Z 2025-09-07T09:26:34.5298152Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_8a7aa47017564b83_.log 2025-09-07T09:26:34.5298804Z 2025-09-07T09:26:34.5299028Z Running cpp/cpu_generator_test 1/1 ... [2025-09-07 09:26:34.529241] 2025-09-07T09:26:34.5299809Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:34.5300951Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/cpu_generator_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e5f8e986645992e0.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:34.529551] 2025-09-07T09:26:34.9452835Z 2025-09-07T09:26:34.9453439Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_f4d0b3174701c11c_.log 2025-09-07T09:26:34.9453898Z 2025-09-07T09:26:34.9454057Z Running cpp/dlconvertor_test 1/1 ... [2025-09-07 09:26:34.944919] 2025-09-07T09:26:34.9454355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:34.9456600Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/dlconvertor_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-41cd2d53a10921ff.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:34.945232] 2025-09-07T09:26:35.3609657Z 2025-09-07T09:26:35.3610324Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_d861bdf802cf368f_.log 2025-09-07T09:26:35.3610807Z 2025-09-07T09:26:35.3610994Z Running cpp/extension_backend_test 1/1 ... [2025-09-07 09:26:35.360584] 2025-09-07T09:26:35.3611330Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:35.3620616Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/extension_backend_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b5e656dd6ded2b8e.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:35.360896] 2025-09-07T09:26:35.7767452Z 2025-09-07T09:26:35.7768065Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_1f31b5879e84ef2d_.log 2025-09-07T09:26:35.7768567Z 2025-09-07T09:26:35.7768736Z Running cpp/lazy_tensor_test 1/1 ... [2025-09-07 09:26:35.776300] 2025-09-07T09:26:35.7769072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:35.7769948Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/lazy_tensor_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-caa3624ff354d96a.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:35.776609] 2025-09-07T09:26:36.1928720Z 2025-09-07T09:26:36.1929414Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_9393116728f0e11f_.log 2025-09-07T09:26:36.1929897Z 2025-09-07T09:26:36.1930062Z Running cpp/legacy_vmap_test 1/1 ... [2025-09-07 09:26:36.192376] 2025-09-07T09:26:36.1930378Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:36.1931186Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/legacy_vmap_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0b6348c2260e3f7e.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:36.192691] 2025-09-07T09:26:36.8092222Z 2025-09-07T09:26:36.8092927Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_019af05db9ff7118_.log 2025-09-07T09:26:36.8093658Z 2025-09-07T09:26:36.8093861Z Running cpp/native_test 1/1 ... [2025-09-07 09:26:36.808730] 2025-09-07T09:26:36.8094276Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:36.8095745Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/native_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9f64650ab48bfea6.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:36.809078] 2025-09-07T09:26:37.3764828Z 2025-09-07T09:26:37.3765567Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_227fa62e1ce8e788_.log 2025-09-07T09:26:37.3766162Z 2025-09-07T09:26:37.3767131Z Running cpp/operators_test 1/1 ... [2025-09-07 09:26:37.376086] 2025-09-07T09:26:37.3767568Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:37.3768704Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/operators_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2d805371ea655ccb.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:37.376411] 2025-09-07T09:26:37.8422920Z 2025-09-07T09:26:37.8423887Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_b0a068ba7d518273_.log 2025-09-07T09:26:37.8424716Z 2025-09-07T09:26:37.8426117Z Running cpp/scalar_tensor_test 1/1 ... [2025-09-07 09:26:37.841887] 2025-09-07T09:26:37.8426564Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:37.8427700Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/scalar_tensor_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1164d68e55ece931.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:37.842212] 2025-09-07T09:26:38.3080833Z 2025-09-07T09:26:38.3081617Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_63b7b4ab705baedb_.log 2025-09-07T09:26:38.3082267Z 2025-09-07T09:26:38.3082475Z Running cpp/scalar_test 1/1 ... [2025-09-07 09:26:38.307724] 2025-09-07T09:26:38.3082891Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:38.3084721Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/scalar_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-574c8e25202f0129.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:38.308027] 2025-09-07T09:26:38.7740604Z 2025-09-07T09:26:38.7741333Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_999f5df4ac061bc1_.log 2025-09-07T09:26:38.7748600Z 2025-09-07T09:26:38.7748804Z Running cpp/tensor_iterator_test 1/1 ... [2025-09-07 09:26:38.773712] 2025-09-07T09:26:38.7749105Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:38.7750465Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/tensor_iterator_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-fe4bd291a4064b19.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:38.774084] 2025-09-07T09:26:39.2400418Z 2025-09-07T09:26:39.2401767Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_61d3fa045d46855a_.log 2025-09-07T09:26:39.2402485Z 2025-09-07T09:26:39.2402726Z Running cpp/undefined_tensor_test 1/1 ... [2025-09-07 09:26:39.239695] 2025-09-07T09:26:39.2403218Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:39.2406751Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/undefined_tensor_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-602e4eb0c6562ac7.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:39.240019] 2025-09-07T09:26:39.7060315Z 2025-09-07T09:26:39.7061515Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_28d08199e09e8d94_.log 2025-09-07T09:26:39.7061793Z 2025-09-07T09:26:39.7068354Z Running cpp/wrapdim_test 1/1 ... [2025-09-07 09:26:39.705673] 2025-09-07T09:26:39.7068670Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:39.7069103Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/wrapdim_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d2c987a2a1fae50f.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:39.706016] 2025-09-07T09:26:40.1220979Z 2025-09-07T09:26:40.1221982Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_54f63311cedc32f7_.log 2025-09-07T09:26:40.1226890Z 2025-09-07T09:26:41.5098947Z /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. 2025-09-07T09:26:41.5099840Z import pkg_resources 2025-09-07T09:26:42.2882412Z Running test batch 'tests to run' cost 10.72 seconds 2025-09-07T09:26:42.7767020Z + run_if_exists tensor_interop_test 2025-09-07T09:26:42.7767427Z + local test_name=tensor_interop_test 2025-09-07T09:26:42.7767700Z + [[ -x build/bin/tensor_interop_test ]] 2025-09-07T09:26:42.7767998Z + echo 'Warning: tensor_interop_test does not exist.' 2025-09-07T09:26:42.7768294Z Warning: tensor_interop_test does not exist. 2025-09-07T09:26:42.7768550Z + run_if_exists cudnn_test 2025-09-07T09:26:42.7768769Z + local test_name=cudnn_test 2025-09-07T09:26:42.7769036Z + [[ -x build/bin/cudnn_test ]] 2025-09-07T09:26:42.7769279Z + echo 'Warning: cudnn_test does not exist.' 2025-09-07T09:26:42.7769533Z Warning: cudnn_test does not exist. 2025-09-07T09:26:42.7769768Z + run_if_exists cuda_generator_test 2025-09-07T09:26:42.7770011Z + local test_name=cuda_generator_test 2025-09-07T09:26:42.7778248Z + [[ -x build/bin/cuda_generator_test ]] 2025-09-07T09:26:42.7778549Z + echo 'Warning: cuda_generator_test does not exist.' 2025-09-07T09:26:42.7778804Z Warning: cuda_generator_test does not exist. 2025-09-07T09:26:42.7779034Z + run_if_exists apply_test 2025-09-07T09:26:42.7779230Z + local test_name=apply_test 2025-09-07T09:26:42.7779430Z + [[ -x build/bin/apply_test ]] 2025-09-07T09:26:42.7779638Z + echo 'Warning: apply_test does not exist.' 2025-09-07T09:26:42.7779855Z Warning: apply_test does not exist. 2025-09-07T09:26:42.7780056Z + run_if_exists stream_test 2025-09-07T09:26:42.7780248Z + local test_name=stream_test 2025-09-07T09:26:42.7780444Z + [[ -x build/bin/stream_test ]] 2025-09-07T09:26:42.7780660Z + echo 'Warning: stream_test does not exist.' 2025-09-07T09:26:42.7780881Z Warning: stream_test does not exist. 2025-09-07T09:26:42.7781083Z + run_if_exists cuda_half_test 2025-09-07T09:26:42.7781277Z + local test_name=cuda_half_test 2025-09-07T09:26:42.7781483Z + [[ -x build/bin/cuda_half_test ]] 2025-09-07T09:26:42.7781707Z + echo 'Warning: cuda_half_test does not exist.' 2025-09-07T09:26:42.7781919Z Warning: cuda_half_test does not exist. 2025-09-07T09:26:42.7784519Z + run_if_exists cuda_vectorized_test 2025-09-07T09:26:42.7784701Z + local test_name=cuda_vectorized_test 2025-09-07T09:26:42.7784860Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-09-07T09:26:42.7785037Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-09-07T09:26:42.7785229Z Warning: cuda_vectorized_test does not exist. 2025-09-07T09:26:42.7785391Z + run_if_exists cuda_distributions_test 2025-09-07T09:26:42.7785554Z + local test_name=cuda_distributions_test 2025-09-07T09:26:42.7785727Z + [[ -x build/bin/cuda_distributions_test ]] 2025-09-07T09:26:42.7786688Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-09-07T09:26:42.7786883Z Warning: cuda_distributions_test does not exist. 2025-09-07T09:26:42.7787060Z + run_if_exists cuda_optional_test 2025-09-07T09:26:42.7788949Z + local test_name=cuda_optional_test 2025-09-07T09:26:42.7789115Z + [[ -x build/bin/cuda_optional_test ]] 2025-09-07T09:26:42.7789288Z + echo 'Warning: cuda_optional_test does not exist.' 2025-09-07T09:26:42.7789570Z Warning: cuda_optional_test does not exist. 2025-09-07T09:26:42.7789733Z + run_if_exists cuda_tensor_interop_test 2025-09-07T09:26:42.7789893Z + local test_name=cuda_tensor_interop_test 2025-09-07T09:26:42.7790061Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-09-07T09:26:42.7790249Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-09-07T09:26:42.7790450Z Warning: cuda_tensor_interop_test does not exist. 2025-09-07T09:26:42.7790616Z + run_if_exists cuda_complex_test 2025-09-07T09:26:42.7790849Z + local test_name=cuda_complex_test 2025-09-07T09:26:42.7792490Z + [[ -x build/bin/cuda_complex_test ]] 2025-09-07T09:26:42.7792645Z + echo 'Warning: cuda_complex_test does not exist.' 2025-09-07T09:26:42.7792795Z Warning: cuda_complex_test does not exist. 2025-09-07T09:26:42.7792923Z + run_if_exists cuda_complex_math_test 2025-09-07T09:26:42.7793049Z + local test_name=cuda_complex_math_test 2025-09-07T09:26:42.7793175Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-09-07T09:26:42.7793323Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-09-07T09:26:42.7793475Z Warning: cuda_complex_math_test does not exist. 2025-09-07T09:26:42.7793603Z + run_if_exists cuda_cub_test 2025-09-07T09:26:42.7793719Z + local test_name=cuda_cub_test 2025-09-07T09:26:42.7793834Z + [[ -x build/bin/cuda_cub_test ]] 2025-09-07T09:26:42.7795364Z + echo 'Warning: cuda_cub_test does not exist.' 2025-09-07T09:26:42.7795498Z Warning: cuda_cub_test does not exist. 2025-09-07T09:26:42.7795623Z + run_if_exists cuda_atomic_ops_test 2025-09-07T09:26:42.7795745Z + local test_name=cuda_atomic_ops_test 2025-09-07T09:26:42.7795872Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-09-07T09:26:42.7796010Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-09-07T09:26:42.7796160Z Warning: cuda_atomic_ops_test does not exist. 2025-09-07T09:26:42.7796285Z + '[' OFF == ON ']' 2025-09-07T09:26:42.7796379Z + [[ -n '' ]] 2025-09-07T09:26:42.7796466Z + assert_git_not_dirty 2025-09-07T09:26:42.7796588Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]] 2025-09-07T09:26:42.7797913Z + test_libtorch 1 2025-09-07T09:26:42.7798001Z + local SHARD=1 2025-09-07T09:26:42.7798095Z + [[ default != \s\l\o\w ]] 2025-09-07T09:26:42.7798203Z + echo 'Testing libtorch' 2025-09-07T09:26:42.7798303Z Testing libtorch 2025-09-07T09:26:42.7798594Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7799063Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libjitbackend_test.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7807176Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2_nvrtc.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7821935Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10_hip.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7831759Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libshm /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libshm.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libshm_windows /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7852298Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_hip.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7863180Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnvfuser*' /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7875919Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7876319Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-09-07T09:26:42.7876640Z + [[ -z 1 ]] 2025-09-07T09:26:42.7876763Z + [[ 1 == \1 ]] 2025-09-07T09:26:42.7876891Z + test_libtorch_api 2025-09-07T09:26:42.7878504Z + MNIST_DIR=/var/lib/jenkins/pytorch/test/cpp/api/mnist 2025-09-07T09:26:42.7882154Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/pytorch/test/cpp/api/mnist 2025-09-07T09:26:42.8170922Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ... 2025-09-07T09:26:42.9218004Z Failed to download (trying next): 2025-09-07T09:26:42.9218187Z HTTP Error 404: Not Found 2025-09-07T09:26:42.9218406Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ... 2025-09-07T09:26:43.3708728Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ... 2025-09-07T09:26:43.3987086Z Failed to download (trying next): 2025-09-07T09:26:43.3988394Z HTTP Error 404: Not Found 2025-09-07T09:26:43.3989086Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ... 2025-09-07T09:26:43.5103925Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ... 2025-09-07T09:26:43.5388347Z Failed to download (trying next): 2025-09-07T09:26:43.5388952Z HTTP Error 404: Not Found 2025-09-07T09:26:43.5389554Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ... 2025-09-07T09:26:43.7825857Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ... 2025-09-07T09:26:43.8101084Z Failed to download (trying next): 2025-09-07T09:26:43.8101258Z HTTP Error 404: Not Found 2025-09-07T09:26:43.8101478Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ... 2025-09-07T09:26:43.9049994Z + [[ linux-noble-rocm-py3.12-mi300 == *asan* ]] 2025-09-07T09:26:43.9050512Z + [[ linux-noble-rocm-py3.12-mi300 == *slow-gradcheck* ]] 2025-09-07T09:26:43.9050903Z + OMP_NUM_THREADS=2 2025-09-07T09:26:43.9051305Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/pytorch/test/cpp/api/mnist 2025-09-07T09:26:43.9051903Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest' 2025-09-07T09:26:45.2541626Z /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. 2025-09-07T09:26:45.2543081Z import pkg_resources 2025-09-07T09:26:45.5847852Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-09-07T09:26:45.5922934Z Found test times from artifacts 2025-09-07T09:26:45.6228096Z Found test times from artifacts 2025-09-07T09:26:45.6234329Z Running all tests 2025-09-07T09:26:45.6235477Z Running parallel tests on 1 processes 2025-09-07T09:26:45.6235732Z Name: tests to run (est. time: 0.0min) 2025-09-07T09:26:45.6235944Z Serial tests (1): 2025-09-07T09:26:45.6242911Z cpp/test_api 1/1 2025-09-07T09:26:45.6243078Z Parallel tests (0): 2025-09-07T09:26:45.6243234Z Name: excluded (est. time: 0.0min) 2025-09-07T09:26:45.6243444Z Serial tests (0): 2025-09-07T09:26:45.6243584Z Parallel tests (0): 2025-09-07T09:26:45.6244944Z Running cpp/test_api 1/1 ... [2025-09-07 09:26:45.623636] 2025-09-07T09:26:45.6245162Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:45.6245932Z Executing ['pytest', '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin/test_api', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-51e84f005a117835.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-09-07 09:26:45.623892] 2025-09-07T09:26:46.2896495Z 2025-09-07T09:26:46.2897273Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_a5bc23783fc32310_.log 2025-09-07T09:26:46.2897588Z 2025-09-07T09:26:46.2902064Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T09:26:46.2902346Z Uploading artifacts took 0.00 seconds 2025-09-07T09:26:47.6202043Z /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. 2025-09-07T09:26:47.6203086Z import pkg_resources 2025-09-07T09:26:48.3488115Z Running test batch 'tests to run' cost 2.72 seconds 2025-09-07T09:26:48.8416744Z + [[ linux-noble-rocm-py3.12-mi300 != *android* ]] 2025-09-07T09:26:48.8417250Z + [[ linux-noble-rocm-py3.12-mi300 != *cuda* ]] 2025-09-07T09:26:48.8417634Z + [[ linux-noble-rocm-py3.12-mi300 != *asan* ]] 2025-09-07T09:26:48.8426654Z + [[ linux-noble-rocm-py3.12-mi300 != *s390x* ]] 2025-09-07T09:26:48.8426963Z + export CPP_TESTS_DIR=build/bin 2025-09-07T09:26:48.8427199Z + CPP_TESTS_DIR=build/bin 2025-09-07T09:26:48.8427506Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test 2025-09-07T09:26:50.1769786Z /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. 2025-09-07T09:26:50.1771249Z import pkg_resources 2025-09-07T09:26:50.4931124Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-09-07T09:26:50.4993959Z Found test times from artifacts 2025-09-07T09:26:50.5271519Z Found test times from artifacts 2025-09-07T09:26:50.5278740Z Running all tests 2025-09-07T09:26:50.5279596Z Running parallel tests on 1 processes 2025-09-07T09:26:50.5280074Z Name: tests to run (est. time: 0.0min) 2025-09-07T09:26:50.5280422Z Serial tests (1): 2025-09-07T09:26:50.5280720Z cpp/static_runtime_test 1/1 2025-09-07T09:26:50.5281040Z Parallel tests (0): 2025-09-07T09:26:50.5281382Z Name: excluded (est. time: 0.0min) 2025-09-07T09:26:50.5281707Z Serial tests (0): 2025-09-07T09:26:50.5281986Z Parallel tests (0): 2025-09-07T09:26:50.5282393Z Running cpp/static_runtime_test 1/1 ... [2025-09-07 09:26:50.527787] 2025-09-07T09:26:50.5288333Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-09-07T09:26:50.5289013Z Executing ['pytest', '/var/lib/jenkins/pytorch/build/bin/static_runtime_test', '-v', '-vv', '-rfEX', '-n', '1', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c54b627e1e899a7b.xml', '-x', '--reruns=2'] ... [2025-09-07 09:26:50.528010] 2025-09-07T09:26:50.9431342Z 2025-09-07T09:26:50.9431987Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_b688537f88591fd6_.log 2025-09-07T09:26:50.9432347Z 2025-09-07T09:26:50.9432486Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-09-07T09:26:50.9432741Z Uploading artifacts took 0.00 seconds 2025-09-07T09:26:52.2803036Z /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. 2025-09-07T09:26:52.2804152Z import pkg_resources 2025-09-07T09:26:53.0289420Z Running test batch 'tests to run' cost 2.5 seconds 2025-09-07T09:26:53.5398484Z + [[ -z 1 ]] 2025-09-07T09:26:53.5398802Z + [[ 1 == \2 ]] 2025-09-07T09:26:53.5399075Z + assert_git_not_dirty 2025-09-07T09:26:53.5399426Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]] 2025-09-07T09:26:53.5399829Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]] 2025-09-07T09:26:53.5400169Z + sccache_epilogue 2025-09-07T09:26:53.5400487Z + echo '::group::Sccache Compilation Log' 2025-09-07T09:26:53.5407361Z ##[group]Sccache Compilation Log 2025-09-07T09:26:53.5407585Z + echo '=================== sccache compilation log ===================' 2025-09-07T09:26:53.5408362Z =================== sccache compilation log =================== 2025-09-07T09:26:53.5408708Z + python /var/lib/jenkins/pytorch/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-09-07T09:26:53.5475626Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-09-07T09:26:53.5475916Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-09-07T09:26:53.5476126Z + sccache --show-stats 2025-09-07T09:26:53.5494538Z Compile requests 7168 2025-09-07T09:26:53.5494698Z Compile requests executed 411 2025-09-07T09:26:53.5494855Z Cache hits 32 2025-09-07T09:26:53.5497570Z Cache hits (C/C++) 32 2025-09-07T09:26:53.5497710Z Cache misses 375 2025-09-07T09:26:53.5497842Z Cache misses (C/C++) 361 2025-09-07T09:26:53.5497968Z Cache misses (HIP) 14 2025-09-07T09:26:53.5498097Z Cache hits rate 7.86 % 2025-09-07T09:26:53.5498229Z Cache hits rate (C/C++) 8.14 % 2025-09-07T09:26:53.5498355Z Cache hits rate (HIP) 0.00 % 2025-09-07T09:26:53.5498480Z Cache timeouts 0 2025-09-07T09:26:53.5498602Z Cache read errors 0 2025-09-07T09:26:53.5498723Z Forced recaches 0 2025-09-07T09:26:53.5498842Z Cache write errors 0 2025-09-07T09:26:53.5500485Z Cache errors 0 2025-09-07T09:26:53.5500609Z Compilations 375 2025-09-07T09:26:53.5500736Z Compilation failures 4 2025-09-07T09:26:53.5500860Z Non-cacheable compilations 0 2025-09-07T09:26:53.5500981Z Non-cacheable calls 203 2025-09-07T09:26:53.5501101Z Non-compilation calls 6554 2025-09-07T09:26:53.5501225Z Unsupported compiler calls 0 2025-09-07T09:26:53.5501352Z Average cache write 0.000 s 2025-09-07T09:26:53.5501480Z Average compiler 2.863 s 2025-09-07T09:26:53.5501610Z Average cache read hit 0.000 s 2025-09-07T09:26:53.5501738Z Failed distributed compilations 0 2025-09-07T09:26:53.5503004Z 2025-09-07T09:26:53.5503050Z Non-cacheable reasons: 2025-09-07T09:26:53.5503154Z unknown source language 158 2025-09-07T09:26:53.5503268Z -E 45 2025-09-07T09:26:53.5503345Z 2025-09-07T09:26:53.5503499Z Cache location Local disk: "/var/lib/jenkins/.cache/sccache" 2025-09-07T09:26:53.5503659Z Use direct/preprocessor mode? yes 2025-09-07T09:26:53.5503779Z Version (client) 0.10.0 2025-09-07T09:26:53.5503897Z Cache size 58 MiB 2025-09-07T09:26:53.5504016Z Max cache size 10 GiB 2025-09-07T09:26:53.5504133Z + sccache --stop-server 2025-09-07T09:26:53.5512249Z Stopping sccache server... 2025-09-07T09:26:53.5514686Z Compile requests 7168 2025-09-07T09:26:53.5516803Z Compile requests executed 411 2025-09-07T09:26:53.5516937Z Cache hits 32 2025-09-07T09:26:53.5517047Z Cache hits (C/C++) 32 2025-09-07T09:26:53.5517213Z Cache misses 375 2025-09-07T09:26:53.5517323Z Cache misses (C/C++) 361 2025-09-07T09:26:53.5517430Z Cache misses (HIP) 14 2025-09-07T09:26:53.5517542Z Cache hits rate 7.86 % 2025-09-07T09:26:53.5517695Z Cache hits rate (C/C++) 8.14 % 2025-09-07T09:26:53.5517815Z Cache hits rate (HIP) 0.00 % 2025-09-07T09:26:53.5517928Z Cache timeouts 0 2025-09-07T09:26:53.5518046Z Cache read errors 0 2025-09-07T09:26:53.5519581Z Forced recaches 0 2025-09-07T09:26:53.5519700Z Cache write errors 0 2025-09-07T09:26:53.5519807Z Cache errors 0 2025-09-07T09:26:53.5519952Z Compilations 375 2025-09-07T09:26:53.5520062Z Compilation failures 4 2025-09-07T09:26:53.5520175Z Non-cacheable compilations 0 2025-09-07T09:26:53.5520286Z Non-cacheable calls 203 2025-09-07T09:26:53.5520398Z Non-compilation calls 6554 2025-09-07T09:26:53.5520510Z Unsupported compiler calls 0 2025-09-07T09:26:53.5520622Z Average cache write 0.000 s 2025-09-07T09:26:53.5520740Z Average compiler 2.863 s 2025-09-07T09:26:53.5520854Z Average cache read hit 0.000 s 2025-09-07T09:26:53.5520973Z Failed distributed compilations 0 2025-09-07T09:26:53.5521053Z 2025-09-07T09:26:53.5521094Z Non-cacheable reasons: 2025-09-07T09:26:53.5521200Z unknown source language 158 2025-09-07T09:26:53.5521316Z -E 45 2025-09-07T09:26:53.5521389Z 2025-09-07T09:26:53.5521468Z Cache location Local disk: "/var/lib/jenkins/.cache/sccache" 2025-09-07T09:26:53.5522811Z Use direct/preprocessor mode? yes 2025-09-07T09:26:53.5522945Z Version (client) 0.10.0 2025-09-07T09:26:53.5523127Z Cache size 58 MiB 2025-09-07T09:26:53.5523302Z Max cache size 10 GiB 2025-09-07T09:26:53.5523470Z + echo ::endgroup:: 2025-09-07T09:26:53.5523718Z ##[endgroup] 2025-09-07T09:26:53.5602165Z ##[group]Run # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct 2025-09-07T09:26:53.5602514Z # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct 2025-09-07T09:26:53.5602890Z docker exec -t "d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526" sh -c "cd ../pytorch && sudo cp -R test/test-reports ../workspace/test" 2025-09-07T09:26:53.5608536Z shell: /usr/bin/bash -e {0} 2025-09-07T09:26:53.5608655Z env: 2025-09-07T09:26:53.5608752Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:53.5608902Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:53.5609081Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:53.5609245Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:53.5609642Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:53.5610005Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:53.5610122Z AWS_REGION: us-east-1 2025-09-07T09:26:53.5610295Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:53.5610446Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:53.5612440Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:53.5612610Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:53.5612791Z ##[endgroup] 2025-09-07T09:26:53.6418179Z ##[group]Run docker exec -t "d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526" sh -c "sudo chown -R 1001:1001 test" 2025-09-07T09:26:53.6418584Z docker exec -t "d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526" sh -c "sudo chown -R 1001:1001 test" 2025-09-07T09:26:53.6425088Z shell: /usr/bin/bash -e {0} 2025-09-07T09:26:53.6425196Z env: 2025-09-07T09:26:53.6425285Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:53.6425428Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:53.6425692Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:53.6425860Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:53.6426244Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:53.6426635Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:53.6426821Z AWS_REGION: us-east-1 2025-09-07T09:26:53.6426990Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:53.6427146Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:53.6431158Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:53.6431334Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:53.6431504Z ##[endgroup] 2025-09-07T09:26:53.7213004Z ##[group]Run cat test/**/*_toprint.log || true 2025-09-07T09:26:53.7213167Z cat test/**/*_toprint.log || true 2025-09-07T09:26:53.7218896Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:26:53.7219115Z env: 2025-09-07T09:26:53.7219245Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:53.7219442Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:53.7219699Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:53.7219895Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:53.7220262Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:53.7220624Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:53.7220734Z AWS_REGION: us-east-1 2025-09-07T09:26:53.7220878Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:53.7221034Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:53.7222928Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:53.7223100Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:53.7223284Z ##[endgroup] 2025-09-07T09:26:53.7291536Z cat: 'test/**/*_toprint.log': No such file or directory 2025-09-07T09:26:53.7372038Z Prepare all required actions 2025-09-07T09:26:53.7372360Z Getting action download info 2025-09-07T09:26:53.9539619Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-09-07T09:26:54.3065842Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-07T09:26:54.7058200Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-09-07T09:26:54.7058354Z with: 2025-09-07T09:26:54.7058445Z use-gha: true 2025-09-07T09:26:54.7058587Z file-suffix: test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520 2025-09-07T09:26:54.7058753Z s3-bucket: gha-artifacts 2025-09-07T09:26:54.7058854Z env: 2025-09-07T09:26:54.7058938Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:54.7059065Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:54.7059251Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:54.7059429Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:54.7059801Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:54.7060157Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:54.7060281Z AWS_REGION: us-east-1 2025-09-07T09:26:54.7060429Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:54.7060567Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:54.7062447Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:54.7062611Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:54.7062778Z ##[endgroup] 2025-09-07T09:26:54.7098889Z ##[group]Run actions/upload-artifact@v4 2025-09-07T09:26:54.7099010Z with: 2025-09-07T09:26:54.7099183Z name: test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip 2025-09-07T09:26:54.7099461Z retention-days: 14 2025-09-07T09:26:54.7107623Z if-no-files-found: warn 2025-09-07T09:26:54.7107754Z path: test/**/*.json 2025-09-07T09:26:54.7107861Z compression-level: 6 2025-09-07T09:26:54.7107956Z overwrite: false 2025-09-07T09:26:54.7108054Z include-hidden-files: false 2025-09-07T09:26:54.7108157Z env: 2025-09-07T09:26:54.7108243Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:54.7108464Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:54.7108631Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:54.7108789Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:54.7109167Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:54.7111743Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:54.7111862Z AWS_REGION: us-east-1 2025-09-07T09:26:54.7111987Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:54.7112135Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:54.7114014Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:54.7114176Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:54.7114347Z ##[endgroup] 2025-09-07T09:26:55.1707803Z With the provided path, there will be 10 files uploaded 2025-09-07T09:26:55.1711130Z Artifact name is valid! 2025-09-07T09:26:55.1712165Z Root directory input is valid! 2025-09-07T09:26:55.2773589Z Beginning upload of artifact content to blob storage 2025-09-07T09:26:55.4469037Z Uploaded bytes 46747 2025-09-07T09:26:55.4786579Z Finished uploading artifact content to blob storage! 2025-09-07T09:26:55.4787597Z SHA256 digest of uploaded artifact zip is b1e53e44019deea913695883abcb5caf89aac2f3b77d4625fd86de3e43a3ed8a 2025-09-07T09:26:55.4788336Z Finalizing artifact upload 2025-09-07T09:26:55.5720211Z Artifact test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip.zip successfully finalized. Artifact ID 3946789120 2025-09-07T09:26:55.5721738Z Artifact test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip has been successfully uploaded! Final size is 46747 bytes. Artifact ID is 3946789120 2025-09-07T09:26:55.5722611Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/17524754565/artifacts/3946789120 2025-09-07T09:26:55.5875905Z ##[group]Run actions/upload-artifact@v4 2025-09-07T09:26:55.5876029Z with: 2025-09-07T09:26:55.5876204Z name: test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip 2025-09-07T09:26:55.5876401Z retention-days: 14 2025-09-07T09:26:55.5876501Z if-no-files-found: ignore 2025-09-07T09:26:55.5876616Z path: test/**/*.xml test/**/*.csv 2025-09-07T09:26:55.5876732Z compression-level: 6 2025-09-07T09:26:55.5876828Z overwrite: false 2025-09-07T09:26:55.5876929Z include-hidden-files: false 2025-09-07T09:26:55.5877032Z env: 2025-09-07T09:26:55.5877115Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:55.5877251Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:55.5877422Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:55.5877587Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:55.5877970Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:55.5878348Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:55.5878471Z AWS_REGION: us-east-1 2025-09-07T09:26:55.5878614Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:55.5878764Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:55.5880655Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:55.5880825Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:55.5881070Z ##[endgroup] 2025-09-07T09:26:56.0915254Z With the provided path, there will be 131 files uploaded 2025-09-07T09:26:56.0917313Z Artifact name is valid! 2025-09-07T09:26:56.0917638Z Root directory input is valid! 2025-09-07T09:26:56.1912192Z Beginning upload of artifact content to blob storage 2025-09-07T09:26:56.7340971Z Uploaded bytes 582132 2025-09-07T09:26:56.7862207Z Finished uploading artifact content to blob storage! 2025-09-07T09:26:56.7871453Z SHA256 digest of uploaded artifact zip is 9f6c189d74ed5700253909a5dac783d897b9a63d85693a7ec2d1858807143217 2025-09-07T09:26:56.7875288Z Finalizing artifact upload 2025-09-07T09:26:56.8684786Z Artifact test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip.zip successfully finalized. Artifact ID 3946789162 2025-09-07T09:26:56.8686160Z Artifact test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip has been successfully uploaded! Final size is 582132 bytes. Artifact ID is 3946789162 2025-09-07T09:26:56.8687871Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/17524754565/artifacts/3946789162 2025-09-07T09:26:56.8855179Z ##[group]Run actions/upload-artifact@v4 2025-09-07T09:26:56.8855321Z with: 2025-09-07T09:26:56.8855486Z name: logs-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip 2025-09-07T09:26:56.8855672Z retention-days: 14 2025-09-07T09:26:56.8855780Z if-no-files-found: ignore 2025-09-07T09:26:56.8855898Z path: usage_log.txt test/**/*.log 2025-09-07T09:26:56.8856026Z compression-level: 6 2025-09-07T09:26:56.8857812Z overwrite: false 2025-09-07T09:26:56.8857919Z include-hidden-files: false 2025-09-07T09:26:56.8858028Z env: 2025-09-07T09:26:56.8858118Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:56.8858253Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:56.8858433Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:56.8858594Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:56.8859131Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:56.8859503Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:56.8859621Z AWS_REGION: us-east-1 2025-09-07T09:26:56.8861339Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:56.8861493Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:56.8863402Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:56.8863645Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:56.8863822Z ##[endgroup] 2025-09-07T09:26:57.3796676Z Multiple search paths detected. Calculating the least common ancestor of all paths 2025-09-07T09:26:57.3798753Z The least common ancestor is /home/runner/_work/pytorch/pytorch. This will be the root directory of the artifact 2025-09-07T09:26:57.3799047Z With the provided path, there will be 123 files uploaded 2025-09-07T09:26:57.3799289Z Artifact name is valid! 2025-09-07T09:26:57.3799406Z Root directory input is valid! 2025-09-07T09:26:57.4822390Z Beginning upload of artifact content to blob storage 2025-09-07T09:26:57.8483104Z Uploaded bytes 714562 2025-09-07T09:26:57.8811908Z Finished uploading artifact content to blob storage! 2025-09-07T09:26:57.8812593Z SHA256 digest of uploaded artifact zip is 8018ff10754d5ac8b9da258def925b41cf8d3325bb702208546dffe2e8ed9806 2025-09-07T09:26:57.8813699Z Finalizing artifact upload 2025-09-07T09:26:57.9709180Z Artifact logs-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip.zip successfully finalized. Artifact ID 3946789200 2025-09-07T09:26:57.9710477Z Artifact logs-runattempt1-test-default-1-6-linux.rocm.gpu.gfx942.1_49774353520.zip has been successfully uploaded! Final size is 714562 bytes. Artifact ID is 3946789200 2025-09-07T09:26:57.9714028Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/17524754565/artifacts/3946789200 2025-09-07T09:26:57.9868272Z ##[group]Run # shellcheck disable=SC2156 2025-09-07T09:26:57.9868439Z # shellcheck disable=SC2156 2025-09-07T09:26:57.9868662Z find . -iname "core.[1-9]*" -exec docker exec "${CONTAINER_NAME}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-09-07T09:26:57.9876614Z shell: /usr/bin/bash -e {0} 2025-09-07T09:26:57.9876733Z env: 2025-09-07T09:26:57.9876828Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:57.9876972Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:57.9877229Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:57.9877398Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:57.9877783Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:57.9878148Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:57.9880482Z AWS_REGION: us-east-1 2025-09-07T09:26:57.9880633Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:57.9880793Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:57.9882698Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:57.9882871Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:57.9883050Z ##[endgroup] 2025-09-07T09:26:58.1504981Z ##[group]Run aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 2025-09-07T09:26:58.1505196Z with: 2025-09-07T09:26:58.1505362Z role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_upload-benchmark-results 2025-09-07T09:26:58.1505557Z role-duration-seconds: 18000 2025-09-07T09:26:58.1505690Z aws-region: us-east-1 2025-09-07T09:26:58.1505815Z audience: sts.amazonaws.com 2025-09-07T09:26:58.1505931Z env: 2025-09-07T09:26:58.1506035Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:58.1506189Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:58.1506380Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:58.1506556Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:58.1506966Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:58.1509494Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:58.1509616Z AWS_REGION: us-east-1 2025-09-07T09:26:58.1509748Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:58.1509903Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:58.1511797Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:58.1511966Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:58.1512143Z ##[endgroup] 2025-09-07T09:26:58.3494723Z Assuming role with OIDC 2025-09-07T09:26:58.4916666Z Authenticated as assumedRoleId AROAUPVRELQNA5GQHA6IA:GitHubActions 2025-09-07T09:26:58.5425938Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-09-07T09:26:58.5426210Z with: 2025-09-07T09:26:58.5426387Z benchmark-results-dir: test/test-reports 2025-09-07T09:26:58.5426576Z dry-run: false 2025-09-07T09:26:58.5426737Z schema-version: v3 2025-09-07T09:26:58.5427081Z github-token: *** 2025-09-07T09:26:58.5427234Z env: 2025-09-07T09:26:58.5427397Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:58.5427612Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:58.5427841Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:58.5428101Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:58.5428548Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:58.5429030Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:58.5429297Z AWS_REGION: us-east-1 2025-09-07T09:26:58.5429484Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:58.5429699Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:58.5431871Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:58.5432358Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:58.5432612Z ##[endgroup] 2025-09-07T09:26:58.5444318Z ##[group]Run set -eux 2025-09-07T09:26:58.5444602Z set -eux 2025-09-07T09:26:58.5444796Z  2025-09-07T09:26:58.5445033Z if [[ -n "" ]]; then 2025-09-07T09:26:58.5445243Z  source "" 2025-09-07T09:26:58.5445394Z fi 2025-09-07T09:26:58.5445631Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T09:26:58.5445863Z  2025-09-07T09:26:58.5446015Z DEVICE_NAME="" 2025-09-07T09:26:58.5446198Z DEVICE_TYPE="" 2025-09-07T09:26:58.5446347Z  2025-09-07T09:26:58.5446531Z if command -v nvidia-smi; then 2025-09-07T09:26:58.5446780Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-09-07T09:26:58.5447074Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-09-07T09:26:58.5447355Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-09-07T09:26:58.5447607Z  python3 -mpip install torch==2.7.1 2025-09-07T09:26:58.5447805Z elif command -v rocminfo; then 2025-09-07T09:26:58.5448379Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-09-07T09:26:58.5448647Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-09-07T09:26:58.5453198Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-09-07T09:26:58.5453406Z  # GPU device name coming from rocminfo instead 2025-09-07T09:26:58.5453558Z  DEVICE_NAME=rocm 2025-09-07T09:26:58.5453764Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-09-07T09:26:58.5453960Z fi 2025-09-07T09:26:58.5454059Z  2025-09-07T09:26:58.5454181Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-09-07T09:26:58.5454349Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-09-07T09:26:58.5458539Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:26:58.5458694Z env: 2025-09-07T09:26:58.5458795Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:26:58.5458944Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:26:58.5459132Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:26:58.5459312Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:26:58.5459785Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:26:58.5460171Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:26:58.5460300Z AWS_REGION: us-east-1 2025-09-07T09:26:58.5460443Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:26:58.5460606Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:26:58.5462716Z AWS_SESSION_TOKEN: *** 2025-09-07T09:26:58.5462895Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:26:58.5463085Z ##[endgroup] 2025-09-07T09:26:58.5496853Z + [[ -n '' ]] 2025-09-07T09:26:58.5497134Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T09:26:58.7017081Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T09:26:59.1766195Z Collecting boto3==1.35.33 2025-09-07T09:26:59.3150839Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-09-07T09:26:59.3656775Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 139.1/139.1 KB 2.7 MB/s eta 0:00:00 2025-09-07T09:26:59.4583861Z Collecting psutil==7.0.0 2025-09-07T09:26:59.4817130Z Downloading psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (277 kB) 2025-09-07T09:26:59.5093595Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 278.0/278.0 KB 10.2 MB/s eta 0:00:00 2025-09-07T09:26:59.5425083Z Collecting pynvml==12.0.0 2025-09-07T09:26:59.5655674Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-09-07T09:26:59.6069279Z Collecting s3transfer<0.11.0,>=0.10.0 2025-09-07T09:26:59.6298920Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-09-07T09:26:59.6329496Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 83.2/83.2 KB 40.0 MB/s eta 0:00:00 2025-09-07T09:26:59.6655542Z Collecting jmespath<2.0.0,>=0.7.1 2025-09-07T09:26:59.6881792Z Downloading jmespath-1.0.1-py3-none-any.whl (20 kB) 2025-09-07T09:27:00.0552885Z Collecting botocore<1.36.0,>=1.35.33 2025-09-07T09:27:00.0785112Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-09-07T09:27:00.4527186Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.3/13.3 MB 32.1 MB/s eta 0:00:00 2025-09-07T09:27:00.5097032Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-09-07T09:27:00.5328148Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-09-07T09:27:00.5355395Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 47.5/47.5 KB 22.8 MB/s eta 0:00:00 2025-09-07T09:27:00.5390157Z Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /home/runner/.local/lib/python3.10/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.26.20) 2025-09-07T09:27:00.5741273Z Collecting python-dateutil<3.0.0,>=2.1 2025-09-07T09:27:00.5962477Z Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB) 2025-09-07T09:27:00.5994213Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 229.9/229.9 KB 101.2 MB/s eta 0:00:00 2025-09-07T09:27:00.6790114Z Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.16.0) 2025-09-07T09:27:00.7431052Z Installing collected packages: nvidia-ml-py, python-dateutil, pynvml, psutil, jmespath, botocore, s3transfer, boto3 2025-09-07T09:27:01.1352212Z Successfully installed boto3-1.35.33 botocore-1.35.99 jmespath-1.0.1 nvidia-ml-py-12.575.51 psutil-7.0.0 pynvml-12.0.0 python-dateutil-2.9.0.post0 s3transfer-0.10.4 2025-09-07T09:27:01.2038446Z + DEVICE_NAME= 2025-09-07T09:27:01.2049816Z + DEVICE_TYPE= 2025-09-07T09:27:01.2050073Z + command -v nvidia-smi 2025-09-07T09:27:01.2050288Z /usr/bin/rocminfo 2025-09-07T09:27:01.2050486Z + command -v rocminfo 2025-09-07T09:27:01.2050705Z + DEVICE_NAME=rocm 2025-09-07T09:27:01.2050884Z ++ rocminfo 2025-09-07T09:27:01.2051432Z ++ grep 'Marketing Name' 2025-09-07T09:27:01.2054671Z ++ tail -n1 2025-09-07T09:27:01.2063351Z ++ awk -F: '{print $2}' 2025-09-07T09:27:01.2063666Z ++ xargs 2025-09-07T09:27:01.2849480Z + DEVICE_TYPE='AMD Instinct Mi325X VF' 2025-09-07T09:27:01.2849663Z + echo DEVICE_NAME=rocm 2025-09-07T09:27:01.2849813Z + echo 'DEVICE_TYPE=AMD Instinct Mi325X VF' 2025-09-07T09:27:01.2885205Z ##[group]Run set -eux 2025-09-07T09:27:01.2885377Z set -eux 2025-09-07T09:27:01.2885495Z  2025-09-07T09:27:01.2885633Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-09-07T09:27:01.2885815Z  echo "Missing github-token input" 2025-09-07T09:27:01.2885974Z  exit 1 2025-09-07T09:27:01.2886099Z fi 2025-09-07T09:27:01.2893477Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.2893653Z env: 2025-09-07T09:27:01.2893768Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.2893929Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.2894128Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.2897132Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.2897524Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.2897990Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.2898116Z AWS_REGION: us-east-1 2025-09-07T09:27:01.2898280Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.2898440Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.2900521Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.2900700Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.2900887Z DEVICE_NAME: rocm 2025-09-07T09:27:01.2900999Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.2901281Z GITHUB_TOKEN: *** 2025-09-07T09:27:01.2901388Z ##[endgroup] 2025-09-07T09:27:01.2932104Z + [[ -z *** ]] 2025-09-07T09:27:01.2978718Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-09-07T09:27:01.2978897Z with: 2025-09-07T09:27:01.2979061Z github-token: *** 2025-09-07T09:27:01.2979162Z env: 2025-09-07T09:27:01.2979260Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.2979419Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.2981536Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.2981722Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.2982113Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.2982492Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.2982623Z AWS_REGION: us-east-1 2025-09-07T09:27:01.2982757Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.2982914Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.2985263Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.2985442Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.2987504Z DEVICE_NAME: rocm 2025-09-07T09:27:01.2987626Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.2987755Z ##[endgroup] 2025-09-07T09:27:01.3001788Z ##[group]Run set -eux 2025-09-07T09:27:01.3001925Z set -eux 2025-09-07T09:27:01.3002024Z  2025-09-07T09:27:01.3002223Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T09:27:01.3009790Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.3009954Z env: 2025-09-07T09:27:01.3010054Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.3010201Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.3010389Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.3010571Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.3010956Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.3011459Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.3011593Z AWS_REGION: us-east-1 2025-09-07T09:27:01.3013551Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.3013713Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.3015896Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.3016071Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.3016258Z DEVICE_NAME: rocm 2025-09-07T09:27:01.3016370Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.3016548Z GITHUB_TOKEN: *** 2025-09-07T09:27:01.3016653Z ##[endgroup] 2025-09-07T09:27:01.3043741Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 17524754565 linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b 2025-09-07T09:27:01.8857698Z setting job-id=49774353520 2025-09-07T09:27:01.8859835Z setting job-name=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T09:27:01.8970165Z ##[group]Run set -eux 2025-09-07T09:27:01.8970322Z set -eux 2025-09-07T09:27:01.8970512Z  2025-09-07T09:27:01.8970617Z if [[ -n "" ]]; then 2025-09-07T09:27:01.8970743Z  source "" 2025-09-07T09:27:01.8973777Z fi 2025-09-07T09:27:01.8973929Z  2025-09-07T09:27:01.8974149Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-09-07T09:27:01.8974503Z  --schema-version "${SCHEMA_VERSION}" \ 2025-09-07T09:27:01.8974651Z  --repo "${REPO}" \ 2025-09-07T09:27:01.8974872Z  --head-branch "${HEAD_BRANCH}" \ 2025-09-07T09:27:01.8975011Z  --head-sha "${HEAD_SHA}" \ 2025-09-07T09:27:01.8975152Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-09-07T09:27:01.8975300Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-09-07T09:27:01.8977402Z  --job-id "${JOB_ID}" \ 2025-09-07T09:27:01.8977535Z  --job-name "${JOB_NAME}" 2025-09-07T09:27:01.8982776Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.8982933Z env: 2025-09-07T09:27:01.8983031Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.8983179Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.8983367Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.8983635Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.8986232Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.8986616Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.8986743Z AWS_REGION: us-east-1 2025-09-07T09:27:01.8986902Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.8987066Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.8989211Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.8989393Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.8989586Z DEVICE_NAME: rocm 2025-09-07T09:27:01.8991577Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.8991715Z SCHEMA_VERSION: v3 2025-09-07T09:27:01.8991827Z REPO: pytorch/pytorch 2025-09-07T09:27:01.8991945Z HEAD_BRANCH: refs/heads/main 2025-09-07T09:27:01.8992088Z HEAD_SHA: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T09:27:01.8992232Z WORKFLOW_RUN_ID: 17524754565 2025-09-07T09:27:01.8992347Z RUN_ATTEMPT: 1 2025-09-07T09:27:01.8992448Z JOB_ID: 49774353520 2025-09-07T09:27:01.8992633Z JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1) 2025-09-07T09:27:01.8994666Z ##[endgroup] 2025-09-07T09:27:01.9022391Z + [[ -n '' ]] 2025-09-07T09:27:01.9031795Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha 93fb23d6fae7c4e82c4239a1033e522088742634 --workflow-id 17524754565 --run-attempt 1 --job-id 49774353520 --job-name 'linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1)' 2025-09-07T09:27:01.9206742Z ##[group]Run set -eux 2025-09-07T09:27:01.9206873Z set -eux 2025-09-07T09:27:01.9206978Z  2025-09-07T09:27:01.9207087Z if [[ -n "" ]]; then 2025-09-07T09:27:01.9207213Z  source "" 2025-09-07T09:27:01.9207324Z fi 2025-09-07T09:27:01.9207425Z  2025-09-07T09:27:01.9207592Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-09-07T09:27:01.9215709Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.9215866Z env: 2025-09-07T09:27:01.9215966Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.9216113Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.9216300Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.9216486Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.9216943Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.9217323Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.9219549Z AWS_REGION: us-east-1 2025-09-07T09:27:01.9219691Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.9219854Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.9221952Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.9222170Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.9222355Z DEVICE_NAME: rocm 2025-09-07T09:27:01.9222468Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.9222596Z ##[endgroup] 2025-09-07T09:27:01.9244605Z + [[ -n '' ]] 2025-09-07T09:27:01.9245035Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-09-07T09:27:01.9463818Z INFO:root:Fail to import torch to get the device name 2025-09-07T09:27:01.9512264Z ##[group]Run set -eux 2025-09-07T09:27:01.9512391Z set -eux 2025-09-07T09:27:01.9512493Z  2025-09-07T09:27:01.9512608Z # TODO (huydhn): Implement this part 2025-09-07T09:27:01.9512773Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-09-07T09:27:01.9519871Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.9520032Z env: 2025-09-07T09:27:01.9520144Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.9520293Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.9520485Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.9520661Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.9521054Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.9521436Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.9523532Z AWS_REGION: us-east-1 2025-09-07T09:27:01.9523680Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.9523840Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.9526039Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.9526218Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.9526403Z DEVICE_NAME: rocm 2025-09-07T09:27:01.9526517Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.9526646Z ##[endgroup] 2025-09-07T09:27:01.9543717Z + echo 'dependencies={}' 2025-09-07T09:27:01.9568737Z ##[group]Run set -eux 2025-09-07T09:27:01.9568904Z set -eux 2025-09-07T09:27:01.9569022Z  2025-09-07T09:27:01.9569145Z if [[ -n "" ]]; then 2025-09-07T09:27:01.9573245Z  source "" 2025-09-07T09:27:01.9573371Z fi 2025-09-07T09:27:01.9573476Z  2025-09-07T09:27:01.9573811Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-09-07T09:27:01.9574007Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-09-07T09:27:01.9574205Z  # We don't want the job to fail if the directory doesn't exist 2025-09-07T09:27:01.9574471Z  exit 0 2025-09-07T09:27:01.9574576Z fi 2025-09-07T09:27:01.9574672Z  2025-09-07T09:27:01.9576790Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-09-07T09:27:01.9577052Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T09:27:01.9577274Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T09:27:01.9577448Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T09:27:01.9577595Z  --runners "${RUNNER_INFO}" \ 2025-09-07T09:27:01.9577741Z  --dependencies "${DEPENDENCIES}" \ 2025-09-07T09:27:01.9577880Z  --dry-run 2025-09-07T09:27:01.9577995Z else 2025-09-07T09:27:01.9578238Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T09:27:01.9581314Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T09:27:01.9581498Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T09:27:01.9581641Z  --runners "${RUNNER_INFO}" \ 2025-09-07T09:27:01.9581784Z  --dependencies "${DEPENDENCIES}" 2025-09-07T09:27:01.9581916Z fi 2025-09-07T09:27:01.9588137Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:01.9588298Z env: 2025-09-07T09:27:01.9588400Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:01.9590786Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:01.9590980Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:01.9591155Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:01.9591553Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:01.9591941Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:01.9592066Z AWS_REGION: us-east-1 2025-09-07T09:27:01.9592247Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:01.9592409Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:01.9596478Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:01.9596675Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:01.9596864Z DEVICE_NAME: rocm 2025-09-07T09:27:01.9596980Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:01.9597125Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-09-07T09:27:01.9597258Z DRY_RUN: false 2025-09-07T09:27:01.9597732Z BENCHMARK_METADATA: {"timestamp": 1757237221, "schema_version": "v3", "name": "linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754565, "run_attempt": 1, "job_id": 49774353520} 2025-09-07T09:27:01.9598390Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 160, "avail_mem_in_gb": 1255, "extra_info": {"hostname": "linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b"}, "name": "rocm", "type": "AMD Instinct Mi325X VF"}] 2025-09-07T09:27:01.9598687Z DEPENDENCIES: {} 2025-09-07T09:27:01.9600467Z ##[endgroup] 2025-09-07T09:27:01.9624484Z + [[ -n '' ]] 2025-09-07T09:27:01.9624930Z + [[ ! -d test/test-reports ]] 2025-09-07T09:27:01.9625098Z + [[ false == \t\r\u\e ]] 2025-09-07T09:27:01.9626475Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1757237221, "schema_version": "v3", "name": "linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.gfx942.1)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754565, "run_attempt": 1, "job_id": 49774353520}' --runners '[{"cpu_info": "x86_64", "cpu_count": 160, "avail_mem_in_gb": 1255, "extra_info": {"hostname": "linux.rocm.gpu.gfx942.1-xb8kr-runner-8vt9b"}, "name": "rocm", "type": "AMD Instinct Mi325X VF"}]' --dependencies '{}' 2025-09-07T09:27:02.0282853Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'lazy/test_ts_opinfo'}], 'excluded': []} from test/test-reports/td_exclusions-041704387828fda2bfd8.json is not a benchmark record, skipping 2025-09-07T09:27:02.0295582Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T09:27:02.0296288Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/test_api'}], 'excluded': []} from test/test-reports/td_exclusions-81ff5e35f87ffeaa2545.json is not a benchmark record, skipping 2025-09-07T09:27:02.0297896Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T09:27:02.0299448Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/Dict_test'}, {'test_file': 'cpp/Dimname_test'}, {'test_file': 'cpp/NamedTensor_test'}, {'test_file': 'cpp/apply_utils_test'}, {'test_file': 'cpp/atest'}, {'test_file': 'cpp/basic'}, {'test_file': 'cpp/broadcast_test'}, {'test_file': 'cpp/cpu_generator_test'}, {'test_file': 'cpp/dlconvertor_test'}, {'test_file': 'cpp/extension_backend_test'}, {'test_file': 'cpp/lazy_tensor_test'}, {'test_file': 'cpp/legacy_vmap_test'}, {'test_file': 'cpp/native_test'}, {'test_file': 'cpp/operators_test'}, {'test_file': 'cpp/scalar_tensor_test'}, {'test_file': 'cpp/scalar_test'}, {'test_file': 'cpp/tensor_iterator_test'}, {'test_file': 'cpp/undefined_tensor_test'}, {'test_file': 'cpp/wrapdim_test'}], 'excluded': []} from test/test-reports/td_exclusions-b781075fa6e0684ea6a5.json is not a benchmark record, skipping 2025-09-07T09:27:02.0300846Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T09:27:02.0301398Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/static_runtime_test'}], 'excluded': []} from test/test-reports/td_exclusions-820dadb9d09fa4962083.json is not a benchmark record, skipping 2025-09-07T09:27:02.0301937Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T09:27:02.0325308Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: 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'torch_np/numpy_tests/lib/test_arraysetops'}, {'test_file': 'torch_np/numpy_tests/lib/test_function_base'}, {'test_file': 'torch_np/numpy_tests/lib/test_histograms'}, {'test_file': 'torch_np/numpy_tests/lib/test_index_tricks'}, {'test_file': 'torch_np/numpy_tests/lib/test_shape_base_'}, {'test_file': 'torch_np/numpy_tests/lib/test_twodim_base'}, {'test_file': 'torch_np/numpy_tests/lib/test_type_check'}, {'test_file': 'torch_np/numpy_tests/linalg/test_linalg'}, {'test_file': 'torch_np/test_basic'}, {'test_file': 'torch_np/test_binary_ufuncs'}, {'test_file': 'torch_np/test_dtype'}, {'test_file': 'torch_np/test_function_base'}, {'test_file': 'torch_np/test_indexing'}, {'test_file': 'torch_np/test_ndarray_methods'}, {'test_file': 'torch_np/test_nep50_examples'}, {'test_file': 'torch_np/test_random'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'torch_np/test_scalars_0D_arrays'}, {'test_file': 'torch_np/test_ufuncs_basic'}, {'test_file': 'torch_np/test_unary_ufuncs'}, {'test_file': 'typing/test_python_operators'}, {'test_file': 'xpu/test_conv'}, {'test_file': 'xpu/test_fusion'}, {'test_file': 'xpu/test_gemm'}], 'excluded': []} from test/test-reports/td_exclusions-8292c9c51df9f7e5fb36.json is not a benchmark record, skipping 2025-09-07T09:27:02.0348463Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T09:27:02.0414242Z Prepare all required actions 2025-09-07T09:27:02.0415090Z Getting action download info 2025-09-07T09:27:02.0437912Z ##[group]Run ./.github/actions/teardown-rocm 2025-09-07T09:27:02.0438079Z env: 2025-09-07T09:27:02.0438204Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:02.0438385Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:02.0438605Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:02.0438825Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:02.0439285Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:02.0439730Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:02.0439881Z AWS_REGION: us-east-1 2025-09-07T09:27:02.0440076Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:02.0440296Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:02.0442482Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:02.0442669Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:02.0442868Z DEVICE_NAME: rocm 2025-09-07T09:27:02.0442998Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:02.0443137Z ##[endgroup] 2025-09-07T09:27:02.0456491Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T09:27:02.0456816Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T09:27:02.0457008Z # shellcheck disable=SC2046 2025-09-07T09:27:02.0457173Z docker stop $(docker ps -q) || true 2025-09-07T09:27:02.0457330Z # Prune all stopped containers. 2025-09-07T09:27:02.0457485Z docker container prune -f 2025-09-07T09:27:02.0462736Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:02.0462900Z env: 2025-09-07T09:27:02.0463078Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:02.0463240Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:02.0463515Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:02.0463708Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:02.0464111Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:02.0464633Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:02.0464780Z AWS_REGION: us-east-1 2025-09-07T09:27:02.0464931Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:02.0465110Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:02.0467220Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:02.0467418Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:02.0467616Z DEVICE_NAME: rocm 2025-09-07T09:27:02.0467752Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:02.0470467Z ##[endgroup] 2025-09-07T09:27:12.6614863Z d61980540b2e 2025-09-07T09:27:17.6211656Z Deleted Containers: 2025-09-07T09:27:17.6211945Z d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:17.6212104Z 2025-09-07T09:27:17.6212176Z Total reclaimed space: 11.65GB 2025-09-07T09:27:17.6238055Z Prepare all required actions 2025-09-07T09:27:17.6260852Z ##[group]Run ./.github/actions/diskspace-cleanup 2025-09-07T09:27:17.6261076Z with: 2025-09-07T09:27:17.6261218Z diskspace-cutoff: 70 2025-09-07T09:27:17.6261332Z env: 2025-09-07T09:27:17.6261437Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:17.6261588Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:17.6261778Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:17.6261956Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:17.6262634Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:17.6263024Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:17.6263155Z AWS_REGION: us-east-1 2025-09-07T09:27:17.6263322Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:17.6263564Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:17.6265834Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:17.6266016Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:17.6266205Z DEVICE_NAME: rocm 2025-09-07T09:27:17.6266318Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:17.6266449Z ##[endgroup] 2025-09-07T09:27:17.6276908Z ##[group]Run set -ex 2025-09-07T09:27:17.6279271Z set -ex 2025-09-07T09:27:17.6279386Z diskspace_cutoff=70 2025-09-07T09:27:17.6279590Z docker_root_dir=$(docker info -f '{{.DockerRootDir}}') 2025-09-07T09:27:17.6279764Z if [ ! -d "$docker_root_dir" ]; then 2025-09-07T09:27:17.6279980Z  echo "Docker root directory ($docker_root_dir) does not exist. Skipping disk space check." 2025-09-07T09:27:17.6280173Z  exit 0 2025-09-07T09:27:17.6280277Z fi 2025-09-07T09:27:17.6280451Z diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-09-07T09:27:17.6282762Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified" 2025-09-07T09:27:17.6283169Z if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then 2025-09-07T09:27:17.6283328Z  docker system prune -af 2025-09-07T09:27:17.6283533Z  diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-09-07T09:27:17.6283764Z  if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then 2025-09-07T09:27:17.6284002Z  echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace." 2025-09-07T09:27:17.6284258Z  echo "$msg" 2025-09-07T09:27:17.6284454Z  exit 1 2025-09-07T09:27:17.6284564Z  else 2025-09-07T09:27:17.6284694Z  difference=$((diskspace - diskspace_new)) 2025-09-07T09:27:17.6284862Z  echo "Diskspace saved: $difference percent" 2025-09-07T09:27:17.6288474Z  fi 2025-09-07T09:27:17.6288577Z fi 2025-09-07T09:27:17.6293799Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T09:27:17.6293950Z env: 2025-09-07T09:27:17.6294051Z GIT_DEFAULT_BRANCH: main 2025-09-07T09:27:17.6294198Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-09-07T09:27:17.6294466Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-09-07T09:27:17.6296528Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-09-07T09:27:17.6296941Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD177 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-09-07T09:27:17.6297324Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T09:27:17.6297449Z AWS_REGION: us-east-1 2025-09-07T09:27:17.6297590Z AWS_ACCESS_KEY_ID: *** 2025-09-07T09:27:17.6297754Z AWS_SECRET_ACCESS_KEY: *** 2025-09-07T09:27:17.6299840Z AWS_SESSION_TOKEN: *** 2025-09-07T09:27:17.6300018Z CONTAINER_NAME: d61980540b2eb5b0463bf533117b614f623a3bd4ec93e859fcdf4a7dc139b526 2025-09-07T09:27:17.6300206Z DEVICE_NAME: rocm 2025-09-07T09:27:17.6301761Z DEVICE_TYPE: AMD Instinct Mi325X VF 2025-09-07T09:27:17.6301889Z ##[endgroup] 2025-09-07T09:27:17.6329381Z + diskspace_cutoff=70 2025-09-07T09:27:17.6332514Z ++ docker info -f '{{.DockerRootDir}}' 2025-09-07T09:27:17.6762382Z + docker_root_dir=/home/runner/docker-data 2025-09-07T09:27:17.6764276Z + '[' '!' -d /home/runner/docker-data ']' 2025-09-07T09:27:17.6771428Z ++ sed -n 2p 2025-09-07T09:27:17.6772550Z ++ df -H --output=pcent /home/runner/docker-data 2025-09-07T09:27:17.6772994Z ++ sed s/%// 2025-09-07T09:27:17.6773280Z ++ sed 's/ //' 2025-09-07T09:27:17.6788497Z + diskspace=18 2025-09-07T09:27:17.6789042Z + 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' 2025-09-07T09:27:17.6789351Z + [[ 18 -ge 70 ]] 2025-09-07T09:27:17.6820046Z Post job cleanup. 2025-09-07T09:27:17.6852371Z Post job cleanup. 2025-09-07T09:27:17.7540715Z Post job cleanup. 2025-09-07T09:27:17.7788440Z Logging out of registry 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T09:27:17.8025354Z Post job cleanup. 2025-09-07T09:27:17.8739009Z Post job cleanup. 2025-09-07T09:27:17.8771778Z Post job cleanup. 2025-09-07T09:27:17.9303239Z [command]/usr/bin/git version 2025-09-07T09:27:17.9346839Z git version 2.51.0 2025-09-07T09:27:17.9363815Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/98b596b5-7ebb-46fe-a356-3ae3512a1747/.gitconfig' 2025-09-07T09:27:17.9371633Z Temporarily overriding HOME='/home/runner/_work/_temp/98b596b5-7ebb-46fe-a356-3ae3512a1747' before making global git config changes 2025-09-07T09:27:17.9372026Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T09:27:17.9374038Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch 2025-09-07T09:27:17.9410631Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T09:27:17.9473308Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2025-09-07T09:27:17.9762876Z Entering 'android/libs/fbjni' 2025-09-07T09:27:17.9797513Z Entering 'third_party/FP16' 2025-09-07T09:27:17.9838875Z Entering 'third_party/FXdiv' 2025-09-07T09:27:17.9901165Z Entering 'third_party/NNPACK' 2025-09-07T09:27:17.9930501Z Entering 'third_party/NVTX' 2025-09-07T09:27:17.9968044Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T09:27:18.0004600Z Entering 'third_party/XNNPACK' 2025-09-07T09:27:18.0043933Z Entering 'third_party/aiter' 2025-09-07T09:27:18.0101561Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T09:27:18.0146191Z Entering 'third_party/benchmark' 2025-09-07T09:27:18.0190268Z Entering 'third_party/composable_kernel' 2025-09-07T09:27:18.0247199Z Entering 'third_party/cpp-httplib' 2025-09-07T09:27:18.0294524Z Entering 'third_party/cpuinfo' 2025-09-07T09:27:18.0352327Z Entering 'third_party/cudnn_frontend' 2025-09-07T09:27:18.0397551Z Entering 'third_party/cutlass' 2025-09-07T09:27:18.0433943Z Entering 'third_party/fbgemm' 2025-09-07T09:27:18.0466063Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T09:27:18.0501626Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T09:27:18.0532234Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T09:27:18.0571392Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T09:27:18.0608040Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T09:27:18.0635046Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T09:27:18.0674080Z Entering 'third_party/fbgemm/external/json' 2025-09-07T09:27:18.0710923Z Entering 'third_party/flash-attention' 2025-09-07T09:27:18.0748288Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T09:27:18.0778537Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T09:27:18.0819826Z Entering 'third_party/flatbuffers' 2025-09-07T09:27:18.0862694Z Entering 'third_party/fmt' 2025-09-07T09:27:18.0909127Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T09:27:18.0946007Z Entering 'third_party/gloo' 2025-09-07T09:27:18.1001448Z Entering 'third_party/googletest' 2025-09-07T09:27:18.1033697Z Entering 'third_party/ideep' 2025-09-07T09:27:18.1076701Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T09:27:18.1111972Z Entering 'third_party/ittapi' 2025-09-07T09:27:18.1159919Z Entering 'third_party/kineto' 2025-09-07T09:27:18.1190444Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T09:27:18.1225451Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T09:27:18.1253194Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T09:27:18.1287922Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T09:27:18.1329191Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T09:27:18.1361904Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T09:27:18.1401987Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T09:27:18.1431096Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T09:27:18.1463617Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T09:27:18.1495118Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T09:27:18.1530670Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T09:27:18.1557674Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T09:27:18.1587507Z Entering 'third_party/kleidiai' 2025-09-07T09:27:18.1630115Z Entering 'third_party/mimalloc' 2025-09-07T09:27:18.1665791Z Entering 'third_party/nlohmann' 2025-09-07T09:27:18.1708794Z Entering 'third_party/onnx' 2025-09-07T09:27:18.1762282Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T09:27:18.1793706Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T09:27:18.1834598Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T09:27:18.1864613Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T09:27:18.1892014Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T09:27:18.1926082Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T09:27:18.1952629Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T09:27:18.1986544Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T09:27:18.2031626Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T09:27:18.2069207Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T09:27:18.2098858Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T09:27:18.2133634Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T09:27:18.2193294Z Entering 'third_party/pocketfft' 2025-09-07T09:27:18.2227056Z Entering 'third_party/protobuf' 2025-09-07T09:27:18.2261574Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T09:27:18.2301210Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T09:27:18.2334759Z Entering 'third_party/psimd' 2025-09-07T09:27:18.2370191Z Entering 'third_party/pthreadpool' 2025-09-07T09:27:18.2399301Z Entering 'third_party/pybind11' 2025-09-07T09:27:18.2449184Z Entering 'third_party/python-peachpy' 2025-09-07T09:27:18.2480147Z Entering 'third_party/sleef' 2025-09-07T09:27:18.2515009Z Entering 'third_party/tensorpipe' 2025-09-07T09:27:18.2543042Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T09:27:18.2572405Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T09:27:18.2609020Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T09:27:18.2636758Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T09:27:18.2663923Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T09:27:18.2713549Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T09:27:18.2733922Z http.https://github.com/.extraheader 2025-09-07T09:27:18.2742815Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-07T09:27:18.2784858Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2025-09-07T09:27:18.2980967Z Entering 'android/libs/fbjni' 2025-09-07T09:27:18.2998786Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3027044Z Entering 'third_party/FP16' 2025-09-07T09:27:18.3046875Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3071852Z Entering 'third_party/FXdiv' 2025-09-07T09:27:18.3090055Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3110897Z Entering 'third_party/NNPACK' 2025-09-07T09:27:18.3129180Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3148664Z Entering 'third_party/NVTX' 2025-09-07T09:27:18.3164295Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3186655Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T09:27:18.3203486Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3226507Z Entering 'third_party/XNNPACK' 2025-09-07T09:27:18.3244144Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3277945Z Entering 'third_party/aiter' 2025-09-07T09:27:18.3288986Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3309179Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T09:27:18.3330996Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3362719Z Entering 'third_party/benchmark' 2025-09-07T09:27:18.3380311Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3398317Z Entering 'third_party/composable_kernel' 2025-09-07T09:27:18.3420766Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3446921Z Entering 'third_party/cpp-httplib' 2025-09-07T09:27:18.3463877Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3483154Z Entering 'third_party/cpuinfo' 2025-09-07T09:27:18.3501279Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3527118Z Entering 'third_party/cudnn_frontend' 2025-09-07T09:27:18.3544297Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3565298Z Entering 'third_party/cutlass' 2025-09-07T09:27:18.3580032Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3609662Z Entering 'third_party/fbgemm' 2025-09-07T09:27:18.3626176Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3651681Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T09:27:18.3668527Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3688337Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T09:27:18.3705933Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3730408Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T09:27:18.3744342Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3766936Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T09:27:18.3780184Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3812315Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T09:27:18.3832881Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3856855Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T09:27:18.3873279Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3893294Z Entering 'third_party/fbgemm/external/json' 2025-09-07T09:27:18.3912887Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3932228Z Entering 'third_party/flash-attention' 2025-09-07T09:27:18.3953317Z http.https://github.com/.extraheader 2025-09-07T09:27:18.3992339Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T09:27:18.4008309Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4031398Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T09:27:18.4047293Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4079246Z Entering 'third_party/flatbuffers' 2025-09-07T09:27:18.4094546Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4118287Z Entering 'third_party/fmt' 2025-09-07T09:27:18.4143747Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4176775Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T09:27:18.4194028Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4215019Z Entering 'third_party/gloo' 2025-09-07T09:27:18.4233842Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4256201Z Entering 'third_party/googletest' 2025-09-07T09:27:18.4276474Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4325278Z Entering 'third_party/ideep' 2025-09-07T09:27:18.4342344Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4368177Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T09:27:18.4384834Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4411458Z Entering 'third_party/ittapi' 2025-09-07T09:27:18.4427164Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4447943Z Entering 'third_party/kineto' 2025-09-07T09:27:18.4464855Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4504781Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T09:27:18.4521554Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4552365Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T09:27:18.4576937Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4597371Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T09:27:18.4628284Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4660851Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T09:27:18.4682661Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4703231Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T09:27:18.4724126Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4744302Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T09:27:18.4762568Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4784727Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T09:27:18.4801288Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4826451Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T09:27:18.4859167Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4882253Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T09:27:18.4903305Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4925276Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T09:27:18.4953776Z http.https://github.com/.extraheader 2025-09-07T09:27:18.4986961Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T09:27:18.5010412Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5035860Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T09:27:18.5052343Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5084727Z Entering 'third_party/kleidiai' 2025-09-07T09:27:18.5109037Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5130436Z Entering 'third_party/mimalloc' 2025-09-07T09:27:18.5165470Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5186474Z Entering 'third_party/nlohmann' 2025-09-07T09:27:18.5206104Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5231409Z Entering 'third_party/onnx' 2025-09-07T09:27:18.5245895Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5275097Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T09:27:18.5297430Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5321698Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T09:27:18.5350306Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5372935Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T09:27:18.5393755Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5414076Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T09:27:18.5437979Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5469669Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T09:27:18.5489250Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5520394Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T09:27:18.5542324Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5563619Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T09:27:18.5587204Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5608376Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T09:27:18.5622565Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5647151Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T09:27:18.5675244Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5702478Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T09:27:18.5720817Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5742596Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T09:27:18.5765479Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5785951Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T09:27:18.5803651Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5839371Z Entering 'third_party/pocketfft' 2025-09-07T09:27:18.5860987Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5885405Z Entering 'third_party/protobuf' 2025-09-07T09:27:18.5903527Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5925528Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T09:27:18.5950166Z http.https://github.com/.extraheader 2025-09-07T09:27:18.5982723Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T09:27:18.6002354Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6027768Z Entering 'third_party/psimd' 2025-09-07T09:27:18.6056083Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6076564Z Entering 'third_party/pthreadpool' 2025-09-07T09:27:18.6102256Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6140744Z Entering 'third_party/pybind11' 2025-09-07T09:27:18.6162156Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6189483Z Entering 'third_party/python-peachpy' 2025-09-07T09:27:18.6208535Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6229632Z Entering 'third_party/sleef' 2025-09-07T09:27:18.6248713Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6269267Z Entering 'third_party/tensorpipe' 2025-09-07T09:27:18.6291759Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6322988Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T09:27:18.6345667Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6366454Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T09:27:18.6382016Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6402330Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T09:27:18.6429011Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6455264Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T09:27:18.6475881Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6502064Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T09:27:18.6522962Z http.https://github.com/.extraheader 2025-09-07T09:27:18.6706914Z Cleaning up orphan processes