2025-08-14T21:14:11.8638833Z Current runner version: '2.328.0'
2025-08-14T21:14:11.8641774Z Runner name: 'linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d'
2025-08-14T21:14:11.8642132Z Runner group name: 'default'
2025-08-14T21:14:11.8642525Z Machine name: 'linux'
2025-08-14T21:14:11.8643759Z ##[group]GITHUB_TOKEN Permissions
2025-08-14T21:14:11.8644875Z Contents: read
2025-08-14T21:14:11.8645086Z Metadata: read
2025-08-14T21:14:11.8645291Z ##[endgroup]
2025-08-14T21:14:11.8646405Z Secret source: Actions
2025-08-14T21:14:11.8646680Z Prepare workflow directory
2025-08-14T21:14:11.8906428Z Prepare all required actions
2025-08-14T21:14:11.8926601Z Getting action download info
2025-08-14T21:14:12.2112786Z Download action repository 'pytorch/pytorch@main' (SHA:47a1db823dfcdacdb99f317428fc3791a18c5812)
2025-08-14T21:14:14.4603084Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722)
2025-08-14T21:14:14.8445016Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076)
2025-08-14T21:14:15.2104539Z Download action repository 'pytorch/test-infra@main' (SHA:83f58f391e939c10dcb8cb6d745e4cefa3b98a84)
2025-08-14T21:14:15.7567295Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02)
2025-08-14T21:14:16.2446420Z Getting action download info
2025-08-14T21:14:16.3627656Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955)
2025-08-14T21:14:16.7180690Z Getting action download info
2025-08-14T21:14:16.8868597Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-08-14T21:14:17.2627845Z Getting action download info
2025-08-14T21:14:17.3913690Z Uses: pytorch/pytorch/.github/workflows/_rocm-test.yml@refs/heads/main (1fc683cf17c8c673044538d10266c00f92987be2)
2025-08-14T21:14:17.3920727Z ##[group] Inputs
2025-08-14T21:14:17.3920929Z   build-environment: linux-noble-rocm-py3.12-mi300
2025-08-14T21:14:17.3921597Z   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"}]}
2025-08-14T21:14:17.3922364Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:14:17.3922649Z   sync-tag: 
2025-08-14T21:14:17.3923266Z   timeout-minutes: 300
2025-08-14T21:14:17.3923366Z   tests-to-include: 
2025-08-14T21:14:17.3923462Z   dashboard-tag: 
2025-08-14T21:14:17.3923729Z   disable-monitor: true
2025-08-14T21:14:17.3923832Z   monitor-log-interval: 5
2025-08-14T21:14:17.3923958Z   monitor-data-collect-interval: 1
2025-08-14T21:14:17.3924076Z ##[endgroup]
2025-08-14T21:14:17.3924252Z Complete job name: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:14:17.4218361Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-08-14T21:14:17.4218608Z with:
2025-08-14T21:14:17.4218689Z   no-sudo: true
2025-08-14T21:14:17.4218777Z   submodules: recursive
2025-08-14T21:14:17.4218868Z   fetch-depth: 0
2025-08-14T21:14:17.4218993Z env:
2025-08-14T21:14:17.4219079Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:14:17.4219199Z ##[endgroup]
2025-08-14T21:14:17.4267208Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-08-14T21:14:17.4267563Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-08-14T21:14:17.4276591Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:14:17.4276737Z env:
2025-08-14T21:14:17.4276822Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:14:17.4276916Z ##[endgroup]
2025-08-14T21:14:17.4395255Z ##[group]Run # Use all available CPUs for fetching
2025-08-14T21:14:17.4395443Z # Use all available CPUs for fetching
2025-08-14T21:14:17.4395576Z cd "${GITHUB_WORKSPACE}"
2025-08-14T21:14:17.4395715Z git config --global fetch.parallel 0
2025-08-14T21:14:17.4395852Z git config --global submodule.fetchJobs 0
2025-08-14T21:14:17.4395977Z 
2025-08-14T21:14:17.4396103Z # Clean workspace. The default checkout action should also do this, but
2025-08-14T21:14:17.4396267Z # do it here as well just in case
2025-08-14T21:14:17.4396382Z if [[ -d .git ]]; then
2025-08-14T21:14:17.4396490Z   if [ -z "${NO_SUDO}" ]; then
2025-08-14T21:14:17.4396607Z     sudo git clean -ffdx
2025-08-14T21:14:17.4396707Z   else
2025-08-14T21:14:17.4396795Z     git clean -ffdx
2025-08-14T21:14:17.4396888Z   fi
2025-08-14T21:14:17.4396965Z fi
2025-08-14T21:14:17.4401095Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:14:17.4401228Z env:
2025-08-14T21:14:17.4401308Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:14:17.4401398Z   NO_SUDO: true
2025-08-14T21:14:17.4404211Z ##[endgroup]
2025-08-14T21:14:17.4542050Z ##[group]Run actions/checkout@v4
2025-08-14T21:14:17.4542206Z with:
2025-08-14T21:14:17.4542306Z   ref: 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:14:17.4542425Z   fetch-depth: 0
2025-08-14T21:14:17.4542515Z   submodules: recursive
2025-08-14T21:14:17.4542612Z   show-progress: false
2025-08-14T21:14:17.4542710Z   repository: pytorch/pytorch
2025-08-14T21:14:17.4543041Z   token: ***
2025-08-14T21:14:17.4543121Z   ssh-strict: true
2025-08-14T21:14:17.4543226Z   ssh-user: git
2025-08-14T21:14:17.4543315Z   persist-credentials: true
2025-08-14T21:14:17.4545978Z   clean: true
2025-08-14T21:14:17.4546080Z   sparse-checkout-cone-mode: true
2025-08-14T21:14:17.4546319Z   fetch-tags: false
2025-08-14T21:14:17.4546403Z   lfs: false
2025-08-14T21:14:17.4546484Z   set-safe-directory: true
2025-08-14T21:14:17.4546589Z env:
2025-08-14T21:14:17.4546666Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:14:17.4546760Z ##[endgroup]
2025-08-14T21:14:17.5115777Z Syncing repository: pytorch/pytorch
2025-08-14T21:14:17.5116339Z ##[group]Getting Git version info
2025-08-14T21:14:17.5116498Z Working directory is '/home/runner/_work/pytorch/pytorch'
2025-08-14T21:14:17.5116738Z [command]/usr/bin/git version
2025-08-14T21:14:17.5118031Z git version 2.50.1
2025-08-14T21:14:17.5140165Z ##[endgroup]
2025-08-14T21:14:17.5147511Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/0fd7ba44-279f-4883-9c9e-82c79393b8d9/.gitconfig'
2025-08-14T21:14:17.5154109Z Temporarily overriding HOME='/home/runner/_work/_temp/0fd7ba44-279f-4883-9c9e-82c79393b8d9' before making global git config changes
2025-08-14T21:14:17.5154473Z Adding repository directory to the temporary git global config as a safe directory
2025-08-14T21:14:17.5157714Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch
2025-08-14T21:14:17.5188915Z Deleting the contents of '/home/runner/_work/pytorch/pytorch'
2025-08-14T21:14:17.5190965Z ##[group]Initializing the repository
2025-08-14T21:14:17.5193248Z [command]/usr/bin/git init /home/runner/_work/pytorch/pytorch
2025-08-14T21:14:17.5225501Z hint: Using 'master' as the name for the initial branch. This default branch name
2025-08-14T21:14:17.5225842Z hint: is subject to change. To configure the initial branch name to use in all
2025-08-14T21:14:17.5226137Z hint: of your new repositories, which will suppress this warning, call:
2025-08-14T21:14:17.5226351Z hint:
2025-08-14T21:14:17.5226549Z hint: 	git config --global init.defaultBranch <name>
2025-08-14T21:14:17.5227020Z hint:
2025-08-14T21:14:17.5227209Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2025-08-14T21:14:17.5227503Z hint: 'development'. The just-created branch can be renamed via this command:
2025-08-14T21:14:17.5227719Z hint:
2025-08-14T21:14:17.5227848Z hint: 	git branch -m <name>
2025-08-14T21:14:17.5228005Z hint:
2025-08-14T21:14:17.5237640Z hint: Disable this message with "git config set advice.defaultBranchName false"
2025-08-14T21:14:17.5237971Z Initialized empty Git repository in /home/runner/_work/pytorch/pytorch/.git/
2025-08-14T21:14:17.5238469Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2025-08-14T21:14:17.5260235Z ##[endgroup]
2025-08-14T21:14:17.5260441Z ##[group]Disabling automatic garbage collection
2025-08-14T21:14:17.5262471Z [command]/usr/bin/git config --local gc.auto 0
2025-08-14T21:14:17.5280313Z ##[endgroup]
2025-08-14T21:14:17.5280488Z ##[group]Setting up auth
2025-08-14T21:14:17.5284077Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-08-14T21:14:17.5306824Z [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' || :"
2025-08-14T21:14:17.5535403Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-08-14T21:14:17.5549240Z [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' || :"
2025-08-14T21:14:17.5769895Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-08-14T21:14:17.5795716Z ##[endgroup]
2025-08-14T21:14:17.5795962Z ##[group]Fetching the repository
2025-08-14T21:14:17.5799710Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-08-14T21:14:43.7445901Z From https://github.com/pytorch/pytorch
2025-08-14T21:14:43.7450947Z  * [new branch]              2.6.0.dev20241004+          -> origin/2.6.0.dev20241004+
2025-08-14T21:14:43.7451792Z  * [new branch]              5addvllmbuild               -> origin/5addvllmbuild
2025-08-14T21:14:43.7452186Z  * [new branch]              AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest
2025-08-14T21:14:43.7452623Z  * [new branch]              HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes
2025-08-14T21:14:43.7453163Z  * [new branch]              JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops
2025-08-14T21:14:43.7453568Z  * [new branch]              PR-AOTInductorNoneBug       -> origin/PR-AOTInductorNoneBug
2025-08-14T21:14:43.7464341Z  * [new branch]              PR-AOTInductorNoneBugFix    -> origin/PR-AOTInductorNoneBugFix
2025-08-14T21:14:43.7464633Z  * [new branch]              PR-FixConfigsIssue          -> origin/PR-FixConfigsIssue
2025-08-14T21:14:43.7464892Z  * [new branch]              PR-NoneBugFix-viable        -> origin/PR-NoneBugFix-viable
2025-08-14T21:14:43.7465183Z  * [new branch]              PR-ResetToZero              -> origin/PR-ResetToZero
2025-08-14T21:14:43.7466058Z  * [new branch]              Update-Flash-Packaging      -> origin/Update-Flash-Packaging
2025-08-14T21:14:43.7466365Z  * [new branch]              add-missing-args-normalization -> origin/add-missing-args-normalization
2025-08-14T21:14:43.7466651Z  * [new branch]              add-user-guide-structure    -> origin/add-user-guide-structure
2025-08-14T21:14:43.7466906Z  * [new branch]              addVllmPin                  -> origin/addVllmPin
2025-08-14T21:14:43.7467166Z  * [new branch]              add_windows_testing_back    -> origin/add_windows_testing_back
2025-08-14T21:14:43.7467410Z  * [new branch]              addbuildvllm                -> origin/addbuildvllm
2025-08-14T21:14:43.7467766Z  * [new branch]              addmm-heuristic             -> origin/addmm-heuristic
2025-08-14T21:14:43.7467982Z  * [new branch]              addsimde                    -> origin/addsimde
2025-08-14T21:14:43.7468204Z  * [new branch]              addvllpinnedfile            -> origin/addvllpinnedfile
2025-08-14T21:14:43.7468433Z  * [new branch]              adi/acl_upgrade             -> origin/adi/acl_upgrade
2025-08-14T21:14:43.7468660Z  * [new branch]              adi/skip_slow_tests         -> origin/adi/skip_slow_tests
2025-08-14T21:14:43.7468874Z  * [new branch]              adi/test                    -> origin/adi/test
2025-08-14T21:14:43.7469083Z  * [new branch]              adi/test_bgemm              -> origin/adi/test_bgemm
2025-08-14T21:14:43.7469296Z  * [new branch]              adi/test_fusions            -> origin/adi/test_fusions
2025-08-14T21:14:43.7469523Z  * [new branch]              adi/test_onednn_v3.9        -> origin/adi/test_onednn_v3.9
2025-08-14T21:14:43.7469770Z  * [new branch]              adi/test_presve_change      -> origin/adi/test_presve_change
2025-08-14T21:14:43.7469993Z  * [new branch]              adi/test_timm               -> origin/adi/test_timm
2025-08-14T21:14:43.7470243Z  * [new branch]              adi/testpresve_change       -> origin/adi/testpresve_change
2025-08-14T21:14:43.7470673Z  * [new branch]              aditew01/test/vec_bf16      -> origin/aditew01/test/vec_bf16
2025-08-14T21:14:43.7471075Z  * [new branch]              ah-globalfeedback-hook      -> origin/ah-globalfeedback-hook
2025-08-14T21:14:43.7471359Z  * [new branch]              albanD-patch-1              -> origin/albanD-patch-1
2025-08-14T21:14:43.7471575Z  * [new branch]              alt-disable                 -> origin/alt-disable
2025-08-14T21:14:43.7471825Z  * [new branch]              angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files
2025-08-14T21:14:43.7472095Z  * [new branch]              angelayi/aoti_inductor_fx   -> origin/angelayi/aoti_inductor_fx
2025-08-14T21:14:43.7472398Z  * [new branch]              angelayi/assert_tensor_metadata_device -> origin/angelayi/assert_tensor_metadata_device
2025-08-14T21:14:43.7472682Z  * [new branch]              angelayi/benchmark          -> origin/angelayi/benchmark
2025-08-14T21:14:43.7472974Z  * [new branch]              angelayi/benchmark2         -> origin/angelayi/benchmark2
2025-08-14T21:14:43.7473210Z  * [new branch]              angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization
2025-08-14T21:14:43.7473439Z  * [new branch]              angelayi/cpp_loader         -> origin/angelayi/cpp_loader
2025-08-14T21:14:43.7473650Z  * [new branch]              angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph
2025-08-14T21:14:43.7473860Z  * [new branch]              angelayi/customop           -> origin/angelayi/customop
2025-08-14T21:14:43.7474051Z  * [new branch]              angelayi/del_lib            -> origin/angelayi/del_lib
2025-08-14T21:14:43.7474238Z  * [new branch]              angelayi/docs               -> origin/angelayi/docs
2025-08-14T21:14:43.7474411Z  * [new branch]              angelayi/docs2              -> origin/angelayi/docs2
2025-08-14T21:14:43.7474595Z  * [new branch]              angelayi/fix_pt2            -> origin/angelayi/fix_pt2
2025-08-14T21:14:43.7474857Z  * [new branch]              angelayi/logging.bak        -> origin/angelayi/logging.bak
2025-08-14T21:14:43.7475075Z  * [new branch]              angelayi/logging2           -> origin/angelayi/logging2
2025-08-14T21:14:43.7475269Z  * [new branch]              angelayi/no_so_weight       -> origin/angelayi/no_so_weight
2025-08-14T21:14:43.7475461Z  * [new branch]              angelayi/pytree             -> origin/angelayi/pytree
2025-08-14T21:14:43.7475648Z  * [new branch]              angelayi/save_error         -> origin/angelayi/save_error
2025-08-14T21:14:43.7475841Z  * [new branch]              angelayi/scan_layers        -> origin/angelayi/scan_layers
2025-08-14T21:14:43.7476097Z  * [new branch]              angelayi/symint_input       -> origin/angelayi/symint_input
2025-08-14T21:14:43.7476313Z  * [new branch]              angelayi/tensor_nn_module_meta -> origin/angelayi/tensor_nn_module_meta
2025-08-14T21:14:43.7476526Z  * [new branch]              angelayi/torch_size         -> origin/angelayi/torch_size
2025-08-14T21:14:43.7476731Z  * [new branch]              aoti-cuda-alloc             -> origin/aoti-cuda-alloc
2025-08-14T21:14:43.7476919Z  * [new branch]              aoti_weight_sharing         -> origin/aoti_weight_sharing
2025-08-14T21:14:43.7477116Z  * [new branch]              arsh/symint_mm_ind_decomp   -> origin/arsh/symint_mm_ind_decomp
2025-08-14T21:14:43.7477354Z  * [new branch]              atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124
2025-08-14T21:14:43.7477821Z  * [new branch]              atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1
2025-08-14T21:14:43.7478237Z  * [new branch]              atalman-patch-1             -> origin/atalman-patch-1
2025-08-14T21:14:43.7479300Z  * [new branch]              atalman-patch-2             -> origin/atalman-patch-2
2025-08-14T21:14:43.7479762Z  * [new branch]              atalman-patch-3             -> origin/atalman-patch-3
2025-08-14T21:14:43.7480151Z  * [new branch]              atalman-patch-6             -> origin/atalman-patch-6
2025-08-14T21:14:43.7480492Z  * [new branch]              atalman-patch-7             -> origin/atalman-patch-7
2025-08-14T21:14:43.7481041Z  * [new branch]              atalman-patch-8             -> origin/atalman-patch-8
2025-08-14T21:14:43.7481568Z  * [new branch]              atalman_inductor_2.3.0      -> origin/atalman_inductor_2.3.0
2025-08-14T21:14:43.7481983Z  * [new branch]              atalman_inductor_2.3.1      -> origin/atalman_inductor_2.3.1
2025-08-14T21:14:43.7482369Z  * [new branch]              atalman_inductor_2.4.0      -> origin/atalman_inductor_2.4.0
2025-08-14T21:14:43.7483050Z  * [new branch]              atalman_inductor_2.4.x      -> origin/atalman_inductor_2.4.x
2025-08-14T21:14:43.7483679Z  * [new branch]              autoupdate-transformers-pin-via-pr -> origin/autoupdate-transformers-pin-via-pr
2025-08-14T21:14:43.7484010Z  * [new branch]              backupvllm                  -> origin/backupvllm
2025-08-14T21:14:43.7485023Z  * [new branch]              base/1.5                    -> origin/base/1.5
2025-08-14T21:14:43.7485366Z  * [new branch]              batching_sdpa_efficient_attention -> origin/batching_sdpa_efficient_attention
2025-08-14T21:14:43.7485879Z  * [new branch]              benchmark-updates           -> origin/benchmark-updates
2025-08-14T21:14:43.7486418Z  * [new branch]              benchmarking-script         -> origin/benchmarking-script
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2025-08-14T21:14:44.0136255Z  * [new tag]                 ciflow/inductor-perf-test-nightly-rocm/160538 -> ciflow/inductor-perf-test-nightly-rocm/160538
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2025-08-14T21:14:44.0138939Z  * [new tag]                 ciflow/inductor/137400      -> ciflow/inductor/137400
2025-08-14T21:14:44.0139209Z  * [new tag]                 ciflow/inductor/144516      -> ciflow/inductor/144516
2025-08-14T21:14:44.0139475Z  * [new tag]                 ciflow/inductor/146506      -> ciflow/inductor/146506
2025-08-14T21:14:44.0139737Z  * [new tag]                 ciflow/inductor/147360      -> ciflow/inductor/147360
2025-08-14T21:14:44.0139975Z  * [new tag]                 ciflow/inductor/147990      -> ciflow/inductor/147990
2025-08-14T21:14:44.0140241Z  * [new tag]                 ciflow/inductor/148180      -> ciflow/inductor/148180
2025-08-14T21:14:44.0140514Z  * [new tag]                 ciflow/inductor/148328      -> ciflow/inductor/148328
2025-08-14T21:14:44.0140824Z  * [new tag]                 ciflow/inductor/148484      -> ciflow/inductor/148484
2025-08-14T21:14:44.0141067Z  * [new tag]                 ciflow/inductor/148492      -> ciflow/inductor/148492
2025-08-14T21:14:44.0141362Z  * [new tag]                 ciflow/inductor/150302      -> ciflow/inductor/150302
2025-08-14T21:14:44.0141618Z  * [new tag]                 ciflow/inductor/151845      -> ciflow/inductor/151845
2025-08-14T21:14:44.0141892Z  * [new tag]                 ciflow/inductor/152198      -> ciflow/inductor/152198
2025-08-14T21:14:44.0142179Z  * [new tag]                 ciflow/inductor/152624      -> ciflow/inductor/152624
2025-08-14T21:14:44.0142451Z  * [new tag]                 ciflow/inductor/153966      -> ciflow/inductor/153966
2025-08-14T21:14:44.0142725Z  * [new tag]                 ciflow/inductor/154193      -> ciflow/inductor/154193
2025-08-14T21:14:44.0143191Z  * [new tag]                 ciflow/inductor/154650      -> ciflow/inductor/154650
2025-08-14T21:14:44.0143828Z  * [new tag]                 ciflow/inductor/154694      -> ciflow/inductor/154694
2025-08-14T21:14:44.0144000Z  * [new tag]                 ciflow/inductor/155072      -> ciflow/inductor/155072
2025-08-14T21:14:44.0144290Z  * [new tag]                 ciflow/inductor/155152      -> ciflow/inductor/155152
2025-08-14T21:14:44.0144551Z  * [new tag]                 ciflow/inductor/155153      -> ciflow/inductor/155153
2025-08-14T21:14:44.0146411Z  * [new tag]                 ciflow/inductor/155154      -> ciflow/inductor/155154
2025-08-14T21:14:44.0146511Z  * [new tag]                 ciflow/inductor/155501      -> ciflow/inductor/155501
2025-08-14T21:14:44.0146656Z  * [new tag]                 ciflow/inductor/155502      -> ciflow/inductor/155502
2025-08-14T21:14:44.0146719Z  * [new tag]                 ciflow/inductor/155503      -> ciflow/inductor/155503
2025-08-14T21:14:44.0146781Z  * [new tag]                 ciflow/inductor/155504      -> ciflow/inductor/155504
2025-08-14T21:14:44.0146844Z  * [new tag]                 ciflow/inductor/155557      -> ciflow/inductor/155557
2025-08-14T21:14:44.0146910Z  * [new tag]                 ciflow/inductor/155608      -> ciflow/inductor/155608
2025-08-14T21:14:44.0146975Z  * [new tag]                 ciflow/inductor/155923      -> ciflow/inductor/155923
2025-08-14T21:14:44.0147197Z  * [new tag]                 ciflow/inductor/155928      -> ciflow/inductor/155928
2025-08-14T21:14:44.0147727Z  * [new tag]                 ciflow/inductor/155958      -> ciflow/inductor/155958
2025-08-14T21:14:44.0147851Z  * [new tag]                 ciflow/inductor/156049      -> ciflow/inductor/156049
2025-08-14T21:14:44.0148058Z  * [new tag]                 ciflow/inductor/156851      -> ciflow/inductor/156851
2025-08-14T21:14:44.0148329Z  * [new tag]                 ciflow/inductor/156967      -> ciflow/inductor/156967
2025-08-14T21:14:44.0148610Z  * [new tag]                 ciflow/inductor/157148      -> ciflow/inductor/157148
2025-08-14T21:14:44.0148874Z  * [new tag]                 ciflow/inductor/157149      -> ciflow/inductor/157149
2025-08-14T21:14:44.0149156Z  * [new tag]                 ciflow/inductor/157152      -> ciflow/inductor/157152
2025-08-14T21:14:44.0149399Z  * [new tag]                 ciflow/inductor/157542      -> ciflow/inductor/157542
2025-08-14T21:14:44.0149683Z  * [new tag]                 ciflow/inductor/157572      -> ciflow/inductor/157572
2025-08-14T21:14:44.0149939Z  * [new tag]                 ciflow/inductor/157635      -> ciflow/inductor/157635
2025-08-14T21:14:44.0150214Z  * [new tag]                 ciflow/inductor/157685      -> ciflow/inductor/157685
2025-08-14T21:14:44.0150462Z  * [new tag]                 ciflow/inductor/157686      -> ciflow/inductor/157686
2025-08-14T21:14:44.0150753Z  * [new tag]                 ciflow/inductor/157689      -> ciflow/inductor/157689
2025-08-14T21:14:44.0150987Z  * [new tag]                 ciflow/inductor/157699      -> ciflow/inductor/157699
2025-08-14T21:14:44.0151368Z  * [new tag]                 ciflow/inductor/157743      -> ciflow/inductor/157743
2025-08-14T21:14:44.0151593Z  * [new tag]                 ciflow/inductor/157944      -> ciflow/inductor/157944
2025-08-14T21:14:44.0151875Z  * [new tag]                 ciflow/inductor/157971      -> ciflow/inductor/157971
2025-08-14T21:14:44.0152172Z  * [new tag]                 ciflow/inductor/157994      -> ciflow/inductor/157994
2025-08-14T21:14:44.0152439Z  * [new tag]                 ciflow/inductor/158061      -> ciflow/inductor/158061
2025-08-14T21:14:44.0152725Z  * [new tag]                 ciflow/inductor/158091      -> ciflow/inductor/158091
2025-08-14T21:14:44.0153092Z  * [new tag]                 ciflow/inductor/158097      -> ciflow/inductor/158097
2025-08-14T21:14:44.0153363Z  * [new tag]                 ciflow/inductor/158098      -> ciflow/inductor/158098
2025-08-14T21:14:44.0153634Z  * [new tag]                 ciflow/inductor/158104      -> ciflow/inductor/158104
2025-08-14T21:14:44.0153919Z  * [new tag]                 ciflow/inductor/158168      -> ciflow/inductor/158168
2025-08-14T21:14:44.0154227Z  * [new tag]                 ciflow/inductor/158250      -> ciflow/inductor/158250
2025-08-14T21:14:44.0154500Z  * [new tag]                 ciflow/inductor/158321      -> ciflow/inductor/158321
2025-08-14T21:14:44.0154762Z  * [new tag]                 ciflow/inductor/158609      -> ciflow/inductor/158609
2025-08-14T21:14:44.0155029Z  * [new tag]                 ciflow/inductor/158647      -> ciflow/inductor/158647
2025-08-14T21:14:44.0155310Z  * [new tag]                 ciflow/inductor/158914      -> ciflow/inductor/158914
2025-08-14T21:14:44.0155874Z  * [new tag]                 ciflow/inductor/158932      -> ciflow/inductor/158932
2025-08-14T21:14:44.0157468Z  * [new tag]                 ciflow/inductor/158987      -> ciflow/inductor/158987
2025-08-14T21:14:44.0157571Z  * [new tag]                 ciflow/inductor/159009      -> ciflow/inductor/159009
2025-08-14T21:14:44.0157641Z  * [new tag]                 ciflow/inductor/159010      -> ciflow/inductor/159010
2025-08-14T21:14:44.0157703Z  * [new tag]                 ciflow/inductor/159093      -> ciflow/inductor/159093
2025-08-14T21:14:44.0157765Z  * [new tag]                 ciflow/inductor/159158      -> ciflow/inductor/159158
2025-08-14T21:14:44.0157828Z  * [new tag]                 ciflow/inductor/159197      -> ciflow/inductor/159197
2025-08-14T21:14:44.0157956Z  * [new tag]                 ciflow/inductor/159274      -> ciflow/inductor/159274
2025-08-14T21:14:44.0158024Z  * [new tag]                 ciflow/inductor/159281      -> ciflow/inductor/159281
2025-08-14T21:14:44.0158144Z  * [new tag]                 ciflow/inductor/159329      -> ciflow/inductor/159329
2025-08-14T21:14:44.0158567Z  * [new tag]                 ciflow/inductor/159361      -> ciflow/inductor/159361
2025-08-14T21:14:44.0158749Z  * [new tag]                 ciflow/inductor/159365      -> ciflow/inductor/159365
2025-08-14T21:14:44.0159042Z  * [new tag]                 ciflow/inductor/159366      -> ciflow/inductor/159366
2025-08-14T21:14:44.0159328Z  * [new tag]                 ciflow/inductor/159367      -> ciflow/inductor/159367
2025-08-14T21:14:44.0159584Z  * [new tag]                 ciflow/inductor/159368      -> ciflow/inductor/159368
2025-08-14T21:14:44.0160046Z  * [new tag]                 ciflow/inductor/159473      -> ciflow/inductor/159473
2025-08-14T21:14:44.0160198Z  * [new tag]                 ciflow/inductor/159483      -> ciflow/inductor/159483
2025-08-14T21:14:44.0160508Z  * [new tag]                 ciflow/inductor/159508      -> ciflow/inductor/159508
2025-08-14T21:14:44.0160749Z  * [new tag]                 ciflow/inductor/159523      -> ciflow/inductor/159523
2025-08-14T21:14:44.0161033Z  * [new tag]                 ciflow/inductor/159530      -> ciflow/inductor/159530
2025-08-14T21:14:44.0161326Z  * [new tag]                 ciflow/inductor/159678      -> ciflow/inductor/159678
2025-08-14T21:14:44.0161616Z  * [new tag]                 ciflow/inductor/159691      -> ciflow/inductor/159691
2025-08-14T21:14:44.0162125Z  * [new tag]                 ciflow/inductor/159778      -> ciflow/inductor/159778
2025-08-14T21:14:44.0162241Z  * [new tag]                 ciflow/inductor/159786      -> ciflow/inductor/159786
2025-08-14T21:14:44.0162553Z  * [new tag]                 ciflow/inductor/159817      -> ciflow/inductor/159817
2025-08-14T21:14:44.0162811Z  * [new tag]                 ciflow/inductor/159842      -> ciflow/inductor/159842
2025-08-14T21:14:44.0163273Z  * [new tag]                 ciflow/inductor/159864      -> ciflow/inductor/159864
2025-08-14T21:14:44.0163484Z  * [new tag]                 ciflow/inductor/159865      -> ciflow/inductor/159865
2025-08-14T21:14:44.0163748Z  * [new tag]                 ciflow/inductor/159869      -> ciflow/inductor/159869
2025-08-14T21:14:44.0164036Z  * [new tag]                 ciflow/inductor/159875      -> ciflow/inductor/159875
2025-08-14T21:14:44.0164317Z  * [new tag]                 ciflow/inductor/159889      -> ciflow/inductor/159889
2025-08-14T21:14:44.0164633Z  * [new tag]                 ciflow/inductor/159902      -> ciflow/inductor/159902
2025-08-14T21:14:44.0164929Z  * [new tag]                 ciflow/inductor/159923      -> ciflow/inductor/159923
2025-08-14T21:14:44.0165250Z  * [new tag]                 ciflow/inductor/159944      -> ciflow/inductor/159944
2025-08-14T21:14:44.0165523Z  * [new tag]                 ciflow/inductor/160004      -> ciflow/inductor/160004
2025-08-14T21:14:44.0165806Z  * [new tag]                 ciflow/inductor/160080      -> ciflow/inductor/160080
2025-08-14T21:14:44.0166080Z  * [new tag]                 ciflow/inductor/160108      -> ciflow/inductor/160108
2025-08-14T21:14:44.0166427Z  * [new tag]                 ciflow/inductor/160109      -> ciflow/inductor/160109
2025-08-14T21:14:44.0166742Z  * [new tag]                 ciflow/inductor/160111      -> ciflow/inductor/160111
2025-08-14T21:14:44.0167038Z  * [new tag]                 ciflow/inductor/160113      -> ciflow/inductor/160113
2025-08-14T21:14:44.0167314Z  * [new tag]                 ciflow/inductor/160127      -> ciflow/inductor/160127
2025-08-14T21:14:44.0168995Z  * [new tag]                 ciflow/inductor/160131      -> ciflow/inductor/160131
2025-08-14T21:14:44.0169108Z  * [new tag]                 ciflow/inductor/160132      -> ciflow/inductor/160132
2025-08-14T21:14:44.0169226Z  * [new tag]                 ciflow/inductor/160136      -> ciflow/inductor/160136
2025-08-14T21:14:44.0169289Z  * [new tag]                 ciflow/inductor/160138      -> ciflow/inductor/160138
2025-08-14T21:14:44.0169352Z  * [new tag]                 ciflow/inductor/160151      -> ciflow/inductor/160151
2025-08-14T21:14:44.0169417Z  * [new tag]                 ciflow/inductor/160152      -> ciflow/inductor/160152
2025-08-14T21:14:44.0169483Z  * [new tag]                 ciflow/inductor/160154      -> ciflow/inductor/160154
2025-08-14T21:14:44.0169670Z  * [new tag]                 ciflow/inductor/160156      -> ciflow/inductor/160156
2025-08-14T21:14:44.0169969Z  * [new tag]                 ciflow/inductor/160161      -> ciflow/inductor/160161
2025-08-14T21:14:44.0170547Z  * [new tag]                 ciflow/inductor/160166      -> ciflow/inductor/160166
2025-08-14T21:14:44.0170627Z  * [new tag]                 ciflow/inductor/160168      -> ciflow/inductor/160168
2025-08-14T21:14:44.0171060Z  * [new tag]                 ciflow/inductor/160174      -> ciflow/inductor/160174
2025-08-14T21:14:44.0171184Z  * [new tag]                 ciflow/inductor/160181      -> ciflow/inductor/160181
2025-08-14T21:14:44.0171562Z  * [new tag]                 ciflow/inductor/160183      -> ciflow/inductor/160183
2025-08-14T21:14:44.0171931Z  * [new tag]                 ciflow/inductor/160190      -> ciflow/inductor/160190
2025-08-14T21:14:44.0172306Z  * [new tag]                 ciflow/inductor/160198      -> ciflow/inductor/160198
2025-08-14T21:14:44.0172688Z  * [new tag]                 ciflow/inductor/160201      -> ciflow/inductor/160201
2025-08-14T21:14:44.0173078Z  * [new tag]                 ciflow/inductor/160209      -> ciflow/inductor/160209
2025-08-14T21:14:44.0173572Z  * [new tag]                 ciflow/inductor/160218      -> ciflow/inductor/160218
2025-08-14T21:14:44.0173818Z  * [new tag]                 ciflow/inductor/160239      -> ciflow/inductor/160239
2025-08-14T21:14:44.0174141Z  * [new tag]                 ciflow/inductor/160250      -> ciflow/inductor/160250
2025-08-14T21:14:44.0174452Z  * [new tag]                 ciflow/inductor/160253      -> ciflow/inductor/160253
2025-08-14T21:14:44.0174684Z  * [new tag]                 ciflow/inductor/160266      -> ciflow/inductor/160266
2025-08-14T21:14:44.0175065Z  * [new tag]                 ciflow/inductor/160282      -> ciflow/inductor/160282
2025-08-14T21:14:44.0175276Z  * [new tag]                 ciflow/inductor/160298      -> ciflow/inductor/160298
2025-08-14T21:14:44.0175601Z  * [new tag]                 ciflow/inductor/160301      -> ciflow/inductor/160301
2025-08-14T21:14:44.0175881Z  * [new tag]                 ciflow/inductor/160310      -> ciflow/inductor/160310
2025-08-14T21:14:44.0176184Z  * [new tag]                 ciflow/inductor/160323      -> ciflow/inductor/160323
2025-08-14T21:14:44.0176741Z  * [new tag]                 ciflow/inductor/160324      -> ciflow/inductor/160324
2025-08-14T21:14:44.0176956Z  * [new tag]                 ciflow/inductor/160325      -> ciflow/inductor/160325
2025-08-14T21:14:44.0177450Z  * [new tag]                 ciflow/inductor/160326      -> ciflow/inductor/160326
2025-08-14T21:14:44.0177663Z  * [new tag]                 ciflow/inductor/160327      -> ciflow/inductor/160327
2025-08-14T21:14:44.0178100Z  * [new tag]                 ciflow/inductor/160328      -> ciflow/inductor/160328
2025-08-14T21:14:44.0178403Z  * [new tag]                 ciflow/inductor/160329      -> ciflow/inductor/160329
2025-08-14T21:14:44.0179052Z  * [new tag]                 ciflow/inductor/160351      -> ciflow/inductor/160351
2025-08-14T21:14:44.0179144Z  * [new tag]                 ciflow/inductor/160353      -> ciflow/inductor/160353
2025-08-14T21:14:44.0179399Z  * [new tag]                 ciflow/inductor/160362      -> ciflow/inductor/160362
2025-08-14T21:14:44.0179729Z  * [new tag]                 ciflow/inductor/160363      -> ciflow/inductor/160363
2025-08-14T21:14:44.0181495Z  * [new tag]                 ciflow/inductor/160364      -> ciflow/inductor/160364
2025-08-14T21:14:44.0181840Z  * [new tag]                 ciflow/inductor/160365      -> ciflow/inductor/160365
2025-08-14T21:14:44.0181976Z  * [new tag]                 ciflow/inductor/160366      -> ciflow/inductor/160366
2025-08-14T21:14:44.0182107Z  * [new tag]                 ciflow/inductor/160367      -> ciflow/inductor/160367
2025-08-14T21:14:44.0182182Z  * [new tag]                 ciflow/inductor/160368      -> ciflow/inductor/160368
2025-08-14T21:14:44.0182250Z  * [new tag]                 ciflow/inductor/160369      -> ciflow/inductor/160369
2025-08-14T21:14:44.0182320Z  * [new tag]                 ciflow/inductor/160371      -> ciflow/inductor/160371
2025-08-14T21:14:44.0182395Z  * [new tag]                 ciflow/inductor/160374      -> ciflow/inductor/160374
2025-08-14T21:14:44.0182464Z  * [new tag]                 ciflow/inductor/160375      -> ciflow/inductor/160375
2025-08-14T21:14:44.0182780Z  * [new tag]                 ciflow/inductor/160377      -> ciflow/inductor/160377
2025-08-14T21:14:44.0183059Z  * [new tag]                 ciflow/inductor/160380      -> ciflow/inductor/160380
2025-08-14T21:14:44.0183567Z  * [new tag]                 ciflow/inductor/160381      -> ciflow/inductor/160381
2025-08-14T21:14:44.0186065Z  * [new tag]                 ciflow/inductor/160383      -> ciflow/inductor/160383
2025-08-14T21:14:44.0186464Z  * [new tag]                 ciflow/inductor/160394      -> ciflow/inductor/160394
2025-08-14T21:14:44.0186548Z  * [new tag]                 ciflow/inductor/160401      -> ciflow/inductor/160401
2025-08-14T21:14:44.0186818Z  * [new tag]                 ciflow/inductor/160402      -> ciflow/inductor/160402
2025-08-14T21:14:44.0186896Z  * [new tag]                 ciflow/inductor/160403      -> ciflow/inductor/160403
2025-08-14T21:14:44.0186976Z  * [new tag]                 ciflow/inductor/160424      -> ciflow/inductor/160424
2025-08-14T21:14:44.0187045Z  * [new tag]                 ciflow/inductor/160426      -> ciflow/inductor/160426
2025-08-14T21:14:44.0187121Z  * [new tag]                 ciflow/inductor/160431      -> ciflow/inductor/160431
2025-08-14T21:14:44.0187191Z  * [new tag]                 ciflow/inductor/160448      -> ciflow/inductor/160448
2025-08-14T21:14:44.0187264Z  * [new tag]                 ciflow/inductor/160450      -> ciflow/inductor/160450
2025-08-14T21:14:44.0187348Z  * [new tag]                 ciflow/inductor/160455      -> ciflow/inductor/160455
2025-08-14T21:14:44.0187565Z  * [new tag]                 ciflow/inductor/160456      -> ciflow/inductor/160456
2025-08-14T21:14:44.0187857Z  * [new tag]                 ciflow/inductor/160461      -> ciflow/inductor/160461
2025-08-14T21:14:44.0188204Z  * [new tag]                 ciflow/inductor/160462      -> ciflow/inductor/160462
2025-08-14T21:14:44.0188476Z  * [new tag]                 ciflow/inductor/160467      -> ciflow/inductor/160467
2025-08-14T21:14:44.0188831Z  * [new tag]                 ciflow/inductor/160470      -> ciflow/inductor/160470
2025-08-14T21:14:44.0189104Z  * [new tag]                 ciflow/inductor/160473      -> ciflow/inductor/160473
2025-08-14T21:14:44.0189454Z  * [new tag]                 ciflow/inductor/160476      -> ciflow/inductor/160476
2025-08-14T21:14:44.0189827Z  * [new tag]                 ciflow/inductor/160480      -> ciflow/inductor/160480
2025-08-14T21:14:44.0190266Z  * [new tag]                 ciflow/inductor/160481      -> ciflow/inductor/160481
2025-08-14T21:14:44.0190468Z  * [new tag]                 ciflow/inductor/160482      -> ciflow/inductor/160482
2025-08-14T21:14:44.0190759Z  * [new tag]                 ciflow/inductor/160483      -> ciflow/inductor/160483
2025-08-14T21:14:44.0191035Z  * [new tag]                 ciflow/inductor/160485      -> ciflow/inductor/160485
2025-08-14T21:14:44.0191577Z  * [new tag]                 ciflow/inductor/160486      -> ciflow/inductor/160486
2025-08-14T21:14:44.0191746Z  * [new tag]                 ciflow/inductor/160503      -> ciflow/inductor/160503
2025-08-14T21:14:44.0192079Z  * [new tag]                 ciflow/inductor/160510      -> ciflow/inductor/160510
2025-08-14T21:14:44.0192283Z  * [new tag]                 ciflow/inductor/160527      -> ciflow/inductor/160527
2025-08-14T21:14:44.0192764Z  * [new tag]                 ciflow/inductor/160530      -> ciflow/inductor/160530
2025-08-14T21:14:44.0194521Z  * [new tag]                 ciflow/inductor/160531      -> ciflow/inductor/160531
2025-08-14T21:14:44.0194635Z  * [new tag]                 ciflow/inductor/160538      -> ciflow/inductor/160538
2025-08-14T21:14:44.0194885Z  * [new tag]                 ciflow/inductor/160539      -> ciflow/inductor/160539
2025-08-14T21:14:44.0194968Z  * [new tag]                 ciflow/inductor/160540      -> ciflow/inductor/160540
2025-08-14T21:14:44.0195034Z  * [new tag]                 ciflow/inductor/160548      -> ciflow/inductor/160548
2025-08-14T21:14:44.0195111Z  * [new tag]                 ciflow/inductor/160561      -> ciflow/inductor/160561
2025-08-14T21:14:44.0195426Z  * [new tag]                 ciflow/inductor/160576      -> ciflow/inductor/160576
2025-08-14T21:14:44.0195801Z  * [new tag]                 ciflow/inductor/160578      -> ciflow/inductor/160578
2025-08-14T21:14:44.0196038Z  * [new tag]                 ciflow/inductor/160580      -> ciflow/inductor/160580
2025-08-14T21:14:44.0196320Z  * [new tag]                 ciflow/inductor/160583      -> ciflow/inductor/160583
2025-08-14T21:14:44.0196830Z  * [new tag]                 ciflow/inductor/160589      -> ciflow/inductor/160589
2025-08-14T21:14:44.0197037Z  * [new tag]                 ciflow/inductor/160590      -> ciflow/inductor/160590
2025-08-14T21:14:44.0197561Z  * [new tag]                 ciflow/inductor/160592      -> ciflow/inductor/160592
2025-08-14T21:14:44.0197736Z  * [new tag]                 ciflow/inductor/160596      -> ciflow/inductor/160596
2025-08-14T21:14:44.0198087Z  * [new tag]                 ciflow/inductor/160601      -> ciflow/inductor/160601
2025-08-14T21:14:44.0198661Z  * [new tag]                 ciflow/inductor/160607      -> ciflow/inductor/160607
2025-08-14T21:14:44.0198894Z  * [new tag]                 ciflow/inductor/160608      -> ciflow/inductor/160608
2025-08-14T21:14:44.0199138Z  * [new tag]                 ciflow/inductor/160611      -> ciflow/inductor/160611
2025-08-14T21:14:44.0199564Z  * [new tag]                 ciflow/inductor/160614      -> ciflow/inductor/160614
2025-08-14T21:14:44.0199790Z  * [new tag]                 ciflow/inductor/160616      -> ciflow/inductor/160616
2025-08-14T21:14:44.0200161Z  * [new tag]                 ciflow/inductor/160619      -> ciflow/inductor/160619
2025-08-14T21:14:44.0200408Z  * [new tag]                 ciflow/inductor/160625      -> ciflow/inductor/160625
2025-08-14T21:14:44.0200827Z  * [new tag]                 ciflow/inductor/160635      -> ciflow/inductor/160635
2025-08-14T21:14:44.0201117Z  * [new tag]                 ciflow/inductor/160649      -> ciflow/inductor/160649
2025-08-14T21:14:44.0201484Z  * [new tag]                 ciflow/inductor/160658      -> ciflow/inductor/160658
2025-08-14T21:14:44.0201736Z  * [new tag]                 ciflow/inductor/160662      -> ciflow/inductor/160662
2025-08-14T21:14:44.0202018Z  * [new tag]                 ciflow/inductor/160668      -> ciflow/inductor/160668
2025-08-14T21:14:44.0202501Z  * [new tag]                 ciflow/inductor/160669      -> ciflow/inductor/160669
2025-08-14T21:14:44.0202760Z  * [new tag]                 ciflow/inductor/160670      -> ciflow/inductor/160670
2025-08-14T21:14:44.0203226Z  * [new tag]                 ciflow/inductor/160671      -> ciflow/inductor/160671
2025-08-14T21:14:44.0203872Z  * [new tag]                 ciflow/inductor/160677      -> ciflow/inductor/160677
2025-08-14T21:14:44.0203950Z  * [new tag]                 ciflow/inductor/160679      -> ciflow/inductor/160679
2025-08-14T21:14:44.0204470Z  * [new tag]                 ciflow/inductor/3b9a386     -> ciflow/inductor/3b9a386
2025-08-14T21:14:44.0204687Z  * [new tag]                 ciflow/inductor/3d4b92b     -> ciflow/inductor/3d4b92b
2025-08-14T21:14:44.0205124Z  * [new tag]                 ciflow/inductor/d224ac7     -> ciflow/inductor/d224ac7
2025-08-14T21:14:44.0205746Z  * [new tag]                 ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855
2025-08-14T21:14:44.0205852Z  * [new tag]                 ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994
2025-08-14T21:14:44.0206075Z  * [new tag]                 ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737
2025-08-14T21:14:44.0207852Z  * [new tag]                 ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078
2025-08-14T21:14:44.0207943Z  * [new tag]                 ciflow/linux-aarch64/160299 -> ciflow/linux-aarch64/160299
2025-08-14T21:14:44.0208186Z  * [new tag]                 ciflow/linux-aarch64/160301 -> ciflow/linux-aarch64/160301
2025-08-14T21:14:44.0208256Z  * [new tag]                 ciflow/mps/155923           -> ciflow/mps/155923
2025-08-14T21:14:44.0208325Z  * [new tag]                 ciflow/mps/157553           -> ciflow/mps/157553
2025-08-14T21:14:44.0208400Z  * [new tag]                 ciflow/mps/157635           -> ciflow/mps/157635
2025-08-14T21:14:44.0208464Z  * [new tag]                 ciflow/mps/160541           -> ciflow/mps/160541
2025-08-14T21:14:44.0208536Z  * [new tag]                 ciflow/nightly/156049       -> ciflow/nightly/156049
2025-08-14T21:14:44.0208616Z  * [new tag]                 ciflow/nightly/158104       -> ciflow/nightly/158104
2025-08-14T21:14:44.0208817Z  * [new tag]                 ciflow/op-benchmark/157994  -> ciflow/op-benchmark/157994
2025-08-14T21:14:44.0209266Z  * [new tag]                 ciflow/periodic-rocm-mi300/139971 -> ciflow/periodic-rocm-mi300/139971
2025-08-14T21:14:44.0209453Z  * [new tag]                 ciflow/periodic-rocm-mi300/160073 -> ciflow/periodic-rocm-mi300/160073
2025-08-14T21:14:44.0209754Z  * [new tag]                 ciflow/periodic-rocm-mi300/160538 -> ciflow/periodic-rocm-mi300/160538
2025-08-14T21:14:44.0210101Z  * [new tag]                 ciflow/periodic/054a2fd     -> ciflow/periodic/054a2fd
2025-08-14T21:14:44.0210358Z  * [new tag]                 ciflow/periodic/131296      -> ciflow/periodic/131296
2025-08-14T21:14:44.0210602Z  * [new tag]                 ciflow/periodic/139971      -> ciflow/periodic/139971
2025-08-14T21:14:44.0210989Z  * [new tag]                 ciflow/periodic/143959      -> ciflow/periodic/143959
2025-08-14T21:14:44.0211186Z  * [new tag]                 ciflow/periodic/154595      -> ciflow/periodic/154595
2025-08-14T21:14:44.0211483Z  * [new tag]                 ciflow/periodic/156703      -> ciflow/periodic/156703
2025-08-14T21:14:44.0211645Z  * [new tag]                 ciflow/periodic/160201      -> ciflow/periodic/160201
2025-08-14T21:14:44.0211965Z  * [new tag]                 ciflow/periodic/160424      -> ciflow/periodic/160424
2025-08-14T21:14:44.0212180Z  * [new tag]                 ciflow/periodic/160538      -> ciflow/periodic/160538
2025-08-14T21:14:44.0212852Z  * [new tag]                 ciflow/periodic/1febab2a89302464f6c7d69cfbef7a24c421ea65 -> ciflow/periodic/1febab2a89302464f6c7d69cfbef7a24c421ea65
2025-08-14T21:14:44.0213169Z  * [new tag]                 ciflow/periodic/2a6d37d     -> ciflow/periodic/2a6d37d
2025-08-14T21:14:44.0213662Z  * [new tag]                 ciflow/periodic/2ee22e435131369a7e4f8cc4732579acc29a941b -> ciflow/periodic/2ee22e435131369a7e4f8cc4732579acc29a941b
2025-08-14T21:14:44.0213746Z  * [new tag]                 ciflow/periodic/317eeb8     -> ciflow/periodic/317eeb8
2025-08-14T21:14:44.0214155Z  * [new tag]                 ciflow/periodic/3c32        -> ciflow/periodic/3c32
2025-08-14T21:14:44.0214504Z  * [new tag]                 ciflow/periodic/3e98831     -> ciflow/periodic/3e98831
2025-08-14T21:14:44.0214993Z  * [new tag]                 ciflow/periodic/4a773e1e867f28a8ff0b15203e5cd9548f74fcee -> ciflow/periodic/4a773e1e867f28a8ff0b15203e5cd9548f74fcee
2025-08-14T21:14:44.0215295Z  * [new tag]                 ciflow/periodic/5f5f508aa836a46dfe88857fb223049616b94e93 -> ciflow/periodic/5f5f508aa836a46dfe88857fb223049616b94e93
2025-08-14T21:14:44.0215521Z  * [new tag]                 ciflow/periodic/94512-point -> ciflow/periodic/94512-point
2025-08-14T21:14:44.0216148Z  * [new tag]                 ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519
2025-08-14T21:14:44.0216441Z  * [new tag]                 ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275
2025-08-14T21:14:44.0216700Z  * [new tag]                 ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761
2025-08-14T21:14:44.0217369Z  * [new tag]                 ciflow/periodic/d7114f05b10de8e6de81ffc567d63944c3117d51 -> ciflow/periodic/d7114f05b10de8e6de81ffc567d63944c3117d51
2025-08-14T21:14:44.0217475Z  * [new tag]                 ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12
2025-08-14T21:14:44.0218099Z  * [new tag]                 ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0
2025-08-14T21:14:44.0218419Z  * [new tag]                 ciflow/periodic/sha-ec5b83  -> ciflow/periodic/sha-ec5b83
2025-08-14T21:14:44.0218716Z  * [new tag]                 ciflow/rocm-mi300/151360    -> ciflow/rocm-mi300/151360
2025-08-14T21:14:44.0220429Z  * [new tag]                 ciflow/rocm-mi300/159158    -> ciflow/rocm-mi300/159158
2025-08-14T21:14:44.0220673Z  * [new tag]                 ciflow/rocm-mi300/160073    -> ciflow/rocm-mi300/160073
2025-08-14T21:14:44.0220755Z  * [new tag]                 ciflow/rocm-mi300/160468    -> ciflow/rocm-mi300/160468
2025-08-14T21:14:44.0220830Z  * [new tag]                 ciflow/rocm-mi300/160538    -> ciflow/rocm-mi300/160538
2025-08-14T21:14:44.0220904Z  * [new tag]                 ciflow/rocm-mi355/160215    -> ciflow/rocm-mi355/160215
2025-08-14T21:14:44.0220970Z  * [new tag]                 ciflow/rocm/148492          -> ciflow/rocm/148492
2025-08-14T21:14:44.0221037Z  * [new tag]                 ciflow/rocm/151360          -> ciflow/rocm/151360
2025-08-14T21:14:44.0221110Z  * [new tag]                 ciflow/rocm/151845          -> ciflow/rocm/151845
2025-08-14T21:14:44.0221314Z  * [new tag]                 ciflow/rocm/154864          -> ciflow/rocm/154864
2025-08-14T21:14:44.0221651Z  * [new tag]                 ciflow/rocm/156491          -> ciflow/rocm/156491
2025-08-14T21:14:44.0222041Z  * [new tag]                 ciflow/rocm/158219          -> ciflow/rocm/158219
2025-08-14T21:14:44.0222272Z  * [new tag]                 ciflow/rocm/158220          -> ciflow/rocm/158220
2025-08-14T21:14:44.0222487Z  * [new tag]                 ciflow/rocm/158224          -> ciflow/rocm/158224
2025-08-14T21:14:44.0222609Z  * [new tag]                 ciflow/rocm/159158          -> ciflow/rocm/159158
2025-08-14T21:14:44.0222886Z  * [new tag]                 ciflow/rocm/160215          -> ciflow/rocm/160215
2025-08-14T21:14:44.0223108Z  * [new tag]                 ciflow/rocm/160468          -> ciflow/rocm/160468
2025-08-14T21:14:44.0223653Z  * [new tag]                 ciflow/rocm/160538          -> ciflow/rocm/160538
2025-08-14T21:14:44.0223753Z  * [new tag]                 ciflow/s390/143959          -> ciflow/s390/143959
2025-08-14T21:14:44.0224261Z  * [new tag]                 ciflow/slow/01c7106         -> ciflow/slow/01c7106
2025-08-14T21:14:44.0224428Z  * [new tag]                 ciflow/slow/0577043         -> ciflow/slow/0577043
2025-08-14T21:14:44.0225165Z  * [new tag]                 ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym
2025-08-14T21:14:44.0225233Z  * [new tag]                 ciflow/slow/0e81104         -> ciflow/slow/0e81104
2025-08-14T21:14:44.0225456Z  * [new tag]                 ciflow/slow/154595          -> ciflow/slow/154595
2025-08-14T21:14:44.0225883Z  * [new tag]                 ciflow/slow/1732077         -> ciflow/slow/1732077
2025-08-14T21:14:44.0226130Z  * [new tag]                 ciflow/slow/187eb7c         -> ciflow/slow/187eb7c
2025-08-14T21:14:44.0226516Z  * [new tag]                 ciflow/slow/1faef89         -> ciflow/slow/1faef89
2025-08-14T21:14:44.0226766Z  * [new tag]                 ciflow/slow/3920ec1         -> ciflow/slow/3920ec1
2025-08-14T21:14:44.0227200Z  * [new tag]                 ciflow/slow/3b7c6b2         -> ciflow/slow/3b7c6b2
2025-08-14T21:14:44.0227406Z  * [new tag]                 ciflow/slow/59a3759         -> ciflow/slow/59a3759
2025-08-14T21:14:44.0227880Z  * [new tag]                 ciflow/slow/70ef0bb         -> ciflow/slow/70ef0bb
2025-08-14T21:14:44.0228033Z  * [new tag]                 ciflow/slow/788ff06         -> ciflow/slow/788ff06
2025-08-14T21:14:44.0228768Z  * [new tag]                 ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym
2025-08-14T21:14:44.0228835Z  * [new tag]                 ciflow/slow/9d85864         -> ciflow/slow/9d85864
2025-08-14T21:14:44.0229060Z  * [new tag]                 ciflow/slow/9ffad5b         -> ciflow/slow/9ffad5b
2025-08-14T21:14:44.0229375Z  * [new tag]                 ciflow/slow/a206e8b         -> ciflow/slow/a206e8b
2025-08-14T21:14:44.0229690Z  * [new tag]                 ciflow/slow/a837609         -> ciflow/slow/a837609
2025-08-14T21:14:44.0230205Z  * [new tag]                 ciflow/slow/af841f3         -> ciflow/slow/af841f3
2025-08-14T21:14:44.0230504Z  * [new tag]                 ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym
2025-08-14T21:14:44.0232152Z  * [new tag]                 ciflow/trunk/131296         -> ciflow/trunk/131296
2025-08-14T21:14:44.0232258Z  * [new tag]                 ciflow/trunk/137400         -> ciflow/trunk/137400
2025-08-14T21:14:44.0232327Z  * [new tag]                 ciflow/trunk/138996         -> ciflow/trunk/138996
2025-08-14T21:14:44.0232389Z  * [new tag]                 ciflow/trunk/139971         -> ciflow/trunk/139971
2025-08-14T21:14:44.0232456Z  * [new tag]                 ciflow/trunk/147360         -> ciflow/trunk/147360
2025-08-14T21:14:44.0232518Z  * [new tag]                 ciflow/trunk/147855         -> ciflow/trunk/147855
2025-08-14T21:14:44.0232582Z  * [new tag]                 ciflow/trunk/148180         -> ciflow/trunk/148180
2025-08-14T21:14:44.0232651Z  * [new tag]                 ciflow/trunk/148328         -> ciflow/trunk/148328
2025-08-14T21:14:44.0232761Z  * [new tag]                 ciflow/trunk/148492         -> ciflow/trunk/148492
2025-08-14T21:14:44.0233275Z  * [new tag]                 ciflow/trunk/150282         -> ciflow/trunk/150282
2025-08-14T21:14:44.0233407Z  * [new tag]                 ciflow/trunk/150302         -> ciflow/trunk/150302
2025-08-14T21:14:44.0233668Z  * [new tag]                 ciflow/trunk/151845         -> ciflow/trunk/151845
2025-08-14T21:14:44.0233927Z  * [new tag]                 ciflow/trunk/152624         -> ciflow/trunk/152624
2025-08-14T21:14:44.0234210Z  * [new tag]                 ciflow/trunk/154193         -> ciflow/trunk/154193
2025-08-14T21:14:44.0234457Z  * [new tag]                 ciflow/trunk/154595         -> ciflow/trunk/154595
2025-08-14T21:14:44.0234727Z  * [new tag]                 ciflow/trunk/154650         -> ciflow/trunk/154650
2025-08-14T21:14:44.0234979Z  * [new tag]                 ciflow/trunk/154694         -> ciflow/trunk/154694
2025-08-14T21:14:44.0235274Z  * [new tag]                 ciflow/trunk/155958         -> ciflow/trunk/155958
2025-08-14T21:14:44.0235589Z  * [new tag]                 ciflow/trunk/156049         -> ciflow/trunk/156049
2025-08-14T21:14:44.0235794Z  * [new tag]                 ciflow/trunk/156703         -> ciflow/trunk/156703
2025-08-14T21:14:44.0236054Z  * [new tag]                 ciflow/trunk/156851         -> ciflow/trunk/156851
2025-08-14T21:14:44.0236304Z  * [new tag]                 ciflow/trunk/157148         -> ciflow/trunk/157148
2025-08-14T21:14:44.0236571Z  * [new tag]                 ciflow/trunk/157152         -> ciflow/trunk/157152
2025-08-14T21:14:44.0236825Z  * [new tag]                 ciflow/trunk/157432         -> ciflow/trunk/157432
2025-08-14T21:14:44.0237078Z  * [new tag]                 ciflow/trunk/157685         -> ciflow/trunk/157685
2025-08-14T21:14:44.0237362Z  * [new tag]                 ciflow/trunk/157689         -> ciflow/trunk/157689
2025-08-14T21:14:44.0237626Z  * [new tag]                 ciflow/trunk/157699         -> ciflow/trunk/157699
2025-08-14T21:14:44.0237896Z  * [new tag]                 ciflow/trunk/157813         -> ciflow/trunk/157813
2025-08-14T21:14:44.0238142Z  * [new tag]                 ciflow/trunk/157994         -> ciflow/trunk/157994
2025-08-14T21:14:44.0238422Z  * [new tag]                 ciflow/trunk/158091         -> ciflow/trunk/158091
2025-08-14T21:14:44.0238659Z  * [new tag]                 ciflow/trunk/158104         -> ciflow/trunk/158104
2025-08-14T21:14:44.0238936Z  * [new tag]                 ciflow/trunk/158219         -> ciflow/trunk/158219
2025-08-14T21:14:44.0239191Z  * [new tag]                 ciflow/trunk/158220         -> ciflow/trunk/158220
2025-08-14T21:14:44.0239462Z  * [new tag]                 ciflow/trunk/158224         -> ciflow/trunk/158224
2025-08-14T21:14:44.0239757Z  * [new tag]                 ciflow/trunk/158529         -> ciflow/trunk/158529
2025-08-14T21:14:44.0240015Z  * [new tag]                 ciflow/trunk/158647         -> ciflow/trunk/158647
2025-08-14T21:14:44.0240281Z  * [new tag]                 ciflow/trunk/158810         -> ciflow/trunk/158810
2025-08-14T21:14:44.0240562Z  * [new tag]                 ciflow/trunk/158812         -> ciflow/trunk/158812
2025-08-14T21:14:44.0240826Z  * [new tag]                 ciflow/trunk/158863         -> ciflow/trunk/158863
2025-08-14T21:14:44.0241065Z  * [new tag]                 ciflow/trunk/158864         -> ciflow/trunk/158864
2025-08-14T21:14:44.0242650Z  * [new tag]                 ciflow/trunk/158883         -> ciflow/trunk/158883
2025-08-14T21:14:44.0242758Z  * [new tag]                 ciflow/trunk/158914         -> ciflow/trunk/158914
2025-08-14T21:14:44.0242820Z  * [new tag]                 ciflow/trunk/158965         -> ciflow/trunk/158965
2025-08-14T21:14:44.0242882Z  * [new tag]                 ciflow/trunk/158987         -> ciflow/trunk/158987
2025-08-14T21:14:44.0243017Z  * [new tag]                 ciflow/trunk/159033         -> ciflow/trunk/159033
2025-08-14T21:14:44.0243074Z  * [new tag]                 ciflow/trunk/159140         -> ciflow/trunk/159140
2025-08-14T21:14:44.0243141Z  * [new tag]                 ciflow/trunk/159158         -> ciflow/trunk/159158
2025-08-14T21:14:44.0243417Z  * [new tag]                 ciflow/trunk/159530         -> ciflow/trunk/159530
2025-08-14T21:14:44.0243663Z  * [new tag]                 ciflow/trunk/159553         -> ciflow/trunk/159553
2025-08-14T21:14:44.0243942Z  * [new tag]                 ciflow/trunk/159562         -> ciflow/trunk/159562
2025-08-14T21:14:44.0244258Z  * [new tag]                 ciflow/trunk/159682         -> ciflow/trunk/159682
2025-08-14T21:14:44.0244511Z  * [new tag]                 ciflow/trunk/159691         -> ciflow/trunk/159691
2025-08-14T21:14:44.0244833Z  * [new tag]                 ciflow/trunk/159842         -> ciflow/trunk/159842
2025-08-14T21:14:44.0245081Z  * [new tag]                 ciflow/trunk/159889         -> ciflow/trunk/159889
2025-08-14T21:14:44.0245338Z  * [new tag]                 ciflow/trunk/159923         -> ciflow/trunk/159923
2025-08-14T21:14:44.0245688Z  * [new tag]                 ciflow/trunk/160004         -> ciflow/trunk/160004
2025-08-14T21:14:44.0245897Z  * [new tag]                 ciflow/trunk/160113         -> ciflow/trunk/160113
2025-08-14T21:14:44.0246190Z  * [new tag]                 ciflow/trunk/160161         -> ciflow/trunk/160161
2025-08-14T21:14:44.0246462Z  * [new tag]                 ciflow/trunk/160168         -> ciflow/trunk/160168
2025-08-14T21:14:44.0246723Z  * [new tag]                 ciflow/trunk/160181         -> ciflow/trunk/160181
2025-08-14T21:14:44.0247023Z  * [new tag]                 ciflow/trunk/160183         -> ciflow/trunk/160183
2025-08-14T21:14:44.0247273Z  * [new tag]                 ciflow/trunk/160190         -> ciflow/trunk/160190
2025-08-14T21:14:44.0247548Z  * [new tag]                 ciflow/trunk/160198         -> ciflow/trunk/160198
2025-08-14T21:14:44.0247848Z  * [new tag]                 ciflow/trunk/160205         -> ciflow/trunk/160205
2025-08-14T21:14:44.0248161Z  * [new tag]                 ciflow/trunk/160219         -> ciflow/trunk/160219
2025-08-14T21:14:44.0248426Z  * [new tag]                 ciflow/trunk/160224         -> ciflow/trunk/160224
2025-08-14T21:14:44.0248699Z  * [new tag]                 ciflow/trunk/160250         -> ciflow/trunk/160250
2025-08-14T21:14:44.0248967Z  * [new tag]                 ciflow/trunk/160253         -> ciflow/trunk/160253
2025-08-14T21:14:44.0249283Z  * [new tag]                 ciflow/trunk/160335         -> ciflow/trunk/160335
2025-08-14T21:14:44.0249762Z  * [new tag]                 ciflow/trunk/160338         -> ciflow/trunk/160338
2025-08-14T21:14:44.0249892Z  * [new tag]                 ciflow/trunk/160383         -> ciflow/trunk/160383
2025-08-14T21:14:44.0250203Z  * [new tag]                 ciflow/trunk/160401         -> ciflow/trunk/160401
2025-08-14T21:14:44.0250474Z  * [new tag]                 ciflow/trunk/160403         -> ciflow/trunk/160403
2025-08-14T21:14:44.0250762Z  * [new tag]                 ciflow/trunk/160430         -> ciflow/trunk/160430
2025-08-14T21:14:44.0251023Z  * [new tag]                 ciflow/trunk/160431         -> ciflow/trunk/160431
2025-08-14T21:14:44.0251481Z  * [new tag]                 ciflow/trunk/160439         -> ciflow/trunk/160439
2025-08-14T21:14:44.0251630Z  * [new tag]                 ciflow/trunk/160449         -> ciflow/trunk/160449
2025-08-14T21:14:44.0252088Z  * [new tag]                 ciflow/trunk/160454         -> ciflow/trunk/160454
2025-08-14T21:14:44.0253715Z  * [new tag]                 ciflow/trunk/160468         -> ciflow/trunk/160468
2025-08-14T21:14:44.0253821Z  * [new tag]                 ciflow/trunk/160481         -> ciflow/trunk/160481
2025-08-14T21:14:44.0253884Z  * [new tag]                 ciflow/trunk/160485         -> ciflow/trunk/160485
2025-08-14T21:14:44.0253943Z  * [new tag]                 ciflow/trunk/160519         -> ciflow/trunk/160519
2025-08-14T21:14:44.0254001Z  * [new tag]                 ciflow/trunk/160527         -> ciflow/trunk/160527
2025-08-14T21:14:44.0254111Z  * [new tag]                 ciflow/trunk/160560         -> ciflow/trunk/160560
2025-08-14T21:14:44.0254224Z  * [new tag]                 ciflow/trunk/160578         -> ciflow/trunk/160578
2025-08-14T21:14:44.0254458Z  * [new tag]                 ciflow/trunk/160589         -> ciflow/trunk/160589
2025-08-14T21:14:44.0254736Z  * [new tag]                 ciflow/trunk/160592         -> ciflow/trunk/160592
2025-08-14T21:14:44.0255014Z  * [new tag]                 ciflow/trunk/160649         -> ciflow/trunk/160649
2025-08-14T21:14:44.0255286Z  * [new tag]                 ciflow/trunk/160656         -> ciflow/trunk/160656
2025-08-14T21:14:44.0255864Z  * [new tag]                 ciflow/unstable/123         -> ciflow/unstable/123
2025-08-14T21:14:44.0256073Z  * [new tag]                 ciflow/vllm/160116          -> ciflow/vllm/160116
2025-08-14T21:14:44.0256327Z  * [new tag]                 ciflow/vllm/160583          -> ciflow/vllm/160583
2025-08-14T21:14:44.0256579Z  * [new tag]                 ciflow/vllm/160619          -> ciflow/vllm/160619
2025-08-14T21:14:44.0256917Z  * [new tag]                 ciflow/vllm/160625          -> ciflow/vllm/160625
2025-08-14T21:14:44.0257103Z  * [new tag]                 ciflow/vllm/160627          -> ciflow/vllm/160627
2025-08-14T21:14:44.0257609Z  * [new tag]                 ciflow/win-arm64/156049     -> ciflow/win-arm64/156049
2025-08-14T21:14:44.0257720Z  * [new tag]                 ciflow/win-arm64/158104     -> ciflow/win-arm64/158104
2025-08-14T21:14:44.0257993Z  * [new tag]                 ciflow/win-arm64/159553     -> ciflow/win-arm64/159553
2025-08-14T21:14:44.0258233Z  * [new tag]                 ciflow/win-arm64/159562     -> ciflow/win-arm64/159562
2025-08-14T21:14:44.0258698Z  * [new tag]                 ciflow/win-arm64/159777     -> ciflow/win-arm64/159777
2025-08-14T21:14:44.0258876Z  * [new tag]                 ciflow/win-arm64/159780     -> ciflow/win-arm64/159780
2025-08-14T21:14:44.0259124Z  * [new tag]                 ciflow/win-arm64/159842     -> ciflow/win-arm64/159842
2025-08-14T21:14:44.0259355Z  * [new tag]                 ciflow/win-arm64/160250     -> ciflow/win-arm64/160250
2025-08-14T21:14:44.0259612Z  * [new tag]                 ciflow/win-arm64/160253     -> ciflow/win-arm64/160253
2025-08-14T21:14:44.0259866Z  * [new tag]                 ciflow/win-arm64/160454     -> ciflow/win-arm64/160454
2025-08-14T21:14:44.0260121Z  * [new tag]                 ciflow/win-arm64/160560     -> ciflow/win-arm64/160560
2025-08-14T21:14:44.0260575Z  * [new tag]                 ciflow/xpu/138996           -> ciflow/xpu/138996
2025-08-14T21:14:44.0260695Z  * [new tag]                 ciflow/xpu/139971           -> ciflow/xpu/139971
2025-08-14T21:14:44.0261180Z  * [new tag]                 ciflow/xpu/140972           -> ciflow/xpu/140972
2025-08-14T21:14:44.0261341Z  * [new tag]                 ciflow/xpu/143553           -> ciflow/xpu/143553
2025-08-14T21:14:44.0261641Z  * [new tag]                 ciflow/xpu/156272           -> ciflow/xpu/156272
2025-08-14T21:14:44.0261889Z  * [new tag]                 ciflow/xpu/156812           -> ciflow/xpu/156812
2025-08-14T21:14:44.0262146Z  * [new tag]                 ciflow/xpu/157699           -> ciflow/xpu/157699
2025-08-14T21:14:44.0262387Z  * [new tag]                 ciflow/xpu/157994           -> ciflow/xpu/157994
2025-08-14T21:14:44.0262660Z  * [new tag]                 ciflow/xpu/158336           -> ciflow/xpu/158336
2025-08-14T21:14:44.0262899Z  * [new tag]                 ciflow/xpu/158733           -> ciflow/xpu/158733
2025-08-14T21:14:44.0263320Z  * [new tag]                 ciflow/xpu/159033           -> ciflow/xpu/159033
2025-08-14T21:14:44.0263487Z  * [new tag]                 ciflow/xpu/159118           -> ciflow/xpu/159118
2025-08-14T21:14:44.0263744Z  * [new tag]                 ciflow/xpu/159140           -> ciflow/xpu/159140
2025-08-14T21:14:44.0265937Z  * [new tag]                 ciflow/xpu/159241           -> ciflow/xpu/159241
2025-08-14T21:14:44.0266009Z  * [new tag]                 ciflow/xpu/159473           -> ciflow/xpu/159473
2025-08-14T21:14:44.0266073Z  * [new tag]                 ciflow/xpu/159474           -> ciflow/xpu/159474
2025-08-14T21:14:44.0266131Z  * [new tag]                 ciflow/xpu/159553           -> ciflow/xpu/159553
2025-08-14T21:14:44.0266187Z  * [new tag]                 ciflow/xpu/159944           -> ciflow/xpu/159944
2025-08-14T21:14:44.0266333Z  * [new tag]                 ciflow/xpu/160062           -> ciflow/xpu/160062
2025-08-14T21:14:44.0266398Z  * [new tag]                 ciflow/xpu/160067           -> ciflow/xpu/160067
2025-08-14T21:14:44.0266462Z  * [new tag]                 ciflow/xpu/160158           -> ciflow/xpu/160158
2025-08-14T21:14:44.0266520Z  * [new tag]                 ciflow/xpu/160173           -> ciflow/xpu/160173
2025-08-14T21:14:44.0266626Z  * [new tag]                 ciflow/xpu/160183           -> ciflow/xpu/160183
2025-08-14T21:14:44.0266908Z  * [new tag]                 ciflow/xpu/160301           -> ciflow/xpu/160301
2025-08-14T21:14:44.0267161Z  * [new tag]                 ciflow/xpu/160403           -> ciflow/xpu/160403
2025-08-14T21:14:44.0267526Z  * [new tag]                 ciflow/xpu/160606           -> ciflow/xpu/160606
2025-08-14T21:14:44.0267808Z  * [new tag]                 cslpull75                   -> cslpull75
2025-08-14T21:14:44.0268099Z  * [new tag]                 cslpull76                   -> cslpull76
2025-08-14T21:14:44.0268407Z  * [new tag]                 cslpull77                   -> cslpull77
2025-08-14T21:14:44.0268701Z  * [new tag]                 cslpull78                   -> cslpull78
2025-08-14T21:14:44.0268971Z  * [new tag]                 cslpull79                   -> cslpull79
2025-08-14T21:14:44.0269383Z  * [new tag]                 cslpull80                   -> cslpull80
2025-08-14T21:14:44.0269592Z  * [new tag]                 cslpull81                   -> cslpull81
2025-08-14T21:14:44.0270033Z  * [new tag]                 cslpull82                   -> cslpull82
2025-08-14T21:14:44.0270202Z  * [new tag]                 cslpull83                   -> cslpull83
2025-08-14T21:14:44.0270507Z  * [new tag]                 cslpull84                   -> cslpull84
2025-08-14T21:14:44.0270792Z  * [new tag]                 cslpull85                   -> cslpull85
2025-08-14T21:14:44.0271105Z  * [new tag]                 cslpull86                   -> cslpull86
2025-08-14T21:14:44.0271396Z  * [new tag]                 cslpull87                   -> cslpull87
2025-08-14T21:14:44.0271829Z  * [new tag]                 cslpull88                   -> cslpull88
2025-08-14T21:14:44.0272000Z  * [new tag]                 cslpull89                   -> cslpull89
2025-08-14T21:14:44.0272254Z  * [new tag]                 cslpull90                   -> cslpull90
2025-08-14T21:14:44.0272866Z  * [new tag]                 cslpull91                   -> cslpull91
2025-08-14T21:14:44.0273100Z  * [new tag]                 cslpull92                   -> cslpull92
2025-08-14T21:14:44.0273490Z  * [new tag]                 flight_5                    -> flight_5
2025-08-14T21:14:44.0273743Z  * [new tag]                 flight_5.1                  -> flight_5.1
2025-08-14T21:14:44.0274179Z  * [new tag]                 flight_5.2                  -> flight_5.2
2025-08-14T21:14:44.0274315Z  * [new tag]                 flight_5.3                  -> flight_5.3
2025-08-14T21:14:44.0274614Z  * [new tag]                 forpull1                    -> forpull1
2025-08-14T21:14:44.0275194Z  * [new tag]                 malfet/tag-2ef5611          -> malfet/tag-2ef5611
2025-08-14T21:14:44.0275374Z  * [new tag]                 malfet/tag-317b1a0          -> malfet/tag-317b1a0
2025-08-14T21:14:44.0275835Z  * [new tag]                 malfet/tag-ec6f767          -> malfet/tag-ec6f767
2025-08-14T21:14:44.0276055Z  * [new tag]                 nightly-binary              -> nightly-binary
2025-08-14T21:14:44.0277751Z  * [new tag]                 sqzhang_flight4_plus        -> sqzhang_flight4_plus
2025-08-14T21:14:44.0277827Z  * [new tag]                 sqzhang_flight_3            -> sqzhang_flight_3
2025-08-14T21:14:44.0277970Z  * [new tag]                 trunk/01584d2a7d029c9749eb73678cf1dc313cc35df6 -> trunk/01584d2a7d029c9749eb73678cf1dc313cc35df6
2025-08-14T21:14:44.0278103Z  * [new tag]                 trunk/017259f9c65b6fad55fb9597d7077e2543eaae46 -> trunk/017259f9c65b6fad55fb9597d7077e2543eaae46
2025-08-14T21:14:44.0278237Z  * [new tag]                 trunk/01bcf9a40dea937637d2cdd530bed2652510943d -> trunk/01bcf9a40dea937637d2cdd530bed2652510943d
2025-08-14T21:14:44.0278371Z  * [new tag]                 trunk/01f66d08d93365015f4af005a252f439c4d4013a -> trunk/01f66d08d93365015f4af005a252f439c4d4013a
2025-08-14T21:14:44.0278500Z  * [new tag]                 trunk/03b254e49f2d4c092e6ca712e5702cf2895aa47e -> trunk/03b254e49f2d4c092e6ca712e5702cf2895aa47e
2025-08-14T21:14:44.0278683Z  * [new tag]                 trunk/05029ad1c30865d3f7e7fd13384db9d826e563eb -> trunk/05029ad1c30865d3f7e7fd13384db9d826e563eb
2025-08-14T21:14:44.0278948Z  * [new tag]                 trunk/05c19d1acecc01b0d2512364183058a6885b9869 -> trunk/05c19d1acecc01b0d2512364183058a6885b9869
2025-08-14T21:14:44.0279365Z  * [new tag]                 trunk/05c417715f791875fbf28cfc3fc86142de1a3206 -> trunk/05c417715f791875fbf28cfc3fc86142de1a3206
2025-08-14T21:14:44.0279603Z  * [new tag]                 trunk/06824f3c7268bb807a422b663047cd0900ddd126 -> trunk/06824f3c7268bb807a422b663047cd0900ddd126
2025-08-14T21:14:44.0279881Z  * [new tag]                 trunk/077cb389746a7d61cfc018aad2ba29a8aa195610 -> trunk/077cb389746a7d61cfc018aad2ba29a8aa195610
2025-08-14T21:14:44.0280224Z  * [new tag]                 trunk/089c4a1ba007ed4abb3e5e0eafd97b7584566057 -> trunk/089c4a1ba007ed4abb3e5e0eafd97b7584566057
2025-08-14T21:14:44.0280555Z  * [new tag]                 trunk/09381f5dacda7bbbfa361f5df76bde5cd309adc1 -> trunk/09381f5dacda7bbbfa361f5df76bde5cd309adc1
2025-08-14T21:14:44.0280901Z  * [new tag]                 trunk/0bd3af4fb87445f4de3a1f9b823e399c8b3cefde -> trunk/0bd3af4fb87445f4de3a1f9b823e399c8b3cefde
2025-08-14T21:14:44.0281219Z  * [new tag]                 trunk/0d3461bac0fb5177e35152d980b301ea3a0aa2c4 -> trunk/0d3461bac0fb5177e35152d980b301ea3a0aa2c4
2025-08-14T21:14:44.0281527Z  * [new tag]                 trunk/0d40ff3b496e68193bc16d5391fa2e3623709f81 -> trunk/0d40ff3b496e68193bc16d5391fa2e3623709f81
2025-08-14T21:14:44.0281863Z  * [new tag]                 trunk/0d71ca2c46753bb268bfdcf815c14415c122a289 -> trunk/0d71ca2c46753bb268bfdcf815c14415c122a289
2025-08-14T21:14:44.0282173Z  * [new tag]                 trunk/0d88593dd826544c9e7bd4aa615ef86847a78d2b -> trunk/0d88593dd826544c9e7bd4aa615ef86847a78d2b
2025-08-14T21:14:44.0282464Z  * [new tag]                 trunk/0e3e377bd5126cfcc69d70c4d77b352d3404cc11 -> trunk/0e3e377bd5126cfcc69d70c4d77b352d3404cc11
2025-08-14T21:14:44.0282780Z  * [new tag]                 trunk/0f3b10b8eebe68e3c75d473d499b87dfe14a2eca -> trunk/0f3b10b8eebe68e3c75d473d499b87dfe14a2eca
2025-08-14T21:14:44.0283200Z  * [new tag]                 trunk/101276f81b4d2a8c31bfd6796b986d4c1bfdf483 -> trunk/101276f81b4d2a8c31bfd6796b986d4c1bfdf483
2025-08-14T21:14:44.0283794Z  * [new tag]                 trunk/1028c5e2d50e121865bf98307e7c035f549a24b2 -> trunk/1028c5e2d50e121865bf98307e7c035f549a24b2
2025-08-14T21:14:44.0283974Z  * [new tag]                 trunk/10bc36fe840cb3510fab84d2ea22663b76702f1e -> trunk/10bc36fe840cb3510fab84d2ea22663b76702f1e
2025-08-14T21:14:44.0284313Z  * [new tag]                 trunk/10e3514c962b58cbbee994257872a626ff76d51b -> trunk/10e3514c962b58cbbee994257872a626ff76d51b
2025-08-14T21:14:44.0284618Z  * [new tag]                 trunk/1128f4c2a822cbe34a9d966306af15097179ffe1 -> trunk/1128f4c2a822cbe34a9d966306af15097179ffe1
2025-08-14T21:14:44.0284942Z  * [new tag]                 trunk/114a6c40434bfb9cfa5abc30e9e34d81300d743e -> trunk/114a6c40434bfb9cfa5abc30e9e34d81300d743e
2025-08-14T21:14:44.0285285Z  * [new tag]                 trunk/118bc97b14c24ac88a4b0c0750a9e7bf93154c76 -> trunk/118bc97b14c24ac88a4b0c0750a9e7bf93154c76
2025-08-14T21:14:44.0285537Z  * [new tag]                 trunk/1196bb1c2e4d5a7edc09f2260e3034132f0c6c91 -> trunk/1196bb1c2e4d5a7edc09f2260e3034132f0c6c91
2025-08-14T21:14:44.0285861Z  * [new tag]                 trunk/11a3565f1872bbad9c253a127e8d4ce7a1b40ec8 -> trunk/11a3565f1872bbad9c253a127e8d4ce7a1b40ec8
2025-08-14T21:14:44.0286199Z  * [new tag]                 trunk/15e49f61643e4c0eef420f0981609709ef55b848 -> trunk/15e49f61643e4c0eef420f0981609709ef55b848
2025-08-14T21:14:44.0286521Z  * [new tag]                 trunk/16d15445f8bd8740095b23de4af89d757af793ca -> trunk/16d15445f8bd8740095b23de4af89d757af793ca
2025-08-14T21:14:44.0286835Z  * [new tag]                 trunk/178515d0ff6833c8e9221482b2a650ab31e00019 -> trunk/178515d0ff6833c8e9221482b2a650ab31e00019
2025-08-14T21:14:44.0287145Z  * [new tag]                 trunk/182efe31dbe43376e7eef7338356aaf94d5bcabe -> trunk/182efe31dbe43376e7eef7338356aaf94d5bcabe
2025-08-14T21:14:44.0287469Z  * [new tag]                 trunk/194fcfcfbdad0add1a1b695321e31a576058f4cf -> trunk/194fcfcfbdad0add1a1b695321e31a576058f4cf
2025-08-14T21:14:44.0287834Z  * [new tag]                 trunk/195b5c2e27eb8f21cbc8ad1e90f42db5a8cfccca -> trunk/195b5c2e27eb8f21cbc8ad1e90f42db5a8cfccca
2025-08-14T21:14:44.0288106Z  * [new tag]                 trunk/198b5fd2d47fa3d5110ceba6827a3b18e0064014 -> trunk/198b5fd2d47fa3d5110ceba6827a3b18e0064014
2025-08-14T21:14:44.0288427Z  * [new tag]                 trunk/199e9abb6a366bbd27c39d1da7c3123b4eea9b5a -> trunk/199e9abb6a366bbd27c39d1da7c3123b4eea9b5a
2025-08-14T21:14:44.0290166Z  * [new tag]                 trunk/19b4283884b2d9b3a0eb364da10b1540d14ab7a7 -> trunk/19b4283884b2d9b3a0eb364da10b1540d14ab7a7
2025-08-14T21:14:44.0290354Z  * [new tag]                 trunk/1c2587119152cec3905647a47c65d3d26619c5a8 -> trunk/1c2587119152cec3905647a47c65d3d26619c5a8
2025-08-14T21:14:44.0290562Z  * [new tag]                 trunk/1c26c53851c212a7c90a325549a72f0571613a8c -> trunk/1c26c53851c212a7c90a325549a72f0571613a8c
2025-08-14T21:14:44.0290694Z  * [new tag]                 trunk/1c2cba17eab2b09d87142883da2bdbdbcf018613 -> trunk/1c2cba17eab2b09d87142883da2bdbdbcf018613
2025-08-14T21:14:44.0290828Z  * [new tag]                 trunk/1d80d697a269234b47ec7ede192faf3bb9b159e3 -> trunk/1d80d697a269234b47ec7ede192faf3bb9b159e3
2025-08-14T21:14:44.0290957Z  * [new tag]                 trunk/1ea688f9a2602fbcde32c0302b822526ca4219dc -> trunk/1ea688f9a2602fbcde32c0302b822526ca4219dc
2025-08-14T21:14:44.0291087Z  * [new tag]                 trunk/1f4057c11ac941fb324386ca594d0a6882185aad -> trunk/1f4057c11ac941fb324386ca594d0a6882185aad
2025-08-14T21:14:44.0291217Z  * [new tag]                 trunk/1fc683cf17c8c673044538d10266c00f92987be2 -> trunk/1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:14:44.0291434Z  * [new tag]                 trunk/1febab2a89302464f6c7d69cfbef7a24c421ea65 -> trunk/1febab2a89302464f6c7d69cfbef7a24c421ea65
2025-08-14T21:14:44.0291765Z  * [new tag]                 trunk/206c1eef6571f906c2792d899a09136b3fce9673 -> trunk/206c1eef6571f906c2792d899a09136b3fce9673
2025-08-14T21:14:44.0292110Z  * [new tag]                 trunk/20bdabbb3c5d6b118a94b2e045c777662563d5bb -> trunk/20bdabbb3c5d6b118a94b2e045c777662563d5bb
2025-08-14T21:14:44.0292425Z  * [new tag]                 trunk/21392c0e06ac2b2621950455975ca6332f0bf641 -> trunk/21392c0e06ac2b2621950455975ca6332f0bf641
2025-08-14T21:14:44.0292678Z  * [new tag]                 trunk/2247aa6d1d43e256255f5c74a781c3190a4387b6 -> trunk/2247aa6d1d43e256255f5c74a781c3190a4387b6
2025-08-14T21:14:44.0293010Z  * [new tag]                 trunk/2259dbed4e0d3f2a8174b5847fd0741aed42451d -> trunk/2259dbed4e0d3f2a8174b5847fd0741aed42451d
2025-08-14T21:14:44.0293364Z  * [new tag]                 trunk/231c72240d80091f099c95e326d3600cba866eee -> trunk/231c72240d80091f099c95e326d3600cba866eee
2025-08-14T21:14:44.0293597Z  * [new tag]                 trunk/24257f5bfaa37795f74d9f64c1b43584128d4b8c -> trunk/24257f5bfaa37795f74d9f64c1b43584128d4b8c
2025-08-14T21:14:44.0294367Z  * [new tag]                 trunk/24f43d0da7ad9c6e95a09a2fee610387728cc1cd -> trunk/24f43d0da7ad9c6e95a09a2fee610387728cc1cd
2025-08-14T21:14:44.0294565Z  * [new tag]                 trunk/2898d3f965e5cd9d02fc2ecdab7c580fd457fea9 -> trunk/2898d3f965e5cd9d02fc2ecdab7c580fd457fea9
2025-08-14T21:14:44.0294890Z  * [new tag]                 trunk/28ccc9e7247798980fe00a11bcd64a8016b5f227 -> trunk/28ccc9e7247798980fe00a11bcd64a8016b5f227
2025-08-14T21:14:44.0295214Z  * [new tag]                 trunk/29712314dd5cf500a8ea3d1c69483a3cb768ca72 -> trunk/29712314dd5cf500a8ea3d1c69483a3cb768ca72
2025-08-14T21:14:44.0295536Z  * [new tag]                 trunk/29d20d49f0b7f4e362e1cefdcdc4b5659969312c -> trunk/29d20d49f0b7f4e362e1cefdcdc4b5659969312c
2025-08-14T21:14:44.0295883Z  * [new tag]                 trunk/2c5e10a5fceb208b11c3d569ae02e348b5893b31 -> trunk/2c5e10a5fceb208b11c3d569ae02e348b5893b31
2025-08-14T21:14:44.0296153Z  * [new tag]                 trunk/2d0cdee394bccadcd0abe19dd4623ed978a331ad -> trunk/2d0cdee394bccadcd0abe19dd4623ed978a331ad
2025-08-14T21:14:44.0296532Z  * [new tag]                 trunk/2e4e5ab4be9e0aeffd9c49b5b2f9f820bd0895b1 -> trunk/2e4e5ab4be9e0aeffd9c49b5b2f9f820bd0895b1
2025-08-14T21:14:44.0296843Z  * [new tag]                 trunk/2ea40fba841b3af8103f332ba62e54f350ba9a51 -> trunk/2ea40fba841b3af8103f332ba62e54f350ba9a51
2025-08-14T21:14:44.0297098Z  * [new tag]                 trunk/2ee22e435131369a7e4f8cc4732579acc29a941b -> trunk/2ee22e435131369a7e4f8cc4732579acc29a941b
2025-08-14T21:14:44.0297428Z  * [new tag]                 trunk/2f4c2226175512af787725c4d5ad7313c60d4db1 -> trunk/2f4c2226175512af787725c4d5ad7313c60d4db1
2025-08-14T21:14:44.0297706Z  * [new tag]                 trunk/3008d985a8fc155eb89374afff50cb33a6bd10d5 -> trunk/3008d985a8fc155eb89374afff50cb33a6bd10d5
2025-08-14T21:14:44.0298024Z  * [new tag]                 trunk/3028fa6ce9d9c96671722ab8213a1a30670d7cf2 -> trunk/3028fa6ce9d9c96671722ab8213a1a30670d7cf2
2025-08-14T21:14:44.0298365Z  * [new tag]                 trunk/303c614f3df95ae2b659c5f6c1838b14e4776ce6 -> trunk/303c614f3df95ae2b659c5f6c1838b14e4776ce6
2025-08-14T21:14:44.0298975Z  * [new tag]                 trunk/305fa2239365ad17ac9c534a68bba8a149c42d67 -> trunk/305fa2239365ad17ac9c534a68bba8a149c42d67
2025-08-14T21:14:44.0299170Z  * [new tag]                 trunk/31c9ac4319c0cc2ed8c6be701c6ccf73f6cb4706 -> trunk/31c9ac4319c0cc2ed8c6be701c6ccf73f6cb4706
2025-08-14T21:14:44.0299498Z  * [new tag]                 trunk/32099961d588fc19ead8afe805d6b5108de75669 -> trunk/32099961d588fc19ead8afe805d6b5108de75669
2025-08-14T21:14:44.0299876Z  * [new tag]                 trunk/32e5e2f596d55bb9441d5d53f3c58bcb55828047 -> trunk/32e5e2f596d55bb9441d5d53f3c58bcb55828047
2025-08-14T21:14:44.0300143Z  * [new tag]                 trunk/334b38ccc4427b1d14981c48a3a0b92180d58225 -> trunk/334b38ccc4427b1d14981c48a3a0b92180d58225
2025-08-14T21:14:44.0300484Z  * [new tag]                 trunk/334ecbd4ffe11858cae7d23d1190ddb4777c2513 -> trunk/334ecbd4ffe11858cae7d23d1190ddb4777c2513
2025-08-14T21:14:44.0300822Z  * [new tag]                 trunk/33d94018668951611b318b7515ae96f04e48eac0 -> trunk/33d94018668951611b318b7515ae96f04e48eac0
2025-08-14T21:14:44.0301139Z  * [new tag]                 trunk/34358f335d95213d96b6cca6a83e7bf3af6a9fcb -> trunk/34358f335d95213d96b6cca6a83e7bf3af6a9fcb
2025-08-14T21:14:44.0301486Z  * [new tag]                 trunk/34ec5ed275f8aa875c80daa97b3e82af0b06f673 -> trunk/34ec5ed275f8aa875c80daa97b3e82af0b06f673
2025-08-14T21:14:44.0303256Z  * [new tag]                 trunk/355462e1278d818deb9ef4a184073d5b66074816 -> trunk/355462e1278d818deb9ef4a184073d5b66074816
2025-08-14T21:14:44.0304362Z  * [new tag]                 trunk/3626ba711b34397d1fbf0a9b1979f85cbf68b919 -> trunk/3626ba711b34397d1fbf0a9b1979f85cbf68b919
2025-08-14T21:14:44.0304494Z  * [new tag]                 trunk/36f46d082a4954921cb8493223f000f2aab79ed7 -> trunk/36f46d082a4954921cb8493223f000f2aab79ed7
2025-08-14T21:14:44.0304800Z  * [new tag]                 trunk/39aa3d1471549b7829c207d634dfdc1d26e346a2 -> trunk/39aa3d1471549b7829c207d634dfdc1d26e346a2
2025-08-14T21:14:44.0305356Z  * [new tag]                 trunk/3a562374401113187ce2566b87e3f1d87d7c53aa -> trunk/3a562374401113187ce2566b87e3f1d87d7c53aa
2025-08-14T21:14:44.0305489Z  * [new tag]                 trunk/3ac86e728dfaa7383ff7f865e9e7d33486188dae -> trunk/3ac86e728dfaa7383ff7f865e9e7d33486188dae
2025-08-14T21:14:44.0305804Z  * [new tag]                 trunk/3cec82a7e9aea040a34dd7a2587ae6d3bd65dba0 -> trunk/3cec82a7e9aea040a34dd7a2587ae6d3bd65dba0
2025-08-14T21:14:44.0306145Z  * [new tag]                 trunk/3cf7b4024ef83e44e9ae223dbff7c7ab68240cb2 -> trunk/3cf7b4024ef83e44e9ae223dbff7c7ab68240cb2
2025-08-14T21:14:44.0306773Z  * [new tag]                 trunk/3ef2e1ef769582a82c6ddf150e9d11bf4bf1c44f -> trunk/3ef2e1ef769582a82c6ddf150e9d11bf4bf1c44f
2025-08-14T21:14:44.0306986Z  * [new tag]                 trunk/3f1636ebef9b45e8a3cb0eb20d327ee6acb74be0 -> trunk/3f1636ebef9b45e8a3cb0eb20d327ee6acb74be0
2025-08-14T21:14:44.0307339Z  * [new tag]                 trunk/3faee0a6318afcbbbb48687009a459214910d820 -> trunk/3faee0a6318afcbbbb48687009a459214910d820
2025-08-14T21:14:44.0307738Z  * [new tag]                 trunk/3fcd79e023da7156ac584992ebab29205d3b7881 -> trunk/3fcd79e023da7156ac584992ebab29205d3b7881
2025-08-14T21:14:44.0308058Z  * [new tag]                 trunk/3fe19a7a0af3f4d692af30476c320be18c7e8ae6 -> trunk/3fe19a7a0af3f4d692af30476c320be18c7e8ae6
2025-08-14T21:14:44.0308419Z  * [new tag]                 trunk/41673110cd7c5960824cc74a6fcaeda1a8bc7a23 -> trunk/41673110cd7c5960824cc74a6fcaeda1a8bc7a23
2025-08-14T21:14:44.0308751Z  * [new tag]                 trunk/4183d4ff3dcc1d87400326a9a7998c3f9e966f60 -> trunk/4183d4ff3dcc1d87400326a9a7998c3f9e966f60
2025-08-14T21:14:44.0309095Z  * [new tag]                 trunk/422bd6808bb98cbbac31d157d9c82ad11ba9732d -> trunk/422bd6808bb98cbbac31d157d9c82ad11ba9732d
2025-08-14T21:14:44.0309415Z  * [new tag]                 trunk/42e51cd4b3973a053fcfa80878a3f346fd158e9f -> trunk/42e51cd4b3973a053fcfa80878a3f346fd158e9f
2025-08-14T21:14:44.0309788Z  * [new tag]                 trunk/4416433c7c625127b7f975c92f8ec98ea4c67fd3 -> trunk/4416433c7c625127b7f975c92f8ec98ea4c67fd3
2025-08-14T21:14:44.0310142Z  * [new tag]                 trunk/45ba7ecda876685b083cbbe932450560c566826b -> trunk/45ba7ecda876685b083cbbe932450560c566826b
2025-08-14T21:14:44.0310414Z  * [new tag]                 trunk/47a1db823dfcdacdb99f317428fc3791a18c5812 -> trunk/47a1db823dfcdacdb99f317428fc3791a18c5812
2025-08-14T21:14:44.0313484Z  * [new tag]                 trunk/4a773e1e867f28a8ff0b15203e5cd9548f74fcee -> trunk/4a773e1e867f28a8ff0b15203e5cd9548f74fcee
2025-08-14T21:14:44.0313623Z  * [new tag]                 trunk/4a90dc0c1f68d1f98832b169f792ed1bb195a0f3 -> trunk/4a90dc0c1f68d1f98832b169f792ed1bb195a0f3
2025-08-14T21:14:44.0313758Z  * [new tag]                 trunk/4cde0acc0e4e795e1a12cbdd9b93c8c04c1fa05d -> trunk/4cde0acc0e4e795e1a12cbdd9b93c8c04c1fa05d
2025-08-14T21:14:44.0313893Z  * [new tag]                 trunk/4d419a74610c32b1372f8802dcc61893740a23cf -> trunk/4d419a74610c32b1372f8802dcc61893740a23cf
2025-08-14T21:14:44.0314034Z  * [new tag]                 trunk/4d5b3f2d5af7c8e4f41da4ffca53fafe8bb86235 -> trunk/4d5b3f2d5af7c8e4f41da4ffca53fafe8bb86235
2025-08-14T21:14:44.0314164Z  * [new tag]                 trunk/4e2ddb5db67617f9f5309c8bba0c17adc84cadbc -> trunk/4e2ddb5db67617f9f5309c8bba0c17adc84cadbc
2025-08-14T21:14:44.0314292Z  * [new tag]                 trunk/50a8c118754a6c5a46968f5c8e215ccba6831d42 -> trunk/50a8c118754a6c5a46968f5c8e215ccba6831d42
2025-08-14T21:14:44.0314420Z  * [new tag]                 trunk/50f23ff6f883db5021dd6bab4c146434f98dd15d -> trunk/50f23ff6f883db5021dd6bab4c146434f98dd15d
2025-08-14T21:14:44.0314550Z  * [new tag]                 trunk/515cb70367e84fcbad23fcc5b39eb1d7706df2aa -> trunk/515cb70367e84fcbad23fcc5b39eb1d7706df2aa
2025-08-14T21:14:44.0314680Z  * [new tag]                 trunk/53e39494958b7e2278cc8176f63636e812e8945f -> trunk/53e39494958b7e2278cc8176f63636e812e8945f
2025-08-14T21:14:44.0314810Z  * [new tag]                 trunk/556e2a73f4f0643f7c2aeb5c7dddda43388a40ce -> trunk/556e2a73f4f0643f7c2aeb5c7dddda43388a40ce
2025-08-14T21:14:44.0314945Z  * [new tag]                 trunk/5665dc9ab76b84d7c90d845ffb0f6349b3621919 -> trunk/5665dc9ab76b84d7c90d845ffb0f6349b3621919
2025-08-14T21:14:44.0315075Z  * [new tag]                 trunk/566c6d52ef1411c8262d7b9cf85e2044fdfbe1a3 -> trunk/566c6d52ef1411c8262d7b9cf85e2044fdfbe1a3
2025-08-14T21:14:44.0315219Z  * [new tag]                 trunk/56c828bef93eada0e18d2cc013207831ca80cc99 -> trunk/56c828bef93eada0e18d2cc013207831ca80cc99
2025-08-14T21:14:44.0315350Z  * [new tag]                 trunk/5737372862253a0ac0292407a5844796f02380ad -> trunk/5737372862253a0ac0292407a5844796f02380ad
2025-08-14T21:14:44.0315724Z  * [new tag]                 trunk/57f738b6357cc8fcdde479a0948e723809a1a44d -> trunk/57f738b6357cc8fcdde479a0948e723809a1a44d
2025-08-14T21:14:44.0315958Z  * [new tag]                 trunk/5a40c5784482255b9baf14086cc4b9349fc6d512 -> trunk/5a40c5784482255b9baf14086cc4b9349fc6d512
2025-08-14T21:14:44.0316371Z  * [new tag]                 trunk/5a9c4cfce42b9eb87da0de40c5633f083115c307 -> trunk/5a9c4cfce42b9eb87da0de40c5633f083115c307
2025-08-14T21:14:44.0316669Z  * [new tag]                 trunk/5ace061254af71aa83d1baae81aa1864c9746add -> trunk/5ace061254af71aa83d1baae81aa1864c9746add
2025-08-14T21:14:44.0317028Z  * [new tag]                 trunk/5dddcd5b07c6644efca8d613f4eca1dc95daa87f -> trunk/5dddcd5b07c6644efca8d613f4eca1dc95daa87f
2025-08-14T21:14:44.0318952Z  * [new tag]                 trunk/5ed4f9177907fe403ec4c4499d0d0e9be6b68fcf -> trunk/5ed4f9177907fe403ec4c4499d0d0e9be6b68fcf
2025-08-14T21:14:44.0319086Z  * [new tag]                 trunk/5f1010fbb3850d99c8fdf9a9de2f79260cdc586a -> trunk/5f1010fbb3850d99c8fdf9a9de2f79260cdc586a
2025-08-14T21:14:44.0319284Z  * [new tag]                 trunk/5f5f508aa836a46dfe88857fb223049616b94e93 -> trunk/5f5f508aa836a46dfe88857fb223049616b94e93
2025-08-14T21:14:44.0319410Z  * [new tag]                 trunk/62bac0798100e0e06a86b7a4cee1788413e3d0ca -> trunk/62bac0798100e0e06a86b7a4cee1788413e3d0ca
2025-08-14T21:14:44.0319539Z  * [new tag]                 trunk/63654ba4c5178fd12220cfc9d1c878af2fdd07cc -> trunk/63654ba4c5178fd12220cfc9d1c878af2fdd07cc
2025-08-14T21:14:44.0319672Z  * [new tag]                 trunk/639778b3ee3b80e0894367fdc4442b58ae1b3a62 -> trunk/639778b3ee3b80e0894367fdc4442b58ae1b3a62
2025-08-14T21:14:44.0319794Z  * [new tag]                 trunk/641ee7478150f26969968f49d8b358e199679a8a -> trunk/641ee7478150f26969968f49d8b358e199679a8a
2025-08-14T21:14:44.0319939Z  * [new tag]                 trunk/65053c03a3d209060cb239d20a229dac37cf9dd1 -> trunk/65053c03a3d209060cb239d20a229dac37cf9dd1
2025-08-14T21:14:44.0320253Z  * [new tag]                 trunk/652a6f5954d039d61dc6e6575ccf89d385d74537 -> trunk/652a6f5954d039d61dc6e6575ccf89d385d74537
2025-08-14T21:14:44.0320594Z  * [new tag]                 trunk/685f15dbea66e8ffa8564752f81ad2f6cb447a14 -> trunk/685f15dbea66e8ffa8564752f81ad2f6cb447a14
2025-08-14T21:14:44.0320893Z  * [new tag]                 trunk/68a4b4b2e336cfd4451ce6546d900568e5ddf96c -> trunk/68a4b4b2e336cfd4451ce6546d900568e5ddf96c
2025-08-14T21:14:44.0321229Z  * [new tag]                 trunk/69a0a9aa7f5e320a02e97fa789d2f72baff1554f -> trunk/69a0a9aa7f5e320a02e97fa789d2f72baff1554f
2025-08-14T21:14:44.0321606Z  * [new tag]                 trunk/6be6d06295c870c77a6eb69f96b3170d983520d5 -> trunk/6be6d06295c870c77a6eb69f96b3170d983520d5
2025-08-14T21:14:44.0321981Z  * [new tag]                 trunk/6c05ea6475beaf3acc05e1bda0f3f8fe3bdc1d49 -> trunk/6c05ea6475beaf3acc05e1bda0f3f8fe3bdc1d49
2025-08-14T21:14:44.0322326Z  * [new tag]                 trunk/6da11d9aafc0d84dc7f66030c181608ff2614f66 -> trunk/6da11d9aafc0d84dc7f66030c181608ff2614f66
2025-08-14T21:14:44.0322704Z  * [new tag]                 trunk/6e8865fbc161270e2ffc52817e6c667df417a3f7 -> trunk/6e8865fbc161270e2ffc52817e6c667df417a3f7
2025-08-14T21:14:44.0323129Z  * [new tag]                 trunk/6ea8376f84232048d6be0f7b2edf82aec1b61d58 -> trunk/6ea8376f84232048d6be0f7b2edf82aec1b61d58
2025-08-14T21:14:44.0323525Z  * [new tag]                 trunk/6ee175195ac7853734d64704171993cc6265eb38 -> trunk/6ee175195ac7853734d64704171993cc6265eb38
2025-08-14T21:14:44.0323851Z  * [new tag]                 trunk/6f0f4e0c3eacd479864319127915f869f64e1935 -> trunk/6f0f4e0c3eacd479864319127915f869f64e1935
2025-08-14T21:14:44.0324149Z  * [new tag]                 trunk/70ccdec44b89e355a2cb03ba14a634284f7750f8 -> trunk/70ccdec44b89e355a2cb03ba14a634284f7750f8
2025-08-14T21:14:44.0324517Z  * [new tag]                 trunk/72009ec6bebca7714f99c18449183787f202af4d -> trunk/72009ec6bebca7714f99c18449183787f202af4d
2025-08-14T21:14:44.0324871Z  * [new tag]                 trunk/731ee31f7b6ba19307daab323f6196172b71aaf8 -> trunk/731ee31f7b6ba19307daab323f6196172b71aaf8
2025-08-14T21:14:44.0325228Z  * [new tag]                 trunk/76a0609b6bddb2bc40f1eb4ade12885023653d59 -> trunk/76a0609b6bddb2bc40f1eb4ade12885023653d59
2025-08-14T21:14:44.0325550Z  * [new tag]                 trunk/781e9a7724c47496e3d38a81e6dd6194cf098c41 -> trunk/781e9a7724c47496e3d38a81e6dd6194cf098c41
2025-08-14T21:14:44.0325925Z  * [new tag]                 trunk/78a2fe1d42edeaa2ef7020b0fa0ac82ee4a640e4 -> trunk/78a2fe1d42edeaa2ef7020b0fa0ac82ee4a640e4
2025-08-14T21:14:44.0326223Z  * [new tag]                 trunk/7a974a88f2c529a614baeabe4debd00fc8a3b299 -> trunk/7a974a88f2c529a614baeabe4debd00fc8a3b299
2025-08-14T21:14:44.0326588Z  * [new tag]                 trunk/7ae0629d64b404e0ef5d9c931433ad25e65d6114 -> trunk/7ae0629d64b404e0ef5d9c931433ad25e65d6114
2025-08-14T21:14:44.0327108Z  * [new tag]                 trunk/7d2ec704e47f4b740cdecda5534b305e8e1875ef -> trunk/7d2ec704e47f4b740cdecda5534b305e8e1875ef
2025-08-14T21:14:44.0327332Z  * [new tag]                 trunk/7d87e358ac8440f666fabbfd99058bb5342be6ac -> trunk/7d87e358ac8440f666fabbfd99058bb5342be6ac
2025-08-14T21:14:44.0327650Z  * [new tag]                 trunk/7e27347fd353928c99620495c8c531a5eba7d56b -> trunk/7e27347fd353928c99620495c8c531a5eba7d56b
2025-08-14T21:14:44.0328237Z  * [new tag]                 trunk/7e91394955721c77645fcdb75a5d47a255d65020 -> trunk/7e91394955721c77645fcdb75a5d47a255d65020
2025-08-14T21:14:44.0328440Z  * [new tag]                 trunk/7f4cb4a3e018a621add2a37a3a2f67b982d51001 -> trunk/7f4cb4a3e018a621add2a37a3a2f67b982d51001
2025-08-14T21:14:44.0328812Z  * [new tag]                 trunk/7fbc22855c17741ae016992803b2e147a13aa22d -> trunk/7fbc22855c17741ae016992803b2e147a13aa22d
2025-08-14T21:14:44.0329180Z  * [new tag]                 trunk/8047421fbb607d70ede13b9cd5a60b7b8bdfe348 -> trunk/8047421fbb607d70ede13b9cd5a60b7b8bdfe348
2025-08-14T21:14:44.0329519Z  * [new tag]                 trunk/8088cfa592504a2897b4c78f8a46fe658ab5c2c2 -> trunk/8088cfa592504a2897b4c78f8a46fe658ab5c2c2
2025-08-14T21:14:44.0329859Z  * [new tag]                 trunk/80cca8307943ba64168208b54028f55b2c71daff -> trunk/80cca8307943ba64168208b54028f55b2c71daff
2025-08-14T21:14:44.0330227Z  * [new tag]                 trunk/8147370733bbdcd034cad54e9212e51885a11892 -> trunk/8147370733bbdcd034cad54e9212e51885a11892
2025-08-14T21:14:44.0330578Z  * [new tag]                 trunk/83875cdb5594ccb3c9206b8eb5745fe1d011cf26 -> trunk/83875cdb5594ccb3c9206b8eb5745fe1d011cf26
2025-08-14T21:14:44.0332452Z  * [new tag]                 trunk/8399cf88ce8399d2be93355f29d4cb69f51c0654 -> trunk/8399cf88ce8399d2be93355f29d4cb69f51c0654
2025-08-14T21:14:44.0332593Z  * [new tag]                 trunk/842cc77ab9aafd518593c2fce077d6abb42a5b7f -> trunk/842cc77ab9aafd518593c2fce077d6abb42a5b7f
2025-08-14T21:14:44.0332723Z  * [new tag]                 trunk/85db508af533649d0b3447ff3f0d5fe083150c84 -> trunk/85db508af533649d0b3447ff3f0d5fe083150c84
2025-08-14T21:14:44.0332850Z  * [new tag]                 trunk/86eb65f7f06016bcd5d7951dc9d74bc3993a827a -> trunk/86eb65f7f06016bcd5d7951dc9d74bc3993a827a
2025-08-14T21:14:44.0333041Z  * [new tag]                 trunk/87e6c4079d8ec7d04aff00ed82096b39836a8367 -> trunk/87e6c4079d8ec7d04aff00ed82096b39836a8367
2025-08-14T21:14:44.0333176Z  * [new tag]                 trunk/89654db1abccf7e5f261989a150db4d1619ea2aa -> trunk/89654db1abccf7e5f261989a150db4d1619ea2aa
2025-08-14T21:14:44.0333306Z  * [new tag]                 trunk/8a37f0c90392a2c38b7c5955471fa49edcaf5cb1 -> trunk/8a37f0c90392a2c38b7c5955471fa49edcaf5cb1
2025-08-14T21:14:44.0333482Z  * [new tag]                 trunk/8ab5868a2199fe485c2d66533b9244ccb97e487d -> trunk/8ab5868a2199fe485c2d66533b9244ccb97e487d
2025-08-14T21:14:44.0333802Z  * [new tag]                 trunk/8ae4d2652f64b8444b3d5314b9232bd2119bcde6 -> trunk/8ae4d2652f64b8444b3d5314b9232bd2119bcde6
2025-08-14T21:14:44.0334163Z  * [new tag]                 trunk/8c41cb800ae0411f02ea5da34bd5ccc3790633b0 -> trunk/8c41cb800ae0411f02ea5da34bd5ccc3790633b0
2025-08-14T21:14:44.0334994Z  * [new tag]                 trunk/8cb91e20bc205b1416648d0ffd98d1ba1f3a6fc4 -> trunk/8cb91e20bc205b1416648d0ffd98d1ba1f3a6fc4
2025-08-14T21:14:44.0335165Z  * [new tag]                 trunk/8cfaf51d4e29c9bd9f49ecc98d955ed53df1a13d -> trunk/8cfaf51d4e29c9bd9f49ecc98d955ed53df1a13d
2025-08-14T21:14:44.0335542Z  * [new tag]                 trunk/8d1cf529229dce7cd5ea04abb0faac83b87ca6d1 -> trunk/8d1cf529229dce7cd5ea04abb0faac83b87ca6d1
2025-08-14T21:14:44.0335808Z  * [new tag]                 trunk/8d3d1c844303cb1d46123a1caa76d4cf83973347 -> trunk/8d3d1c844303cb1d46123a1caa76d4cf83973347
2025-08-14T21:14:44.0336364Z  * [new tag]                 trunk/8d6d3246316e1767a57d5e855acd6208da753b75 -> trunk/8d6d3246316e1767a57d5e855acd6208da753b75
2025-08-14T21:14:44.0336623Z  * [new tag]                 trunk/8e6a3138581152ab827a0997f34c470271399f5e -> trunk/8e6a3138581152ab827a0997f34c470271399f5e
2025-08-14T21:14:44.0336961Z  * [new tag]                 trunk/8eee08d2279b98af2522debb6512d37e837e89e3 -> trunk/8eee08d2279b98af2522debb6512d37e837e89e3
2025-08-14T21:14:44.0337307Z  * [new tag]                 trunk/90b78ee50f73b5c963996076a3d54b74b1b965be -> trunk/90b78ee50f73b5c963996076a3d54b74b1b965be
2025-08-14T21:14:44.0337584Z  * [new tag]                 trunk/94b91a876327820a4bb6f5d39d156f13f2553ab6 -> trunk/94b91a876327820a4bb6f5d39d156f13f2553ab6
2025-08-14T21:14:44.0338141Z  * [new tag]                 trunk/95210cc409dd578988c7116b47725c304dea54c7 -> trunk/95210cc409dd578988c7116b47725c304dea54c7
2025-08-14T21:14:44.0338321Z  * [new tag]                 trunk/96bd33b2de79598566df395f32e27c4d33673f05 -> trunk/96bd33b2de79598566df395f32e27c4d33673f05
2025-08-14T21:14:44.0338875Z  * [new tag]                 trunk/9708fcf92db88b80b9010c68662d634434da3106 -> trunk/9708fcf92db88b80b9010c68662d634434da3106
2025-08-14T21:14:44.0339063Z  * [new tag]                 trunk/97c8c98f8dcb9c5c188b691d156e0043dba6c7f8 -> trunk/97c8c98f8dcb9c5c188b691d156e0043dba6c7f8
2025-08-14T21:14:44.0339403Z  * [new tag]                 trunk/9903ca4f70bdc1653016256f5b4fd74fdfc609f8 -> trunk/9903ca4f70bdc1653016256f5b4fd74fdfc609f8
2025-08-14T21:14:44.0339745Z  * [new tag]                 trunk/99bc2f94c1955657e950ebdad5f77e518785ccbd -> trunk/99bc2f94c1955657e950ebdad5f77e518785ccbd
2025-08-14T21:14:44.0340029Z  * [new tag]                 trunk/9a06e6d0310da9d8a59ae05e8ec9c0201b55cacd -> trunk/9a06e6d0310da9d8a59ae05e8ec9c0201b55cacd
2025-08-14T21:14:44.0340360Z  * [new tag]                 trunk/9a0f7a3bb01b235ea04581ee540970a098071b72 -> trunk/9a0f7a3bb01b235ea04581ee540970a098071b72
2025-08-14T21:14:44.0340727Z  * [new tag]                 trunk/9b803cdbe298009f08340c1aaccb25aafbca95d8 -> trunk/9b803cdbe298009f08340c1aaccb25aafbca95d8
2025-08-14T21:14:44.0341261Z  * [new tag]                 trunk/9ccd0f5e31ea54fcf42101dfbaacc103494e34df -> trunk/9ccd0f5e31ea54fcf42101dfbaacc103494e34df
2025-08-14T21:14:44.0341456Z  * [new tag]                 trunk/9d37c960a4fc44d5ac334ca8bf775f85b95d76fc -> trunk/9d37c960a4fc44d5ac334ca8bf775f85b95d76fc
2025-08-14T21:14:44.0341822Z  * [new tag]                 trunk/9e07673deb212c87b1c6fea23799a97474c476ed -> trunk/9e07673deb212c87b1c6fea23799a97474c476ed
2025-08-14T21:14:44.0342121Z  * [new tag]                 trunk/9eedd2a20b64302d0d116ea2802b50948d2ebb09 -> trunk/9eedd2a20b64302d0d116ea2802b50948d2ebb09
2025-08-14T21:14:44.0342474Z  * [new tag]                 trunk/9fa8ce26cf638504469852cbc3e7d04579fc8674 -> trunk/9fa8ce26cf638504469852cbc3e7d04579fc8674
2025-08-14T21:14:44.0342836Z  * [new tag]                 trunk/a06ec54d40013c97fbffc174ea8f524ea5a95715 -> trunk/a06ec54d40013c97fbffc174ea8f524ea5a95715
2025-08-14T21:14:44.0343211Z  * [new tag]                 trunk/a288b15ea9f87ddd665f249d492e0fb0861f5a69 -> trunk/a288b15ea9f87ddd665f249d492e0fb0861f5a69
2025-08-14T21:14:44.0343572Z  * [new tag]                 trunk/a2fd106d670bb4990cebfd00f25ecbae4145e76c -> trunk/a2fd106d670bb4990cebfd00f25ecbae4145e76c
2025-08-14T21:14:44.0344107Z  * [new tag]                 trunk/a354fa91e26b376d96385a2206c5ff5b42aa4600 -> trunk/a354fa91e26b376d96385a2206c5ff5b42aa4600
2025-08-14T21:14:44.0344292Z  * [new tag]                 trunk/a4f69a5da08eace1c1e6469dec6a18aa842da73b -> trunk/a4f69a5da08eace1c1e6469dec6a18aa842da73b
2025-08-14T21:14:44.0344693Z  * [new tag]                 trunk/a53d14d5f846ac44f6c205abb1c5bc4d2f3126ae -> trunk/a53d14d5f846ac44f6c205abb1c5bc4d2f3126ae
2025-08-14T21:14:44.0346431Z  * [new tag]                 trunk/a5652407e4f3d772fc44486ac2abf756decf0861 -> trunk/a5652407e4f3d772fc44486ac2abf756decf0861
2025-08-14T21:14:44.0346572Z  * [new tag]                 trunk/a7abf57aabec0ce686092e2d66e53ba185dbc56b -> trunk/a7abf57aabec0ce686092e2d66e53ba185dbc56b
2025-08-14T21:14:44.0346698Z  * [new tag]                 trunk/a84b60c0c4016785fd93b7b8a0c04f2d0770d332 -> trunk/a84b60c0c4016785fd93b7b8a0c04f2d0770d332
2025-08-14T21:14:44.0346833Z  * [new tag]                 trunk/aa75e917bdb0f95bb6dee81853c2d3c4ab3e1883 -> trunk/aa75e917bdb0f95bb6dee81853c2d3c4ab3e1883
2025-08-14T21:14:44.0346964Z  * [new tag]                 trunk/adcca7d9a1c053495e99012de801b2ea237faad0 -> trunk/adcca7d9a1c053495e99012de801b2ea237faad0
2025-08-14T21:14:44.0347153Z  * [new tag]                 trunk/af10f1f86cc4effc93142a447693d8be55966615 -> trunk/af10f1f86cc4effc93142a447693d8be55966615
2025-08-14T21:14:44.0347288Z  * [new tag]                 trunk/af3cabc55d5699f4da528e1ca39d83338f84ae8c -> trunk/af3cabc55d5699f4da528e1ca39d83338f84ae8c
2025-08-14T21:14:44.0347536Z  * [new tag]                 trunk/b0df7715e8c590c0001d1f9cdb97057be80c9107 -> trunk/b0df7715e8c590c0001d1f9cdb97057be80c9107
2025-08-14T21:14:44.0347904Z  * [new tag]                 trunk/b149c7204c218e7c4d6594a89dd74f72bd480ec5 -> trunk/b149c7204c218e7c4d6594a89dd74f72bd480ec5
2025-08-14T21:14:44.0348257Z  * [new tag]                 trunk/b1a602762e6a6674b406a3137e7e7a678885a97b -> trunk/b1a602762e6a6674b406a3137e7e7a678885a97b
2025-08-14T21:14:44.0348587Z  * [new tag]                 trunk/b1f43548cad8fc0e30bda250f6e196310fa7a4bc -> trunk/b1f43548cad8fc0e30bda250f6e196310fa7a4bc
2025-08-14T21:14:44.0348939Z  * [new tag]                 trunk/b219ca2a00a305753c4f1ea4c9c5d23243d54753 -> trunk/b219ca2a00a305753c4f1ea4c9c5d23243d54753
2025-08-14T21:14:44.0349306Z  * [new tag]                 trunk/b4596895b9d85a686c2cb978938b0a7797b3690a -> trunk/b4596895b9d85a686c2cb978938b0a7797b3690a
2025-08-14T21:14:44.0349637Z  * [new tag]                 trunk/b5fd7223b1bf44720dc9183bda7dfcf7aeccff02 -> trunk/b5fd7223b1bf44720dc9183bda7dfcf7aeccff02
2025-08-14T21:14:44.0349975Z  * [new tag]                 trunk/b602ea9cab7d43a7ee7b4051227090f23fbd3dbf -> trunk/b602ea9cab7d43a7ee7b4051227090f23fbd3dbf
2025-08-14T21:14:44.0350479Z  * [new tag]                 trunk/b6b74aed604bd2e96389ff99aaaf39abc64fdc64 -> trunk/b6b74aed604bd2e96389ff99aaaf39abc64fdc64
2025-08-14T21:14:44.0350742Z  * [new tag]                 trunk/b7db86600a2614adc71c92ca42d359a7ac534d78 -> trunk/b7db86600a2614adc71c92ca42d359a7ac534d78
2025-08-14T21:14:44.0351052Z  * [new tag]                 trunk/b9003ed3d87699e81e436719625a21996a6654e5 -> trunk/b9003ed3d87699e81e436719625a21996a6654e5
2025-08-14T21:14:44.0351378Z  * [new tag]                 trunk/b90feeac86bda00afc2789321bcd706015ff44e3 -> trunk/b90feeac86bda00afc2789321bcd706015ff44e3
2025-08-14T21:14:44.0352111Z  * [new tag]                 trunk/b9d7de3a094598c3dc0dd52e57bce30eb684c9d8 -> trunk/b9d7de3a094598c3dc0dd52e57bce30eb684c9d8
2025-08-14T21:14:44.0352320Z  * [new tag]                 trunk/ba47821f524eee50a214ed39fa2e7765d54aabf4 -> trunk/ba47821f524eee50a214ed39fa2e7765d54aabf4
2025-08-14T21:14:44.0352671Z  * [new tag]                 trunk/ba4ccf5d67e3d237f435eacc2bce3c6025f08491 -> trunk/ba4ccf5d67e3d237f435eacc2bce3c6025f08491
2025-08-14T21:14:44.0353118Z  * [new tag]                 trunk/bcf23ecc476df2bd7479f142567213e2623308ee -> trunk/bcf23ecc476df2bd7479f142567213e2623308ee
2025-08-14T21:14:44.0353480Z  * [new tag]                 trunk/be53f609aaf6f01e2863f490975ea9eaac3ee9ff -> trunk/be53f609aaf6f01e2863f490975ea9eaac3ee9ff
2025-08-14T21:14:44.0353817Z  * [new tag]                 trunk/beb4d7816dedc67a5de1f82e5a45b5910f407941 -> trunk/beb4d7816dedc67a5de1f82e5a45b5910f407941
2025-08-14T21:14:44.0354199Z  * [new tag]                 trunk/bfc873d02ec413344717493e4175a902921359fd -> trunk/bfc873d02ec413344717493e4175a902921359fd
2025-08-14T21:14:44.0354605Z  * [new tag]                 trunk/c184cb3852f0ff2d16a489d61abc3739c309e6ca -> trunk/c184cb3852f0ff2d16a489d61abc3739c309e6ca
2025-08-14T21:14:44.0354933Z  * [new tag]                 trunk/c24ca7f4bf79f62fd623d76346ca27e53f731431 -> trunk/c24ca7f4bf79f62fd623d76346ca27e53f731431
2025-08-14T21:14:44.0355275Z  * [new tag]                 trunk/c3dc8dc4122977893004c49d10e4676cd0a97da4 -> trunk/c3dc8dc4122977893004c49d10e4676cd0a97da4
2025-08-14T21:14:44.0355603Z  * [new tag]                 trunk/c5ec5458a547f7a774468ea0eb2258d3de596492 -> trunk/c5ec5458a547f7a774468ea0eb2258d3de596492
2025-08-14T21:14:44.0355969Z  * [new tag]                 trunk/c5efc5c8a66eca84865015058b3221013ebfe685 -> trunk/c5efc5c8a66eca84865015058b3221013ebfe685
2025-08-14T21:14:44.0356334Z  * [new tag]                 trunk/c6563341208003f64c131854a9cf029555f786d2 -> trunk/c6563341208003f64c131854a9cf029555f786d2
2025-08-14T21:14:44.0356694Z  * [new tag]                 trunk/c6d78d4dbda53837d298d23a5fbc09af90a42d9e -> trunk/c6d78d4dbda53837d298d23a5fbc09af90a42d9e
2025-08-14T21:14:44.0356982Z  * [new tag]                 trunk/c8205cb35435f39d2c26f6c94b45e4adeb6dcb23 -> trunk/c8205cb35435f39d2c26f6c94b45e4adeb6dcb23
2025-08-14T21:14:44.0357432Z  * [new tag]                 trunk/c859ba7114b1fcb49527e090745fa17091d1f8d5 -> trunk/c859ba7114b1fcb49527e090745fa17091d1f8d5
2025-08-14T21:14:44.0357750Z  * [new tag]                 trunk/c86040a8e68f754b90a84099187d3624954c7f36 -> trunk/c86040a8e68f754b90a84099187d3624954c7f36
2025-08-14T21:14:44.0358119Z  * [new tag]                 trunk/c9671dc865aa0fc1cb86df754e355b44d8e02bb4 -> trunk/c9671dc865aa0fc1cb86df754e355b44d8e02bb4
2025-08-14T21:14:44.0358507Z  * [new tag]                 trunk/ca7315c17162ea21b1ca5ba23f4bf6168766c7b9 -> trunk/ca7315c17162ea21b1ca5ba23f4bf6168766c7b9
2025-08-14T21:14:44.0358856Z  * [new tag]                 trunk/cae2b5e3d223829bdc553fc8601df4b1c1554cff -> trunk/cae2b5e3d223829bdc553fc8601df4b1c1554cff
2025-08-14T21:14:44.0360571Z  * [new tag]                 trunk/cbffde774557752cf20447d42d99ec6102673c31 -> trunk/cbffde774557752cf20447d42d99ec6102673c31
2025-08-14T21:14:44.0360711Z  * [new tag]                 trunk/cd8d8c18f5bafdc1c73d5ac0129e7b4d76ab45bc -> trunk/cd8d8c18f5bafdc1c73d5ac0129e7b4d76ab45bc
2025-08-14T21:14:44.0360841Z  * [new tag]                 trunk/cf0a0dcb0afa5e84b95461cc542f862b51ca96bf -> trunk/cf0a0dcb0afa5e84b95461cc542f862b51ca96bf
2025-08-14T21:14:44.0360970Z  * [new tag]                 trunk/cf4964be68fa9f4ffc334f01cce42d7424b1cc81 -> trunk/cf4964be68fa9f4ffc334f01cce42d7424b1cc81
2025-08-14T21:14:44.0361098Z  * [new tag]                 trunk/d0e2240f680ea2a553f7ee8188f52482e130bfd0 -> trunk/d0e2240f680ea2a553f7ee8188f52482e130bfd0
2025-08-14T21:14:44.0361234Z  * [new tag]                 trunk/d1950d4bb5cba8fb6b23e4d283eea5b9801737e2 -> trunk/d1950d4bb5cba8fb6b23e4d283eea5b9801737e2
2025-08-14T21:14:44.0361408Z  * [new tag]                 trunk/d20c4c20e61adecf00335c4d8c22eb1ace472cd3 -> trunk/d20c4c20e61adecf00335c4d8c22eb1ace472cd3
2025-08-14T21:14:44.0361765Z  * [new tag]                 trunk/d25c4f954d599ea512e2f70cd6df101c21479d4c -> trunk/d25c4f954d599ea512e2f70cd6df101c21479d4c
2025-08-14T21:14:44.0362103Z  * [new tag]                 trunk/d3d359dbafa89173a371e2637f22b47398e94a24 -> trunk/d3d359dbafa89173a371e2637f22b47398e94a24
2025-08-14T21:14:44.0362489Z  * [new tag]                 trunk/d46768db04499d07a5b0db984112a6d1b7d3b0c1 -> trunk/d46768db04499d07a5b0db984112a6d1b7d3b0c1
2025-08-14T21:14:44.0362857Z  * [new tag]                 trunk/d4c1a08c89f37d249a0146ff511c82ecc5c53b8f -> trunk/d4c1a08c89f37d249a0146ff511c82ecc5c53b8f
2025-08-14T21:14:44.0363848Z  * [new tag]                 trunk/d556586448f3caab85673c7da0978fe31c7748f7 -> trunk/d556586448f3caab85673c7da0978fe31c7748f7
2025-08-14T21:14:44.0364098Z  * [new tag]                 trunk/d670304001429a1a833255a918ed788d7ec4989a -> trunk/d670304001429a1a833255a918ed788d7ec4989a
2025-08-14T21:14:44.0364537Z  * [new tag]                 trunk/d6786741a77aba200c78002646cc069b7a1799b0 -> trunk/d6786741a77aba200c78002646cc069b7a1799b0
2025-08-14T21:14:44.0364997Z  * [new tag]                 trunk/d68c323692dedcbb74e670801e3502944fd790ff -> trunk/d68c323692dedcbb74e670801e3502944fd790ff
2025-08-14T21:14:44.0365288Z  * [new tag]                 trunk/d8cb3db5339b45e4b745b2b883ef3ecde9843e2c -> trunk/d8cb3db5339b45e4b745b2b883ef3ecde9843e2c
2025-08-14T21:14:44.0365565Z  * [new tag]                 trunk/da1f608ca33f3062535d0a4866d95db19e72fcbd -> trunk/da1f608ca33f3062535d0a4866d95db19e72fcbd
2025-08-14T21:14:44.0365883Z  * [new tag]                 trunk/db0b7f1cc9bb3fe71aaf8b964a644147ae8e1c35 -> trunk/db0b7f1cc9bb3fe71aaf8b964a644147ae8e1c35
2025-08-14T21:14:44.0366234Z  * [new tag]                 trunk/db32b60662b2f2bdcad980127d5dc4b66b02a7e4 -> trunk/db32b60662b2f2bdcad980127d5dc4b66b02a7e4
2025-08-14T21:14:44.0366615Z  * [new tag]                 trunk/db763b17175553ba09637362eb9773a91997a7ad -> trunk/db763b17175553ba09637362eb9773a91997a7ad
2025-08-14T21:14:44.0366954Z  * [new tag]                 trunk/db78943a1ca13a32a3d6045eb15e2b719ee13a2f -> trunk/db78943a1ca13a32a3d6045eb15e2b719ee13a2f
2025-08-14T21:14:44.0367314Z  * [new tag]                 trunk/dc0d18e023d9b7e314ebba0f234b6cb1579dbcfd -> trunk/dc0d18e023d9b7e314ebba0f234b6cb1579dbcfd
2025-08-14T21:14:44.0367664Z  * [new tag]                 trunk/dd21c8a578038ab2841a7ba809a06921093ac9d8 -> trunk/dd21c8a578038ab2841a7ba809a06921093ac9d8
2025-08-14T21:14:44.0367998Z  * [new tag]                 trunk/deea71a90e05eb320c04bebfead5317746637f0d -> trunk/deea71a90e05eb320c04bebfead5317746637f0d
2025-08-14T21:14:44.0368369Z  * [new tag]                 trunk/df55ec7d4b35f6d21691e9dd41c82f27de762948 -> trunk/df55ec7d4b35f6d21691e9dd41c82f27de762948
2025-08-14T21:14:44.0368700Z  * [new tag]                 trunk/e1cf0d496ea85d1807c8c740f296e77bf7bdc1df -> trunk/e1cf0d496ea85d1807c8c740f296e77bf7bdc1df
2025-08-14T21:14:44.0369058Z  * [new tag]                 trunk/e248719ac03c103767ab72034f6b9fd56855bf98 -> trunk/e248719ac03c103767ab72034f6b9fd56855bf98
2025-08-14T21:14:44.0369428Z  * [new tag]                 trunk/e49762026070f66be41bfa6537fbcf9bfc24e558 -> trunk/e49762026070f66be41bfa6537fbcf9bfc24e558
2025-08-14T21:14:44.0369785Z  * [new tag]                 trunk/e4de93f6a3e342bab34d3757cf90ec0ccc87e168 -> trunk/e4de93f6a3e342bab34d3757cf90ec0ccc87e168
2025-08-14T21:14:44.0370093Z  * [new tag]                 trunk/e619c6bb90b9dedaccd3cbeed86a288993a4e33f -> trunk/e619c6bb90b9dedaccd3cbeed86a288993a4e33f
2025-08-14T21:14:44.0370450Z  * [new tag]                 trunk/e63c2b21c186a7d2ab8a8953b8aa1535f2e96e58 -> trunk/e63c2b21c186a7d2ab8a8953b8aa1535f2e96e58
2025-08-14T21:14:44.0370800Z  * [new tag]                 trunk/e7152ff8a6a929a0db7f3f4a72a5b6d471769cd3 -> trunk/e7152ff8a6a929a0db7f3f4a72a5b6d471769cd3
2025-08-14T21:14:44.0371161Z  * [new tag]                 trunk/e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a -> trunk/e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a
2025-08-14T21:14:44.0371518Z  * [new tag]                 trunk/e9eb2096a59a79e7a94c3e28a0715e040369f34c -> trunk/e9eb2096a59a79e7a94c3e28a0715e040369f34c
2025-08-14T21:14:44.0371871Z  * [new tag]                 trunk/eac2d9d695a32dd456050f45cac35134ec3809f4 -> trunk/eac2d9d695a32dd456050f45cac35134ec3809f4
2025-08-14T21:14:44.0372212Z  * [new tag]                 trunk/ecde76c764752540edf9ef62a97936c86d984b17 -> trunk/ecde76c764752540edf9ef62a97936c86d984b17
2025-08-14T21:14:44.0372502Z  * [new tag]                 trunk/ecea81117b2fdc52907c97b3c32d779e07b5d55b -> trunk/ecea81117b2fdc52907c97b3c32d779e07b5d55b
2025-08-14T21:14:44.0372870Z  * [new tag]                 trunk/edaa151d0d5a4e75fbec9843f49cc78770eb61fb -> trunk/edaa151d0d5a4e75fbec9843f49cc78770eb61fb
2025-08-14T21:14:44.0373189Z  * [new tag]                 trunk/ee1b0412b919dfb358d5a697b3be49621497fbc2 -> trunk/ee1b0412b919dfb358d5a697b3be49621497fbc2
2025-08-14T21:14:44.0374989Z  * [new tag]                 trunk/ee1fb43450c2e985657f95a91b68328d6f20f24e -> trunk/ee1fb43450c2e985657f95a91b68328d6f20f24e
2025-08-14T21:14:44.0375245Z  * [new tag]                 trunk/ee89cc7a0acd69de25f98fe4ef828546db7b444c -> trunk/ee89cc7a0acd69de25f98fe4ef828546db7b444c
2025-08-14T21:14:44.0375380Z  * [new tag]                 trunk/ee9f8ba11d664b871a9e0c7933fdc8571635b78c -> trunk/ee9f8ba11d664b871a9e0c7933fdc8571635b78c
2025-08-14T21:14:44.0375514Z  * [new tag]                 trunk/eed9dbf70f43ee529fec78ac00ed9a4fd74c6e76 -> trunk/eed9dbf70f43ee529fec78ac00ed9a4fd74c6e76
2025-08-14T21:14:44.0375643Z  * [new tag]                 trunk/f077c2402e4eb5b0ed562b4ee5b7a0503f26ef94 -> trunk/f077c2402e4eb5b0ed562b4ee5b7a0503f26ef94
2025-08-14T21:14:44.0375773Z  * [new tag]                 trunk/f0980fc0bbd656d6c02d23ad97e945353b314f35 -> trunk/f0980fc0bbd656d6c02d23ad97e945353b314f35
2025-08-14T21:14:44.0375965Z  * [new tag]                 trunk/f15ada5c6fad97a7dcbfa4673f067b6942dda640 -> trunk/f15ada5c6fad97a7dcbfa4673f067b6942dda640
2025-08-14T21:14:44.0376197Z  * [new tag]                 trunk/f27232a2134150cb5e55d26a74d8c36c6a961ca5 -> trunk/f27232a2134150cb5e55d26a74d8c36c6a961ca5
2025-08-14T21:14:44.0376546Z  * [new tag]                 trunk/f33ce40bc062a281e1a1f57e8c1926d0a7d155cc -> trunk/f33ce40bc062a281e1a1f57e8c1926d0a7d155cc
2025-08-14T21:14:44.0376889Z  * [new tag]                 trunk/f341077ce4710172da20cfad916ee37159bfe9fe -> trunk/f341077ce4710172da20cfad916ee37159bfe9fe
2025-08-14T21:14:44.0377252Z  * [new tag]                 trunk/f3a4d742ece08de4cb0e59dcc62e0093a7d0b0c7 -> trunk/f3a4d742ece08de4cb0e59dcc62e0093a7d0b0c7
2025-08-14T21:14:44.0377583Z  * [new tag]                 trunk/f3f159ff8c4bad2edec99c68a941c628e983d04c -> trunk/f3f159ff8c4bad2edec99c68a941c628e983d04c
2025-08-14T21:14:44.0377938Z  * [new tag]                 trunk/f60454cce8b93e5bbf67f2f3c88c8ac01ed65457 -> trunk/f60454cce8b93e5bbf67f2f3c88c8ac01ed65457
2025-08-14T21:14:44.0378488Z  * [new tag]                 trunk/f7b2f3314cf7aede67d5fa5c75e4243208484344 -> trunk/f7b2f3314cf7aede67d5fa5c75e4243208484344
2025-08-14T21:14:44.0378757Z  * [new tag]                 trunk/f8f0414a5983ff481a2188e0c18594150430c8c5 -> trunk/f8f0414a5983ff481a2188e0c18594150430c8c5
2025-08-14T21:14:44.0379083Z  * [new tag]                 trunk/f95b58c2844b3444cd8446fed8570729dc4216eb -> trunk/f95b58c2844b3444cd8446fed8570729dc4216eb
2025-08-14T21:14:44.0379441Z  * [new tag]                 trunk/f990490a23815ea6ee27e487c70ba2cf513ba43d -> trunk/f990490a23815ea6ee27e487c70ba2cf513ba43d
2025-08-14T21:14:44.0379782Z  * [new tag]                 trunk/fb887c3bb588cfe782615e67f6c26db636b8539b -> trunk/fb887c3bb588cfe782615e67f6c26db636b8539b
2025-08-14T21:14:44.0380586Z  * [new tag]                 trunk/fc25c68f20f772290927a7031b998b92615259cf -> trunk/fc25c68f20f772290927a7031b998b92615259cf
2025-08-14T21:14:44.0380815Z  * [new tag]                 trunk/fc80f6859e0ccf66513a40f04b9e735e759d4ddb -> trunk/fc80f6859e0ccf66513a40f04b9e735e759d4ddb
2025-08-14T21:14:44.0381202Z  * [new tag]                 trunk/fdfd69bb05488d76123db9cc1cdd90ac4137bbfb -> trunk/fdfd69bb05488d76123db9cc1cdd90ac4137bbfb
2025-08-14T21:14:44.0381558Z  * [new tag]                 trunk/fe3f5fe4ea2ff6f56406dc5d954636ebb08d0a08 -> trunk/fe3f5fe4ea2ff6f56406dc5d954636ebb08d0a08
2025-08-14T21:14:44.0381902Z  * [new tag]                 trunk/fea7e9dd37c02c334b130f6624af6163fde6b2ab -> trunk/fea7e9dd37c02c334b130f6624af6163fde6b2ab
2025-08-14T21:14:44.0382202Z  * [new tag]                 trunk/ff0d56d03592aa03f3ced8359241d21df1783393 -> trunk/ff0d56d03592aa03f3ced8359241d21df1783393
2025-08-14T21:14:44.0382491Z  * [new tag]                 v0.1.1                      -> v0.1.1
2025-08-14T21:14:44.0383056Z  * [new tag]                 v0.1.10                     -> v0.1.10
2025-08-14T21:14:44.0383205Z  * [new tag]                 v0.1.11                     -> v0.1.11
2025-08-14T21:14:44.0383653Z  * [new tag]                 v0.1.12                     -> v0.1.12
2025-08-14T21:14:44.0383824Z  * [new tag]                 v0.1.2                      -> v0.1.2
2025-08-14T21:14:44.0384250Z  * [new tag]                 v0.1.3                      -> v0.1.3
2025-08-14T21:14:44.0384449Z  * [new tag]                 v0.1.4                      -> v0.1.4
2025-08-14T21:14:44.0384876Z  * [new tag]                 v0.1.5                      -> v0.1.5
2025-08-14T21:14:44.0385124Z  * [new tag]                 v0.1.6                      -> v0.1.6
2025-08-14T21:14:44.0385568Z  * [new tag]                 v0.1.7                      -> v0.1.7
2025-08-14T21:14:44.0385727Z  * [new tag]                 v0.1.8                      -> v0.1.8
2025-08-14T21:14:44.0386036Z  * [new tag]                 v0.1.9                      -> v0.1.9
2025-08-14T21:14:44.0386404Z  * [new tag]                 v0.2.0                      -> v0.2.0
2025-08-14T21:14:44.0386719Z  * [new tag]                 v0.3.0                      -> v0.3.0
2025-08-14T21:14:44.0387210Z  * [new tag]                 v0.3.1                      -> v0.3.1
2025-08-14T21:14:44.0389016Z  * [new tag]                 v0.4.0                      -> v0.4.0
2025-08-14T21:14:44.0389102Z  * [new tag]                 v0.4.1                      -> v0.4.1
2025-08-14T21:14:44.0389160Z  * [new tag]                 v1.0.0                      -> v1.0.0
2025-08-14T21:14:44.0389216Z  * [new tag]                 v1.0.0a0                    -> v1.0.0a0
2025-08-14T21:14:44.0389266Z  * [new tag]                 v1.0.1                      -> v1.0.1
2025-08-14T21:14:44.0389317Z  * [new tag]                 v1.0rc0                     -> v1.0rc0
2025-08-14T21:14:44.0389431Z  * [new tag]                 v1.0rc1                     -> v1.0rc1
2025-08-14T21:14:44.0389924Z  * [new tag]                 v1.1.0                      -> v1.1.0
2025-08-14T21:14:44.0390122Z  * [new tag]                 v1.1.0a0                    -> v1.1.0a0
2025-08-14T21:14:44.0390785Z  * [new tag]                 v1.10.0                     -> v1.10.0
2025-08-14T21:14:44.0390980Z  * [new tag]                 v1.10.0-rc1                 -> v1.10.0-rc1
2025-08-14T21:14:44.0391428Z  * [new tag]                 v1.10.0-rc2                 -> v1.10.0-rc2
2025-08-14T21:14:44.0391590Z  * [new tag]                 v1.10.0-rc3                 -> v1.10.0-rc3
2025-08-14T21:14:44.0392085Z  * [new tag]                 v1.10.1                     -> v1.10.1
2025-08-14T21:14:44.0392252Z  * [new tag]                 v1.10.1-rc1                 -> v1.10.1-rc1
2025-08-14T21:14:44.0392521Z  * [new tag]                 v1.10.2                     -> v1.10.2
2025-08-14T21:14:44.0392764Z  * [new tag]                 v1.10.2-rc1                 -> v1.10.2-rc1
2025-08-14T21:14:44.0393418Z  * [new tag]                 v1.11.0                     -> v1.11.0
2025-08-14T21:14:44.0393564Z  * [new tag]                 v1.11.0-rc1                 -> v1.11.0-rc1
2025-08-14T21:14:44.0394067Z  * [new tag]                 v1.11.0-rc2                 -> v1.11.0-rc2
2025-08-14T21:14:44.0394384Z  * [new tag]                 v1.11.0-rc3                 -> v1.11.0-rc3
2025-08-14T21:14:44.0394831Z  * [new tag]                 v1.11.0-rc4                 -> v1.11.0-rc4
2025-08-14T21:14:44.0395156Z  * [new tag]                 v1.11.0-rc5                 -> v1.11.0-rc5
2025-08-14T21:14:44.0395404Z  * [new tag]                 v1.11.0-rc6                 -> v1.11.0-rc6
2025-08-14T21:14:44.0395665Z  * [new tag]                 v1.11.0-rc7                 -> v1.11.0-rc7
2025-08-14T21:14:44.0396102Z  * [new tag]                 v1.12.0                     -> v1.12.0
2025-08-14T21:14:44.0396361Z  * [new tag]                 v1.12.0-rc1                 -> v1.12.0-rc1
2025-08-14T21:14:44.0396969Z  * [new tag]                 v1.12.0-rc2                 -> v1.12.0-rc2
2025-08-14T21:14:44.0397153Z  * [new tag]                 v1.12.0-rc3                 -> v1.12.0-rc3
2025-08-14T21:14:44.0397623Z  * [new tag]                 v1.12.0-rc4                 -> v1.12.0-rc4
2025-08-14T21:14:44.0397874Z  * [new tag]                 v1.12.0-rc5                 -> v1.12.0-rc5
2025-08-14T21:14:44.0398311Z  * [new tag]                 v1.12.0-rc6                 -> v1.12.0-rc6
2025-08-14T21:14:44.0398551Z  * [new tag]                 v1.12.0-rc7                 -> v1.12.0-rc7
2025-08-14T21:14:44.0398777Z  * [new tag]                 v1.12.0-rc8                 -> v1.12.0-rc8
2025-08-14T21:14:44.0399048Z  * [new tag]                 v1.12.1                     -> v1.12.1
2025-08-14T21:14:44.0399570Z  * [new tag]                 v1.12.1-rc1                 -> v1.12.1-rc1
2025-08-14T21:14:44.0399867Z  * [new tag]                 v1.12.1-rc2                 -> v1.12.1-rc2
2025-08-14T21:14:44.0400340Z  * [new tag]                 v1.12.1-rc3                 -> v1.12.1-rc3
2025-08-14T21:14:44.0400617Z  * [new tag]                 v1.12.1-rc4                 -> v1.12.1-rc4
2025-08-14T21:14:44.0402313Z  * [new tag]                 v1.12.1-rc5                 -> v1.12.1-rc5
2025-08-14T21:14:44.0402413Z  * [new tag]                 v1.13.0                     -> v1.13.0
2025-08-14T21:14:44.0402474Z  * [new tag]                 v1.13.0-rc1                 -> v1.13.0-rc1
2025-08-14T21:14:44.0402528Z  * [new tag]                 v1.13.0-rc2                 -> v1.13.0-rc2
2025-08-14T21:14:44.0402581Z  * [new tag]                 v1.13.0-rc3                 -> v1.13.0-rc3
2025-08-14T21:14:44.0402730Z  * [new tag]                 v1.13.0-rc4                 -> v1.13.0-rc4
2025-08-14T21:14:44.0403204Z  * [new tag]                 v1.13.0-rc5                 -> v1.13.0-rc5
2025-08-14T21:14:44.0403280Z  * [new tag]                 v1.13.0-rc6                 -> v1.13.0-rc6
2025-08-14T21:14:44.0403842Z  * [new tag]                 v1.13.1                     -> v1.13.1
2025-08-14T21:14:44.0403950Z  * [new tag]                 v1.13.1-rc1                 -> v1.13.1-rc1
2025-08-14T21:14:44.0404489Z  * [new tag]                 v1.2.0                      -> v1.2.0
2025-08-14T21:14:44.0404667Z  * [new tag]                 v1.2.0a0                    -> v1.2.0a0
2025-08-14T21:14:44.0405117Z  * [new tag]                 v1.3.0                      -> v1.3.0
2025-08-14T21:14:44.0405429Z  * [new tag]                 v1.3.0a0                    -> v1.3.0a0
2025-08-14T21:14:44.0405740Z  * [new tag]                 v1.3.1                      -> v1.3.1
2025-08-14T21:14:44.0406155Z  * [new tag]                 v1.4.0                      -> v1.4.0
2025-08-14T21:14:44.0406431Z  * [new tag]                 v1.4.0a0                    -> v1.4.0a0
2025-08-14T21:14:44.0406701Z  * [new tag]                 v1.4.1                      -> v1.4.1
2025-08-14T21:14:44.0407233Z  * [new tag]                 v1.5.0                      -> v1.5.0
2025-08-14T21:14:44.0407482Z  * [new tag]                 v1.5.0-rc1                  -> v1.5.0-rc1
2025-08-14T21:14:44.0407956Z  * [new tag]                 v1.5.0-rc2                  -> v1.5.0-rc2
2025-08-14T21:14:44.0408440Z  * [new tag]                 v1.5.0-rc3                  -> v1.5.0-rc3
2025-08-14T21:14:44.0408579Z  * [new tag]                 v1.5.0-rc4                  -> v1.5.0-rc4
2025-08-14T21:14:44.0408867Z  * [new tag]                 v1.5.0-rc5                  -> v1.5.0-rc5
2025-08-14T21:14:44.0409355Z  * [new tag]                 v1.5.1                      -> v1.5.1
2025-08-14T21:14:44.0409506Z  * [new tag]                 v1.5.1-rc1                  -> v1.5.1-rc1
2025-08-14T21:14:44.0409782Z  * [new tag]                 v1.6.0                      -> v1.6.0
2025-08-14T21:14:44.0410277Z  * [new tag]                 v1.6.0-rc1                  -> v1.6.0-rc1
2025-08-14T21:14:44.0410532Z  * [new tag]                 v1.6.0-rc2                  -> v1.6.0-rc2
2025-08-14T21:14:44.0410997Z  * [new tag]                 v1.6.0-rc3                  -> v1.6.0-rc3
2025-08-14T21:14:44.0411297Z  * [new tag]                 v1.6.0-rc4                  -> v1.6.0-rc4
2025-08-14T21:14:44.0411725Z  * [new tag]                 v1.6.0-rc5                  -> v1.6.0-rc5
2025-08-14T21:14:44.0411992Z  * [new tag]                 v1.6.0-rc6                  -> v1.6.0-rc6
2025-08-14T21:14:44.0412264Z  * [new tag]                 v1.6.0-rc7                  -> v1.6.0-rc7
2025-08-14T21:14:44.0412830Z  * [new tag]                 v1.7.0                      -> v1.7.0
2025-08-14T21:14:44.0413024Z  * [new tag]                 v1.7.0-rc1                  -> v1.7.0-rc1
2025-08-14T21:14:44.0413926Z  * [new tag]                 v1.7.0-rc2                  -> v1.7.0-rc2
2025-08-14T21:14:44.0414189Z  * [new tag]                 v1.7.0-rc3                  -> v1.7.0-rc3
2025-08-14T21:14:44.0414475Z  * [new tag]                 v1.7.0-rc4                  -> v1.7.0-rc4
2025-08-14T21:14:44.0414978Z  * [new tag]                 v1.7.1                      -> v1.7.1
2025-08-14T21:14:44.0416528Z  * [new tag]                 v1.7.1-rc1                  -> v1.7.1-rc1
2025-08-14T21:14:44.0416585Z  * [new tag]                 v1.7.1-rc2                  -> v1.7.1-rc2
2025-08-14T21:14:44.0416706Z  * [new tag]                 v1.7.1-rc3                  -> v1.7.1-rc3
2025-08-14T21:14:44.0416757Z  * [new tag]                 v1.8.0                      -> v1.8.0
2025-08-14T21:14:44.0416806Z  * [new tag]                 v1.8.0-rc1                  -> v1.8.0-rc1
2025-08-14T21:14:44.0417061Z  * [new tag]                 v1.8.0-rc2                  -> v1.8.0-rc2
2025-08-14T21:14:44.0417678Z  * [new tag]                 v1.8.0-rc3                  -> v1.8.0-rc3
2025-08-14T21:14:44.0417750Z  * [new tag]                 v1.8.0-rc4                  -> v1.8.0-rc4
2025-08-14T21:14:44.0418003Z  * [new tag]                 v1.8.0-rc5                  -> v1.8.0-rc5
2025-08-14T21:14:44.0418290Z  * [new tag]                 v1.8.1                      -> v1.8.1
2025-08-14T21:14:44.0418763Z  * [new tag]                 v1.8.1-rc1                  -> v1.8.1-rc1
2025-08-14T21:14:44.0418945Z  * [new tag]                 v1.8.1-rc2                  -> v1.8.1-rc2
2025-08-14T21:14:44.0419224Z  * [new tag]                 v1.8.1-rc3                  -> v1.8.1-rc3
2025-08-14T21:14:44.0419968Z  * [new tag]                 v1.8.2                      -> v1.8.2
2025-08-14T21:14:44.0420097Z  * [new tag]                 v1.8.2-rc1                  -> v1.8.2-rc1
2025-08-14T21:14:44.0420569Z  * [new tag]                 v1.9.0                      -> v1.9.0
2025-08-14T21:14:44.0420825Z  * [new tag]                 v1.9.0-rc1                  -> v1.9.0-rc1
2025-08-14T21:14:44.0421335Z  * [new tag]                 v1.9.0-rc2                  -> v1.9.0-rc2
2025-08-14T21:14:44.0421782Z  * [new tag]                 v1.9.0-rc3                  -> v1.9.0-rc3
2025-08-14T21:14:44.0421911Z  * [new tag]                 v1.9.0-rc4                  -> v1.9.0-rc4
2025-08-14T21:14:44.0422397Z  * [new tag]                 v1.9.1                      -> v1.9.1
2025-08-14T21:14:44.0423020Z  * [new tag]                 v1.9.1-rc1                  -> v1.9.1-rc1
2025-08-14T21:14:44.0423126Z  * [new tag]                 v1.9.1-rc2                  -> v1.9.1-rc2
2025-08-14T21:14:44.0423698Z  * [new tag]                 v2.0.0                      -> v2.0.0
2025-08-14T21:14:44.0423951Z  * [new tag]                 v2.0.0-rc1                  -> v2.0.0-rc1
2025-08-14T21:14:44.0424439Z  * [new tag]                 v2.0.0-rc2                  -> v2.0.0-rc2
2025-08-14T21:14:44.0424816Z  * [new tag]                 v2.0.0-rc3                  -> v2.0.0-rc3
2025-08-14T21:14:44.0425269Z  * [new tag]                 v2.0.0-rc4                  -> v2.0.0-rc4
2025-08-14T21:14:44.0425517Z  * [new tag]                 v2.0.0-rc5                  -> v2.0.0-rc5
2025-08-14T21:14:44.0425819Z  * [new tag]                 v2.0.0-rc6                  -> v2.0.0-rc6
2025-08-14T21:14:44.0426339Z  * [new tag]                 v2.0.1                      -> v2.0.1
2025-08-14T21:14:44.0426649Z  * [new tag]                 v2.0.1-rc1                  -> v2.0.1-rc1
2025-08-14T21:14:44.0426909Z  * [new tag]                 v2.0.1-rc2                  -> v2.0.1-rc2
2025-08-14T21:14:44.0427330Z  * [new tag]                 v2.0.1-rc3                  -> v2.0.1-rc3
2025-08-14T21:14:44.0427510Z  * [new tag]                 v2.0.1-rc4                  -> v2.0.1-rc4
2025-08-14T21:14:44.0428233Z  * [new tag]                 v2.1.0                      -> v2.1.0
2025-08-14T21:14:44.0428576Z  * [new tag]                 v2.1.0-rc1                  -> v2.1.0-rc1
2025-08-14T21:14:44.0429125Z  * [new tag]                 v2.1.0-rc2                  -> v2.1.0-rc2
2025-08-14T21:14:44.0429331Z  * [new tag]                 v2.1.0-rc3                  -> v2.1.0-rc3
2025-08-14T21:14:44.0429870Z  * [new tag]                 v2.1.0-rc4                  -> v2.1.0-rc4
2025-08-14T21:14:44.0431649Z  * [new tag]                 v2.1.0-rc5                  -> v2.1.0-rc5
2025-08-14T21:14:44.0431748Z  * [new tag]                 v2.1.0-rc6                  -> v2.1.0-rc6
2025-08-14T21:14:44.0431806Z  * [new tag]                 v2.1.1                      -> v2.1.1
2025-08-14T21:14:44.0431909Z  * [new tag]                 v2.1.1-rc1                  -> v2.1.1-rc1
2025-08-14T21:14:44.0431959Z  * [new tag]                 v2.1.1-rc2                  -> v2.1.1-rc2
2025-08-14T21:14:44.0432021Z  * [new tag]                 v2.1.1-rc3                  -> v2.1.1-rc3
2025-08-14T21:14:44.0432558Z  * [new tag]                 v2.1.1-rc4                  -> v2.1.1-rc4
2025-08-14T21:14:44.0432780Z  * [new tag]                 v2.1.1-rc5                  -> v2.1.1-rc5
2025-08-14T21:14:44.0433142Z  * [new tag]                 v2.1.1-rc6                  -> v2.1.1-rc6
2025-08-14T21:14:44.0433847Z  * [new tag]                 v2.1.2                      -> v2.1.2
2025-08-14T21:14:44.0434066Z  * [new tag]                 v2.1.2-rc1                  -> v2.1.2-rc1
2025-08-14T21:14:44.0434589Z  * [new tag]                 v2.1.2-rc2                  -> v2.1.2-rc2
2025-08-14T21:14:44.0434775Z  * [new tag]                 v2.1.2-rc3                  -> v2.1.2-rc3
2025-08-14T21:14:44.0435270Z  * [new tag]                 v2.2.0                      -> v2.2.0
2025-08-14T21:14:44.0435719Z  * [new tag]                 v2.2.0-rc1                  -> v2.2.0-rc1
2025-08-14T21:14:44.0435937Z  * [new tag]                 v2.2.0-rc2                  -> v2.2.0-rc2
2025-08-14T21:14:44.0436406Z  * [new tag]                 v2.2.0-rc3                  -> v2.2.0-rc3
2025-08-14T21:14:44.0436644Z  * [new tag]                 v2.2.0-rc4                  -> v2.2.0-rc4
2025-08-14T21:14:44.0437093Z  * [new tag]                 v2.2.0-rc5                  -> v2.2.0-rc5
2025-08-14T21:14:44.0437585Z  * [new tag]                 v2.2.0-rc6                  -> v2.2.0-rc6
2025-08-14T21:14:44.0437702Z  * [new tag]                 v2.2.0-rc7                  -> v2.2.0-rc7
2025-08-14T21:14:44.0437999Z  * [new tag]                 v2.2.0-rc8                  -> v2.2.0-rc8
2025-08-14T21:14:44.0438448Z  * [new tag]                 v2.2.1                      -> v2.2.1
2025-08-14T21:14:44.0439016Z  * [new tag]                 v2.2.1-rc1                  -> v2.2.1-rc1
2025-08-14T21:14:44.0439087Z  * [new tag]                 v2.2.1-rc2                  -> v2.2.1-rc2
2025-08-14T21:14:44.0439396Z  * [new tag]                 v2.2.1-rc3                  -> v2.2.1-rc3
2025-08-14T21:14:44.0439692Z  * [new tag]                 v2.2.2                      -> v2.2.2
2025-08-14T21:14:44.0440174Z  * [new tag]                 v2.2.2-rc1                  -> v2.2.2-rc1
2025-08-14T21:14:44.0440367Z  * [new tag]                 v2.2.2-rc2                  -> v2.2.2-rc2
2025-08-14T21:14:44.0440671Z  * [new tag]                 v2.2.2-rc3                  -> v2.2.2-rc3
2025-08-14T21:14:44.0441121Z  * [new tag]                 v2.3.0                      -> v2.3.0
2025-08-14T21:14:44.0441400Z  * [new tag]                 v2.3.0-rc1                  -> v2.3.0-rc1
2025-08-14T21:14:44.0441999Z  * [new tag]                 v2.3.0-rc10                 -> v2.3.0-rc10
2025-08-14T21:14:44.0442197Z  * [new tag]                 v2.3.0-rc11                 -> v2.3.0-rc11
2025-08-14T21:14:44.0442490Z  * [new tag]                 v2.3.0-rc12                 -> v2.3.0-rc12
2025-08-14T21:14:44.0442979Z  * [new tag]                 v2.3.0-rc2                  -> v2.3.0-rc2
2025-08-14T21:14:44.0443422Z  * [new tag]                 v2.3.0-rc3                  -> v2.3.0-rc3
2025-08-14T21:14:44.0443753Z  * [new tag]                 v2.3.0-rc4                  -> v2.3.0-rc4
2025-08-14T21:14:44.0444299Z  * [new tag]                 v2.3.0-rc5                  -> v2.3.0-rc5
2025-08-14T21:14:44.0445889Z  * [new tag]                 v2.3.0-rc6                  -> v2.3.0-rc6
2025-08-14T21:14:44.0445978Z  * [new tag]                 v2.3.0-rc7                  -> v2.3.0-rc7
2025-08-14T21:14:44.0446369Z  * [new tag]                 v2.3.0-rc8                  -> v2.3.0-rc8
2025-08-14T21:14:44.0446429Z  * [new tag]                 v2.3.0-rc9                  -> v2.3.0-rc9
2025-08-14T21:14:44.0446494Z  * [new tag]                 v2.3.1                      -> v2.3.1
2025-08-14T21:14:44.0446622Z  * [new tag]                 v2.3.1-rc1                  -> v2.3.1-rc1
2025-08-14T21:14:44.0446673Z  * [new tag]                 v2.3.1-rc2                  -> v2.3.1-rc2
2025-08-14T21:14:44.0447296Z  * [new tag]                 v2.3.1-rc3                  -> v2.3.1-rc3
2025-08-14T21:14:44.0447575Z  * [new tag]                 v2.4.0                      -> v2.4.0
2025-08-14T21:14:44.0448135Z  * [new tag]                 v2.4.0-rc1                  -> v2.4.0-rc1
2025-08-14T21:14:44.0448269Z  * [new tag]                 v2.4.0-rc2                  -> v2.4.0-rc2
2025-08-14T21:14:44.0448751Z  * [new tag]                 v2.4.0-rc3                  -> v2.4.0-rc3
2025-08-14T21:14:44.0448954Z  * [new tag]                 v2.4.0-rc4                  -> v2.4.0-rc4
2025-08-14T21:14:44.0449514Z  * [new tag]                 v2.4.0-rc5                  -> v2.4.0-rc5
2025-08-14T21:14:44.0449728Z  * [new tag]                 v2.4.0-rc6                  -> v2.4.0-rc6
2025-08-14T21:14:44.0450252Z  * [new tag]                 v2.4.0-rc7                  -> v2.4.0-rc7
2025-08-14T21:14:44.0450463Z  * [new tag]                 v2.4.0-rc8                  -> v2.4.0-rc8
2025-08-14T21:14:44.0451003Z  * [new tag]                 v2.4.0-rc9                  -> v2.4.0-rc9
2025-08-14T21:14:44.0451151Z  * [new tag]                 v2.4.1                      -> v2.4.1
2025-08-14T21:14:44.0451697Z  * [new tag]                 v2.4.1-rc1                  -> v2.4.1-rc1
2025-08-14T21:14:44.0452151Z  * [new tag]                 v2.4.1-rc2                  -> v2.4.1-rc2
2025-08-14T21:14:44.0452431Z  * [new tag]                 v2.4.1-rc3                  -> v2.4.1-rc3
2025-08-14T21:14:44.0452775Z  * [new tag]                 v2.5.0                      -> v2.5.0
2025-08-14T21:14:44.0453210Z  * [new tag]                 v2.5.0-rc1                  -> v2.5.0-rc1
2025-08-14T21:14:44.0453822Z  * [new tag]                 v2.5.0-rc10                 -> v2.5.0-rc10
2025-08-14T21:14:44.0454238Z  * [new tag]                 v2.5.0-rc2                  -> v2.5.0-rc2
2025-08-14T21:14:44.0454564Z  * [new tag]                 v2.5.0-rc3                  -> v2.5.0-rc3
2025-08-14T21:14:44.0455275Z  * [new tag]                 v2.5.0-rc4                  -> v2.5.0-rc4
2025-08-14T21:14:44.0455360Z  * [new tag]                 v2.5.0-rc5                  -> v2.5.0-rc5
2025-08-14T21:14:44.0455925Z  * [new tag]                 v2.5.0-rc6                  -> v2.5.0-rc6
2025-08-14T21:14:44.0456160Z  * [new tag]                 v2.5.0-rc7                  -> v2.5.0-rc7
2025-08-14T21:14:44.0456506Z  * [new tag]                 v2.5.0-rc8                  -> v2.5.0-rc8
2025-08-14T21:14:44.0457000Z  * [new tag]                 v2.5.0-rc9                  -> v2.5.0-rc9
2025-08-14T21:14:44.0457119Z  * [new tag]                 v2.5.1                      -> v2.5.1
2025-08-14T21:14:44.0457436Z  * [new tag]                 v2.5.1-rc1                  -> v2.5.1-rc1
2025-08-14T21:14:44.0457699Z  * [new tag]                 v2.6.0                      -> v2.6.0
2025-08-14T21:14:44.0458238Z  * [new tag]                 v2.6.0-rc1                  -> v2.6.0-rc1
2025-08-14T21:14:44.0458613Z  * [new tag]                 v2.6.0-rc2                  -> v2.6.0-rc2
2025-08-14T21:14:44.0458935Z  * [new tag]                 v2.6.0-rc3                  -> v2.6.0-rc3
2025-08-14T21:14:44.0462008Z  * [new tag]                 v2.6.0-rc4                  -> v2.6.0-rc4
2025-08-14T21:14:44.0462111Z  * [new tag]                 v2.6.0-rc5                  -> v2.6.0-rc5
2025-08-14T21:14:44.0462172Z  * [new tag]                 v2.6.0-rc6                  -> v2.6.0-rc6
2025-08-14T21:14:44.0462231Z  * [new tag]                 v2.6.0-rc7                  -> v2.6.0-rc7
2025-08-14T21:14:44.0462286Z  * [new tag]                 v2.6.0-rc8                  -> v2.6.0-rc8
2025-08-14T21:14:44.0462343Z  * [new tag]                 v2.6.0-rc9                  -> v2.6.0-rc9
2025-08-14T21:14:44.0462404Z  * [new tag]                 v2.7.0                      -> v2.7.0
2025-08-14T21:14:44.0462574Z  * [new tag]                 v2.7.0-rc1                  -> v2.7.0-rc1
2025-08-14T21:14:44.0462639Z  * [new tag]                 v2.7.0-rc10                 -> v2.7.0-rc10
2025-08-14T21:14:44.0463414Z  * [new tag]                 v2.7.0-rc2                  -> v2.7.0-rc2
2025-08-14T21:14:44.0463628Z  * [new tag]                 v2.7.0-rc3                  -> v2.7.0-rc3
2025-08-14T21:14:44.0463887Z  * [new tag]                 v2.7.0-rc4                  -> v2.7.0-rc4
2025-08-14T21:14:44.0464349Z  * [new tag]                 v2.7.0-rc5                  -> v2.7.0-rc5
2025-08-14T21:14:44.0464578Z  * [new tag]                 v2.7.0-rc6                  -> v2.7.0-rc6
2025-08-14T21:14:44.0465518Z  * [new tag]                 v2.7.0-rc7                  -> v2.7.0-rc7
2025-08-14T21:14:44.0465621Z  * [new tag]                 v2.7.0-rc8                  -> v2.7.0-rc8
2025-08-14T21:14:44.0466085Z  * [new tag]                 v2.7.0-rc9                  -> v2.7.0-rc9
2025-08-14T21:14:44.0466175Z  * [new tag]                 v2.7.1                      -> v2.7.1
2025-08-14T21:14:44.0466743Z  * [new tag]                 v2.7.1-rc1                  -> v2.7.1-rc1
2025-08-14T21:14:44.0467103Z  * [new tag]                 v2.7.1-rc2                  -> v2.7.1-rc2
2025-08-14T21:14:44.0467347Z  * [new tag]                 v2.7.1-rc3                  -> v2.7.1-rc3
2025-08-14T21:14:44.0467746Z  * [new tag]                 v2.7.1-rc4                  -> v2.7.1-rc4
2025-08-14T21:14:44.0467931Z  * [new tag]                 v2.7.1-rc5                  -> v2.7.1-rc5
2025-08-14T21:14:44.0468293Z  * [new tag]                 v2.8.0                      -> v2.8.0
2025-08-14T21:14:44.0468658Z  * [new tag]                 v2.8.0-rc1                  -> v2.8.0-rc1
2025-08-14T21:14:44.0469021Z  * [new tag]                 v2.8.0-rc2                  -> v2.8.0-rc2
2025-08-14T21:14:44.0469739Z  * [new tag]                 v2.8.0-rc3                  -> v2.8.0-rc3
2025-08-14T21:14:44.0469920Z  * [new tag]                 v2.8.0-rc4                  -> v2.8.0-rc4
2025-08-14T21:14:44.0470220Z  * [new tag]                 v2.8.0-rc5                  -> v2.8.0-rc5
2025-08-14T21:14:44.0470724Z  * [new tag]                 v2.8.0-rc6                  -> v2.8.0-rc6
2025-08-14T21:14:44.0470984Z  * [new tag]                 v2.8.0-rc7                  -> v2.8.0-rc7
2025-08-14T21:14:44.0471497Z  * [new tag]                 v2.8.0-rc8                  -> v2.8.0-rc8
2025-08-14T21:14:44.0471716Z  * [new tag]                 whc_flight_1                -> whc_flight_1
2025-08-14T21:14:44.0472047Z  * [new tag]                 whc_flight_2                -> whc_flight_2
2025-08-14T21:14:44.0472338Z  * [new tag]                 whc_flight_4                -> whc_flight_4
2025-08-14T21:14:44.0882329Z [command]/usr/bin/git rev-parse --verify --quiet 1fc683cf17c8c673044538d10266c00f92987be2^{object}
2025-08-14T21:14:44.0912960Z 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:14:44.0917591Z ##[endgroup]
2025-08-14T21:14:44.0917721Z ##[group]Determining the checkout info
2025-08-14T21:14:44.0917890Z ##[endgroup]
2025-08-14T21:14:44.0918555Z [command]/usr/bin/git sparse-checkout disable
2025-08-14T21:14:44.0949286Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig
2025-08-14T21:14:44.0980801Z ##[group]Checking out the ref
2025-08-14T21:14:44.0983013Z [command]/usr/bin/git checkout --progress --force 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:14:44.6998150Z Note: switching to '1fc683cf17c8c673044538d10266c00f92987be2'.
2025-08-14T21:14:44.6998523Z 
2025-08-14T21:14:44.6998771Z You are in 'detached HEAD' state. You can look around, make experimental
2025-08-14T21:14:44.6999307Z changes and commit them, and you can discard any commits you make in this
2025-08-14T21:14:44.6999847Z state without impacting any branches by switching back to a branch.
2025-08-14T21:14:44.7000155Z 
2025-08-14T21:14:44.7000365Z If you want to create a new branch to retain commits you create, you may
2025-08-14T21:14:44.7000855Z do so (now or later) by using -c with the switch command. Example:
2025-08-14T21:14:44.7001510Z 
2025-08-14T21:14:44.7001647Z   git switch -c <new-branch-name>
2025-08-14T21:14:44.7001848Z 
2025-08-14T21:14:44.7001964Z Or undo this operation with:
2025-08-14T21:14:44.7002150Z 
2025-08-14T21:14:44.7002250Z   git switch -
2025-08-14T21:14:44.7002391Z 
2025-08-14T21:14:44.7002649Z Turn off this advice by setting config variable advice.detachedHead to false
2025-08-14T21:14:44.7003230Z 
2025-08-14T21:14:44.7003621Z HEAD is now at 1fc683cf17c [Inductor] Allow indexing a flexible layout for extract_input_node_reduction_ranges (#160645)
2025-08-14T21:14:44.7062499Z ##[endgroup]
2025-08-14T21:14:44.7062741Z ##[group]Setting up auth for fetching submodules
2025-08-14T21:14:44.7069602Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-08-14T21:14:44.7105453Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2025-08-14T21:14:44.7134996Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2025-08-14T21:14:44.7161840Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2025-08-14T21:14:44.7186516Z ##[endgroup]
2025-08-14T21:14:44.7186761Z ##[group]Fetching submodules
2025-08-14T21:14:44.7188977Z [command]/usr/bin/git submodule sync --recursive
2025-08-14T21:14:44.7431574Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-08-14T21:14:44.7652328Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2025-08-14T21:14:44.7654741Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2025-08-14T21:14:44.7656338Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2025-08-14T21:14:44.7658845Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2025-08-14T21:14:44.7660701Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX'
2025-08-14T21:14:44.7663217Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2025-08-14T21:14:44.7665701Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2025-08-14T21:14:44.7667501Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter'
2025-08-14T21:14:44.7673165Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2025-08-14T21:14:44.7673928Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel'
2025-08-14T21:14:44.7677379Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib'
2025-08-14T21:14:44.7680383Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2025-08-14T21:14:44.7683781Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2025-08-14T21:14:44.7686311Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2025-08-14T21:14:44.7688729Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2025-08-14T21:14:44.7691269Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention'
2025-08-14T21:14:44.7693513Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2025-08-14T21:14:44.7696790Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2025-08-14T21:14:44.7699207Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2025-08-14T21:14:44.7702656Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo'
2025-08-14T21:14:44.7704638Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2025-08-14T21:14:44.7706603Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2025-08-14T21:14:44.7708697Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2025-08-14T21:14:44.7710735Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2025-08-14T21:14:44.7712809Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai'
2025-08-14T21:14:44.7714920Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc'
2025-08-14T21:14:44.7717013Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2025-08-14T21:14:44.7718948Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2025-08-14T21:14:44.7723003Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp'
2025-08-14T21:14:44.7723398Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2025-08-14T21:14:44.7725314Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2025-08-14T21:14:44.7728137Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2025-08-14T21:14:44.7729677Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2025-08-14T21:14:44.7731960Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2025-08-14T21:14:44.7734363Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2025-08-14T21:14:44.7736448Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef'
2025-08-14T21:14:44.7738705Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe'
2025-08-14T21:14:44.7765667Z Cloning into '/home/runner/_work/pytorch/pytorch/android/libs/fbjni'...
2025-08-14T21:14:45.1676424Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FXdiv'...
2025-08-14T21:14:45.1676734Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FP16'...
2025-08-14T21:14:45.1676988Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/psimd'...
2025-08-14T21:14:45.1677530Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pocketfft'...
2025-08-14T21:14:45.1808959Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NNPACK'...
2025-08-14T21:14:45.2052244Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NVTX'...
2025-08-14T21:14:45.5181051Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pthreadpool'...
2025-08-14T21:14:45.5181332Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'...
2025-08-14T21:14:45.5181554Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/benchmark'...
2025-08-14T21:14:45.5181751Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gloo'...
2025-08-14T21:14:45.5188841Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep'...
2025-08-14T21:14:45.5189141Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/python-peachpy'...
2025-08-14T21:14:45.5189350Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kleidiai'...
2025-08-14T21:14:45.5189571Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention'...
2025-08-14T21:14:45.5189782Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpp-httplib'...
2025-08-14T21:14:45.5218361Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'...
2025-08-14T21:14:46.4341641Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ittapi'...
2025-08-14T21:14:46.4342061Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe'...
2025-08-14T21:14:46.4342401Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpuinfo'...
2025-08-14T21:14:46.4342729Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/sleef'...
2025-08-14T21:14:46.4343352Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/googletest'...
2025-08-14T21:14:46.4351606Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/mimalloc'...
2025-08-14T21:14:46.4351962Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pybind11'...
2025-08-14T21:14:46.4959715Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/XNNPACK'...
2025-08-14T21:14:53.5839518Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cudnn_frontend'...
2025-08-14T21:14:53.5840226Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto'...
2025-08-14T21:14:53.5840743Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fmt'...
2025-08-14T21:14:53.5841266Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flatbuffers'...
2025-08-14T21:14:53.5841816Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cutlass'...
2025-08-14T21:14:53.5850584Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm'...
2025-08-14T21:14:53.5851144Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx'...
2025-08-14T21:14:53.5851555Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/composable_kernel'...
2025-08-14T21:14:53.5851911Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter'...
2025-08-14T21:14:53.5852254Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'...
2025-08-14T21:14:53.5852611Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/nlohmann'...
2025-08-14T21:14:53.5853068Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf'...
2025-08-14T21:14:53.5947988Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-08-14T21:14:53.6055606Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-08-14T21:14:53.6136349Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-08-14T21:14:53.6270524Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-08-14T21:14:53.6699615Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07'
2025-08-14T21:14:53.6993124Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1'
2025-08-14T21:14:54.0268449Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-08-14T21:14:54.1014927Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150'
2025-08-14T21:14:54.1039955Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel'
2025-08-14T21:14:54.1074447Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'...
2025-08-14T21:14:56.4705552Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf'
2025-08-14T21:14:56.4871336Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f'
2025-08-14T21:14:56.6338835Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977'
2025-08-14T21:14:56.6692766Z Submodule path 'third_party/cpp-httplib': checked out '3af7f2c16147f3fbc6e4d717032daf505dc1652c'
2025-08-14T21:14:56.7235185Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d'
2025-08-14T21:14:56.7499351Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6'
2025-08-14T21:14:57.0435636Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491'
2025-08-14T21:14:57.1113252Z Submodule path 'third_party/fbgemm': checked out '21c7d30c526c0f1ad873ecc632dca6cfa8a69067'
2025-08-14T21:14:57.1138961Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit'
2025-08-14T21:14:57.1140943Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel'
2025-08-14T21:14:57.1143389Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo'
2025-08-14T21:14:57.1145677Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass'
2025-08-14T21:14:57.1147897Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest'
2025-08-14T21:14:57.1149463Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch'
2025-08-14T21:14:57.1154128Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json'
2025-08-14T21:14:57.1182873Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'...
2025-08-14T21:14:58.2348507Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'...
2025-08-14T21:14:58.2348882Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'...
2025-08-14T21:14:58.2349139Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'...
2025-08-14T21:14:58.2349404Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'...
2025-08-14T21:14:58.3348982Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'...
2025-08-14T21:14:58.5447160Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'...
2025-08-14T21:15:00.4165636Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea'
2025-08-14T21:15:00.5317101Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9'
2025-08-14T21:15:00.5877852Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349'
2025-08-14T21:15:00.8764642Z Submodule path 'third_party/fbgemm/external/cutlass': checked out 'b40777404c174b9694a870bff5c13ce6b7f656ad'
2025-08-14T21:15:00.9021607Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723'
2025-08-14T21:15:00.9113270Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out 'a4337c69fe0e2552a7b7b0669178926beeed828c'
2025-08-14T21:15:00.9663914Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03'
2025-08-14T21:15:01.0048878Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-08-14T21:15:01.0070919Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel'
2025-08-14T21:15:01.0071924Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass'
2025-08-14T21:15:01.0103394Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'...
2025-08-14T21:15:03.2296643Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'...
2025-08-14T21:15:03.3425146Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-08-14T21:15:03.6164155Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-08-14T21:15:03.6824484Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757'
2025-08-14T21:15:03.7031415Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21'
2025-08-14T21:15:03.7250360Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-08-14T21:15:03.7361024Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2'
2025-08-14T21:15:03.7629503Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723'
2025-08-14T21:15:03.7730335Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3'
2025-08-14T21:15:03.7747055Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn'
2025-08-14T21:15:03.7773804Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'...
2025-08-14T21:15:10.4411106Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d'
2025-08-14T21:15:10.4579022Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959'
2025-08-14T21:15:10.5085739Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658'
2025-08-14T21:15:10.5103395Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog'
2025-08-14T21:15:10.5105707Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt'
2025-08-14T21:15:10.5107305Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest'
2025-08-14T21:15:10.5140388Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'...
2025-08-14T21:15:11.2567450Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'...
2025-08-14T21:15:11.5726762Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'...
2025-08-14T21:15:11.6168464Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-08-14T21:15:11.6189042Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-08-14T21:15:11.6196289Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-08-14T21:15:11.6199064Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-08-14T21:15:11.6200338Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-08-14T21:15:11.6201436Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-08-14T21:15:11.6203286Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-08-14T21:15:11.6204434Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T21:15:11.6205492Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-08-14T21:15:11.6226581Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'...
2025-08-14T21:15:12.6925085Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'...
2025-08-14T21:15:12.6925982Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'...
2025-08-14T21:15:12.6935349Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'...
2025-08-14T21:15:12.6936019Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'...
2025-08-14T21:15:12.6936677Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'...
2025-08-14T21:15:12.6937323Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'...
2025-08-14T21:15:12.7924531Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'...
2025-08-14T21:15:15.0382831Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-08-14T21:15:15.0496752Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-08-14T21:15:15.0718429Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-08-14T21:15:15.0813697Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-08-14T21:15:15.0823797Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-08-14T21:15:15.0841249Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'...
2025-08-14T21:15:15.5269785Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-08-14T21:15:15.5400399Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-08-14T21:15:15.5630865Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-08-14T21:15:15.6146577Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-08-14T21:15:15.6247602Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-08-14T21:15:15.6484710Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-08-14T21:15:15.6799780Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-08-14T21:15:15.7037318Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7'
2025-08-14T21:15:15.7260815Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e'
2025-08-14T21:15:15.7825666Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72'
2025-08-14T21:15:15.9598673Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83'
2025-08-14T21:15:15.9627123Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11'
2025-08-14T21:15:15.9655871Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'...
2025-08-14T21:15:16.9427542Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-08-14T21:15:16.9770857Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-08-14T21:15:16.9786560Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-08-14T21:15:16.9788045Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest'
2025-08-14T21:15:16.9789427Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-08-14T21:15:16.9797634Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-08-14T21:15:16.9798436Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-08-14T21:15:16.9799197Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-08-14T21:15:16.9799764Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-08-14T21:15:16.9800240Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-08-14T21:15:16.9821992Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'...
2025-08-14T21:15:17.5467726Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'...
2025-08-14T21:15:17.5468476Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'...
2025-08-14T21:15:17.5469135Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'...
2025-08-14T21:15:17.5477814Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'...
2025-08-14T21:15:17.6468606Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'...
2025-08-14T21:15:17.9471397Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'...
2025-08-14T21:15:20.4096387Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'...
2025-08-14T21:15:22.6463280Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-08-14T21:15:22.6703190Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2025-08-14T21:15:22.6801383Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2025-08-14T21:15:22.7335076Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2025-08-14T21:15:22.7427383Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2025-08-14T21:15:22.7513646Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2025-08-14T21:15:22.7608319Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2025-08-14T21:15:22.7625327Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-08-14T21:15:22.7626955Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-08-14T21:15:22.7650610Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'...
2025-08-14T21:15:24.0488376Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'...
2025-08-14T21:15:24.1791668Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2025-08-14T21:15:24.2060680Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2025-08-14T21:15:24.4081921Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2025-08-14T21:15:24.4187389Z Submodule path 'third_party/pocketfft': checked out '0fa0ef591e38c2758e3184c6c23e497b9f732ffa'
2025-08-14T21:15:24.5616978Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2025-08-14T21:15:24.5639686Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark'
2025-08-14T21:15:24.5642052Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest'
2025-08-14T21:15:24.5671875Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'...
2025-08-14T21:15:25.2336593Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'...
2025-08-14T21:15:25.4130333Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2025-08-14T21:15:25.4526780Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2025-08-14T21:15:25.4611868Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2025-08-14T21:15:25.4709322Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2025-08-14T21:15:25.4919386Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-08-14T21:15:25.5064068Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2025-08-14T21:15:25.5288325Z Submodule path 'third_party/sleef': checked out '5a1d179df9cf652951b59010a2d2075372d67f68'
2025-08-14T21:15:25.5432595Z Submodule path 'third_party/tensorpipe': checked out 'dacda0567d9f23d4bc503e1c4f84aa65f33ac38a'
2025-08-14T21:15:25.5443451Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest'
2025-08-14T21:15:25.5444604Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop'
2025-08-14T21:15:25.5446578Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv'
2025-08-14T21:15:25.5447895Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11'
2025-08-14T21:15:25.5465932Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'...
2025-08-14T21:15:26.3985502Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'...
2025-08-14T21:15:26.4606957Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'...
2025-08-14T21:15:26.6071314Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'...
2025-08-14T21:15:26.6400233Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2025-08-14T21:15:26.6486944Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2025-08-14T21:15:26.6870548Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '5152db2cbfeb5582e9c27c5ea1dba2cd9e10759b'
2025-08-14T21:15:26.7031991Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2025-08-14T21:15:26.7051086Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-08-14T21:15:26.7080898Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'...
2025-08-14T21:15:27.0745840Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2025-08-14T21:15:27.0795777Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2025-08-14T21:15:27.1030885Z Entering 'android/libs/fbjni'
2025-08-14T21:15:27.1063374Z Entering 'third_party/FP16'
2025-08-14T21:15:27.1093671Z Entering 'third_party/FXdiv'
2025-08-14T21:15:27.1135480Z Entering 'third_party/NNPACK'
2025-08-14T21:15:27.1163079Z Entering 'third_party/NVTX'
2025-08-14T21:15:27.1189062Z Entering 'third_party/VulkanMemoryAllocator'
2025-08-14T21:15:27.1226654Z Entering 'third_party/XNNPACK'
2025-08-14T21:15:27.1270687Z Entering 'third_party/aiter'
2025-08-14T21:15:27.1304793Z Entering 'third_party/aiter/3rdparty/composable_kernel'
2025-08-14T21:15:27.1342615Z Entering 'third_party/benchmark'
2025-08-14T21:15:27.1378151Z Entering 'third_party/composable_kernel'
2025-08-14T21:15:27.1414645Z Entering 'third_party/cpp-httplib'
2025-08-14T21:15:27.1447474Z Entering 'third_party/cpuinfo'
2025-08-14T21:15:27.1472713Z Entering 'third_party/cudnn_frontend'
2025-08-14T21:15:27.1504640Z Entering 'third_party/cutlass'
2025-08-14T21:15:27.1537430Z Entering 'third_party/fbgemm'
2025-08-14T21:15:27.1572133Z Entering 'third_party/fbgemm/external/asmjit'
2025-08-14T21:15:27.1605113Z Entering 'third_party/fbgemm/external/composable_kernel'
2025-08-14T21:15:27.1649826Z Entering 'third_party/fbgemm/external/cpuinfo'
2025-08-14T21:15:27.1681889Z Entering 'third_party/fbgemm/external/cutlass'
2025-08-14T21:15:27.1716900Z Entering 'third_party/fbgemm/external/googletest'
2025-08-14T21:15:27.1755023Z Entering 'third_party/fbgemm/external/hipify_torch'
2025-08-14T21:15:27.1777711Z Entering 'third_party/fbgemm/external/json'
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2025-08-14T21:15:27.2476599Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T21:15:27.2505623Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
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2025-08-14T21:15:27.2739558Z Entering 'third_party/onnx/third_party/pybind11'
2025-08-14T21:15:27.2779090Z Entering 'third_party/opentelemetry-cpp'
2025-08-14T21:15:27.2808954Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
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2025-08-14T21:15:27.2876941Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-08-14T21:15:27.2899989Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-08-14T21:15:27.2928954Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-08-14T21:15:27.2956350Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-08-14T21:15:27.2980143Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-08-14T21:15:27.3007003Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-08-14T21:15:27.3036039Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-08-14T21:15:27.3070572Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-08-14T21:15:27.3104716Z Entering 'third_party/pocketfft'
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2025-08-14T21:15:27.3303098Z Entering 'third_party/pybind11'
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2025-08-14T21:15:27.3426827Z Entering 'third_party/tensorpipe/third_party/googletest'
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2025-08-14T21:15:27.3561584Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-08-14T21:15:27.3612005Z ##[endgroup]
2025-08-14T21:15:27.3612224Z ##[group]Persisting credentials for submodules
2025-08-14T21:15:27.3612662Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
2025-08-14T21:15:27.3823100Z Entering 'android/libs/fbjni'
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2025-08-14T21:15:27.4042502Z Entering 'third_party/XNNPACK'
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2025-08-14T21:15:27.4282013Z Entering 'third_party/cpuinfo'
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2025-08-14T21:15:27.4675885Z Entering 'third_party/flash-attention'
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2025-08-14T21:15:27.5086818Z Entering 'third_party/ittapi'
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2025-08-14T21:15:27.5187278Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-08-14T21:15:27.5226459Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-08-14T21:15:27.5269553Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-08-14T21:15:27.5304535Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-08-14T21:15:27.5344256Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-08-14T21:15:27.5381586Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-08-14T21:15:27.5423141Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-08-14T21:15:27.5458113Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T21:15:27.5494201Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-08-14T21:15:27.5523635Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-08-14T21:15:27.5563381Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-08-14T21:15:27.5603231Z Entering 'third_party/kleidiai'
2025-08-14T21:15:27.5642047Z Entering 'third_party/mimalloc'
2025-08-14T21:15:27.5680149Z Entering 'third_party/nlohmann'
2025-08-14T21:15:27.5726505Z Entering 'third_party/onnx'
2025-08-14T21:15:27.5768725Z Entering 'third_party/onnx/third_party/pybind11'
2025-08-14T21:15:27.5800600Z Entering 'third_party/opentelemetry-cpp'
2025-08-14T21:15:27.5834322Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-08-14T21:15:27.5876027Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-08-14T21:15:27.5913622Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-08-14T21:15:27.5946279Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-08-14T21:15:27.5978986Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-08-14T21:15:27.6020457Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-08-14T21:15:27.6052730Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-08-14T21:15:27.6082435Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-08-14T21:15:27.6114762Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-08-14T21:15:27.6149126Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-08-14T21:15:27.6193021Z Entering 'third_party/pocketfft'
2025-08-14T21:15:27.6226769Z Entering 'third_party/protobuf'
2025-08-14T21:15:27.6264061Z Entering 'third_party/protobuf/third_party/benchmark'
2025-08-14T21:15:27.6293233Z Entering 'third_party/protobuf/third_party/googletest'
2025-08-14T21:15:27.6326716Z Entering 'third_party/psimd'
2025-08-14T21:15:27.6365375Z Entering 'third_party/pthreadpool'
2025-08-14T21:15:27.6402156Z Entering 'third_party/pybind11'
2025-08-14T21:15:27.6436000Z Entering 'third_party/python-peachpy'
2025-08-14T21:15:27.6473503Z Entering 'third_party/sleef'
2025-08-14T21:15:27.6511589Z Entering 'third_party/tensorpipe'
2025-08-14T21:15:27.6541527Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-08-14T21:15:27.6574155Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-08-14T21:15:27.6604506Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-08-14T21:15:27.6641646Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-08-14T21:15:27.6680314Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-08-14T21:15:27.6732057Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2025-08-14T21:15:27.6930241Z Entering 'android/libs/fbjni'
2025-08-14T21:15:27.6964175Z file:/home/runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-08-14T21:15:27.6976469Z Entering 'third_party/FP16'
2025-08-14T21:15:27.7012084Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-08-14T21:15:27.7024657Z Entering 'third_party/FXdiv'
2025-08-14T21:15:27.7056098Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-08-14T21:15:27.7072856Z Entering 'third_party/NNPACK'
2025-08-14T21:15:27.7099776Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-08-14T21:15:27.7113816Z Entering 'third_party/NVTX'
2025-08-14T21:15:27.7152973Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
2025-08-14T21:15:27.7164848Z Entering 'third_party/VulkanMemoryAllocator'
2025-08-14T21:15:27.7199363Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2025-08-14T21:15:27.7211694Z Entering 'third_party/XNNPACK'
2025-08-14T21:15:27.7252999Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2025-08-14T21:15:27.7270315Z Entering 'third_party/aiter'
2025-08-14T21:15:27.7299208Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/config	remote.origin.url
2025-08-14T21:15:27.7310247Z Entering 'third_party/aiter/3rdparty/composable_kernel'
2025-08-14T21:15:27.7345221Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/modules/3rdparty/composable_kernel/config	remote.origin.url
2025-08-14T21:15:27.7361623Z Entering 'third_party/benchmark'
2025-08-14T21:15:27.7401511Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2025-08-14T21:15:27.7416768Z Entering 'third_party/composable_kernel'
2025-08-14T21:15:27.7461448Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2025-08-14T21:15:27.7482281Z Entering 'third_party/cpp-httplib'
2025-08-14T21:15:27.7522729Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
2025-08-14T21:15:27.7534366Z Entering 'third_party/cpuinfo'
2025-08-14T21:15:27.7564033Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2025-08-14T21:15:27.7574424Z Entering 'third_party/cudnn_frontend'
2025-08-14T21:15:27.7610527Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2025-08-14T21:15:27.7620891Z Entering 'third_party/cutlass'
2025-08-14T21:15:27.7651712Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2025-08-14T21:15:27.7667130Z Entering 'third_party/fbgemm'
2025-08-14T21:15:27.7701449Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2025-08-14T21:15:27.7718426Z Entering 'third_party/fbgemm/external/asmjit'
2025-08-14T21:15:27.7747950Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/asmjit/config	remote.origin.url
2025-08-14T21:15:27.7758152Z Entering 'third_party/fbgemm/external/composable_kernel'
2025-08-14T21:15:27.7789182Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/composable_kernel/config	remote.origin.url
2025-08-14T21:15:27.7802735Z Entering 'third_party/fbgemm/external/cpuinfo'
2025-08-14T21:15:27.7828996Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/cpuinfo/config	remote.origin.url
2025-08-14T21:15:27.7841032Z Entering 'third_party/fbgemm/external/cutlass'
2025-08-14T21:15:27.7880730Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/cutlass/config	remote.origin.url
2025-08-14T21:15:27.7893701Z Entering 'third_party/fbgemm/external/googletest'
2025-08-14T21:15:27.7915736Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/googletest/config	remote.origin.url
2025-08-14T21:15:27.7926209Z Entering 'third_party/fbgemm/external/hipify_torch'
2025-08-14T21:15:27.7959678Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/hipify_torch/config	remote.origin.url
2025-08-14T21:15:27.7976909Z Entering 'third_party/fbgemm/external/json'
2025-08-14T21:15:27.8012728Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/json/config	remote.origin.url
2025-08-14T21:15:27.8031551Z Entering 'third_party/flash-attention'
2025-08-14T21:15:27.8054490Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config	remote.origin.url
2025-08-14T21:15:27.8071658Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-08-14T21:15:27.8111857Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
2025-08-14T21:15:27.8127032Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-08-14T21:15:27.8152044Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
2025-08-14T21:15:27.8167266Z Entering 'third_party/flatbuffers'
2025-08-14T21:15:27.8197504Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-08-14T21:15:27.8216277Z Entering 'third_party/fmt'
2025-08-14T21:15:27.8252184Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2025-08-14T21:15:27.8270973Z Entering 'third_party/gemmlowp/gemmlowp'
2025-08-14T21:15:27.8304921Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2025-08-14T21:15:27.8315037Z Entering 'third_party/gloo'
2025-08-14T21:15:27.8355567Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2025-08-14T21:15:27.8372990Z Entering 'third_party/googletest'
2025-08-14T21:15:27.8399132Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2025-08-14T21:15:27.8413418Z Entering 'third_party/ideep'
2025-08-14T21:15:27.8444221Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2025-08-14T21:15:27.8456039Z Entering 'third_party/ideep/mkl-dnn'
2025-08-14T21:15:27.8497860Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2025-08-14T21:15:27.8514708Z Entering 'third_party/ittapi'
2025-08-14T21:15:27.8554847Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
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2025-08-14T21:15:28.4070839Z Entering 'third_party/fbgemm/external/asmjit'
2025-08-14T21:15:28.4101884Z Entering 'third_party/fbgemm/external/composable_kernel'
2025-08-14T21:15:28.4138361Z Entering 'third_party/fbgemm/external/cpuinfo'
2025-08-14T21:15:28.4165131Z Entering 'third_party/fbgemm/external/cutlass'
2025-08-14T21:15:28.4206647Z Entering 'third_party/fbgemm/external/googletest'
2025-08-14T21:15:28.4240647Z Entering 'third_party/fbgemm/external/hipify_torch'
2025-08-14T21:15:28.4271809Z Entering 'third_party/fbgemm/external/json'
2025-08-14T21:15:28.4305282Z Entering 'third_party/flash-attention'
2025-08-14T21:15:28.4334202Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-08-14T21:15:28.4365015Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-08-14T21:15:28.4399933Z Entering 'third_party/flatbuffers'
2025-08-14T21:15:28.4432771Z Entering 'third_party/fmt'
2025-08-14T21:15:28.4464675Z Entering 'third_party/gemmlowp/gemmlowp'
2025-08-14T21:15:28.4497048Z Entering 'third_party/gloo'
2025-08-14T21:15:28.4525075Z Entering 'third_party/googletest'
2025-08-14T21:15:28.4554426Z Entering 'third_party/ideep'
2025-08-14T21:15:28.4581133Z Entering 'third_party/ideep/mkl-dnn'
2025-08-14T21:15:28.4611124Z Entering 'third_party/ittapi'
2025-08-14T21:15:28.4643123Z Entering 'third_party/kineto'
2025-08-14T21:15:28.4672829Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-08-14T21:15:28.4697794Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-08-14T21:15:28.4728578Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-08-14T21:15:28.4770812Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-08-14T21:15:28.4799665Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-08-14T21:15:28.4824053Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-08-14T21:15:28.4856236Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-08-14T21:15:28.4884181Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-08-14T21:15:28.4907969Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T21:15:28.4942588Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-08-14T21:15:28.4984572Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-08-14T21:15:28.5009471Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-08-14T21:15:28.5038289Z Entering 'third_party/kleidiai'
2025-08-14T21:15:28.5066442Z Entering 'third_party/mimalloc'
2025-08-14T21:15:28.5096538Z Entering 'third_party/nlohmann'
2025-08-14T21:15:28.5132792Z Entering 'third_party/onnx'
2025-08-14T21:15:28.5169281Z Entering 'third_party/onnx/third_party/pybind11'
2025-08-14T21:15:28.5200035Z Entering 'third_party/opentelemetry-cpp'
2025-08-14T21:15:28.5231816Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-08-14T21:15:28.5258027Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-08-14T21:15:28.5291748Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-08-14T21:15:28.5323023Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-08-14T21:15:28.5398759Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-08-14T21:15:28.5420669Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-08-14T21:15:28.5447216Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-08-14T21:15:28.5475820Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-08-14T21:15:28.5504804Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-08-14T21:15:28.5535645Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-08-14T21:15:28.5569774Z Entering 'third_party/pocketfft'
2025-08-14T21:15:28.5595271Z Entering 'third_party/protobuf'
2025-08-14T21:15:28.5620450Z Entering 'third_party/protobuf/third_party/benchmark'
2025-08-14T21:15:28.5644184Z Entering 'third_party/protobuf/third_party/googletest'
2025-08-14T21:15:28.5672709Z Entering 'third_party/psimd'
2025-08-14T21:15:28.5701839Z Entering 'third_party/pthreadpool'
2025-08-14T21:15:28.5724433Z Entering 'third_party/pybind11'
2025-08-14T21:15:28.5747103Z Entering 'third_party/python-peachpy'
2025-08-14T21:15:28.5780701Z Entering 'third_party/sleef'
2025-08-14T21:15:28.5803781Z Entering 'third_party/tensorpipe'
2025-08-14T21:15:28.5832295Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-08-14T21:15:28.5858451Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-08-14T21:15:28.5897737Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-08-14T21:15:28.5934312Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-08-14T21:15:28.5961618Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-08-14T21:15:28.6010772Z ##[endgroup]
2025-08-14T21:15:28.6046693Z [command]/usr/bin/git log -1 --format=%H
2025-08-14T21:15:28.6068268Z 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:15:28.6231617Z Prepare all required actions
2025-08-14T21:15:28.6231883Z Getting action download info
2025-08-14T21:15:28.8184911Z ##[group]Run ./.github/actions/setup-rocm
2025-08-14T21:15:28.8185176Z env:
2025-08-14T21:15:28.8185266Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8185397Z ##[endgroup]
2025-08-14T21:15:28.8203977Z ##[group]Run dpkg -l | grep -E "  rocm"
2025-08-14T21:15:28.8204110Z dpkg -l | grep -E "  rocm"
2025-08-14T21:15:28.8209357Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.8209491Z env:
2025-08-14T21:15:28.8209571Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8209666Z ##[endgroup]
2025-08-14T21:15:28.8279283Z ii  rocm-cmake                   0.14.0.60401-83~22.04                   amd64        rocm-cmake built using CMake
2025-08-14T21:15:28.8279558Z ii  rocm-core                    6.4.1.60401-83~22.04                    amd64        ROCm Runtime software stack
2025-08-14T21:15:28.8279891Z ii  rocm-dbgapi                  0.77.2.60401-83~22.04                   amd64        Library to provide AMD GPU debugger API
2025-08-14T21:15:28.8280166Z ii  rocm-debug-agent             2.0.4.60401-83~22.04                    amd64        Radeon Open Compute Debug Agent (ROCdebug-agent)
2025-08-14T21:15:28.8280446Z ii  rocm-dev                     6.4.1.60401-83~22.04                    amd64        Radeon Open Compute (ROCm) Runtime software stack
2025-08-14T21:15:28.8286575Z ii  rocm-device-libs             1.0.0.60401-83~22.04                    amd64        Radeon Open Compute - device libraries
2025-08-14T21:15:28.8286912Z ii  rocm-gdb                     15.2.60401-83~22.04                     amd64        ROCgdb
2025-08-14T21:15:28.8287123Z ii  rocm-llvm                    19.0.0.25184.60401-83~22.04             amd64        ROCm core compiler
2025-08-14T21:15:28.8287346Z ii  rocm-opencl                  2.0.0.60401-83~22.04                    amd64        clr built using CMake
2025-08-14T21:15:28.8287575Z ii  rocm-opencl-dev              2.0.0.60401-83~22.04                    amd64        clr built using CMake
2025-08-14T21:15:28.8287808Z ii  rocm-smi-lib                 7.5.0.60401-83~22.04                    amd64        AMD System Management libraries
2025-08-14T21:15:28.8288055Z ii  rocm-utils                   6.4.1.60401-83~22.04                    amd64        Radeon Open Compute (ROCm) Runtime software stack
2025-08-14T21:15:28.8288309Z ii  rocminfo                     1.0.0.60401-83~22.04                    amd64        Radeon Open Compute (ROCm) Runtime rocminfo tool
2025-08-14T21:15:28.8300603Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-08-14T21:15:28.8300832Z # ignore expansion of "docker ps -q" since it could be empty
2025-08-14T21:15:28.8300984Z # shellcheck disable=SC2046
2025-08-14T21:15:28.8301111Z docker stop $(docker ps -q) || true
2025-08-14T21:15:28.8301240Z # Prune all stopped containers.
2025-08-14T21:15:28.8301370Z docker container prune -f
2025-08-14T21:15:28.8306762Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.8306906Z env:
2025-08-14T21:15:28.8306991Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8307096Z ##[endgroup]
2025-08-14T21:15:28.8589853Z docker: 'docker stop' requires at least 1 argument
2025-08-14T21:15:28.8589973Z 
2025-08-14T21:15:28.8590054Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-08-14T21:15:28.8590169Z 
2025-08-14T21:15:28.8590237Z See 'docker stop --help' for more information
2025-08-14T21:15:28.8720630Z Total reclaimed space: 0B
2025-08-14T21:15:28.8756482Z ##[group]Run cat /etc/os-release || true
2025-08-14T21:15:28.8756628Z cat /etc/os-release || true
2025-08-14T21:15:28.8756768Z cat /etc/apt/sources.list.d/rocm.list || true
2025-08-14T21:15:28.8756905Z cat /opt/rocm/.info/version || true
2025-08-14T21:15:28.8757017Z whoami
2025-08-14T21:15:28.8762471Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.8762612Z env:
2025-08-14T21:15:28.8762704Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8762805Z ##[endgroup]
2025-08-14T21:15:28.8790321Z PRETTY_NAME="Ubuntu 22.04.5 LTS"
2025-08-14T21:15:28.8790630Z NAME="Ubuntu"
2025-08-14T21:15:28.8790737Z VERSION_ID="22.04"
2025-08-14T21:15:28.8790842Z VERSION="22.04.5 LTS (Jammy Jellyfish)"
2025-08-14T21:15:28.8790967Z VERSION_CODENAME=jammy
2025-08-14T21:15:28.8791070Z ID=ubuntu
2025-08-14T21:15:28.8791152Z ID_LIKE=debian
2025-08-14T21:15:28.8792594Z HOME_URL="https://www.ubuntu.com/"
2025-08-14T21:15:28.8792736Z SUPPORT_URL="https://help.ubuntu.com/"
2025-08-14T21:15:28.8793211Z BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
2025-08-14T21:15:28.8793447Z PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
2025-08-14T21:15:28.8793631Z UBUNTU_CODENAME=jammy
2025-08-14T21:15:28.8800077Z deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.4.1 jammy main
2025-08-14T21:15:28.8809281Z 6.4.1-83
2025-08-14T21:15:28.8820511Z runner
2025-08-14T21:15:28.8829460Z ##[group]Run dpkg -l | grep -E "  amdgpu"
2025-08-14T21:15:28.8829597Z dpkg -l | grep -E "  amdgpu"
2025-08-14T21:15:28.8833364Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.8833496Z env:
2025-08-14T21:15:28.8833675Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8833797Z ##[endgroup]
2025-08-14T21:15:28.8900149Z ii  amdgpu-core                  1:6.4.60401-2164967.22.04               all          Core meta package for unified amdgpu driver.
2025-08-14T21:15:28.8900769Z ii  amdgpu-install               6.4.60401-2164967.22.04                 all          AMDGPU driver repository and installer
2025-08-14T21:15:28.8916050Z ##[group]Run rocm-smi
2025-08-14T21:15:28.8916191Z rocm-smi
2025-08-14T21:15:28.8921692Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.8921839Z env:
2025-08-14T21:15:28.8921933Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.8922062Z ##[endgroup]
2025-08-14T21:15:28.9385544Z 
2025-08-14T21:15:28.9385653Z 
2025-08-14T21:15:28.9385816Z ============================================ ROCm System Management Interface ============================================
2025-08-14T21:15:28.9386502Z ====================================================== Concise Info ======================================================
2025-08-14T21:15:28.9387144Z Device  Node  IDs              Temp        Power     Partitions          SCLK    MCLK    Fan  Perf  PwrCap   VRAM%  GPU%  
2025-08-14T21:15:28.9388201Z               (DID,     GUID)  (Junction)  (Socket)  (Mem, Compute, ID)                                                   
2025-08-14T21:15:28.9397409Z ==========================================================================================================================
2025-08-14T21:15:28.9398399Z 0       7     0x74b9,   26434  46.0°C      127.0W    NPS1, SPX, 0        148Mhz  900Mhz  0%   auto  1000.0W  0%     0%    
2025-08-14T21:15:28.9398613Z ==========================================================================================================================
2025-08-14T21:15:28.9398773Z ================================================== End of ROCm SMI Log ===================================================
2025-08-14T21:15:28.9444590Z ##[group]Run rocminfo
2025-08-14T21:15:28.9444704Z rocminfo
2025-08-14T21:15:28.9448920Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:28.9449065Z env:
2025-08-14T21:15:28.9449155Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:28.9449264Z ##[endgroup]
2025-08-14T21:15:29.0111541Z ROCk module version 6.12.12 is loaded
2025-08-14T21:15:29.0111927Z =====================    
2025-08-14T21:15:29.0112204Z HSA System Attributes    
2025-08-14T21:15:29.0112466Z =====================    
2025-08-14T21:15:29.0112713Z Runtime Version:         1.15
2025-08-14T21:15:29.0113123Z Runtime Ext Version:     1.7
2025-08-14T21:15:29.0113798Z System Timestamp Freq.:  1000.000000MHz
2025-08-14T21:15:29.0114367Z Sig. Max Wait Duration:  18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
2025-08-14T21:15:29.0114896Z Machine Model:           LARGE                              
2025-08-14T21:15:29.0120305Z System Endianness:       LITTLE                             
2025-08-14T21:15:29.0120462Z Mwaitx:                  DISABLED
2025-08-14T21:15:29.0120579Z XNACK enabled:           NO
2025-08-14T21:15:29.0120740Z DMAbuf Support:          YES
2025-08-14T21:15:29.0120841Z VMM Support:             YES
2025-08-14T21:15:29.0120906Z 
2025-08-14T21:15:29.0120944Z ==========               
2025-08-14T21:15:29.0121033Z HSA Agents               
2025-08-14T21:15:29.0121122Z ==========               
2025-08-14T21:15:29.0121207Z *******                  
2025-08-14T21:15:29.0121294Z Agent 1                  
2025-08-14T21:15:29.0121382Z *******                  
2025-08-14T21:15:29.0121495Z   Name:                    AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:15:29.0121675Z   Uuid:                    CPU-XX                             
2025-08-14T21:15:29.0121833Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:15:29.0121988Z   Vendor Name:             CPU                                
2025-08-14T21:15:29.0122140Z   Feature:                 None specified                     
2025-08-14T21:15:29.0122288Z   Profile:                 FULL_PROFILE                       
2025-08-14T21:15:29.0122440Z   Float Round Mode:        NEAR                               
2025-08-14T21:15:29.0122592Z   Max Queue Number:        0(0x0)                             
2025-08-14T21:15:29.0122738Z   Queue Min Size:          0(0x0)                             
2025-08-14T21:15:29.0122890Z   Queue Max Size:          0(0x0)                             
2025-08-14T21:15:29.0123165Z   Queue Type:              MULTI                              
2025-08-14T21:15:29.0123301Z   Node:                    0                                  
2025-08-14T21:15:29.0123442Z   Device Type:             CPU                                
2025-08-14T21:15:29.0123575Z   Cache Info:              
2025-08-14T21:15:29.0123695Z     L1:                      65536(0x10000) KB                  
2025-08-14T21:15:29.0123831Z   Chip ID:                 0(0x0)                             
2025-08-14T21:15:29.0123976Z   ASIC Revision:           0(0x0)                             
2025-08-14T21:15:29.0124124Z   Cacheline Size:          64(0x40)                           
2025-08-14T21:15:29.0124280Z   Max Clock Freq. (MHz):   0                                  
2025-08-14T21:15:29.0124422Z   BDFID:                   0                                  
2025-08-14T21:15:29.0124561Z   Internal Node ID:        0                                  
2025-08-14T21:15:29.0124710Z   Compute Unit:            80                                 
2025-08-14T21:15:29.0124853Z   SIMDs per CU:            0                                  
2025-08-14T21:15:29.0124998Z   Shader Engines:          0                                  
2025-08-14T21:15:29.0125151Z   Shader Arrs. per Eng.:   0                                  
2025-08-14T21:15:29.0125310Z   WatchPts on Addr. Ranges:1                                  
2025-08-14T21:15:29.0125453Z   Memory Properties:       
2025-08-14T21:15:29.0125556Z   Features:                None
2025-08-14T21:15:29.0125661Z   Pool Info:               
2025-08-14T21:15:29.0125759Z     Pool 1                   
2025-08-14T21:15:29.0125889Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:15:29.0126036Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:15:29.0126188Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0126342Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0126513Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0126682Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0126903Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0127046Z     Pool 2                   
2025-08-14T21:15:29.0127173Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:15:29.0127355Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:15:29.0127491Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0127639Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0127795Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0127952Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0128110Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0128241Z     Pool 3                   
2025-08-14T21:15:29.0128366Z       Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
2025-08-14T21:15:29.0128510Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:15:29.0128652Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0128799Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0128951Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0129100Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0129244Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0129373Z     Pool 4                   
2025-08-14T21:15:29.0129489Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:15:29.0129632Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:15:29.0129774Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0129918Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0131766Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0131917Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0132069Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0132197Z   ISA Info:                
2025-08-14T21:15:29.0132289Z *******                  
2025-08-14T21:15:29.0132375Z Agent 2                  
2025-08-14T21:15:29.0132460Z *******                  
2025-08-14T21:15:29.0132564Z   Name:                    AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:15:29.0132695Z   Uuid:                    CPU-XX                             
2025-08-14T21:15:29.0132833Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:15:29.0133063Z   Vendor Name:             CPU                                
2025-08-14T21:15:29.0133200Z   Feature:                 None specified                     
2025-08-14T21:15:29.0133335Z   Profile:                 FULL_PROFILE                       
2025-08-14T21:15:29.0133475Z   Float Round Mode:        NEAR                               
2025-08-14T21:15:29.0133613Z   Max Queue Number:        0(0x0)                             
2025-08-14T21:15:29.0135061Z   Queue Min Size:          0(0x0)                             
2025-08-14T21:15:29.0135200Z   Queue Max Size:          0(0x0)                             
2025-08-14T21:15:29.0135331Z   Queue Type:              MULTI                              
2025-08-14T21:15:29.0135457Z   Node:                    1                                  
2025-08-14T21:15:29.0135584Z   Device Type:             CPU                                
2025-08-14T21:15:29.0135705Z   Cache Info:              
2025-08-14T21:15:29.0135807Z     L1:                      65536(0x10000) KB                  
2025-08-14T21:15:29.0135930Z   Chip ID:                 0(0x0)                             
2025-08-14T21:15:29.0136060Z   ASIC Revision:           0(0x0)                             
2025-08-14T21:15:29.0136254Z   Cacheline Size:          64(0x40)                           
2025-08-14T21:15:29.0136394Z   Max Clock Freq. (MHz):   0                                  
2025-08-14T21:15:29.0136523Z   BDFID:                   0                                  
2025-08-14T21:15:29.0137755Z   Internal Node ID:        1                                  
2025-08-14T21:15:29.0137898Z   Compute Unit:            80                                 
2025-08-14T21:15:29.0138030Z   SIMDs per CU:            0                                  
2025-08-14T21:15:29.0138164Z   Shader Engines:          0                                  
2025-08-14T21:15:29.0138302Z   Shader Arrs. per Eng.:   0                                  
2025-08-14T21:15:29.0138447Z   WatchPts on Addr. Ranges:1                                  
2025-08-14T21:15:29.0138574Z   Memory Properties:       
2025-08-14T21:15:29.0138670Z   Features:                None
2025-08-14T21:15:29.0138765Z   Pool Info:               
2025-08-14T21:15:29.0138857Z     Pool 1                   
2025-08-14T21:15:29.0138978Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:15:29.0139115Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:15:29.0139249Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0140394Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0140541Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0140688Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0140831Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0140956Z     Pool 2                   
2025-08-14T21:15:29.0141071Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:15:29.0141205Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:15:29.0141336Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0141478Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0141623Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0141767Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0141913Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0143145Z     Pool 3                   
2025-08-14T21:15:29.0143265Z       Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
2025-08-14T21:15:29.0143398Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:15:29.0143529Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0143669Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0143812Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0143957Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0144101Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0144224Z     Pool 4                   
2025-08-14T21:15:29.0144338Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:15:29.0144473Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:15:29.0144604Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0144744Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0145875Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:15:29.0146022Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0146163Z       Accessible by all:       TRUE                               
2025-08-14T21:15:29.0146288Z   ISA Info:                
2025-08-14T21:15:29.0146377Z *******                  
2025-08-14T21:15:29.0146464Z Agent 3                  
2025-08-14T21:15:29.0146550Z *******                  
2025-08-14T21:15:29.0146704Z   Name:                    gfx942                             
2025-08-14T21:15:29.0146836Z   Uuid:                    GPU-4b54e616aa7823b9               
2025-08-14T21:15:29.0147019Z   Marketing Name:          AMD Instinct Mi325X VF             
2025-08-14T21:15:29.0147163Z   Vendor Name:             AMD                                
2025-08-14T21:15:29.0147298Z   Feature:                 KERNEL_DISPATCH                    
2025-08-14T21:15:29.0148421Z   Profile:                 BASE_PROFILE                       
2025-08-14T21:15:29.0148563Z   Float Round Mode:        NEAR                               
2025-08-14T21:15:29.0148701Z   Max Queue Number:        128(0x80)                          
2025-08-14T21:15:29.0148837Z   Queue Min Size:          64(0x40)                           
2025-08-14T21:15:29.0148970Z   Queue Max Size:          131072(0x20000)                    
2025-08-14T21:15:29.0149103Z   Queue Type:              MULTI                              
2025-08-14T21:15:29.0149232Z   Node:                    2                                  
2025-08-14T21:15:29.0149360Z   Device Type:             GPU                                
2025-08-14T21:15:29.0149479Z   Cache Info:              
2025-08-14T21:15:29.0149586Z     L1:                      32(0x20) KB                        
2025-08-14T21:15:29.0149706Z     L2:                      4096(0x1000) KB                    
2025-08-14T21:15:29.0149821Z     L3:                      262144(0x40000) KB                 
2025-08-14T21:15:29.0150909Z   Chip ID:                 29881(0x74b9)                      
2025-08-14T21:15:29.0151048Z   ASIC Revision:           1(0x1)                             
2025-08-14T21:15:29.0151187Z   Cacheline Size:          128(0x80)                          
2025-08-14T21:15:29.0151327Z   Max Clock Freq. (MHz):   2100                               
2025-08-14T21:15:29.0151458Z   BDFID:                   37632                              
2025-08-14T21:15:29.0151590Z   Internal Node ID:        2                                  
2025-08-14T21:15:29.0151728Z   Compute Unit:            304                                
2025-08-14T21:15:29.0151862Z   SIMDs per CU:            4                                  
2025-08-14T21:15:29.0151998Z   Shader Engines:          32                                 
2025-08-14T21:15:29.0152137Z   Shader Arrs. per Eng.:   1                                  
2025-08-14T21:15:29.0152281Z   WatchPts on Addr. Ranges:4                                  
2025-08-14T21:15:29.0152426Z   Coherent Host Access:    FALSE                              
2025-08-14T21:15:29.0152553Z   Memory Properties:       
2025-08-14T21:15:29.0153741Z   Features:                KERNEL_DISPATCH 
2025-08-14T21:15:29.0153871Z   Fast F16 Operation:      TRUE                               
2025-08-14T21:15:29.0154012Z   Wavefront Size:          64(0x40)                           
2025-08-14T21:15:29.0154153Z   Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:15:29.0154286Z   Workgroup Max Size per Dimension:
2025-08-14T21:15:29.0154400Z     x                        1024(0x400)                        
2025-08-14T21:15:29.0154515Z     y                        1024(0x400)                        
2025-08-14T21:15:29.0154630Z     z                        1024(0x400)                        
2025-08-14T21:15:29.0154754Z   Max Waves Per CU:        32(0x20)                           
2025-08-14T21:15:29.0154896Z   Max Work-item Per CU:    2048(0x800)                        
2025-08-14T21:15:29.0155035Z   Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:15:29.0155159Z   Grid Max Size per Dimension:
2025-08-14T21:15:29.0156243Z     x                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0156364Z     y                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0156480Z     z                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0156613Z   Max fbarriers/Workgrp:   32                                 
2025-08-14T21:15:29.0162006Z   Packet Processor uCode:: 177                                
2025-08-14T21:15:29.0162169Z   SDMA engine uCode::      24                                 
2025-08-14T21:15:29.0162355Z   IOMMU Support::          None                               
2025-08-14T21:15:29.0162479Z   Pool Info:               
2025-08-14T21:15:29.0162573Z     Pool 1                   
2025-08-14T21:15:29.0162692Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:15:29.0162832Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:15:29.0164643Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0164787Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0164934Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:15:29.0165081Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0165237Z       Accessible by all:       FALSE                              
2025-08-14T21:15:29.0165365Z     Pool 2                   
2025-08-14T21:15:29.0165483Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:15:29.0165622Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:15:29.0165754Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0165897Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0166041Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:15:29.0166186Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0166329Z       Accessible by all:       FALSE                              
2025-08-14T21:15:29.0166454Z     Pool 3                   
2025-08-14T21:15:29.0167627Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:15:29.0167759Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:15:29.0167896Z       Allocatable:             TRUE                               
2025-08-14T21:15:29.0168035Z       Alloc Granule:           4KB                                
2025-08-14T21:15:29.0168182Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:15:29.0168328Z       Alloc Alignment:         4KB                                
2025-08-14T21:15:29.0168469Z       Accessible by all:       FALSE                              
2025-08-14T21:15:29.0168593Z     Pool 4                   
2025-08-14T21:15:29.0168703Z       Segment:                 GROUP                              
2025-08-14T21:15:29.0168831Z       Size:                    64(0x40) KB                        
2025-08-14T21:15:29.0168961Z       Allocatable:             FALSE                              
2025-08-14T21:15:29.0169100Z       Alloc Granule:           0KB                                
2025-08-14T21:15:29.0170150Z       Alloc Recommended Granule:0KB                                
2025-08-14T21:15:29.0170303Z       Alloc Alignment:         0KB                                
2025-08-14T21:15:29.0170445Z       Accessible by all:       FALSE                              
2025-08-14T21:15:29.0170575Z   ISA Info:                
2025-08-14T21:15:29.0170667Z     ISA 1                    
2025-08-14T21:15:29.0170785Z       Name:                    amdgcn-amd-amdhsa--gfx942:sramecc+:xnack-
2025-08-14T21:15:29.0170936Z       Machine Models:          HSA_MACHINE_MODEL_LARGE            
2025-08-14T21:15:29.0171082Z       Profiles:                HSA_PROFILE_BASE                   
2025-08-14T21:15:29.0171227Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:15:29.0171376Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:15:29.0171515Z       Fast f16:                TRUE                               
2025-08-14T21:15:29.0171654Z       Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:15:29.0171842Z       Workgroup Max Size per Dimension:
2025-08-14T21:15:29.0172865Z         x                        1024(0x400)                        
2025-08-14T21:15:29.0173148Z         y                        1024(0x400)                        
2025-08-14T21:15:29.0173306Z         z                        1024(0x400)                        
2025-08-14T21:15:29.0173433Z       Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:15:29.0173558Z       Grid Max Size per Dimension:
2025-08-14T21:15:29.0173666Z         x                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0173782Z         y                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0173898Z         z                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0174093Z       FBarrier Max Size:       32                                 
2025-08-14T21:15:29.0174218Z     ISA 2                    
2025-08-14T21:15:29.0174349Z       Name:                    amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack-
2025-08-14T21:15:29.0174510Z       Machine Models:          HSA_MACHINE_MODEL_LARGE            
2025-08-14T21:15:29.0175601Z       Profiles:                HSA_PROFILE_BASE                   
2025-08-14T21:15:29.0175752Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:15:29.0175900Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:15:29.0176037Z       Fast f16:                TRUE                               
2025-08-14T21:15:29.0176174Z       Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:15:29.0176304Z       Workgroup Max Size per Dimension:
2025-08-14T21:15:29.0176416Z         x                        1024(0x400)                        
2025-08-14T21:15:29.0176530Z         y                        1024(0x400)                        
2025-08-14T21:15:29.0176642Z         z                        1024(0x400)                        
2025-08-14T21:15:29.0176772Z       Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:15:29.0176895Z       Grid Max Size per Dimension:
2025-08-14T21:15:29.0177002Z         x                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0180872Z         y                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0181007Z         z                        4294967295(0xffffffff)             
2025-08-14T21:15:29.0181144Z       FBarrier Max Size:       32                                 
2025-08-14T21:15:29.0181270Z *** Done ***             
2025-08-14T21:15:29.0195009Z ##[group]Run ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx')
2025-08-14T21:15:29.0195188Z ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx')
2025-08-14T21:15:29.0195451Z 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"
2025-08-14T21:15:29.0195697Z if [[ $ngpu -eq 0 ]]; then
2025-08-14T21:15:29.0195835Z     echo "Error: Failed to detect any GPUs on the runner"
2025-08-14T21:15:29.0195970Z     echo "$msg"
2025-08-14T21:15:29.0196059Z     exit 1
2025-08-14T21:15:29.0196141Z fi
2025-08-14T21:15:29.0200530Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.0200665Z env:
2025-08-14T21:15:29.0200747Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.0200844Z ##[endgroup]
2025-08-14T21:15:29.0926083Z ##[group]Run pytorch/pytorch/.github/actions/diskspace-cleanup@main
2025-08-14T21:15:29.0926233Z with:
2025-08-14T21:15:29.0926319Z   diskspace-cutoff: 70
2025-08-14T21:15:29.0926410Z env:
2025-08-14T21:15:29.0926496Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.0926596Z ##[endgroup]
2025-08-14T21:15:29.0947239Z ##[group]Run set -ex
2025-08-14T21:15:29.0947367Z set -ex
2025-08-14T21:15:29.0947460Z diskspace_cutoff=70
2025-08-14T21:15:29.0947600Z docker_root_dir=$(docker info -f '{{.DockerRootDir}}')
2025-08-14T21:15:29.0947847Z if [ ! -d "$docker_root_dir" ]; then
2025-08-14T21:15:29.0948038Z     echo "Docker root directory ($docker_root_dir) does not exist. Skipping disk space check."
2025-08-14T21:15:29.0948215Z     exit 0
2025-08-14T21:15:29.0948391Z fi
2025-08-14T21:15:29.0948542Z diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
2025-08-14T21:15:29.0948856Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified"
2025-08-14T21:15:29.0949126Z if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then
2025-08-14T21:15:29.0949264Z     docker system prune -af
2025-08-14T21:15:29.0949453Z     diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
2025-08-14T21:15:29.0949658Z     if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then
2025-08-14T21:15:29.0949891Z         echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace."
2025-08-14T21:15:29.0950085Z         echo "$msg"
2025-08-14T21:15:29.0950185Z         exit 1
2025-08-14T21:15:29.0950279Z     else
2025-08-14T21:15:29.0950386Z         difference=$((diskspace - diskspace_new))
2025-08-14T21:15:29.0950534Z         echo "Diskspace saved: $difference percent"
2025-08-14T21:15:29.0950654Z     fi
2025-08-14T21:15:29.0950737Z fi
2025-08-14T21:15:29.0955755Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.0955890Z env:
2025-08-14T21:15:29.0955969Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.0956065Z ##[endgroup]
2025-08-14T21:15:29.0976026Z + diskspace_cutoff=70
2025-08-14T21:15:29.0979145Z ++ docker info -f '{{.DockerRootDir}}'
2025-08-14T21:15:29.1353500Z + docker_root_dir=/home/runner/docker-data
2025-08-14T21:15:29.1353711Z + '[' '!' -d /home/runner/docker-data ']'
2025-08-14T21:15:29.1359290Z ++ df -H --output=pcent /home/runner/docker-data
2025-08-14T21:15:29.1359781Z ++ sed -n 2p
2025-08-14T21:15:29.1361277Z ++ sed s/%//
2025-08-14T21:15:29.1362313Z ++ sed 's/ //'
2025-08-14T21:15:29.1375127Z + diskspace=' 5'
2025-08-14T21:15:29.1375387Z + msg='Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified'
2025-08-14T21:15:29.1375639Z + [[  5 -ge 70 ]]
2025-08-14T21:15:29.1396520Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts"
2025-08-14T21:15:29.1396698Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts"
2025-08-14T21:15:29.1401132Z rm -rf "${RUNNER_ARTIFACT_DIR}"
2025-08-14T21:15:29.1401262Z mkdir -p "${RUNNER_ARTIFACT_DIR}"
2025-08-14T21:15:29.1401426Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}"
2025-08-14T21:15:29.1401581Z 
2025-08-14T21:15:29.1401693Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results"
2025-08-14T21:15:29.1401854Z rm -rf "${RUNNER_TEST_RESULTS_DIR}"
2025-08-14T21:15:29.1401980Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}"
2025-08-14T21:15:29.1402154Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}"
2025-08-14T21:15:29.1402320Z 
2025-08-14T21:15:29.1402413Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs"
2025-08-14T21:15:29.1402538Z rm -rf "${RUNNER_DOCS_DIR}"
2025-08-14T21:15:29.1402652Z mkdir -p "${RUNNER_DOCS_DIR}"
2025-08-14T21:15:29.1402800Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}"
2025-08-14T21:15:29.1406731Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.1406860Z env:
2025-08-14T21:15:29.1406939Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.1407031Z ##[endgroup]
2025-08-14T21:15:29.1508850Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2025-08-14T21:15:29.1512075Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2025-08-14T21:15:29.1512284Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
2025-08-14T21:15:29.1516827Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.1517042Z env:
2025-08-14T21:15:29.1517126Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.1517253Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.1517421Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.1517578Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.1517703Z ##[endgroup]
2025-08-14T21:15:29.1572262Z ##[group]Run # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py.
2025-08-14T21:15:29.1572535Z # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py.
2025-08-14T21:15:29.1572724Z # Add render group for container creation.
2025-08-14T21:15:29.1573014Z render_gid=`cat /etc/group | grep render | cut -d: -f3`
2025-08-14T21:15:29.1573209Z # Ensure GPU isolation if pod is part of kubernetes setup with DEVICE_FLAG.
2025-08-14T21:15:29.1573397Z if [ -f "/etc/podinfo/gha-render-devices" ]; then
2025-08-14T21:15:29.1573559Z   DEVICE_FLAG=$(cat /etc/podinfo/gha-render-devices)
2025-08-14T21:15:29.1573692Z else
2025-08-14T21:15:29.1573787Z   DEVICE_FLAG="--device /dev/dri"
2025-08-14T21:15:29.1573897Z fi
2025-08-14T21:15:29.1574157Z # The --group-add daemon and --group-add bin are needed in the Ubuntu 24.04 and Almalinux OSs respectively.
2025-08-14T21:15:29.1574423Z # This is due to the device files (/dev/kfd & /dev/dri) being owned by video group on bare metal.
2025-08-14T21:15:29.1574666Z # This video group ID maps to subgid 1 inside the docker image due to the /etc/subgid entries.
2025-08-14T21:15:29.1574918Z # The group name corresponding to group ID 1 can change depending on the OS, so both are necessary.
2025-08-14T21:15:29.1575346Z 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}"
2025-08-14T21:15:29.1582036Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.1582182Z env:
2025-08-14T21:15:29.1582263Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.1582388Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.1582549Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.1582704Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.1582818Z ##[endgroup]
2025-08-14T21:15:29.1691654Z ##[group]Run aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722
2025-08-14T21:15:29.1691840Z with:
2025-08-14T21:15:29.1691973Z   role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
2025-08-14T21:15:29.1692129Z   aws-region: us-east-1
2025-08-14T21:15:29.1692225Z   role-duration-seconds: 18000
2025-08-14T21:15:29.1692332Z   audience: sts.amazonaws.com
2025-08-14T21:15:29.1692437Z env:
2025-08-14T21:15:29.1692514Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.1692631Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.1692788Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.1693062Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.1693436Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:29.1693770Z ##[endgroup]
2025-08-14T21:15:29.3769383Z Assuming role with OIDC
2025-08-14T21:15:29.5041612Z Authenticated as assumedRoleId AROAUPVRELQNLLCOPFEJR:GitHubActions
2025-08-14T21:15:29.5465376Z ##[group]Run aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076
2025-08-14T21:15:29.5465566Z with:
2025-08-14T21:15:29.5465664Z   mask-password: true
2025-08-14T21:15:29.5465870Z   registry-type: private
2025-08-14T21:15:29.5465982Z   skip-logout: false
2025-08-14T21:15:29.5466078Z env:
2025-08-14T21:15:29.5466337Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.5466527Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.5466725Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.5466955Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.5467407Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:29.5467836Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:29.5468049Z   AWS_REGION: us-east-1
2025-08-14T21:15:29.5468325Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:29.5468535Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:29.5470711Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:29.5470855Z ##[endgroup]
2025-08-14T21:15:29.7465560Z Logging into registry 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9766034Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-08-14T21:15:29.9766221Z with:
2025-08-14T21:15:29.9766490Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9766792Z   use-custom-docker-registry: true
2025-08-14T21:15:29.9766916Z   docker-build-dir: .ci/docker
2025-08-14T21:15:29.9767037Z   docker-build-script: ./build.sh
2025-08-14T21:15:29.9767156Z   working-directory: .
2025-08-14T21:15:29.9767292Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9767443Z   force-push: false
2025-08-14T21:15:29.9767536Z env:
2025-08-14T21:15:29.9767625Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.9767760Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.9767931Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.9768132Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.9768511Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:29.9768877Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:29.9768995Z   AWS_REGION: us-east-1
2025-08-14T21:15:29.9769362Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:29.9769511Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:29.9771566Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:29.9771668Z ##[endgroup]
2025-08-14T21:15:29.9785552Z ##[group]Run set -ex
2025-08-14T21:15:29.9785673Z set -ex
2025-08-14T21:15:29.9785762Z 
2025-08-14T21:15:29.9785912Z # If the docker build directory or the build script doesn't exist, the action will
2025-08-14T21:15:29.9786148Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-08-14T21:15:29.9786360Z # job could then download the pre-built image as usual
2025-08-14T21:15:29.9786607Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then
2025-08-14T21:15:29.9786832Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9786956Z else
2025-08-14T21:15:29.9787056Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9787218Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9787362Z 
2025-08-14T21:15:29.9787560Z   echo "Not using custom ECR registry.  Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..."
2025-08-14T21:15:29.9787778Z   exit 0
2025-08-14T21:15:29.9787862Z fi
2025-08-14T21:15:29.9787942Z 
2025-08-14T21:15:29.9788181Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-08-14T21:15:29.9788396Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-08-14T21:15:29.9788585Z   # use it as it is, but first let's extract the tag
2025-08-14T21:15:29.9788759Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-08-14T21:15:29.9788945Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9789119Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9789260Z else
2025-08-14T21:15:29.9789363Z   if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then
2025-08-14T21:15:29.9789505Z     CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:}
2025-08-14T21:15:29.9789646Z     DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*}
2025-08-14T21:15:29.9789766Z   fi
2025-08-14T21:15:29.9789994Z   DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-08-14T21:15:29.9790215Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9790439Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9790683Z   echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9790832Z fi
2025-08-14T21:15:29.9796347Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.9796484Z env:
2025-08-14T21:15:29.9796570Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.9796699Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.9796867Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.9797024Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.9797396Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:29.9797751Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:29.9797862Z   AWS_REGION: us-east-1
2025-08-14T21:15:29.9797995Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:29.9798138Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:29.9800178Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:29.9800285Z   REPO_NAME: pytorch
2025-08-14T21:15:29.9800560Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9800844Z   DOCKER_BUILD_DIR: .ci/docker
2025-08-14T21:15:29.9800958Z   DOCKER_BUILD_SCRIPT: ./build.sh
2025-08-14T21:15:29.9801107Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9801258Z   USE_CUSTOM_DOCKER_REGISTRY: true
2025-08-14T21:15:29.9801370Z   CUSTOM_TAG_PREFIX: 
2025-08-14T21:15:29.9801468Z ##[endgroup]
2025-08-14T21:15:29.9823661Z + [[ -d .ci/docker ]]
2025-08-14T21:15:29.9823869Z + [[ -f .ci/docker/./build.sh ]]
2025-08-14T21:15:29.9824034Z + [[ true == \t\r\u\e ]]
2025-08-14T21:15:29.9824182Z + echo skip=false
2025-08-14T21:15:29.9824733Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe == *\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* ]]
2025-08-14T21:15:29.9830503Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9831000Z ++ awk -F '[:,]' '{print $2}'
2025-08-14T21:15:29.9844262Z + DOCKER_TAG=pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9844636Z + echo docker-tag=pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9845222Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9874677Z ##[group]Run set +e
2025-08-14T21:15:29.9874802Z set +e
2025-08-14T21:15:29.9874891Z set -x
2025-08-14T21:15:29.9874976Z 
2025-08-14T21:15:29.9875060Z login() {
2025-08-14T21:15:29.9875246Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-08-14T21:15:29.9875437Z }
2025-08-14T21:15:29.9875526Z 
2025-08-14T21:15:29.9877748Z retry () {
2025-08-14T21:15:29.9877863Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-08-14T21:15:29.9877982Z }
2025-08-14T21:15:29.9878063Z 
2025-08-14T21:15:29.9878149Z retry login "${DOCKER_REGISTRY}"
2025-08-14T21:15:29.9878260Z 
2025-08-14T21:15:29.9878344Z START_TIME=$(date +%s)
2025-08-14T21:15:29.9878457Z # Wait up to 120 minutes
2025-08-14T21:15:29.9878715Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-08-14T21:15:29.9878892Z   # Check if image already exists, if it does then skip building it
2025-08-14T21:15:29.9879069Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-08-14T21:15:29.9880835Z     exit 0
2025-08-14T21:15:29.9880930Z   fi
2025-08-14T21:15:29.9881012Z 
2025-08-14T21:15:29.9881155Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-08-14T21:15:29.9881389Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-08-14T21:15:29.9881618Z   # latter, it will wait for the Docker images to become available before continuing
2025-08-14T21:15:29.9881805Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-08-14T21:15:29.9881954Z     # It's a Docker build job, let's build the image
2025-08-14T21:15:29.9882085Z     break
2025-08-14T21:15:29.9882179Z   else
2025-08-14T21:15:29.9882305Z     # It's a regular build job, wait for the image to become available
2025-08-14T21:15:29.9884230Z     sleep 300
2025-08-14T21:15:29.9884332Z   fi
2025-08-14T21:15:29.9884414Z done
2025-08-14T21:15:29.9884495Z 
2025-08-14T21:15:29.9884624Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-08-14T21:15:29.9884822Z # be empty.  The default action would be to continue rebuild the image
2025-08-14T21:15:29.9885003Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-08-14T21:15:29.9885166Z   # if we're on the base branch then use the parent commit
2025-08-14T21:15:29.9885315Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-08-14T21:15:29.9885427Z else
2025-08-14T21:15:29.9885547Z   # otherwise we're on a PR, so use the most recent base commit
2025-08-14T21:15:29.9887192Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-08-14T21:15:29.9887329Z fi
2025-08-14T21:15:29.9887409Z 
2025-08-14T21:15:29.9887499Z if [[ -z "${MERGE_BASE}" ]]; then
2025-08-14T21:15:29.9887631Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9887754Z 
2025-08-14T21:15:29.9887918Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-08-14T21:15:29.9888108Z   exit 0
2025-08-14T21:15:29.9888194Z fi
2025-08-14T21:15:29.9888274Z 
2025-08-14T21:15:29.9888388Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-08-14T21:15:29.9890059Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-08-14T21:15:29.9890264Z   exit 1
2025-08-14T21:15:29.9890348Z fi
2025-08-14T21:15:29.9890504Z 
2025-08-14T21:15:29.9890639Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-08-14T21:15:29.9890867Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-08-14T21:15:29.9891073Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-08-14T21:15:29.9891311Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-08-14T21:15:29.9891573Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-08-14T21:15:29.9891732Z fi
2025-08-14T21:15:29.9891812Z 
2025-08-14T21:15:29.9893502Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-08-14T21:15:29.9897983Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:29.9898122Z env:
2025-08-14T21:15:29.9898211Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:29.9898346Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:29.9898574Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:29.9898732Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:29.9899102Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:29.9899456Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:29.9899566Z   AWS_REGION: us-east-1
2025-08-14T21:15:29.9901528Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:29.9901689Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:29.9903807Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:29.9903916Z   DOCKER_BUILD_DIR: .ci/docker
2025-08-14T21:15:29.9904049Z   BASE_REVISION: 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:15:29.9904358Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9904705Z   DOCKER_TAG: pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:29.9904926Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9905068Z   DOCKER_PUSH: 
2025-08-14T21:15:29.9905159Z ##[endgroup]
2025-08-14T21:15:29.9930121Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9930428Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9932871Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:29.9933327Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:29.9933762Z /home/runner/_work/_temp/98000ddf-5045-49a0-95c2-480a9d65868a.sh: line 5: aws: command not found
2025-08-14T21:15:30.0038625Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:30.0049336Z + sleep 1
2025-08-14T21:15:31.0064152Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:31.0067589Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:31.0068291Z /home/runner/_work/_temp/98000ddf-5045-49a0-95c2-480a9d65868a.sh: line 5: aws: command not found
2025-08-14T21:15:31.0069072Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:31.0196760Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:31.0216830Z + sleep 2
2025-08-14T21:15:33.0240899Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.0241989Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:33.0242539Z /home/runner/_work/_temp/98000ddf-5045-49a0-95c2-480a9d65868a.sh: line 5: aws: command not found
2025-08-14T21:15:33.0243350Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.0358055Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:33.0375539Z ++ date +%s
2025-08-14T21:15:33.0386537Z + START_TIME=1755206133
2025-08-14T21:15:33.0389434Z ++ date +%s
2025-08-14T21:15:33.0396937Z + [[ 1755198933 -lt 1755206133 ]]
2025-08-14T21:15:33.0397495Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
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2025-08-14T21:15:33.5480828Z + exit 0
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2025-08-14T21:15:33.5481209Z 		},
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2025-08-14T21:15:33.5481844Z 		},
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2025-08-14T21:15:33.5484419Z 		},
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2025-08-14T21:15:33.5494269Z 		{
2025-08-14T21:15:33.5494383Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5494524Z 			"size": 724,
2025-08-14T21:15:33.5494668Z 			"digest": "sha256:8c242d215a8f77589f52e21fffc3510999687d6960f9eabf3e28fca0608dae60"
2025-08-14T21:15:33.5494828Z 		},
2025-08-14T21:15:33.5495935Z 		{
2025-08-14T21:15:33.5496050Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5496193Z 			"size": 118,
2025-08-14T21:15:33.5496339Z 			"digest": "sha256:e09eab04c40bfe1ad50914a20fd48c8e8ee93a452fe47182ca68295ae2a93212"
2025-08-14T21:15:33.5496503Z 		},
2025-08-14T21:15:33.5496571Z 		{
2025-08-14T21:15:33.5496686Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5496827Z 			"size": 136,
2025-08-14T21:15:33.5496972Z 			"digest": "sha256:6976913d3d27ea16b90b5e5193d67e2325d51de8eb75d3004f04d0ee92a750b0"
2025-08-14T21:15:33.5497185Z 		},
2025-08-14T21:15:33.5497256Z 		{
2025-08-14T21:15:33.5498371Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5498513Z 			"size": 140,
2025-08-14T21:15:33.5498665Z 			"digest": "sha256:f10c466a8b1fa8360c3fb66deedcfbcfa34cd1bdb6cfeed1bd5d5ca20c970b2f"
2025-08-14T21:15:33.5498832Z 		},
2025-08-14T21:15:33.5498900Z 		{
2025-08-14T21:15:33.5499015Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5499157Z 			"size": 32,
2025-08-14T21:15:33.5499305Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-08-14T21:15:33.5499466Z 		},
2025-08-14T21:15:33.5499534Z 		{
2025-08-14T21:15:33.5499648Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5504827Z 			"size": 214,
2025-08-14T21:15:33.5504992Z 			"digest": "sha256:5df1a93e20a44fef21d1e02475b4c7b2240117cc8c781c7ce9883c687857ddd8"
2025-08-14T21:15:33.5505166Z 		},
2025-08-14T21:15:33.5505239Z 		{
2025-08-14T21:15:33.5505359Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5505505Z 			"size": 346,
2025-08-14T21:15:33.5505654Z 			"digest": "sha256:1cd1e92d3a227192f9c7bfd1e1e785d77b91ca2fda5f1fbc0350aff08d28075b"
2025-08-14T21:15:33.5505818Z 		},
2025-08-14T21:15:33.5505888Z 		{
2025-08-14T21:15:33.5506006Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5506153Z 			"size": 88298,
2025-08-14T21:15:33.5507383Z 			"digest": "sha256:05ae8e902c000a23e70d8446f27999e656872780ff22458142ff3499d473cfe2"
2025-08-14T21:15:33.5507549Z 		},
2025-08-14T21:15:33.5507617Z 		{
2025-08-14T21:15:33.5507733Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5507875Z 			"size": 106,
2025-08-14T21:15:33.5508018Z 			"digest": "sha256:761f4417515e2a9c3895fe65225874ec8aa52681947c8880580e2f6f0c49bd74"
2025-08-14T21:15:33.5508180Z 		},
2025-08-14T21:15:33.5508248Z 		{
2025-08-14T21:15:33.5508367Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5508510Z 			"size": 1666,
2025-08-14T21:15:33.5508657Z 			"digest": "sha256:7a6e3cce4230c64f44c2e0326a932e88a29a0e267a0e4885ea77e55157b563e5"
2025-08-14T21:15:33.5509689Z 		},
2025-08-14T21:15:33.5509765Z 		{
2025-08-14T21:15:33.5509881Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5510022Z 			"size": 724,
2025-08-14T21:15:33.5510165Z 			"digest": "sha256:8c242d215a8f77589f52e21fffc3510999687d6960f9eabf3e28fca0608dae60"
2025-08-14T21:15:33.5510324Z 		},
2025-08-14T21:15:33.5510394Z 		{
2025-08-14T21:15:33.5510508Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5510649Z 			"size": 137,
2025-08-14T21:15:33.5510793Z 			"digest": "sha256:46ad26a64652af41c44440c16fd6d6021e0287507ea557ced55416a0adfb9660"
2025-08-14T21:15:33.5510951Z 		},
2025-08-14T21:15:33.5511898Z 		{
2025-08-14T21:15:33.5512016Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5512160Z 			"size": 120,
2025-08-14T21:15:33.5512303Z 			"digest": "sha256:9dabacb81b976642d40261923555e1f7c539948627bef41f1c5795bda9939ecf"
2025-08-14T21:15:33.5512463Z 		},
2025-08-14T21:15:33.5512531Z 		{
2025-08-14T21:15:33.5512644Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5512791Z 			"size": 5365418237,
2025-08-14T21:15:33.5513058Z 			"digest": "sha256:7965eab551ab5adc25ae243ce3b7816d4d73e976b7c7b1efd0c94a623541466b"
2025-08-14T21:15:33.5513221Z 		},
2025-08-14T21:15:33.5513290Z 		{
2025-08-14T21:15:33.5514187Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5514332Z 			"size": 175,
2025-08-14T21:15:33.5514481Z 			"digest": "sha256:613cbabab8b32dc6cca4974439d9f999ee14bd024fd796a8065065ff9c24e9e6"
2025-08-14T21:15:33.5514643Z 		},
2025-08-14T21:15:33.5514712Z 		{
2025-08-14T21:15:33.5514826Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5515030Z 			"size": 1897,
2025-08-14T21:15:33.5515176Z 			"digest": "sha256:519ae890867f4173280e6f2468d371266dcebc7fdce7a196afd5103b4bbbe0d3"
2025-08-14T21:15:33.5515337Z 		},
2025-08-14T21:15:33.5515406Z 		{
2025-08-14T21:15:33.5515520Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5516447Z 			"size": 162671329,
2025-08-14T21:15:33.5516604Z 			"digest": "sha256:d7fa12a3cc9a32dd23cf3853f4a3917b3adbb7d8f4a91633b305c2833992d7fe"
2025-08-14T21:15:33.5516768Z 		},
2025-08-14T21:15:33.5516836Z 		{
2025-08-14T21:15:33.5516950Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5517093Z 			"size": 304,
2025-08-14T21:15:33.5517240Z 			"digest": "sha256:9357b732b9013df1c10a59f95a145d1e1e1d35857d62007bb1fbe207f949a263"
2025-08-14T21:15:33.5517398Z 		},
2025-08-14T21:15:33.5517466Z 		{
2025-08-14T21:15:33.5517579Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5517726Z 			"size": 32,
2025-08-14T21:15:33.5518650Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-08-14T21:15:33.5518815Z 		},
2025-08-14T21:15:33.5518884Z 		{
2025-08-14T21:15:33.5518998Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5519140Z 			"size": 108,
2025-08-14T21:15:33.5519284Z 			"digest": "sha256:32e9496eefa1b203b724dbfd737f94565f53eba0e2fc3f05c602515693a1ee09"
2025-08-14T21:15:33.5519446Z 		},
2025-08-14T21:15:33.5519514Z 		{
2025-08-14T21:15:33.5519628Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-08-14T21:15:33.5519772Z 			"size": 54145699,
2025-08-14T21:15:33.5519928Z 			"digest": "sha256:d9fa8827580f35de4cfd9cc3e127ebeb104d3f8922dd79c813d955474085e07a"
2025-08-14T21:15:33.5520862Z 		}
2025-08-14T21:15:33.5520934Z 	]
2025-08-14T21:15:33.5521005Z }
2025-08-14T21:15:33.5544691Z ##[group]Run set -eux
2025-08-14T21:15:33.5544815Z set -eux
2025-08-14T21:15:33.5545142Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin
2025-08-14T21:15:33.5551009Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:33.5551151Z env:
2025-08-14T21:15:33.5551239Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:33.5553392Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:33.5553563Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:33.5553722Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:33.5554086Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:33.5554445Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:33.5554636Z   AWS_REGION: us-east-1
2025-08-14T21:15:33.5554881Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:33.5555028Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:33.5557013Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:33.5557117Z ##[endgroup]
2025-08-14T21:15:33.5587579Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-08-14T21:15:33.5587819Z + jq --raw-output .SecretString
2025-08-14T21:15:33.5587954Z + jq -r .docker_hub_readonly_token
2025-08-14T21:15:33.5588166Z /home/runner/_work/_temp/9077e03a-29f0-41c9-a486-72d595d28bd0.sh: line 2: aws: command not found
2025-08-14T21:15:33.5588419Z + docker login --username pytorchbot --password-stdin
2025-08-14T21:15:33.5686882Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:33.5705943Z ##[error]Process completed with exit code 1.
2025-08-14T21:15:33.5770030Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-08-14T21:15:33.5770190Z with:
2025-08-14T21:15:33.5770445Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:33.5770769Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.5770912Z env:
2025-08-14T21:15:33.5771005Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:33.5771139Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:33.5771310Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:33.5771468Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:33.5771841Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:33.5774892Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:33.5775005Z   AWS_REGION: us-east-1
2025-08-14T21:15:33.5775159Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:33.5775314Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:33.5777288Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:33.5777391Z ##[endgroup]
2025-08-14T21:15:33.5786569Z ##[group]Run set -x
2025-08-14T21:15:33.5786685Z set -x
2025-08-14T21:15:33.5786771Z set +e
2025-08-14T21:15:33.5786854Z 
2025-08-14T21:15:33.5786934Z login() {
2025-08-14T21:15:33.5789741Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-08-14T21:15:33.5789926Z }
2025-08-14T21:15:33.5790006Z 
2025-08-14T21:15:33.5790085Z retry () {
2025-08-14T21:15:33.5790192Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-08-14T21:15:33.5790308Z }
2025-08-14T21:15:33.5790386Z 
2025-08-14T21:15:33.5790478Z retry login "${DOCKER_REGISTRY}"
2025-08-14T21:15:33.5790591Z 
2025-08-14T21:15:33.5790765Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024')
2025-08-14T21:15:33.5791004Z echo "Compressed size of image in MB: ${IMAGE_SIZE}"
2025-08-14T21:15:33.5793361Z 
2025-08-14T21:15:33.5793444Z set -e
2025-08-14T21:15:33.5793574Z # ignore output since only exit code is used for conditional
2025-08-14T21:15:33.5793748Z # only pull docker image if it's not available locally
2025-08-14T21:15:33.5793941Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-08-14T21:15:33.5794121Z   retry docker pull "${DOCKER_IMAGE}"
2025-08-14T21:15:33.5794236Z fi
2025-08-14T21:15:33.5797960Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:15:33.5798094Z env:
2025-08-14T21:15:33.5798181Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:15:33.5800535Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:15:33.5800700Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:15:33.5800941Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:15:33.5801308Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:15:33.5801659Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:15:33.5801768Z   AWS_REGION: us-east-1
2025-08-14T21:15:33.5801902Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:15:33.5802049Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:15:33.5804322Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:15:33.5804585Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:33.5804973Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.5807335Z ##[endgroup]
2025-08-14T21:15:33.5827334Z + set +e
2025-08-14T21:15:33.5827502Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.5827708Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.5830390Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:33.5831268Z /home/runner/_work/_temp/e0483f8f-f742-43a1-8fa8-fc552c031a61.sh: line 5: aws: command not found
2025-08-14T21:15:33.5832149Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:33.5938949Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:33.5950149Z + sleep 1
2025-08-14T21:15:34.5964021Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:34.5967316Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:34.5978073Z /home/runner/_work/_temp/e0483f8f-f742-43a1-8fa8-fc552c031a61.sh: line 5: aws: command not found
2025-08-14T21:15:34.5978829Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:34.6086591Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:34.6100634Z + sleep 2
2025-08-14T21:15:36.6115846Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:36.6119175Z + aws ecr get-login-password --region us-east-1
2025-08-14T21:15:36.6120435Z /home/runner/_work/_temp/e0483f8f-f742-43a1-8fa8-fc552c031a61.sh: line 5: aws: command not found
2025-08-14T21:15:36.6121248Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T21:15:36.6228934Z Error: Cannot perform an interactive login from a non TTY device
2025-08-14T21:15:36.6245521Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:36.6253983Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024'
2025-08-14T21:15:37.1804347Z + IMAGE_SIZE=18041.78188228607
2025-08-14T21:15:37.1804808Z + echo 'Compressed size of image in MB: 18041.78188228607'
2025-08-14T21:15:37.1805224Z + set -e
2025-08-14T21:15:37.1806014Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:37.1806905Z Compressed size of image in MB: 18041.78188228607
2025-08-14T21:15:37.1968522Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:37.1969811Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:15:37.6920753Z pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe: Pulling from pytorch/ci-image
2025-08-14T21:15:37.6921487Z baa9e71a063a: Pulling fs layer
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2025-08-14T21:18:49.3460700Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
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2025-08-14T21:18:49.3546058Z Prepare all required actions
2025-08-14T21:18:49.3567583Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-08-14T21:18:49.3567716Z with:
2025-08-14T21:18:49.3567967Z   github-token: ***
2025-08-14T21:18:49.3568056Z env:
2025-08-14T21:18:49.3568142Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:18:49.3568273Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:18:49.3568445Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:18:49.3568605Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:18:49.3568983Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:18:49.3571469Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:18:49.3571582Z   AWS_REGION: us-east-1
2025-08-14T21:18:49.3571711Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:18:49.3571870Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:18:49.3574143Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:18:49.3574251Z ##[endgroup]
2025-08-14T21:18:49.3584764Z ##[group]Run set -eux
2025-08-14T21:18:49.3584873Z set -eux
2025-08-14T21:18:49.3585034Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-08-14T21:18:49.3593562Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:18:49.3593706Z env:
2025-08-14T21:18:49.3593796Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:18:49.3593926Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:18:49.3594095Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:18:49.3594255Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:18:49.3594747Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:18:49.3595103Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:18:49.3595215Z   AWS_REGION: us-east-1
2025-08-14T21:18:49.3595348Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:18:49.3597616Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:18:49.3599608Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:18:49.3599756Z   GITHUB_TOKEN: ***
2025-08-14T21:18:49.3599849Z ##[endgroup]
2025-08-14T21:18:49.3632097Z + python3 .github/scripts/get_workflow_job_id.py 16976255041 linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d
2025-08-14T21:18:49.8494745Z Setting output job-id=48127863093
2025-08-14T21:18:49.8495399Z Setting output job-name=linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:18:49.8637731Z Prepare all required actions
2025-08-14T21:18:49.8637962Z Getting action download info
2025-08-14T21:18:50.0647461Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-08-14T21:18:50.4940392Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093)
2025-08-14T21:18:50.8944832Z ##[group]Run ./.github/actions/download-build-artifacts
2025-08-14T21:18:50.8945068Z with:
2025-08-14T21:18:50.8945223Z   name: linux-noble-rocm-py3.12-mi300
2025-08-14T21:18:50.8945405Z   s3-bucket: gha-artifacts
2025-08-14T21:18:50.8945516Z env:
2025-08-14T21:18:50.8950808Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:18:50.8950968Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:18:50.8951148Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:18:50.8951312Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:18:50.8951726Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:18:50.8952103Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:18:50.8952218Z   AWS_REGION: us-east-1
2025-08-14T21:18:50.8952403Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:18:50.8952564Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:18:50.8954708Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:18:50.8954813Z ##[endgroup]
2025-08-14T21:18:50.8976635Z ##[group]Run seemethere/download-artifact-s3@v4
2025-08-14T21:18:50.8976772Z with:
2025-08-14T21:18:50.8976880Z   name: linux-noble-rocm-py3.12-mi300
2025-08-14T21:18:50.8977015Z   s3-bucket: gha-artifacts
2025-08-14T21:18:50.8977131Z   region: us-east-1
2025-08-14T21:18:50.8977223Z env:
2025-08-14T21:18:50.8977316Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:18:50.8977452Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:18:50.8977631Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:18:50.8977804Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:18:50.8978181Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:18:50.8981333Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:18:50.8981451Z   AWS_REGION: us-east-1
2025-08-14T21:18:50.8981592Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:18:50.8981742Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:18:50.8983890Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:18:50.8983991Z ##[endgroup]
2025-08-14T21:18:51.1232563Z (node:4719) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-08-14T21:18:51.1233312Z 
2025-08-14T21:18:51.1233533Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-08-14T21:18:51.1234117Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-08-14T21:18:51.1234937Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-08-14T21:18:51.2294584Z Found 1 objects with prefix pytorch/pytorch/16976255041/linux-noble-rocm-py3.12-mi300/
2025-08-14T21:18:51.2295305Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip
2025-08-14T21:19:19.9035118Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip
2025-08-14T21:19:19.9049105Z Artifact download has finished successfully
2025-08-14T21:19:19.9193752Z ##[group]Run unzip -o artifacts.zip
2025-08-14T21:19:19.9193916Z unzip -o artifacts.zip
2025-08-14T21:19:19.9199358Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:19.9199509Z env:
2025-08-14T21:19:19.9199603Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:19.9199742Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:19.9200088Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:19.9200264Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:19.9200682Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:19.9201055Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:19.9201172Z   AWS_REGION: us-east-1
2025-08-14T21:19:19.9204059Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:19.9204210Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:19.9206192Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:19.9206294Z ##[endgroup]
2025-08-14T21:19:19.9244787Z Archive:  artifacts.zip
2025-08-14T21:19:19.9245838Z    creating: dist/
2025-08-14T21:19:22.8842031Z   inflating: dist/torch-2.9.0a0+git1fc683c-cp312-cp312-linux_x86_64.whl  
2025-08-14T21:19:22.8922686Z   inflating: dist/.ninja_log         
2025-08-14T21:19:22.8923818Z    creating: build/custom_test_artifacts/
2025-08-14T21:19:22.8931020Z    creating: build/custom_test_artifacts/custom-op-build/
2025-08-14T21:19:22.8931407Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/
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2025-08-14T21:19:22.8932696Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/
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2025-08-14T21:19:26.6707542Z   inflating: build/bin/c10_hip_HIPTest  
2025-08-14T21:19:26.7085770Z   inflating: build/bin/vec_test_all_types_AVX512  
2025-08-14T21:19:26.7461758Z   inflating: build/bin/vec_test_all_types_DEFAULT  
2025-08-14T21:19:26.7841352Z   inflating: build/bin/vec_test_all_types_AVX2  
2025-08-14T21:19:26.7876157Z   inflating: build/bin/BackoffTest   
2025-08-14T21:19:26.7911839Z   inflating: build/bin/FileStoreTest  
2025-08-14T21:19:26.7950212Z   inflating: build/bin/TCPStoreTest  
2025-08-14T21:19:26.7989706Z   inflating: build/bin/HashStoreTest  
2025-08-14T21:19:26.8030535Z   inflating: build/bin/ProcessGroupGlooTest  
2025-08-14T21:19:26.8031864Z   inflating: build/bin/example_allreduce  
2025-08-14T21:19:26.8034151Z   inflating: build/bin/torch_shm_manager  
2025-08-14T21:19:26.8070177Z   inflating: build/bin/static_runtime_bench  
2025-08-14T21:19:26.8229265Z   inflating: build/bin/static_runtime_test  
2025-08-14T21:19:26.8277669Z   inflating: build/bin/Dict_test     
2025-08-14T21:19:26.8312768Z   inflating: build/bin/Dimname_test  
2025-08-14T21:19:26.8356571Z   inflating: build/bin/MaybeOwned_test  
2025-08-14T21:19:26.8394769Z   inflating: build/bin/NamedTensor_test  
2025-08-14T21:19:26.8433901Z   inflating: build/bin/apply_utils_test  
2025-08-14T21:19:26.8473442Z   inflating: build/bin/atest         
2025-08-14T21:19:26.8515845Z   inflating: build/bin/basic         
2025-08-14T21:19:26.8553178Z   inflating: build/bin/broadcast_test  
2025-08-14T21:19:26.8587568Z   inflating: build/bin/cpu_allocator_test  
2025-08-14T21:19:26.8626254Z   inflating: build/bin/cpu_generator_test  
2025-08-14T21:19:26.8661865Z   inflating: build/bin/cpu_profiling_allocator_test  
2025-08-14T21:19:26.8722376Z   inflating: build/bin/cpu_rng_test  
2025-08-14T21:19:26.8759919Z   inflating: build/bin/dlconvertor_test  
2025-08-14T21:19:26.8795749Z   inflating: build/bin/extension_backend_test  
2025-08-14T21:19:26.8832992Z   inflating: build/bin/half_test     
2025-08-14T21:19:26.8866735Z   inflating: build/bin/lazy_tensor_test  
2025-08-14T21:19:26.8929406Z   inflating: build/bin/ivalue_test   
2025-08-14T21:19:26.8965298Z   inflating: build/bin/math_kernel_test  
2025-08-14T21:19:26.9001286Z   inflating: build/bin/memory_format_test  
2025-08-14T21:19:26.9037581Z   inflating: build/bin/memory_overlapping_test  
2025-08-14T21:19:26.9073249Z   inflating: build/bin/mobile_memory_cleanup  
2025-08-14T21:19:26.9110824Z   inflating: build/bin/native_test   
2025-08-14T21:19:26.9145901Z   inflating: build/bin/operator_name_test  
2025-08-14T21:19:26.9180265Z   inflating: build/bin/operators_test  
2025-08-14T21:19:26.9215286Z   inflating: build/bin/packedtensoraccessor_test  
2025-08-14T21:19:26.9259699Z   inflating: build/bin/pow_test      
2025-08-14T21:19:26.9298340Z   inflating: build/bin/quantized_test  
2025-08-14T21:19:26.9331785Z   inflating: build/bin/reduce_ops_test  
2025-08-14T21:19:26.9366235Z   inflating: build/bin/reportMemoryUsage_test  
2025-08-14T21:19:26.9404231Z   inflating: build/bin/scalar_tensor_test  
2025-08-14T21:19:26.9438672Z   inflating: build/bin/StorageUtils_test  
2025-08-14T21:19:26.9477808Z   inflating: build/bin/scalar_test   
2025-08-14T21:19:26.9512755Z   inflating: build/bin/stride_properties_test  
2025-08-14T21:19:26.9564782Z   inflating: build/bin/tensor_iterator_test  
2025-08-14T21:19:26.9601291Z   inflating: build/bin/test_parallel  
2025-08-14T21:19:26.9638209Z   inflating: build/bin/type_ptr_test  
2025-08-14T21:19:26.9674592Z   inflating: build/bin/thread_init_test  
2025-08-14T21:19:26.9707179Z   inflating: build/bin/undefined_tensor_test  
2025-08-14T21:19:26.9748574Z   inflating: build/bin/type_test     
2025-08-14T21:19:26.9780580Z   inflating: build/bin/verify_api_visibility  
2025-08-14T21:19:26.9826721Z   inflating: build/bin/legacy_vmap_test  
2025-08-14T21:19:26.9861125Z   inflating: build/bin/weakref_test  
2025-08-14T21:19:26.9895750Z   inflating: build/bin/wrapdim_test  
2025-08-14T21:19:26.9964748Z   inflating: build/bin/List_test     
2025-08-14T21:19:27.0004665Z   inflating: build/bin/IListRef_test  
2025-08-14T21:19:27.0039258Z   inflating: build/bin/xla_tensor_test  
2025-08-14T21:19:27.0116697Z   inflating: build/bin/kernel_function_legacy_test  
2025-08-14T21:19:27.0179105Z   inflating: build/bin/kernel_function_test  
2025-08-14T21:19:27.0222860Z   inflating: build/bin/KernelFunction_test  
2025-08-14T21:19:27.0304151Z   inflating: build/bin/kernel_lambda_legacy_test  
2025-08-14T21:19:27.0344993Z   inflating: build/bin/kernel_stackbased_test  
2025-08-14T21:19:27.0410845Z   inflating: build/bin/kernel_lambda_test  
2025-08-14T21:19:27.0473032Z   inflating: build/bin/make_boxed_from_unboxed_functor_test  
2025-08-14T21:19:27.0507417Z   inflating: build/bin/CppSignature_test  
2025-08-14T21:19:27.0540600Z   inflating: build/bin/op_allowlist_test  
2025-08-14T21:19:27.0735944Z   inflating: build/bin/op_registration_test  
2025-08-14T21:19:27.0768759Z   inflating: build/bin/hip_complex_math_test  
2025-08-14T21:19:27.0813770Z   inflating: build/bin/inline_container_test  
2025-08-14T21:19:27.0850426Z   inflating: build/bin/backend_fallback_test  
2025-08-14T21:19:27.0883605Z   inflating: build/bin/hip_complex_test  
2025-08-14T21:19:27.0919241Z   inflating: build/bin/hip_apply_test  
2025-08-14T21:19:27.0952568Z   inflating: build/bin/hip_distributions_test  
2025-08-14T21:19:27.0985631Z   inflating: build/bin/hip_generator_test  
2025-08-14T21:19:27.1018945Z   inflating: build/bin/hip_half_test  
2025-08-14T21:19:27.1052083Z   inflating: build/bin/hip_integer_divider_test  
2025-08-14T21:19:27.1085493Z   inflating: build/bin/hip_optional_test  
2025-08-14T21:19:27.1118513Z   inflating: build/bin/hip_packedtensoraccessor_test  
2025-08-14T21:19:27.1154157Z   inflating: build/bin/hip_vectorized_test  
2025-08-14T21:19:27.1186145Z   inflating: build/bin/hip_dlconvertor_test  
2025-08-14T21:19:27.1873501Z   inflating: build/bin/test_jit      
2025-08-14T21:19:27.2072616Z   inflating: build/bin/test_nativert  
2025-08-14T21:19:27.2108987Z   inflating: build/bin/test_dist_autograd  
2025-08-14T21:19:27.2153764Z   inflating: build/bin/test_cpp_rpc  
2025-08-14T21:19:27.2155195Z   inflating: build/bin/parallel_benchmark  
2025-08-14T21:19:27.2869742Z   inflating: build/bin/test_api      
2025-08-14T21:19:27.3084427Z   inflating: build/bin/test_lazy     
2025-08-14T21:19:27.3084812Z    creating: .additional_ci_files/
2025-08-14T21:19:27.3132581Z   inflating: .additional_ci_files/test-times.json  
2025-08-14T21:19:27.3318129Z   inflating: .additional_ci_files/test-class-times.json  
2025-08-14T21:19:27.3355867Z ##[group]Run rm artifacts.zip
2025-08-14T21:19:27.3356024Z rm artifacts.zip
2025-08-14T21:19:27.3363698Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:27.3363839Z env:
2025-08-14T21:19:27.3364067Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:27.3364201Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:27.3364389Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:27.3364543Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:27.3364909Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:27.3365263Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:27.3365372Z   AWS_REGION: us-east-1
2025-08-14T21:19:27.3365565Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:27.3367482Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:27.3369476Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:27.3369577Z ##[endgroup]
2025-08-14T21:19:27.4797795Z ##[group]Run df -H
2025-08-14T21:19:27.4797946Z df -H
2025-08-14T21:19:27.4802110Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:27.4802266Z env:
2025-08-14T21:19:27.4802373Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:27.4802521Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:27.4802708Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:27.4802882Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:27.4806938Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:27.4807321Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:27.4807439Z   AWS_REGION: us-east-1
2025-08-14T21:19:27.4807583Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:27.4807733Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:27.4809938Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:27.4810033Z ##[endgroup]
2025-08-14T21:19:27.4843557Z Filesystem      Size  Used Avail Use% Mounted on
2025-08-14T21:19:27.4843818Z overlay         2.2T  162G  2.0T   8% /
2025-08-14T21:19:27.4844057Z tmpfs            68M     0   68M   0% /dev
2025-08-14T21:19:27.4844250Z /dev/vda1       2.2T  162G  2.0T   8% /run
2025-08-14T21:19:27.4844452Z shm              68M  4.1k   68M   1% /dev/shm
2025-08-14T21:19:27.4844704Z tmpfs           1.4T   13k  1.4T   1% /run/secrets/kubernetes.io/serviceaccount
2025-08-14T21:19:27.4845188Z tmpfs           675G     0  675G   0% /proc/acpi
2025-08-14T21:19:27.4845419Z tmpfs           675G     0  675G   0% /proc/scsi
2025-08-14T21:19:27.4845625Z tmpfs           675G     0  675G   0% /sys/firmware
2025-08-14T21:19:27.4882593Z Prepare all required actions
2025-08-14T21:19:27.4882845Z Getting action download info
2025-08-14T21:19:27.6731357Z ##[group]Run ./.github/actions/download-td-artifacts
2025-08-14T21:19:27.6731492Z with:
2025-08-14T21:19:27.6731576Z env:
2025-08-14T21:19:27.6731664Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:27.6731797Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:27.6732098Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:27.6732254Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:27.6734970Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:27.6735330Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:27.6735445Z   AWS_REGION: us-east-1
2025-08-14T21:19:27.6735613Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:27.6735780Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:27.6737784Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:27.6737887Z ##[endgroup]
2025-08-14T21:19:27.6756115Z ##[group]Run seemethere/download-artifact-s3@v4
2025-08-14T21:19:27.6756246Z with:
2025-08-14T21:19:27.6756329Z   name: td_results
2025-08-14T21:19:27.6756427Z   s3-bucket: gha-artifacts
2025-08-14T21:19:27.6756527Z   region: us-east-1
2025-08-14T21:19:27.6756621Z env:
2025-08-14T21:19:27.6759196Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:27.6759338Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:27.6759509Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:27.6759667Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:27.6760039Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:27.6760399Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:27.6760509Z   AWS_REGION: us-east-1
2025-08-14T21:19:27.6760638Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:27.6760784Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:27.6762837Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:27.6763036Z ##[endgroup]
2025-08-14T21:19:27.9062308Z (node:4752) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-08-14T21:19:27.9062538Z 
2025-08-14T21:19:27.9062966Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-08-14T21:19:27.9063213Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-08-14T21:19:27.9063454Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-08-14T21:19:28.0200109Z Found 1 objects with prefix pytorch/pytorch/16976255041/td_results/
2025-08-14T21:19:28.0200476Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json
2025-08-14T21:19:28.1812455Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json
2025-08-14T21:19:28.1815232Z Artifact download has finished successfully
2025-08-14T21:19:28.1981412Z ##[group]Run mkdir -p .additional_ci_files
2025-08-14T21:19:28.1981598Z mkdir -p .additional_ci_files
2025-08-14T21:19:28.1981792Z mv td_results.json .additional_ci_files/td_results.json || true
2025-08-14T21:19:28.1988327Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:28.1988521Z env:
2025-08-14T21:19:28.1988634Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:28.1988793Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:28.1988993Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:28.1989184Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:28.1989798Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:28.1990232Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:28.1990363Z   AWS_REGION: us-east-1
2025-08-14T21:19:28.1990540Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:28.1990696Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:28.1992717Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:28.1992822Z ##[endgroup]
2025-08-14T21:19:28.2063085Z ##[group]Run .github/scripts/parse_ref.py
2025-08-14T21:19:28.2063362Z .github/scripts/parse_ref.py
2025-08-14T21:19:28.2076188Z shell: /usr/bin/bash -e {0}
2025-08-14T21:19:28.2076436Z env:
2025-08-14T21:19:28.2076602Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:28.2076834Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:28.2077129Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:28.2077398Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:28.2078042Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:28.2078656Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:28.2078854Z   AWS_REGION: us-east-1
2025-08-14T21:19:28.2079161Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:28.2079448Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:28.2081941Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:28.2082094Z ##[endgroup]
2025-08-14T21:19:28.2184669Z Setting output branch=main
2025-08-14T21:19:28.2249137Z Prepare all required actions
2025-08-14T21:19:28.2249349Z Getting action download info
2025-08-14T21:19:28.3590841Z ##[group]Run ./.github/actions/filter-test-configs
2025-08-14T21:19:28.3590981Z with:
2025-08-14T21:19:28.3591249Z   github-token: ***
2025-08-14T21:19:28.3591839Z   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"}]}
2025-08-14T21:19:28.3592507Z   job-name: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:28.3592742Z env:
2025-08-14T21:19:28.3592837Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:28.3593092Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:28.3593268Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:28.3593427Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:28.3593806Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:28.3594162Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:28.3594277Z   AWS_REGION: us-east-1
2025-08-14T21:19:28.3594428Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:28.3594608Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:28.3596743Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:28.3596850Z ##[endgroup]
2025-08-14T21:19:28.3612647Z ##[group]Run nick-fields/retry@v3.0.0
2025-08-14T21:19:28.3612776Z with:
2025-08-14T21:19:28.3612863Z   shell: bash
2025-08-14T21:19:28.3613051Z   timeout_minutes: 10
2025-08-14T21:19:28.3613154Z   max_attempts: 5
2025-08-14T21:19:28.3613254Z   retry_wait_seconds: 30
2025-08-14T21:19:28.3613542Z   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

2025-08-14T21:19:28.3613895Z   polling_interval_seconds: 1
2025-08-14T21:19:28.3614010Z   warning_on_retry: true
2025-08-14T21:19:28.3614119Z   continue_on_error: false
2025-08-14T21:19:28.3614226Z env:
2025-08-14T21:19:28.3614320Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:28.3614456Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:28.3614663Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:28.3614829Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:28.3615336Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:28.3615695Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:28.3615811Z   AWS_REGION: us-east-1
2025-08-14T21:19:28.3615945Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:28.3616099Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:28.3618140Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:28.3618293Z   GITHUB_TOKEN: ***
2025-08-14T21:19:28.3618395Z ##[endgroup]
2025-08-14T21:19:28.4040554Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2
2025-08-14T21:19:28.5438570Z Defaulting to user installation because normal site-packages is not writeable
2025-08-14T21:19:28.7096373Z Collecting requests==2.27.1
2025-08-14T21:19:28.8119099Z   Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
2025-08-14T21:19:28.8481404Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.1/63.1 KB 1.6 MB/s eta 0:00:00
2025-08-14T21:19:28.9219150Z Collecting pyyaml==6.0.2
2025-08-14T21:19:28.9475054Z   Downloading PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (751 kB)
2025-08-14T21:19:29.0271425Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 751.2/751.2 KB 9.6 MB/s eta 0:00:00
2025-08-14T21:19:29.0668562Z Collecting idna<4,>=2.5
2025-08-14T21:19:29.0935059Z   Downloading idna-3.10-py3-none-any.whl (70 kB)
2025-08-14T21:19:29.0958307Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 70.4/70.4 KB 45.4 MB/s eta 0:00:00
2025-08-14T21:19:29.1359404Z Collecting certifi>=2017.4.17
2025-08-14T21:19:29.1622687Z   Downloading certifi-2025.8.3-py3-none-any.whl (161 kB)
2025-08-14T21:19:29.1661863Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 161.2/161.2 KB 50.7 MB/s eta 0:00:00
2025-08-14T21:19:29.2161010Z Collecting urllib3<1.27,>=1.21.1
2025-08-14T21:19:29.2426942Z   Downloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB)
2025-08-14T21:19:29.2460217Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 144.2/144.2 KB 55.8 MB/s eta 0:00:00
2025-08-14T21:19:29.3714642Z Collecting charset-normalizer~=2.0.0
2025-08-14T21:19:29.3986636Z   Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
2025-08-14T21:19:29.4531608Z Installing collected packages: urllib3, pyyaml, idna, charset-normalizer, certifi, requests
2025-08-14T21:19:29.5488053Z   WARNING: The script normalizer is installed in '/home/runner/.local/bin' which is not on PATH.
2025-08-14T21:19:29.5488912Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-08-14T21:19:29.5694022Z 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
2025-08-14T21:19:30.4047509Z Command completed after 1 attempt(s).
2025-08-14T21:19:30.4113185Z ##[group]Run set -x
2025-08-14T21:19:30.4113336Z set -x
2025-08-14T21:19:30.4113444Z 
2025-08-14T21:19:30.4113613Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-08-14T21:19:30.4113845Z # in runner workspace
2025-08-14T21:19:30.4114015Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-08-14T21:19:30.4119705Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:30.4119871Z env:
2025-08-14T21:19:30.4119976Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:30.4120117Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:30.4120293Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:30.4120459Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:30.4120837Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:30.4121192Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:30.4121301Z   AWS_REGION: us-east-1
2025-08-14T21:19:30.4121606Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:30.4121758Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:30.4123918Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:30.4124019Z ##[endgroup]
2025-08-14T21:19:30.4154644Z + python3 /home/runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-08-14T21:19:30.4240731Z Setting output branch=main
2025-08-14T21:19:30.4285813Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-08-14T21:19:30.4285989Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-08-14T21:19:30.4286127Z echo "Job name: ${JOB_NAME}"
2025-08-14T21:19:30.4286250Z 
2025-08-14T21:19:30.4286403Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-08-14T21:19:30.4286591Z # in runner workspace
2025-08-14T21:19:30.4286759Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-08-14T21:19:30.4286949Z   --workflow "${GITHUB_WORKFLOW}" \
2025-08-14T21:19:30.4287098Z   --job-name "${JOB_NAME}" \
2025-08-14T21:19:30.4287908Z   --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"}]}" \
2025-08-14T21:19:30.4290202Z   --selected-test-configs "" \
2025-08-14T21:19:30.4290337Z   --pr-number "${PR_NUMBER}" \
2025-08-14T21:19:30.4290456Z   --tag "${TAG}" \
2025-08-14T21:19:30.4290569Z   --event-name "${EVENT_NAME}" \
2025-08-14T21:19:30.4290688Z   --schedule "${SCHEDULE}" \
2025-08-14T21:19:30.4290806Z   --branch "${HEAD_BRANCH}"
2025-08-14T21:19:30.4296608Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:30.4296748Z env:
2025-08-14T21:19:30.4296836Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:30.4296969Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:30.4297139Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:30.4297297Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:30.4299199Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:30.4299565Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:30.4299674Z   AWS_REGION: us-east-1
2025-08-14T21:19:30.4299825Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:30.4299970Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:30.4301987Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:30.4302176Z   GITHUB_TOKEN: ***
2025-08-14T21:19:30.4302344Z   JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:30.4302521Z   PR_NUMBER: 
2025-08-14T21:19:30.4302608Z   TAG: 
2025-08-14T21:19:30.4302688Z   EVENT_NAME: push
2025-08-14T21:19:30.4304313Z   SCHEDULE: 
2025-08-14T21:19:30.4304399Z   HEAD_BRANCH: main
2025-08-14T21:19:30.4304488Z ##[endgroup]
2025-08-14T21:19:30.4325135Z Workflow: rocm-mi300
2025-08-14T21:19:30.4325475Z Job name: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:30.6943325Z Setting output keep-going=True
2025-08-14T21:19:30.6943770Z Setting output ci-verbose-test-logs=False
2025-08-14T21:19:30.6944144Z Setting output ci-test-showlocals=False
2025-08-14T21:19:30.6951658Z Setting output ci-no-test-timeout=False
2025-08-14T21:19:30.6951830Z Setting output ci-no-td=False
2025-08-14T21:19:30.6951973Z Setting output ci-td-distributed=False
2025-08-14T21:19:30.6952353Z Setting output is-unstable=False
2025-08-14T21:19:30.6952501Z Setting output reenabled-issues=
2025-08-14T21:19:30.6953423Z 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"}]}
2025-08-14T21:19:30.6954177Z Setting output is-test-matrix-empty=False
2025-08-14T21:19:30.7017440Z ##[group]Run echo "Filtered matrix:"
2025-08-14T21:19:30.7017589Z echo "Filtered matrix:"
2025-08-14T21:19:30.7018329Z 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"}]}"
2025-08-14T21:19:30.7018947Z 
2025-08-14T21:19:30.7019033Z echo
2025-08-14T21:19:30.7019142Z echo "Is the current job unstable? False"
2025-08-14T21:19:30.7019267Z 
2025-08-14T21:19:30.7019346Z echo
2025-08-14T21:19:30.7019445Z echo "Is keep-going label set? True"
2025-08-14T21:19:30.7019562Z 
2025-08-14T21:19:30.7021314Z echo
2025-08-14T21:19:30.7021409Z echo "Reenabled issues? "
2025-08-14T21:19:30.7026432Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:30.7026574Z env:
2025-08-14T21:19:30.7026666Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:30.7026795Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:30.7026961Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:30.7027117Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:30.7027486Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:30.7029427Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:30.7029544Z   AWS_REGION: us-east-1
2025-08-14T21:19:30.7029700Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:30.7029842Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:30.7031857Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:30.7031956Z ##[endgroup]
2025-08-14T21:19:30.7057751Z Filtered matrix:
2025-08-14T21:19:30.7058363Z {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}]}
2025-08-14T21:19:30.7059059Z 
2025-08-14T21:19:30.7059136Z Is the current job unstable? False
2025-08-14T21:19:30.7059216Z 
2025-08-14T21:19:30.7059266Z Is keep-going label set? True
2025-08-14T21:19:30.7059338Z 
2025-08-14T21:19:30.7059375Z Reenabled issues? 
2025-08-14T21:19:30.7085676Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-08-14T21:19:30.7085875Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-08-14T21:19:30.7089538Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T21:19:30.7089793Z env:
2025-08-14T21:19:30.7089880Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:30.7090010Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:30.7090175Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:30.7092436Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:30.7092801Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:30.7093303Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:30.7093413Z   AWS_REGION: us-east-1
2025-08-14T21:19:30.7093563Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:30.7093709Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:30.7095800Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:30.7095897Z   JOB_TIMEOUT: 300
2025-08-14T21:19:30.7095987Z ##[endgroup]
2025-08-14T21:19:30.7158418Z ##[group]Run set -x
2025-08-14T21:19:30.7158547Z set -x
2025-08-14T21:19:30.7158632Z 
2025-08-14T21:19:30.7158732Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-08-14T21:19:30.7158881Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-08-14T21:19:30.7159030Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-08-14T21:19:30.7159165Z   TEST_COMMAND=.ci/caffe2/test.sh
2025-08-14T21:19:30.7159276Z else
2025-08-14T21:19:30.7159373Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-08-14T21:19:30.7159492Z fi
2025-08-14T21:19:30.7161206Z 
2025-08-14T21:19:30.7161332Z # detached container should get cleaned up by teardown_ec2_linux
2025-08-14T21:19:30.7161524Z # TODO: Stop building test binaries as part of the build phase
2025-08-14T21:19:30.7161695Z # Used for GPU_FLAG since that doesn't play nice
2025-08-14T21:19:30.7161856Z # shellcheck disable=SC2086,SC2090
2025-08-14T21:19:30.7161987Z container_name=$(docker run \
2025-08-14T21:19:30.7162105Z   ${GPU_FLAG:-} \
2025-08-14T21:19:30.7162215Z   -e BUILD_ENVIRONMENT \
2025-08-14T21:19:30.7162327Z   -e PR_NUMBER \
2025-08-14T21:19:30.7162430Z   -e GITHUB_ACTIONS \
2025-08-14T21:19:30.7162538Z   -e GITHUB_REPOSITORY \
2025-08-14T21:19:30.7164177Z   -e GITHUB_WORKFLOW \
2025-08-14T21:19:30.7164286Z   -e GITHUB_JOB \
2025-08-14T21:19:30.7164387Z   -e GITHUB_RUN_ID \
2025-08-14T21:19:30.7164492Z   -e GITHUB_RUN_NUMBER \
2025-08-14T21:19:30.7164605Z   -e GITHUB_RUN_ATTEMPT \
2025-08-14T21:19:30.7164713Z   -e JOB_ID \
2025-08-14T21:19:30.7164810Z   -e JOB_NAME \
2025-08-14T21:19:30.7164907Z   -e BRANCH \
2025-08-14T21:19:30.7165001Z   -e SHA1 \
2025-08-14T21:19:30.7165096Z   -e AWS_DEFAULT_REGION \
2025-08-14T21:19:30.7165209Z   -e IN_WHEEL_TEST \
2025-08-14T21:19:30.7165316Z   -e SHARD_NUMBER \
2025-08-14T21:19:30.7166714Z   -e TEST_CONFIG \
2025-08-14T21:19:30.7166825Z   -e NUM_TEST_SHARDS \
2025-08-14T21:19:30.7166932Z   -e REENABLED_ISSUES \
2025-08-14T21:19:30.7167044Z   -e CONTINUE_THROUGH_ERROR \
2025-08-14T21:19:30.7167160Z   -e VERBOSE_TEST_LOGS \
2025-08-14T21:19:30.7167266Z   -e TEST_SHOWLOCALS \
2025-08-14T21:19:30.7167370Z   -e NO_TEST_TIMEOUT \
2025-08-14T21:19:30.7167471Z   -e NO_TD \
2025-08-14T21:19:30.7167581Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-08-14T21:19:30.7167713Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-08-14T21:19:30.7167846Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-08-14T21:19:30.7169200Z   -e TESTS_TO_INCLUDE \
2025-08-14T21:19:30.7169313Z   -e DASHBOARD_TAG \
2025-08-14T21:19:30.7169451Z   --env-file="${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" \
2025-08-14T21:19:30.7169716Z   --ulimit stack=10485760:83886080 \
2025-08-14T21:19:30.7169831Z   --ulimit core=0 \
2025-08-14T21:19:30.7169948Z   --security-opt seccomp=unconfined \
2025-08-14T21:19:30.7170072Z   --cap-add=SYS_PTRACE \
2025-08-14T21:19:30.7170181Z   --shm-size="8g" \
2025-08-14T21:19:30.7170278Z   --tty \
2025-08-14T21:19:30.7170370Z   --detach \
2025-08-14T21:19:30.7170471Z   --name="${container_name}" \
2025-08-14T21:19:30.7171846Z   --user jenkins \
2025-08-14T21:19:30.7171976Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-08-14T21:19:30.7172124Z   -w /var/lib/jenkins/workspace \
2025-08-14T21:19:30.7172238Z   "${DOCKER_IMAGE}"
2025-08-14T21:19:30.7172333Z )
2025-08-14T21:19:30.7172426Z # save container name for later step
2025-08-14T21:19:30.7172655Z echo "CONTAINER_NAME=${container_name}" >> "$GITHUB_ENV"
2025-08-14T21:19:30.7173017Z # jenkins user does not have write permission to mounted workspace; work-around by copying within container to jenkins home
2025-08-14T21:19:30.7173347Z docker exec -t "${container_name}" sh -c "cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && ${TEST_COMMAND}"
2025-08-14T21:19:30.7178638Z shell: /usr/bin/bash -e {0}
2025-08-14T21:19:30.7180298Z env:
2025-08-14T21:19:30.7180387Z   GIT_DEFAULT_BRANCH: main
2025-08-14T21:19:30.7180515Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T21:19:30.7180682Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T21:19:30.7180839Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T21:19:30.7181215Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T21:19:30.7181573Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T21:19:30.7181677Z   AWS_REGION: us-east-1
2025-08-14T21:19:30.7181833Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T21:19:30.7181976Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T21:19:30.7185488Z   AWS_SESSION_TOKEN: ***
2025-08-14T21:19:30.7185620Z   BUILD_ENVIRONMENT: linux-noble-rocm-py3.12-mi300
2025-08-14T21:19:30.7185752Z   PR_NUMBER: 
2025-08-14T21:19:30.7185848Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-08-14T21:19:30.7185966Z   GITHUB_WORKFLOW: rocm-mi300
2025-08-14T21:19:30.7186068Z   GITHUB_JOB: test
2025-08-14T21:19:30.7186161Z   GITHUB_RUN_ID: 16976255041
2025-08-14T21:19:30.7186264Z   GITHUB_RUN_NUMBER: 8364
2025-08-14T21:19:30.7186363Z   GITHUB_RUN_ATTEMPT: 1
2025-08-14T21:19:30.7186458Z   JOB_ID: 48127863093
2025-08-14T21:19:30.7186623Z   JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:30.7188106Z   BRANCH: main
2025-08-14T21:19:30.7188214Z   SHA1: 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:30.7188342Z   CONTINUE_THROUGH_ERROR: True
2025-08-14T21:19:30.7188451Z   VERBOSE_TEST_LOGS: False
2025-08-14T21:19:30.7188552Z   TEST_SHOWLOCALS: False
2025-08-14T21:19:30.7188650Z   NO_TEST_TIMEOUT: False
2025-08-14T21:19:30.7188741Z   NO_TD: False
2025-08-14T21:19:30.7188829Z   TEST_CONFIG: default
2025-08-14T21:19:30.7188920Z   SHARD_NUMBER: 4
2025-08-14T21:19:30.7189007Z   NUM_TEST_SHARDS: 6
2025-08-14T21:19:30.7189098Z   REENABLED_ISSUES: 
2025-08-14T21:19:30.7190636Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:19:30.7190920Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-08-14T21:19:30.7191041Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-08-14T21:19:30.7191151Z   TESTS_TO_INCLUDE: 
2025-08-14T21:19:30.7191244Z   DASHBOARD_TAG: 
2025-08-14T21:19:30.7191333Z ##[endgroup]
2025-08-14T21:19:30.7213873Z + [[ default == \m\u\l\t\i\g\p\u ]]
2025-08-14T21:19:30.7214549Z + [[ linux-noble-rocm-py3.12-mi300 == *onnx* ]]
2025-08-14T21:19:30.7214774Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-08-14T21:19:30.7220023Z +++ nproc --ignore=2
2025-08-14T21:19:30.7237747Z ++ docker run --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --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_16976255041 --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-bfa89110622ba7202628e9faac705f183070defe
2025-08-14T21:19:33.1424215Z + container_name=4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T21:19:33.1424963Z + echo CONTAINER_NAME=4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T21:19:33.1426026Z + docker exec -t 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49 sh -c 'cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && .ci/pytorch/test.sh'
2025-08-14T21:19:37.1683324Z Processing ./dist/torch-2.9.0a0+git1fc683c-cp312-cp312-linux_x86_64.whl
2025-08-14T21:19:37.6901227Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (3.18.0)
2025-08-14T21:19:37.6902518Z 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+git1fc683c) (4.14.1)
2025-08-14T21:19:37.6906427Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (80.9.0)
2025-08-14T21:19:37.6907486Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (1.13.3)
2025-08-14T21:19:37.6908541Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (2.8.8)
2025-08-14T21:19:37.6909560Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (3.1.6)
2025-08-14T21:19:37.6919124Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+git1fc683c) (2025.5.1)
2025-08-14T21:19:37.6956599Z 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+git1fc683c) (1.3.0)
2025-08-14T21:19:37.6977751Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.9.0a0+git1fc683c) (3.0.2)
2025-08-14T21:19:37.8115301Z Installing collected packages: torch
2025-08-14T21:19:43.2687763Z 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.
2025-08-14T21:19:43.2688176Z helion 0.1.0 requires filecheck, which is not installed.
2025-08-14T21:19:43.2688395Z Successfully installed torch-2.9.0a0+git1fc683c
2025-08-14T21:19:43.3071561Z + export TERM=vt100
2025-08-14T21:19:43.3071698Z + TERM=vt100
2025-08-14T21:19:43.3079765Z ++ dirname .ci/pytorch/test.sh
2025-08-14T21:19:43.3089400Z + source .ci/pytorch/common.sh
2025-08-14T21:19:43.3093306Z +++ dirname .ci/pytorch/common.sh
2025-08-14T21:19:43.3102264Z ++ source .ci/pytorch/common_utils.sh
2025-08-14T21:19:43.3102596Z +++ declare -f -t trap_add
2025-08-14T21:19:43.3104813Z ++ set -ex -o pipefail
2025-08-14T21:19:43.3105155Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]]
2025-08-14T21:19:43.3105391Z ++ unset HIP_PLATFORM
2025-08-14T21:19:43.3105585Z ++ export PYTORCH_TEST_WITH_ROCM=1
2025-08-14T21:19:43.3105772Z ++ PYTORCH_TEST_WITH_ROCM=1
2025-08-14T21:19:43.3105955Z ++ BUILD_TEST_LIBTORCH=0
2025-08-14T21:19:43.3109113Z ++ dirname .ci/pytorch/test.sh
2025-08-14T21:19:43.3118059Z + source .ci/pytorch/common-build.sh
2025-08-14T21:19:43.3118432Z ++ [[ linux-noble-rocm-py3.12-mi300 != *win-* ]]
2025-08-14T21:19:43.3126837Z ++++ dirname .ci/pytorch/common-build.sh
2025-08-14T21:19:43.3139696Z +++ cd .ci/pytorch
2025-08-14T21:19:43.3139956Z +++ pwd -P
2025-08-14T21:19:43.3141675Z ++ script_dir=/var/lib/jenkins/pytorch/.ci/pytorch
2025-08-14T21:19:43.3141882Z ++ [[ linux-noble-rocm-py3.12-mi300 == *-pch* ]]
2025-08-14T21:19:43.3142012Z ++ which sccache
2025-08-14T21:19:43.3152677Z ++ [[ -z '' ]]
2025-08-14T21:19:43.3152818Z ++ unset SCCACHE_BUCKET
2025-08-14T21:19:43.3153163Z ++ unset SCCACHE_REGION
2025-08-14T21:19:43.3153330Z ++ sccache --stop-server
2025-08-14T21:19:43.3174687Z ++ true
2025-08-14T21:19:43.3174961Z ++ rm -f /var/lib/jenkins/sccache_error.log
2025-08-14T21:19:43.3183929Z ++ trap_add sccache_epilogue EXIT
2025-08-14T21:19:43.3184650Z ++ trap_add_cmd=sccache_epilogue
2025-08-14T21:19:43.3185850Z ++ shift
2025-08-14T21:19:43.3186040Z ++ for trap_add_name in "$@"
2025-08-14T21:19:43.3190767Z ++++ trap -p EXIT
2025-08-14T21:19:43.3192135Z +++ eval 'extract_trap_cmd '
2025-08-14T21:19:43.3192453Z ++++ extract_trap_cmd
2025-08-14T21:19:43.3192602Z ++++ printf '%s\n' ''
2025-08-14T21:19:43.3192736Z +++ printf '%s\n' sccache_epilogue
2025-08-14T21:19:43.3194564Z ++ trap -- '
2025-08-14T21:19:43.3194763Z sccache_epilogue' EXIT
2025-08-14T21:19:43.3194898Z ++ [[ -n '' ]]
2025-08-14T21:19:43.3195050Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]]
2025-08-14T21:19:43.3195233Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log
2025-08-14T21:19:43.3195410Z ++ SCCACHE_IDLE_TIMEOUT=0
2025-08-14T21:19:43.3195537Z ++ sccache --start-server
2025-08-14T21:19:43.3211925Z sccache: Starting the server...
2025-08-14T21:19:43.3501922Z sccache: Listening on address 127.0.0.1:4226
2025-08-14T21:19:43.3512250Z ++ sccache --zero-stats
2025-08-14T21:19:43.3531410Z Statistics zeroed.
2025-08-14T21:19:43.3533082Z ++ which ccache
2025-08-14T21:19:43.3547050Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]]
2025-08-14T21:19:43.3547423Z + echo 'Environment variables:'
2025-08-14T21:19:43.3547579Z Environment variables:
2025-08-14T21:19:43.3547694Z + env
2025-08-14T21:19:43.3550611Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch
2025-08-14T21:19:43.3550768Z CONTINUE_THROUGH_ERROR=True
2025-08-14T21:19:43.3550943Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300
2025-08-14T21:19:43.3551187Z HOSTNAME=linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d
2025-08-14T21:19:43.3551442Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3551648Z GITHUB_ACTION=__self
2025-08-14T21:19:43.3555925Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-08-14T21:19:43.3556064Z GITHUB_RUN_NUMBER=8364
2025-08-14T21:19:43.3556164Z TEST_CONFIG=default
2025-08-14T21:19:43.3556262Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-08-14T21:19:43.3556384Z AWS_DEFAULT_REGION=us-east-1
2025-08-14T21:19:43.3556504Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-08-14T21:19:43.3556622Z GITHUB_REF_TYPE=branch
2025-08-14T21:19:43.3556955Z ***
2025-08-14T21:19:43.3557043Z GITHUB_REPOSITORY_ID=65600975
2025-08-14T21:19:43.3557150Z GITHUB_ACTIONS=true
2025-08-14T21:19:43.3557262Z SHA1=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3557417Z GITHUB_SHA=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3559493Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main
2025-08-14T21:19:43.3559671Z UCC_HOME=/usr
2025-08-14T21:19:43.3559759Z VERBOSE_TEST_LOGS=False
2025-08-14T21:19:43.3559859Z GITHUB_REF=refs/heads/main
2025-08-14T21:19:43.3559957Z SHARD_NUMBER=4
2025-08-14T21:19:43.3560048Z GITHUB_REF_PROTECTED=true
2025-08-14T21:19:43.3560147Z HOME=/var/lib/jenkins
2025-08-14T21:19:43.3560256Z GITHUB_API_URL=https://api.github.com
2025-08-14T21:19:43.3560384Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-08-14T21:19:43.3560498Z LANG=C.UTF-8
2025-08-14T21:19:43.3560607Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
2025-08-14T21:19:43.3560740Z PYTORCH_TEST_WITH_ROCM=1
2025-08-14T21:19:43.3562019Z NUM_TEST_SHARDS=6
2025-08-14T21:19:43.3562107Z UCX_HOME=/usr
2025-08-14T21:19:43.3562287Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3562652Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:43.3562840Z MAGMA_HOME=/opt/rocm/magma
2025-08-14T21:19:43.3563170Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3563405Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json
2025-08-14T21:19:43.3563555Z GITHUB_EVENT_NAME=push
2025-08-14T21:19:43.3563700Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0
2025-08-14T21:19:43.3563857Z DASHBOARD_TAG=
2025-08-14T21:19:43.3563944Z GITHUB_RUN_ID=16976255041
2025-08-14T21:19:43.3565280Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3565500Z GITHUB_ACTOR=pytorchmergebot
2025-08-14T21:19:43.3565611Z PR_NUMBER=
2025-08-14T21:19:43.3565692Z GITHUB_RUN_ATTEMPT=1
2025-08-14T21:19:43.3565790Z ANACONDA_PYTHON_VERSION=3.12
2025-08-14T21:19:43.3565922Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-08-14T21:19:43.3566055Z TERM=vt100
2025-08-14T21:19:43.3566135Z INSTALLED_VISION=yes
2025-08-14T21:19:43.3566224Z BRANCH=main
2025-08-14T21:19:43.3566311Z OPENSSL_ROOT_DIR=/opt/openssl
2025-08-14T21:19:43.3566412Z TESTS_TO_INCLUDE=
2025-08-14T21:19:43.3567665Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm
2025-08-14T21:19:43.3567848Z GITHUB_SERVER_URL=https://github.com
2025-08-14T21:19:43.3567970Z PYTORCH_ROCM_ARCH=gfx90a;gfx942
2025-08-14T21:19:43.3568095Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
2025-08-14T21:19:43.3568215Z REENABLED_ISSUES=
2025-08-14T21:19:43.3568298Z SHLVL=1
2025-08-14T21:19:43.3568374Z MAX_JOBS=158
2025-08-14T21:19:43.3568459Z GITHUB_ACTOR_ID=97764156
2025-08-14T21:19:43.3568587Z GITHUB_WORKFLOW_SHA=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3568725Z GITHUB_REF_NAME=main
2025-08-14T21:19:43.3568815Z ROCM_PATH=/opt/rocm
2025-08-14T21:19:43.3569867Z GITHUB_JOB=test
2025-08-14T21:19:43.3569954Z NO_TEST_TIMEOUT=False
2025-08-14T21:19:43.3570068Z GITHUB_REPOSITORY=pytorch/pytorch
2025-08-14T21:19:43.3570174Z LC_ALL=C.UTF-8
2025-08-14T21:19:43.3570260Z GITHUB_RETENTION_DAYS=90
2025-08-14T21:19:43.3570359Z OPENSSL_DIR=/opt/openssl
2025-08-14T21:19:43.3570458Z GITHUB_ACTION_REPOSITORY=
2025-08-14T21:19:43.3570802Z 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
2025-08-14T21:19:43.3571144Z GITHUB_BASE_REF=
2025-08-14T21:19:43.3571227Z CI=true
2025-08-14T21:19:43.3571317Z GITHUB_REPOSITORY_OWNER=pytorch
2025-08-14T21:19:43.3572360Z JOB_ID=48127863093
2025-08-14T21:19:43.3572446Z GITHUB_HEAD_REF=
2025-08-14T21:19:43.3572531Z GITHUB_ACTION_REF=
2025-08-14T21:19:43.3572619Z TEST_SHOWLOCALS=False
2025-08-14T21:19:43.3572716Z GITHUB_WORKFLOW=rocm-mi300
2025-08-14T21:19:43.3572822Z DEBIAN_FRONTEND=noninteractive
2025-08-14T21:19:43.3573173Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3573434Z NO_TD=False
2025-08-14T21:19:43.3573519Z OLDPWD=/var/lib/jenkins
2025-08-14T21:19:43.3573612Z _=/usr/bin/env
2025-08-14T21:19:43.3573735Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-08-14T21:19:43.3624045Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch
2025-08-14T21:19:43.3624280Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin
2025-08-14T21:19:43.3625610Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib
2025-08-14T21:19:43.3626013Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test
2025-08-14T21:19:43.3626265Z + BUILD_DIR=build
2025-08-14T21:19:43.3626437Z + BUILD_RENAMED_DIR=build_renamed
2025-08-14T21:19:43.3626866Z + BUILD_BIN_DIR=build/bin
2025-08-14T21:19:43.3627021Z + SHARD_NUMBER=4
2025-08-14T21:19:43.3627165Z + NUM_TEST_SHARDS=6
2025-08-14T21:19:43.3627749Z + export TORCH_SERIALIZATION_DEBUG=1
2025-08-14T21:19:43.3633744Z + TORCH_SERIALIZATION_DEBUG=1
2025-08-14T21:19:43.3633922Z + export VALGRIND=ON
2025-08-14T21:19:43.3634059Z + VALGRIND=ON
2025-08-14T21:19:43.3634208Z + [[ linux-noble-rocm-py3.12-mi300 == *clang9* ]]
2025-08-14T21:19:43.3634409Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]]
2025-08-14T21:19:43.3634597Z + [[ linux-noble-rocm-py3.12-mi300 == *s390x* ]]
2025-08-14T21:19:43.3634753Z + [[ 0 == \1 ]]
2025-08-14T21:19:43.3634865Z + [[ True == \1 ]]
2025-08-14T21:19:43.3635011Z + [[ linux-noble-rocm-py3.12-mi300 != *bazel* ]]
2025-08-14T21:19:43.3635205Z ++ realpath build/custom_test_artifacts
2025-08-14T21:19:43.3643774Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/pytorch/build/custom_test_artifacts
2025-08-14T21:19:43.3643999Z + [[ -n '' ]]
2025-08-14T21:19:43.3644107Z + echo 'Environment variables'
2025-08-14T21:19:43.3644225Z Environment variables
2025-08-14T21:19:43.3644320Z + env
2025-08-14T21:19:43.3658265Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch
2025-08-14T21:19:43.3658410Z CONTINUE_THROUGH_ERROR=True
2025-08-14T21:19:43.3659056Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300
2025-08-14T21:19:43.3659300Z HOSTNAME=linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d
2025-08-14T21:19:43.3659602Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3665146Z GITHUB_ACTION=__self
2025-08-14T21:19:43.3665318Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-08-14T21:19:43.3665457Z GITHUB_RUN_NUMBER=8364
2025-08-14T21:19:43.3665574Z TEST_CONFIG=default
2025-08-14T21:19:43.3665695Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-08-14T21:19:43.3665833Z AWS_DEFAULT_REGION=us-east-1
2025-08-14T21:19:43.3665980Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-08-14T21:19:43.3666118Z GITHUB_REF_TYPE=branch
2025-08-14T21:19:43.3666396Z ***
2025-08-14T21:19:43.3666485Z GITHUB_REPOSITORY_ID=65600975
2025-08-14T21:19:43.3666595Z GITHUB_ACTIONS=true
2025-08-14T21:19:43.3666762Z SHA1=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3666926Z GITHUB_SHA=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3667133Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main
2025-08-14T21:19:43.3667319Z UCC_HOME=/usr
2025-08-14T21:19:43.3667423Z TORCH_SERIALIZATION_DEBUG=1
2025-08-14T21:19:43.3669505Z VERBOSE_TEST_LOGS=False
2025-08-14T21:19:43.3669625Z GITHUB_REF=refs/heads/main
2025-08-14T21:19:43.3669723Z SHARD_NUMBER=4
2025-08-14T21:19:43.3669816Z GITHUB_REF_PROTECTED=true
2025-08-14T21:19:43.3669920Z HOME=/var/lib/jenkins
2025-08-14T21:19:43.3670031Z GITHUB_API_URL=https://api.github.com
2025-08-14T21:19:43.3670167Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-08-14T21:19:43.3670311Z LANG=C.UTF-8
2025-08-14T21:19:43.3670463Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
2025-08-14T21:19:43.3670594Z PYTORCH_TEST_WITH_ROCM=1
2025-08-14T21:19:43.3670693Z NUM_TEST_SHARDS=6
2025-08-14T21:19:43.3672064Z UCX_HOME=/usr
2025-08-14T21:19:43.3672260Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3672792Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T21:19:43.3673455Z MAGMA_HOME=/opt/rocm/magma
2025-08-14T21:19:43.3673706Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3674029Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json
2025-08-14T21:19:43.3674239Z GITHUB_EVENT_NAME=push
2025-08-14T21:19:43.3674437Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0
2025-08-14T21:19:43.3674650Z DASHBOARD_TAG=
2025-08-14T21:19:43.3674776Z GITHUB_RUN_ID=16976255041
2025-08-14T21:19:43.3675051Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3676736Z GITHUB_ACTOR=pytorchmergebot
2025-08-14T21:19:43.3676850Z PR_NUMBER=
2025-08-14T21:19:43.3677070Z GITHUB_RUN_ATTEMPT=1
2025-08-14T21:19:43.3677161Z VALGRIND=ON
2025-08-14T21:19:43.3677287Z ANACONDA_PYTHON_VERSION=3.12
2025-08-14T21:19:43.3677415Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-08-14T21:19:43.3677545Z TERM=vt100
2025-08-14T21:19:43.3677625Z INSTALLED_VISION=yes
2025-08-14T21:19:43.3677713Z BRANCH=main
2025-08-14T21:19:43.3677801Z OPENSSL_ROOT_DIR=/opt/openssl
2025-08-14T21:19:43.3677905Z TESTS_TO_INCLUDE=
2025-08-14T21:19:43.3679229Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm
2025-08-14T21:19:43.3679416Z GITHUB_SERVER_URL=https://github.com
2025-08-14T21:19:43.3679538Z PYTORCH_ROCM_ARCH=gfx90a;gfx942
2025-08-14T21:19:43.3679662Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
2025-08-14T21:19:43.3679782Z REENABLED_ISSUES=
2025-08-14T21:19:43.3679865Z SHLVL=1
2025-08-14T21:19:43.3679941Z MAX_JOBS=158
2025-08-14T21:19:43.3680028Z GITHUB_ACTOR_ID=97764156
2025-08-14T21:19:43.3680160Z GITHUB_WORKFLOW_SHA=1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T21:19:43.3680300Z GITHUB_REF_NAME=main
2025-08-14T21:19:43.3680391Z ROCM_PATH=/opt/rocm
2025-08-14T21:19:43.3681533Z GITHUB_JOB=test
2025-08-14T21:19:43.3681625Z NO_TEST_TIMEOUT=False
2025-08-14T21:19:43.3681728Z GITHUB_REPOSITORY=pytorch/pytorch
2025-08-14T21:19:43.3681835Z LC_ALL=C.UTF-8
2025-08-14T21:19:43.3681924Z GITHUB_RETENTION_DAYS=90
2025-08-14T21:19:43.3682023Z OPENSSL_DIR=/opt/openssl
2025-08-14T21:19:43.3682123Z GITHUB_ACTION_REPOSITORY=
2025-08-14T21:19:43.3682466Z 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
2025-08-14T21:19:43.3682806Z GITHUB_BASE_REF=
2025-08-14T21:19:43.3682889Z CI=true
2025-08-14T21:19:43.3683104Z GITHUB_REPOSITORY_OWNER=pytorch
2025-08-14T21:19:43.3684290Z JOB_ID=48127863093
2025-08-14T21:19:43.3684380Z GITHUB_HEAD_REF=
2025-08-14T21:19:43.3684464Z GITHUB_ACTION_REF=
2025-08-14T21:19:43.3684564Z TEST_SHOWLOCALS=False
2025-08-14T21:19:43.3684662Z GITHUB_WORKFLOW=rocm-mi300
2025-08-14T21:19:43.3684769Z DEBIAN_FRONTEND=noninteractive
2025-08-14T21:19:43.3684967Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_60ae9f9a-aa42-4a4e-ba04-56cc27ca4792
2025-08-14T21:19:43.3685165Z NO_TD=False
2025-08-14T21:19:43.3685247Z OLDPWD=/var/lib/jenkins
2025-08-14T21:19:43.3685341Z _=/usr/bin/env
2025-08-14T21:19:43.3685427Z + echo 'Testing pytorch'
2025-08-14T21:19:43.3686521Z Testing pytorch
2025-08-14T21:19:43.3686609Z + export LANG=C.UTF-8
2025-08-14T21:19:43.3686702Z + LANG=C.UTF-8
2025-08-14T21:19:43.3686783Z + PR_NUMBER=
2025-08-14T21:19:43.3686871Z + [[ default == \d\e\f\a\u\l\t ]]
2025-08-14T21:19:43.3686981Z + export CUDA_VISIBLE_DEVICES=0
2025-08-14T21:19:43.3687085Z + CUDA_VISIBLE_DEVICES=0
2025-08-14T21:19:43.3687185Z + export HIP_VISIBLE_DEVICES=0
2025-08-14T21:19:43.3687288Z + HIP_VISIBLE_DEVICES=0
2025-08-14T21:19:43.3687429Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-08-14T21:19:43.3687619Z + [[ default == \s\l\o\w ]]
2025-08-14T21:19:43.3688796Z + [[ linux-noble-rocm-py3.12-mi300 == *slow-gradcheck* ]]
2025-08-14T21:19:43.3688952Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]]
2025-08-14T21:19:43.3689085Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]]
2025-08-14T21:19:43.3689215Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2025-08-14T21:19:43.3689341Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2025-08-14T21:19:43.3689457Z + [[ default == *crossref* ]]
2025-08-14T21:19:43.3689571Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]]
2025-08-14T21:19:43.3689689Z + export VALGRIND=OFF
2025-08-14T21:19:43.3689780Z + VALGRIND=OFF
2025-08-14T21:19:43.3689862Z + rocminfo
2025-08-14T21:19:43.3874470Z ROCk module version 6.12.12 is loaded
2025-08-14T21:19:43.4317048Z =====================    
2025-08-14T21:19:43.4317246Z HSA System Attributes    
2025-08-14T21:19:43.4318455Z =====================    
2025-08-14T21:19:43.4318956Z Runtime Version:         1.15
2025-08-14T21:19:43.4319843Z Runtime Ext Version:     1.7
2025-08-14T21:19:43.4320171Z System Timestamp Freq.:  1000.000000MHz
2025-08-14T21:19:43.4320678Z Sig. Max Wait Duration:  18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
2025-08-14T21:19:43.4321236Z Machine Model:           LARGE                              
2025-08-14T21:19:43.4321678Z System Endianness:       LITTLE                             
2025-08-14T21:19:43.4322092Z Mwaitx:                  DISABLED
2025-08-14T21:19:43.4322399Z XNACK enabled:           NO
2025-08-14T21:19:43.4322698Z DMAbuf Support:          YES
2025-08-14T21:19:43.4323249Z VMM Support:             YES
2025-08-14T21:19:43.4323439Z 
2025-08-14T21:19:43.4323545Z ==========               
2025-08-14T21:19:43.4323832Z HSA Agents               
2025-08-14T21:19:43.4324099Z ==========               
2025-08-14T21:19:43.4324355Z *******                  
2025-08-14T21:19:43.4324615Z Agent 1                  
2025-08-14T21:19:43.4324872Z *******                  
2025-08-14T21:19:43.4325221Z   Name:                    AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4325627Z   Uuid:                    CPU-XX                             
2025-08-14T21:19:43.4326046Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4326475Z   Vendor Name:             CPU                                
2025-08-14T21:19:43.4326881Z   Feature:                 None specified                     
2025-08-14T21:19:43.4327279Z   Profile:                 FULL_PROFILE                       
2025-08-14T21:19:43.4327695Z   Float Round Mode:        NEAR                               
2025-08-14T21:19:43.4328025Z   Max Queue Number:        0(0x0)                             
2025-08-14T21:19:43.4328377Z   Queue Min Size:          0(0x0)                             
2025-08-14T21:19:43.4334458Z   Queue Max Size:          0(0x0)                             
2025-08-14T21:19:43.4334649Z   Queue Type:              MULTI                              
2025-08-14T21:19:43.4334806Z   Node:                    0                                  
2025-08-14T21:19:43.4334964Z   Device Type:             CPU                                
2025-08-14T21:19:43.4335114Z   Cache Info:              
2025-08-14T21:19:43.4335240Z     L1:                      65536(0x10000) KB                  
2025-08-14T21:19:43.4335391Z   Chip ID:                 0(0x0)                             
2025-08-14T21:19:43.4335547Z   ASIC Revision:           0(0x0)                             
2025-08-14T21:19:43.4335707Z   Cacheline Size:          64(0x40)                           
2025-08-14T21:19:43.4335867Z   Max Clock Freq. (MHz):   0                                  
2025-08-14T21:19:43.4336013Z   BDFID:                   0                                  
2025-08-14T21:19:43.4336157Z   Internal Node ID:        0                                  
2025-08-14T21:19:43.4336303Z   Compute Unit:            80                                 
2025-08-14T21:19:43.4336447Z   SIMDs per CU:            0                                  
2025-08-14T21:19:43.4336724Z   Shader Engines:          0                                  
2025-08-14T21:19:43.4336875Z   Shader Arrs. per Eng.:   0                                  
2025-08-14T21:19:43.4337031Z   WatchPts on Addr. Ranges:1                                  
2025-08-14T21:19:43.4337167Z   Memory Properties:       
2025-08-14T21:19:43.4337271Z   Features:                None
2025-08-14T21:19:43.4337374Z   Pool Info:               
2025-08-14T21:19:43.4337476Z     Pool 1                   
2025-08-14T21:19:43.4337603Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:19:43.4337750Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:19:43.4337895Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4338045Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4338200Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4338401Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4338553Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4338686Z     Pool 2                   
2025-08-14T21:19:43.4338813Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:19:43.4338959Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:19:43.4339097Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4339247Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4339400Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4339552Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4339702Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4339835Z     Pool 3                   
2025-08-14T21:19:43.4339973Z       Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
2025-08-14T21:19:43.4340133Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:19:43.4341818Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4341977Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4342132Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4342285Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4342434Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4342566Z     Pool 4                   
2025-08-14T21:19:43.4342689Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:19:43.4342829Z       Size:                    660201688(0x2759e0d8) KB           
2025-08-14T21:19:43.4343053Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4343208Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4343364Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4344634Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4344788Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4344922Z   ISA Info:                
2025-08-14T21:19:43.4345020Z *******                  
2025-08-14T21:19:43.4345116Z Agent 2                  
2025-08-14T21:19:43.4345208Z *******                  
2025-08-14T21:19:43.4345321Z   Name:                    AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4345459Z   Uuid:                    CPU-XX                             
2025-08-14T21:19:43.4345605Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4345757Z   Vendor Name:             CPU                                
2025-08-14T21:19:43.4345901Z   Feature:                 None specified                     
2025-08-14T21:19:43.4347107Z   Profile:                 FULL_PROFILE                       
2025-08-14T21:19:43.4347259Z   Float Round Mode:        NEAR                               
2025-08-14T21:19:43.4347403Z   Max Queue Number:        0(0x0)                             
2025-08-14T21:19:43.4347546Z   Queue Min Size:          0(0x0)                             
2025-08-14T21:19:43.4347686Z   Queue Max Size:          0(0x0)                             
2025-08-14T21:19:43.4347827Z   Queue Type:              MULTI                              
2025-08-14T21:19:43.4347959Z   Node:                    1                                  
2025-08-14T21:19:43.4348135Z   Device Type:             CPU                                
2025-08-14T21:19:43.4348264Z   Cache Info:              
2025-08-14T21:19:43.4348375Z     L1:                      65536(0x10000) KB                  
2025-08-14T21:19:43.4348506Z   Chip ID:                 0(0x0)                             
2025-08-14T21:19:43.4349730Z   ASIC Revision:           0(0x0)                             
2025-08-14T21:19:43.4349883Z   Cacheline Size:          64(0x40)                           
2025-08-14T21:19:43.4350029Z   Max Clock Freq. (MHz):   0                                  
2025-08-14T21:19:43.4350166Z   BDFID:                   0                                  
2025-08-14T21:19:43.4350303Z   Internal Node ID:        1                                  
2025-08-14T21:19:43.4350447Z   Compute Unit:            80                                 
2025-08-14T21:19:43.4350587Z   SIMDs per CU:            0                                  
2025-08-14T21:19:43.4350729Z   Shader Engines:          0                                  
2025-08-14T21:19:43.4350878Z   Shader Arrs. per Eng.:   0                                  
2025-08-14T21:19:43.4351030Z   WatchPts on Addr. Ranges:1                                  
2025-08-14T21:19:43.4351164Z   Memory Properties:       
2025-08-14T21:19:43.4352280Z   Features:                None
2025-08-14T21:19:43.4352385Z   Pool Info:               
2025-08-14T21:19:43.4352492Z     Pool 1                   
2025-08-14T21:19:43.4352616Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:19:43.4352761Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:19:43.4353010Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4353158Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4353312Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4353468Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4353619Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4353749Z     Pool 2                   
2025-08-14T21:19:43.4354872Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:19:43.4355014Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:19:43.4355157Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4355305Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4355458Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4355611Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4355762Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4355893Z     Pool 3                   
2025-08-14T21:19:43.4356016Z       Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
2025-08-14T21:19:43.4356155Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:19:43.4356294Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4357421Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4357573Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4357731Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4357930Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4358060Z     Pool 4                   
2025-08-14T21:19:43.4358181Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:19:43.4358319Z       Size:                    656372808(0x271f7448) KB           
2025-08-14T21:19:43.4358458Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4358606Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4358759Z       Alloc Recommended Granule:4KB                                
2025-08-14T21:19:43.4359884Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4360038Z       Accessible by all:       TRUE                               
2025-08-14T21:19:43.4360171Z   ISA Info:                
2025-08-14T21:19:43.4360268Z *******                  
2025-08-14T21:19:43.4360404Z Agent 3                  
2025-08-14T21:19:43.4360503Z *******                  
2025-08-14T21:19:43.4360611Z   Name:                    gfx942                             
2025-08-14T21:19:43.4360749Z   Uuid:                    GPU-4b54e616aa7823b9               
2025-08-14T21:19:43.4360897Z   Marketing Name:          AMD Instinct Mi325X VF             
2025-08-14T21:19:43.4361047Z   Vendor Name:             AMD                                
2025-08-14T21:19:43.4361190Z   Feature:                 KERNEL_DISPATCH                    
2025-08-14T21:19:43.4362304Z   Profile:                 BASE_PROFILE                       
2025-08-14T21:19:43.4362453Z   Float Round Mode:        NEAR                               
2025-08-14T21:19:43.4362599Z   Max Queue Number:        128(0x80)                          
2025-08-14T21:19:43.4362742Z   Queue Min Size:          64(0x40)                           
2025-08-14T21:19:43.4362882Z   Queue Max Size:          131072(0x20000)                    
2025-08-14T21:19:43.4363105Z   Queue Type:              MULTI                              
2025-08-14T21:19:43.4363240Z   Node:                    2                                  
2025-08-14T21:19:43.4363373Z   Device Type:             GPU                                
2025-08-14T21:19:43.4363501Z   Cache Info:              
2025-08-14T21:19:43.4363613Z     L1:                      32(0x20) KB                        
2025-08-14T21:19:43.4363740Z     L2:                      4096(0x1000) KB                    
2025-08-14T21:19:43.4364859Z     L3:                      262144(0x40000) KB                 
2025-08-14T21:19:43.4364992Z   Chip ID:                 29881(0x74b9)                      
2025-08-14T21:19:43.4365132Z   ASIC Revision:           1(0x1)                             
2025-08-14T21:19:43.4365279Z   Cacheline Size:          128(0x80)                          
2025-08-14T21:19:43.4365426Z   Max Clock Freq. (MHz):   2100                               
2025-08-14T21:19:43.4365568Z   BDFID:                   37632                              
2025-08-14T21:19:43.4365711Z   Internal Node ID:        2                                  
2025-08-14T21:19:43.4365855Z   Compute Unit:            304                                
2025-08-14T21:19:43.4365996Z   SIMDs per CU:            4                                  
2025-08-14T21:19:43.4366139Z   Shader Engines:          32                                 
2025-08-14T21:19:43.4366287Z   Shader Arrs. per Eng.:   1                                  
2025-08-14T21:19:43.4367411Z   WatchPts on Addr. Ranges:4                                  
2025-08-14T21:19:43.4367567Z   Coherent Host Access:    FALSE                              
2025-08-14T21:19:43.4367701Z   Memory Properties:       
2025-08-14T21:19:43.4367814Z   Features:                KERNEL_DISPATCH 
2025-08-14T21:19:43.4367950Z   Fast F16 Operation:      TRUE                               
2025-08-14T21:19:43.4368098Z   Wavefront Size:          64(0x40)                           
2025-08-14T21:19:43.4368252Z   Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:19:43.4368440Z   Workgroup Max Size per Dimension:
2025-08-14T21:19:43.4368561Z     x                        1024(0x400)                        
2025-08-14T21:19:43.4368684Z     y                        1024(0x400)                        
2025-08-14T21:19:43.4368803Z     z                        1024(0x400)                        
2025-08-14T21:19:43.4369926Z   Max Waves Per CU:        32(0x20)                           
2025-08-14T21:19:43.4370078Z   Max Work-item Per CU:    2048(0x800)                        
2025-08-14T21:19:43.4370225Z   Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:19:43.4370356Z   Grid Max Size per Dimension:
2025-08-14T21:19:43.4370467Z     x                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4370590Z     y                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4370712Z     z                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4370905Z   Max fbarriers/Workgrp:   32                                 
2025-08-14T21:19:43.4371115Z   Packet Processor uCode:: 177                                
2025-08-14T21:19:43.4371271Z   SDMA engine uCode::      24                                 
2025-08-14T21:19:43.4371419Z   IOMMU Support::          None                               
2025-08-14T21:19:43.4372628Z   Pool Info:               
2025-08-14T21:19:43.4372744Z     Pool 1                   
2025-08-14T21:19:43.4372870Z       Segment:                 GLOBAL; FLAGS: COARSE GRAINED      
2025-08-14T21:19:43.4373105Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:19:43.4373250Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4373400Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4373553Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:19:43.4373714Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4373950Z       Accessible by all:       FALSE                              
2025-08-14T21:19:43.4374084Z     Pool 2                   
2025-08-14T21:19:43.4374209Z       Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
2025-08-14T21:19:43.4375581Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:19:43.4375739Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4375886Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4376039Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:19:43.4376192Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4376343Z       Accessible by all:       FALSE                              
2025-08-14T21:19:43.4376474Z     Pool 3                   
2025-08-14T21:19:43.4376593Z       Segment:                 GLOBAL; FLAGS: FINE GRAINED        
2025-08-14T21:19:43.4376737Z       Size:                    268107776(0xffb0000) KB            
2025-08-14T21:19:43.4376877Z       Allocatable:             TRUE                               
2025-08-14T21:19:43.4377023Z       Alloc Granule:           4KB                                
2025-08-14T21:19:43.4378173Z       Alloc Recommended Granule:2048KB                             
2025-08-14T21:19:43.4378329Z       Alloc Alignment:         4KB                                
2025-08-14T21:19:43.4378479Z       Accessible by all:       FALSE                              
2025-08-14T21:19:43.4378611Z     Pool 4                   
2025-08-14T21:19:43.4378728Z       Segment:                 GROUP                              
2025-08-14T21:19:43.4378863Z       Size:                    64(0x40) KB                        
2025-08-14T21:19:43.4379002Z       Allocatable:             FALSE                              
2025-08-14T21:19:43.4379150Z       Alloc Granule:           0KB                                
2025-08-14T21:19:43.4379366Z       Alloc Recommended Granule:0KB                                
2025-08-14T21:19:43.4379520Z       Alloc Alignment:         0KB                                
2025-08-14T21:19:43.4379670Z       Accessible by all:       FALSE                              
2025-08-14T21:19:43.4380764Z   ISA Info:                
2025-08-14T21:19:43.4380868Z     ISA 1                    
2025-08-14T21:19:43.4380995Z       Name:                    amdgcn-amd-amdhsa--gfx942:sramecc+:xnack-
2025-08-14T21:19:43.4381155Z       Machine Models:          HSA_MACHINE_MODEL_LARGE            
2025-08-14T21:19:43.4381308Z       Profiles:                HSA_PROFILE_BASE                   
2025-08-14T21:19:43.4381462Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:19:43.4381618Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:19:43.4381764Z       Fast f16:                TRUE                               
2025-08-14T21:19:43.4381963Z       Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:19:43.4382113Z       Workgroup Max Size per Dimension:
2025-08-14T21:19:43.4382238Z         x                        1024(0x400)                        
2025-08-14T21:19:43.4383456Z         y                        1024(0x400)                        
2025-08-14T21:19:43.4383580Z         z                        1024(0x400)                        
2025-08-14T21:19:43.4383717Z       Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:19:43.4383850Z       Grid Max Size per Dimension:
2025-08-14T21:19:43.4383966Z         x                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4384091Z         y                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4384216Z         z                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4384354Z       FBarrier Max Size:       32                                 
2025-08-14T21:19:43.4384482Z     ISA 2                    
2025-08-14T21:19:43.4384630Z       Name:                    amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack-
2025-08-14T21:19:43.4384799Z       Machine Models:          HSA_MACHINE_MODEL_LARGE            
2025-08-14T21:19:43.4385921Z       Profiles:                HSA_PROFILE_BASE                   
2025-08-14T21:19:43.4386073Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:19:43.4386228Z       Default Rounding Mode:   NEAR                               
2025-08-14T21:19:43.4386373Z       Fast f16:                TRUE                               
2025-08-14T21:19:43.4386517Z       Workgroup Max Size:      1024(0x400)                        
2025-08-14T21:19:43.4386655Z       Workgroup Max Size per Dimension:
2025-08-14T21:19:43.4386776Z         x                        1024(0x400)                        
2025-08-14T21:19:43.4386898Z         y                        1024(0x400)                        
2025-08-14T21:19:43.4387024Z         z                        1024(0x400)                        
2025-08-14T21:19:43.4387162Z       Grid Max Size:           4294967295(0xffffffff)             
2025-08-14T21:19:43.4387294Z       Grid Max Size per Dimension:
2025-08-14T21:19:43.4388375Z         x                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4388500Z         y                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4388623Z         z                        4294967295(0xffffffff)             
2025-08-14T21:19:43.4388761Z       FBarrier Max Size:       32                                 
2025-08-14T21:19:43.4388890Z *** Done ***             
2025-08-14T21:19:43.4388989Z + rocminfo
2025-08-14T21:19:43.4389087Z + grep -E 'Name:.*\sgfx|Marketing'
2025-08-14T21:19:43.4943598Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4944106Z   Marketing Name:          AMD EPYC 9575F 64-Core Processor   
2025-08-14T21:19:43.4944525Z   Name:                    gfx942                             
2025-08-14T21:19:43.4948656Z   Marketing Name:          AMD Instinct Mi325X VF             
2025-08-14T21:19:43.4990827Z + MAYBE_ROCM=rocm/
2025-08-14T21:19:43.4990973Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]]
2025-08-14T21:19:43.4991728Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]]
2025-08-14T21:19:43.4992099Z + pip_install ninja==1.10.2
2025-08-14T21:19:43.4992459Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-08-14T21:19:43.4992878Z + python3 -m pip install --progress-bar off ninja==1.10.2
2025-08-14T21:19:43.7483214Z Collecting ninja==1.10.2
2025-08-14T21:19:43.8336370Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-08-14T21:19:43.8655666Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-08-14T21:19:44.9012228Z Installing collected packages: ninja
2025-08-14T21:19:44.9012422Z   Attempting uninstall: ninja
2025-08-14T21:19:44.9022668Z     Found existing installation: ninja 1.11.1.3
2025-08-14T21:19:44.9033313Z     Uninstalling ninja-1.11.1.3:
2025-08-14T21:19:45.2345391Z       Successfully uninstalled ninja-1.11.1.3
2025-08-14T21:19:45.6515683Z Successfully installed ninja-1.10.2
2025-08-14T21:19:45.6833920Z + 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
2025-08-14T21:19:45.6836027Z + 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
2025-08-14T21:19:45.6837165Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]]
2025-08-14T21:19:45.6837600Z + [[ linux-noble-rocm-py3.12-mi300 == *asan* ]]
2025-08-14T21:19:45.6837998Z + [[ linux-noble-rocm-py3.12-mi300 == *-debug* ]]
2025-08-14T21:19:45.6838489Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]]
2025-08-14T21:19:45.6839319Z + echo 'We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass'
2025-08-14T21:19:45.6839994Z We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass
2025-08-14T21:19:45.6840471Z + cd test
2025-08-14T21:19:45.6840736Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-08-14T21:19:46.5686161Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-08-14T21:19:46.5686443Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-08-14T21:19:46.5686678Z + [[ default == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]]
2025-08-14T21:19:46.5686914Z + DYNAMO_BENCHMARK_FLAGS=()
2025-08-14T21:19:46.5687104Z + [[ default == *pr_time_benchmarks* ]]
2025-08-14T21:19:46.5687298Z + [[ default == *dynamo_eager* ]]
2025-08-14T21:19:46.5687479Z + [[ default == *aot_eager* ]]
2025-08-14T21:19:46.5687659Z + [[ default == *aot_inductor* ]]
2025-08-14T21:19:46.5687841Z + [[ default == *max_autotune_inductor* ]]
2025-08-14T21:19:46.5688001Z + [[ default == *inductor* ]]
2025-08-14T21:19:46.5688142Z + [[ default == *dynamic* ]]
2025-08-14T21:19:46.5690334Z + [[ default == *cpu* ]]
2025-08-14T21:19:46.5690488Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
2025-08-14T21:19:46.5701096Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]]
2025-08-14T21:19:46.5701300Z + [[ linux-noble-rocm-py3.12-mi300 == *-bazel-* ]]
2025-08-14T21:19:46.5705534Z + cd test
2025-08-14T21:19:46.5705895Z + python -c 'import torch; print(torch.__config__.show())'
2025-08-14T21:19:47.2748628Z PyTorch built with:
2025-08-14T21:19:47.2748937Z   - GCC 11.4
2025-08-14T21:19:47.2749155Z   - C++ Version: 201703
2025-08-14T21:19:47.2757908Z   - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
2025-08-14T21:19:47.2758389Z   - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-08-14T21:19:47.2758681Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-08-14T21:19:47.2759385Z   - LAPACK is enabled (usually provided by MKL)
2025-08-14T21:19:47.2759597Z   - NNPACK is enabled
2025-08-14T21:19:47.2759785Z   - CPU capability usage: AVX512
2025-08-14T21:19:47.2759978Z   - HIP Runtime 6.4.43484
2025-08-14T21:19:47.2760147Z   - MIOpen 3.4.0
2025-08-14T21:19:47.2760288Z   - Magma 2.7.2
2025-08-14T21:19:47.2763153Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1fc683cf17c8c673044538d10266c00f92987be2, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.9.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=ON, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 
2025-08-14T21:19:47.2765770Z 
2025-08-14T21:19:47.4662509Z + cd test
2025-08-14T21:19:47.4662798Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-08-14T21:19:48.1335876Z ATen/Parallel:
2025-08-14T21:19:48.1336085Z 	at::get_num_threads() : 160
2025-08-14T21:19:48.1336251Z 	at::get_num_interop_threads() : 160
2025-08-14T21:19:48.1336399Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-08-14T21:19:48.1336536Z 	omp_get_max_threads() : 160
2025-08-14T21:19:48.1336793Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
2025-08-14T21:19:48.1337044Z 	mkl_get_max_threads() : 160
2025-08-14T21:19:48.1342569Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-08-14T21:19:48.1342789Z std::thread::hardware_concurrency() : 160
2025-08-14T21:19:48.1343175Z Environment variables:
2025-08-14T21:19:48.1343293Z 	OMP_NUM_THREADS : [not set]
2025-08-14T21:19:48.1343407Z 	MKL_NUM_THREADS : [not set]
2025-08-14T21:19:48.1343535Z ATen parallel backend: OpenMP
2025-08-14T21:19:48.1343613Z 
2025-08-14T21:19:48.3448473Z + [[ default == *numpy_2* ]]
2025-08-14T21:19:48.3449991Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]]
2025-08-14T21:19:48.3450331Z + [[ default == *backward* ]]
2025-08-14T21:19:48.3450520Z + [[ default == *xla* ]]
2025-08-14T21:19:48.3450688Z + [[ default == *executorch* ]]
2025-08-14T21:19:48.3458203Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]]
2025-08-14T21:19:48.3458414Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]]
2025-08-14T21:19:48.3458658Z + [[ default == distributed ]]
2025-08-14T21:19:48.3458836Z + [[ default == *operator_benchmark* ]]
2025-08-14T21:19:48.3459001Z + [[ default == *inductor_distributed* ]]
2025-08-14T21:19:48.3459177Z + [[ default == *inductor-halide* ]]
2025-08-14T21:19:48.3459351Z + [[ default == *inductor-triton-cpu* ]]
2025-08-14T21:19:48.3459527Z + [[ default == *inductor-micro-benchmark* ]]
2025-08-14T21:19:48.3459683Z + [[ default == *huggingface* ]]
2025-08-14T21:19:48.3459810Z + [[ default == *timm* ]]
2025-08-14T21:19:48.3459944Z + [[ default == cachebench ]]
2025-08-14T21:19:48.3460084Z + [[ default == verify_cachebench ]]
2025-08-14T21:19:48.3460231Z + [[ default == *torchbench* ]]
2025-08-14T21:19:48.3460383Z + [[ default == *inductor_cpp_wrapper* ]]
2025-08-14T21:19:48.3462365Z + [[ default == *inductor* ]]
2025-08-14T21:19:48.3462519Z + [[ default == *einops* ]]
2025-08-14T21:19:48.3462656Z + [[ default == *dynamo_wrapped* ]]
2025-08-14T21:19:48.3462813Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]]
2025-08-14T21:19:48.3463404Z + [[ -n '' ]]
2025-08-14T21:19:48.3463506Z + [[ 4 == 1 ]]
2025-08-14T21:19:48.3463605Z + [[ 4 == 2 ]]
2025-08-14T21:19:48.3463703Z + [[ 4 -gt 2 ]]
2025-08-14T21:19:48.3463825Z + install_torchvision
2025-08-14T21:19:48.3463944Z + local orig_preload
2025-08-14T21:19:48.3464053Z + local commit
2025-08-14T21:19:48.3465604Z ++ get_pinned_commit vision
2025-08-14T21:19:48.3465752Z ++ cat .github/ci_commit_pins/vision.txt
2025-08-14T21:19:48.3473227Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:48.3473364Z + orig_preload=
2025-08-14T21:19:48.3473450Z + '[' -n '' ']'
2025-08-14T21:19:48.3473551Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]]
2025-08-14T21:19:48.3473832Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision
2025-08-14T21:19:48.3474147Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:48.3474351Z + local wheel_dir=dist/vision
2025-08-14T21:19:48.3474625Z + local found_whl=0
2025-08-14T21:19:48.3474734Z + for file in "${wheel_dir}"/*.whl
2025-08-14T21:19:48.3476358Z + [[ -f dist/vision/*.whl ]]
2025-08-14T21:19:48.3476470Z + '[' 0 == 0 ']'
2025-08-14T21:19:48.3476748Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:48.4851908Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:48.4854266Z   Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-8hgs1ibm
2025-08-14T21:19:48.4869196Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-8hgs1ibm
2025-08-14T21:19:52.5607108Z   Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66'
2025-08-14T21:19:52.5625433Z   Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:52.7914872Z   Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:53.1633142Z   Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66
2025-08-14T21:19:54.4760341Z   Preparing metadata (setup.py) ... [?25l- \ | done
2025-08-14T21:19:54.4792494Z [?25hBuilding wheels for collected packages: torchvision
2025-08-14T21:19:54.4840506Z   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
2025-08-14T21:20:22.5329723Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
2025-08-14T21:20:22.5346933Z [?25h  Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl size=1572035 sha256=cb96144407a1d32ef7d148ae47e4ad24b167f1c8060bf3113c819a647d637d62
2025-08-14T21:20:22.5347659Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/10/ba/61/eb5228b3631dc6bb4f478b3aa59575551a5473e4596e4c001a
2025-08-14T21:20:22.5368878Z Successfully built torchvision
2025-08-14T21:20:22.5846820Z + for file in "${wheel_dir}"/*.whl
2025-08-14T21:20:22.5847249Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl
2025-08-14T21:20:22.5847619Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl')
2025-08-14T21:20:22.5847866Z + local args
2025-08-14T21:20:22.5848132Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl == *\ * ]]
2025-08-14T21:20:22.5848809Z + for path in "${args[@]}"
2025-08-14T21:20:22.5849077Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl'
2025-08-14T21:20:22.5849430Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl
2025-08-14T21:20:22.5849849Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl
2025-08-14T21:20:22.7263821Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl
2025-08-14T21:20:22.7305908Z Installing collected packages: torchvision
2025-08-14T21:20:22.9545482Z Successfully installed torchvision-0.22.0a0+966da7e
2025-08-14T21:20:22.9818330Z + '[' -n '' ']'
2025-08-14T21:20:22.9818508Z + test_python_shard 4
2025-08-14T21:20:22.9818644Z + [[ -z 6 ]]
2025-08-14T21:20:22.9818974Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 4 6 --verbose --upload-artifacts-while-running
2025-08-14T21:20:24.4664565Z /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.
2025-08-14T21:20:24.4665874Z   import pkg_resources
2025-08-14T21:20:24.7954315Z Excluding test_cuda_nvml_based_avail on ROCm
2025-08-14T21:20:25.1295585Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json
2025-08-14T21:20:25.3033613Z Ignoring disabled issues:  ['']
2025-08-14T21:20:25.3089903Z Found test times from artifacts
2025-08-14T21:20:25.3315942Z Found test times from artifacts
2025-08-14T21:20:25.3322240Z Running all tests
2025-08-14T21:20:25.3485928Z Running parallel tests on 1 processes
2025-08-14T21:20:25.3486744Z Name: tests to run (est. time: 144.09min)
2025-08-14T21:20:25.3487323Z   Serial tests (83):
2025-08-14T21:20:25.3487698Z     inductor/test_aot_inductor 3/3
2025-08-14T21:20:25.3488103Z     inductor/test_torchinductor_codegen_dynamic_shapes 1/3
2025-08-14T21:20:25.3495601Z     inductor/test_torchinductor_opinfo 4/8
2025-08-14T21:20:25.3495936Z     inductor/test_cpu_repro 2/4
2025-08-14T21:20:25.3496107Z     dynamo/test_repros 1/1
2025-08-14T21:20:25.3496270Z     inductor/test_perf 1/1
2025-08-14T21:20:25.3496422Z     inductor/test_pattern_matcher 1/1
2025-08-14T21:20:25.3496593Z     inductor/test_inductor_freezing 1/1
2025-08-14T21:20:25.3496774Z     dynamo/test_higher_order_ops 1/1
2025-08-14T21:20:25.3496952Z     dynamo/test_modules 1/1
2025-08-14T21:20:25.3497107Z     dynamo/test_backends 1/1
2025-08-14T21:20:25.3497270Z     dynamo/test_activation_checkpointing 1/1
2025-08-14T21:20:25.3500072Z     inductor/test_select_algorithm 1/1
2025-08-14T21:20:25.3500243Z     inductor/test_compiled_autograd 1/2
2025-08-14T21:20:25.3500415Z     dynamo/test_cudagraphs 1/1
2025-08-14T21:20:25.3500580Z     dynamo/test_exc 1/1
2025-08-14T21:20:25.3500725Z     inductor/test_binary_folding 1/1
2025-08-14T21:20:25.3500884Z     inductor/test_minifier 1/1
2025-08-14T21:20:25.3502822Z     inductor/test_flex_attention 4/4
2025-08-14T21:20:25.3503306Z     inductor/test_cpu_select_algorithm 1/1
2025-08-14T21:20:25.3503493Z     inductor/test_flex_decoding 1/2
2025-08-14T21:20:25.3503657Z     test_testing 1/1
2025-08-14T21:20:25.3510058Z     export/test_export_training_ir_to_run_decomp 1/1
2025-08-14T21:20:25.3510250Z     test_quantization 3/5
2025-08-14T21:20:25.3510399Z     dynamo/test_package 1/1
2025-08-14T21:20:25.3510545Z     export/test_db 1/1
2025-08-14T21:20:25.3510692Z     export/test_schema 1/1
2025-08-14T21:20:25.3510840Z     export/test_sparse 1/1
2025-08-14T21:20:25.3510974Z     export/test_swap 1/1
2025-08-14T21:20:25.3511105Z     inductor/test_autoheuristic 1/1
2025-08-14T21:20:25.3511271Z     inductor/test_benchmarking 1/1
2025-08-14T21:20:25.3511464Z     inductor/test_block_analysis 1/1
2025-08-14T21:20:25.3511885Z     inductor/test_codegen_triton 1/1
2025-08-14T21:20:25.3512023Z     inductor/test_compile 1/1
2025-08-14T21:20:25.3512178Z     inductor/test_custom_post_grad_passes 1/1
2025-08-14T21:20:25.3512339Z     inductor/test_distributed_patterns 1/1
2025-08-14T21:20:25.3512495Z     inductor/test_kernel_optimization 1/1
2025-08-14T21:20:25.3512649Z     inductor/test_minifier_utils 1/1
2025-08-14T21:20:25.3512794Z     inductor/test_op_completeness 1/1
2025-08-14T21:20:25.3513025Z     inductor/test_ordered_set 1/1
2025-08-14T21:20:25.3513166Z     inductor/test_split_cat_fx_aten_passes 1/1
2025-08-14T21:20:25.3513318Z     test_sparse_semi_structured 1/1
2025-08-14T21:20:25.3513462Z     test_dynamic_shapes 1/1
2025-08-14T21:20:25.3515413Z     test_reductions 1/1
2025-08-14T21:20:25.3515511Z     test_ops 4/4
2025-08-14T21:20:25.3515598Z     test_hop_infra 1/1
2025-08-14T21:20:25.3515690Z     test_fx 1/2
2025-08-14T21:20:25.3515789Z     benchmark_utils/test_benchmark_utils 1/1
2025-08-14T21:20:25.3516059Z     test_decomp 1/12
2025-08-14T21:20:25.3516150Z     test_decomp 7/12
2025-08-14T21:20:25.3516248Z     test_expanded_weights 1/1
2025-08-14T21:20:25.3516355Z     doctests 1/1
2025-08-14T21:20:25.3516443Z     test_tensorboard 1/1
2025-08-14T21:20:25.3516549Z     test_transformers_privateuse1 1/1
2025-08-14T21:20:25.3517790Z     test_cpp_extensions_mtia_backend 1/1
2025-08-14T21:20:25.3517904Z     test_autograd 1/1
2025-08-14T21:20:25.3518004Z     profiler/test_memory_profiler 1/1
2025-08-14T21:20:25.3518118Z     functorch/test_ops 1/4
2025-08-14T21:20:25.3518218Z     test_ops_jit 1/1
2025-08-14T21:20:25.3518312Z     optim/test_swa_utils 1/1
2025-08-14T21:20:25.3518415Z     test_mkldnn_verbose 1/1
2025-08-14T21:20:25.3518517Z     test_mobile_optimizer 1/1
2025-08-14T21:20:25.3518622Z     test_module_tracker 1/1
2025-08-14T21:20:25.3518717Z     test_monitor 1/1
2025-08-14T21:20:25.3518812Z     test_multiprocessing 1/1
2025-08-14T21:20:25.3519924Z     test_multiprocessing_spawn 1/1
2025-08-14T21:20:25.3520049Z     test_segment_reductions 1/1
2025-08-14T21:20:25.3520156Z     test_serialization 1/1
2025-08-14T21:20:25.3520267Z     test_set_default_mobile_cpu_allocator 1/1
2025-08-14T21:20:25.3520385Z     test_shape_ops 1/1
2025-08-14T21:20:25.3520482Z     test_show_pickle 1/1
2025-08-14T21:20:25.3520580Z     test_sort_and_select 1/1
2025-08-14T21:20:25.3520679Z     test_sparse_csr 1/2
2025-08-14T21:20:25.3520786Z     torch_np/numpy_tests/lib/test_arraypad 1/1
2025-08-14T21:20:25.3520925Z     torch_np/numpy_tests/lib/test_function_base 1/1
2025-08-14T21:20:25.3522039Z     torch_np/numpy_tests/lib/test_index_tricks 1/1
2025-08-14T21:20:25.3522181Z     torch_np/numpy_tests/lib/test_shape_base_ 1/1
2025-08-14T21:20:25.3522315Z     torch_np/numpy_tests/lib/test_twodim_base 1/1
2025-08-14T21:20:25.3522449Z     torch_np/numpy_tests/lib/test_type_check 1/1
2025-08-14T21:20:25.3522569Z     torch_np/test_basic 1/1
2025-08-14T21:20:25.3522675Z     torch_np/test_ndarray_methods 1/1
2025-08-14T21:20:25.3522799Z     torch_np/test_nep50_examples 1/1
2025-08-14T21:20:25.3522971Z     xpu/test_gemm 1/1
2025-08-14T21:20:25.3523067Z   Parallel tests (0):
2025-08-14T21:20:25.3523168Z Name: excluded (est. time: 0.0min)
2025-08-14T21:20:25.3523276Z   Serial tests (0):
2025-08-14T21:20:25.3524341Z   Parallel tests (0):
2025-08-14T21:20:25.3524484Z Running inductor/test_aot_inductor 3/3 ... [2025-08-14 21:20:25.348582]
2025-08-14T21:20:25.3524643Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:20:25.3525028Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_aot_inductor.py', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:20:25.348739]
2025-08-14T21:28:54.7425659Z 
2025-08-14T21:28:54.7427534Z inductor/test_aot_inductor 3/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_3.3_255fc67e27127b65_.log
2025-08-14T21:28:54.7484497Z Running 281 items in this shard: test/inductor/test_aot_inductor.py::TestAOTInductorConfig::test_compile_standalone_explicit_set, test/inductor/test_aot_inductor.py::TestAOTInductorConfig::test_compile_standalone_package_cpp_false_raises, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test__int_mm_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test__weight_int4pack_mm_m_32_n_64_q_group_32_num_groups_2_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test__weight_int4pack_mm_m_32_n_64_q_group_64_num_groups_1_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test__weight_int4pack_mm_m_32_n_64_q_group_64_num_groups_2_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_32_num_groups_2_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_addmm_multiple_dynamic_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_amp_fallback_random_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_aoti_constant_tensor_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_aoti_debug_printing_model_inputs_codegen_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_aoti_profiler_enable_kernel_profile_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_aoti_profiler_enable_kernel_profile_True_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_aoti_runtime_asserts_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_assert_tensor_meta_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_autotune_with_constant_folding_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_autotuning_args_reuse_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_bool_input_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_buffer_mutation_2_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_buffer_mutation_4_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_buffer_mutation_and_force_mmap_weights_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_buffer_reuse_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_composed_dynamic_size_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_cond_mismatched_branch_output_dynamic_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_cond_share_predicte_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_cond_unbacked_symint_closure_dynamic_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_cond_with_outer_code_before_after_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_cond_with_parameters_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_constant_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_constant_folding_with_update_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_conv_freezing_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_convolution_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_copy_non_blocking_is_pinned_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_deconv_freezing_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_dup_unbacked_sym_decl_with_refinement_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_duplicate_constant_folding_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_embedding_bag_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_empty_cat_dtype_promotion_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_empty_graph_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_extract_constants_map_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_fallback_kernel_with_symexpr_output_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_fallback_mem_leak_fix_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_index_put_fallback_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_index_put_with_none_index_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_load_package_multiple_gpus_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_misc_1_max_autotune_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_multiple_output_alias_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_narrow_fallback_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_non_contiguous_output_alias_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_non_tensor_input_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_on_gpu_device1_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_output_misaligned_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_pad_fallback_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_quantized_linear_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_repeated_user_defined_triton_kernel_embed_kernel_binary_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_replicate_on_devices_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_run_with_grad_enabled_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_runtime_checks_device_type_failed_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_runtime_checks_dtype_failed_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_runtime_checks_large_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_runtime_checks_shape_failed_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_scatter_reduce_fallback_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_seq_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_shifted_constraint_ranges_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_simple_embed_kernel_binary_False_max_autotune_True_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_size_with_unbacked_add_and_mul_expr_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_so_without_weight_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_stride_with_unbacked_expr_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_sympy_cpp_printer_min_max_minmax1_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_torchvision_transforms_functional_tensor_resize_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_dynamic_launcher_grid_infer_from_tensor_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_bool_param_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_dynamic_grid_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_dynamic_shape_with_div_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_equal_to_1_arg_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_extern_kernel_arg_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_grid_type_1_num_dims_1_dynamic_False_autotune_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_grid_type_2_num_dims_2_dynamic_False_autotune_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_multi_output_arg_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_on_device_tma_dynamic_True_tma_version_old_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_reinterpret_view_mem_leak_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_tma_descriptor_1d_dynamic_False_tma_version_new_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_tma_descriptor_2d_dynamic_True_tma_version_old_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_weird_param_order_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_triton_kernel_with_none_inputs_and_equal_to_1_arg_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_unbacked_equals_input_size_runtime_assertion_mark_unbacked_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_update_inactive_constant_buffer_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_view_outputs_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_while_loop_with_conv_dynamic_True_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_while_loop_with_mixed_device_dynamic_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_while_loop_with_mixed_device_dynamic_True_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_while_loop_with_parameters_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_while_loop_with_unbacked_symint_closure_dynamic_False_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_with_no_triton_profiler_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_with_offset_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_with_profiler_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_zero_grid_with_backed_symbols_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_zero_size_buffer_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleCpu::test_zero_size_weight_cpu, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__int_mm_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__weight_int4pack_mm_m_32_n_64_q_group_32_num_groups_2_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__weight_int4pack_mm_m_32_n_64_q_group_64_num_groups_1_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_32_num_groups_1_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_32_num_groups_2_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_64_num_groups_1_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_addmm_multiple_dynamic_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aliased_buffer_reuse_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_constant_tensor_name_collision_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_debug_printer_cpp_kernel_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_debug_printer_sym_inputs_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_debug_printing_model_inputs_codegen_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_profiler_enable_kernel_profile_False_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_aoti_profiler_enable_kernel_profile_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_assert_tensor_meta_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_autotuning_args_reuse_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_backward_no_op_logging_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_buffer_mutation_1_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_buffer_mutation_4_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_buffer_mutation_and_force_mmap_weights_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_buffer_reuse_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_cond_share_predicte_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_cond_with_outer_code_before_after_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_cond_with_parameters_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_conv3d_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_copy_non_blocking_is_pinned_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_deconv_freezing_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_duplicate_constant_folding_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_dynamic_cat_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_extract_constants_map_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_fake_tensor_device_validation_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_foreach_multiple_dynamic_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_fp8_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_index_put_fallback_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_inf_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_input_codegen_with_sympy_expr_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_load_package_multiple_gpus_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_masked_select_dynamic_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_misaligned_input_1_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_misaligned_input_2_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_misc_1_max_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_multi_device_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_multiple_output_alias_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_nested_tensor_from_jagged_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_non_tensor_input_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_none_args_aot_codegen_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_normal_functional_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_output_path_2_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_pad_fallback_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_proxy_executor_squeeze_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_quantized_linear_bias_none_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_repeated_calling_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_repeated_user_defined_triton_kernel_embed_kernel_binary_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_return_view_constant_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_reuse_kernel_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_run_with_grad_enabled_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_runtime_checks_device_type_failed_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_scatter_fallback_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_seq_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_simple_embed_kernel_binary_False_max_autotune_False_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_simple_embed_kernel_binary_False_max_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_simple_embed_kernel_binary_True_max_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_simple_split_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_size_with_unbacked_add_expr_transitive_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_symbool_item_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_torchvision_transforms_functional_tensor_resize_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_dynamic_shape_with_div_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_equal_to_1_float_arg_dynamic_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_grid_type_1_num_dims_1_dynamic_False_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_multi_output_arg_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_on_device_tma_dynamic_False_tma_version_new_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_reinterpret_view_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_sympy_fn_like_arg_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_unbacked_symint_in_grid_dynamic_False_autotuning_False_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_unbacked_symint_in_grid_dynamic_False_autotuning_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_with_none_input_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_kernel_with_none_inputs_and_equal_to_1_arg_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_triton_next_power_of_2_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_unbacked_equals_input_size_runtime_assertion_mark_unbacked_False_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_update_inactive_constant_buffer_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_upper_bound_i64_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_view_outputs_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_weight_on_disk_legacy_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_while_loop_with_mixed_device_dynamic_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_while_loop_with_outer_buffers_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_while_loop_with_parameters_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_while_loop_with_sym_expr_cond_dynamic_True_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_with_cudagraphs_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_with_profiler_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleGpu::test_zero_size_buffer_cuda, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test__int_mm_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test__weight_int4pack_mm_m_32_n_64_q_group_32_num_groups_1_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test__weight_int4pack_mm_with_scales_and_zeros_m_32_n_64_q_group_64_num_groups_2_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_addmm_multiple_dynamic_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_aoti_constant_tensor_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_aoti_debug_printing_model_inputs_codegen_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_aoti_runtime_asserts_backed_symint_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_aoti_runtime_asserts_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_assert_async_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_assert_tensor_meta_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_backward_no_op_logging_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_buffer_mutation_1_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_buffer_mutation_3_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_clamp_decomposition_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_non_tensor_predicates_dynamic_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_unbacked_symint_closure_dynamic_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_unbacked_symint_closure_dynamic_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_use_buffers_from_outer_scope_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_with_parameters_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_cond_with_reinterpret_view_inputs_outputs_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_copy_non_blocking_is_pinned_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_d2h_copy_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_deconv_freezing_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_dup_unbacked_sym_decl_with_refinement_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_dynamic_cat_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_fft_c2c_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_fp8_view_of_param_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_fx_gm_return_tuple_validation_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_index_put_fallback_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_index_put_with_none_index_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_input_codegen_with_sympy_expr_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_large_dynamic_dim_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_large_mmaped_weights_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_linear_freezing_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_misc_1_max_autotune_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_multi_device_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_no_args_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_normal_functional_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_output_path_2_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_poi_multiple_dynamic_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_proxy_executor_abs_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_proxy_executor_squeeze_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_quanatized_int8_linear_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_repeat_interleave_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_repeat_output_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_repeated_calling_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_runtime_checks_device_type_failed_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_runtime_checks_shape_failed_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_sdpa_2_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_shifted_constraint_ranges_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_simple_embed_kernel_binary_False_max_autotune_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_simple_embed_kernel_binary_True_max_autotune_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_size_from_multi_output_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_size_with_unbacked_add_and_mul_expr_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_stft_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_subclasses_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_symbool_item_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_symfloat_item_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_symint_item_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_sympy_cpp_printer_min_max_minmax0_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_torchvision_transforms_functional_tensor_resize_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_bool_param_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_dynamic_shape_with_div_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_equal_to_1_arg_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_1_num_dims_1_dynamic_False_autotune_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_3_num_dims_2_dynamic_True_autotune_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_grid_type_3_num_dims_2_dynamic_True_autotune_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_on_device_tma_dynamic_True_tma_version_new_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_sympy_expr_arg_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_tma_descriptor_1d_dynamic_False_tma_version_new_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_tma_descriptor_1d_dynamic_False_tma_version_old_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_tma_descriptor_1d_dynamic_True_tma_version_old_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_tma_descriptor_2d_dynamic_False_tma_version_old_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_tma_descriptor_2d_dynamic_True_tma_version_new_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_triton_kernel_weird_param_order_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_update_constant_buffer_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_while_loop_with_sym_expr_cond_dynamic_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_while_loop_with_unbacked_symint_closure_dynamic_False_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_while_loop_with_unbacked_symint_closure_dynamic_True_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_zero_size_buffer_mps, test/inductor/test_aot_inductor.py::AOTInductorTestABICompatibleMps::test_zero_size_weight_mps
2025-08-14T21:28:54.7526900Z 
2025-08-14T21:28:54.7527000Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T21:28:54.7527189Z Uploading artifacts took 0.00 seconds
2025-08-14T21:28:54.7527389Z Running inductor/test_torchinductor_codegen_dynamic_shapes 1/3 ... [2025-08-14 21:28:54.743193]
2025-08-14T21:28:54.7527594Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:28:54.7528023Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_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'] ... [2025-08-14 21:28:54.743471]
2025-08-14T21:39:36.4619497Z 
2025-08-14T21:39:36.4620446Z inductor/test_torchinductor_codegen_dynamic_shapes 1/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_1.3_e13fae18ac80a504_.log
2025-08-14T21:39:36.4735621Z Running 573 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__unsafe_masked_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d1_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_max_pool2d1_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_inplace_permuted_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_alexnet_prefix_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aliased_buffer_reuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_angle_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_cache_hit_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_with_persistent_cache_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_duplicates_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_pool2d3_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_avg_pool2d_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool_errors_with_uint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_batch_norm_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bernoulli1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_both_scalars_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_int_int16_int64_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_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int64_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_uint8_int32_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_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_zero_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_negative_dim_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_cat_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_check_stack_no_cycles_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_config_option_dont_assume_alignment_recompiles_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_consecutive_split_cumprod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_const_int32_to_float_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_2d_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_constant_pad_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_nd_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_channels_last_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_with_as_strided_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_copy_non_blocking_is_pinned_use_cat_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cudnn_rnn_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_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_div2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div9_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_by_zero_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_presicion_accuracy_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_dont_constant_fold_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_trivial_0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_trivial_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtype_mismatch_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtype_sympy_expr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_bfloat16_bfloat16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_bfloat16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_bfloat16_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int32_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_float32_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_fusion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_bfloat16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_float64_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_int32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_bfloat16_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_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int8_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_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_bag_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_erfinv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_exact_stride_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_exp2_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_fft_real_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fill1_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_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_floordiv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmin_fmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d5_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_like_sliced_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_like_transposed_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_tiled_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_generated_code_has_size_stride_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_mutation_real_name_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_no_inputs_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_horizonal_fusion2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_abs_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_flip_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_remainder_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_put_failed_reinplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_reinplace_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_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_inductor_triton_bucketize_respects_masking_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_add_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_where_pointwise_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_insignificant_strides_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isin_tensor_scalar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isinf2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_issue102546_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_l1_loss_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_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_layer_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_lgamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear2_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_linspace3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_list_clearing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logcumsumexp_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logsumexp_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_masked_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_matmul_layer_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d1_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_pool2d5_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_misaligned_address_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mm_mixed_dtype_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_multi_gpu_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_mutable_custom_op_fixed_layout_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nan_sort_stable_False_descending_False_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_narrow_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_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nll_loss_forward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_no_mega_fusion_during_lowering_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_output_strides_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_permute1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_philox_rand_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_t_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_digamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_exp2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_gammainc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_log_ndtr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_multigammaln_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_ndtri_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_psi_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_w_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_xlogy_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_pow2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_profiler_mark_wrapper_call_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_int64_mod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_kernel_count_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_generator_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randn_like_empty_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_reduction1_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_reflection_pad2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_copy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_slice_scatter_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_int32_nd_1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_replication_pad_errors_with_bool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_require_stride_expanded_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_reuse_buffers_with_aliasing_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_round_correctness_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_rsqrt_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scalar_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scalar_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter2_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_scatter6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_bf16_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_scheduler_vertical_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_unaligned_mask_freezing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_select_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_shape_padding_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_should_pad_bench_for_bmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sigmoid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sign_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_silu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sin_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_single_elem_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_slice1_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_slice_mutation3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_bool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_stable_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_special_polygamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumprod_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumsum_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_failed_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_with_integer_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_stack_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_stride_preservation_with_stride_modifying_fx_pass_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_int_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_tensor_index_put_slice_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_device_constant_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_topk_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_triu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_uint4x2_mixed_mm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_uint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbacked_floordiv_simplify_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unroll_small_reduction_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_int16_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_bilinear2d_a_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bilinear2d_b_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_upsample_nearest3d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_mean_div_by_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_mean_tile_reduction_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vertical_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_uint8_through_differing_bitwidths_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views1_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_views3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views5_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_where_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_zero_dim_reductions_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_zero_element_mutation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_adaptive_avg_pool2d1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_adaptive_avg_pool2d_low_prec_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_adaptive_max_pool2d1_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_addmv_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_aliased_buffer_reuse_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_aoti_eager_support_str_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_aoti_eager_with_scalar_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_argmax_argmin2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_argmax_argmin3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_argmax_argmin_with_nan_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_argmax_min_int32_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_avg_pool2d1_dynamic_shapes_cuda, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bitwise3_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_bucketize_add_autotune_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bucketize_computed_offsets_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_bucketize_int_int16_int16_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_int32_int64_dynamic_shapes_cuda, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_airy_ai_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_chebyshev_polynomial_v_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_entr_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_erfcx_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_erfinv_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_exp2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_i1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_i1e_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_log1p_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_log_ndtr_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_logit_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_modified_bessel_k0_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_multigammaln_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_ndtr_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_psi_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_scaled_modified_bessel_k1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_shifted_chebyshev_polynomial_u_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_shifted_chebyshev_polynomial_w_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_spherical_bessel_j0_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_xlog1py_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pointwise_zeta_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_polar_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pow2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pow_by_natural_log2_dynamic_shapes_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_pow_symfloat_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_prod_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_randint_distribution_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_randint_int64_mod_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_reduction3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_reflection_pad2d_backward_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_reflection_pad2d_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_relu_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_remainder_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_remove_noop_clone_dynamic_shapes_cuda, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_rsqrt_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_select_scatter_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_should_pad_bench_for_bmm_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_signbit_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_simplify_loops_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_slice_scatter4_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_slice_scatter_reinplace_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_softmax_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_sort_transpose_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_special_polygamma_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_cumprod_low_prec_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_cumsum_index_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_cumsum_low_prec_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_split_with_sizes_with_unbacked_symints_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sqrt_dynamic_shapes_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_squeeze_varargs_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_strided_inputs_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sum2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_sum5_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tensor1_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tensor2_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_tensor_index_put_slice_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_issue3_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_topk_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_triton_kernel_bool_param_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_unspec_inputs_bfloat16_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_int32_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_unspec_inputs_int64_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_unspec_inputs_int8_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_unsqueeze_inplace_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_upsample_cat_conv_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_upsample_nearest1d_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_upsample_nearest3d_dynamic_shapes_cuda, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenGPUTests::test_vertical_fusion1_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_zero_element_mutation_dynamic_shapes_cuda
2025-08-14T21:39:36.4840092Z 
2025-08-14T21:39:36.4840200Z Running inductor/test_torchinductor_opinfo 4/8 ... [2025-08-14 21:39:36.462860]
2025-08-14T21:39:36.4840388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:39:36.4840788Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '--shard-id=4', '--num-shards=8', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:39:36.463077]
2025-08-14T21:50:00.9533553Z 
2025-08-14T21:50:00.9535115Z inductor/test_torchinductor_opinfo 4/8 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_4.8_859583886aae741a_.log
2025-08-14T21:50:00.9617302Z Running 436 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_H_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_H_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_T_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_T_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rdiv___cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmod___cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rmul___cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rpow___cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive___rpow___cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__chunk_cat_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__chunk_cat_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__segment_reduce_offsets_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive__softmax_backward_data_cuda_float16, 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_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_abs_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_abs_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_abs_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acos_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acos_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_acosh_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_add_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_add_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addmm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_addmv_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amax_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_amax_cuda_int64, 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_arange_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmin_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmin_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argmin_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argsort_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_argwhere_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_as_strided_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_asin_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atan_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_2d_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_atleast_3d_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bfloat16_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_bfloat16_cuda_uint8, 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_byte_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_byte_cuda_int64, 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_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cat_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ceil_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chalf_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chalf_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chalf_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_char_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chunk_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_chunk_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_max_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clamp_min_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_clone_cuda_float32, 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_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_combinations_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_complex_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_conj_physical_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_constant_pad_nd_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_contiguous_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_copysign_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_copysign_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cos_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cos_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cosh_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cov_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummax_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_cummin_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_deg2rad_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diag_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diag_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diag_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagflat_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_diagonal_scatter_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_digamma_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_div_floor_rounding_cuda_int64, 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_dsplit_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_dsplit_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_permuted_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_empty_strided_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eq_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erf_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_erfinv_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp2_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_exp_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_as_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_copy_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expand_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expm1_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expm1_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_expm1_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_eye_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_fft_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft2_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft2_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft2_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfft_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_hfftn_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifft_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifftn_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ifftshift_cuda_float64, 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_ihfft2_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fft_ihfft_cuda_bool, 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_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fill_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_flatten_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_float_power_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_floor_divide_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fmax_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_fmin_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_frexp_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_like_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_full_like_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_geometric_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_gt_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_half_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hash_tensor_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hash_tensor_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_heaviside_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_histc_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_hypot_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_copy_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_fill_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_fill_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_put_cuda_bool, 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_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_amin_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_reduce_mean_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_index_select_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_int_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isclose_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isclose_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isfinite_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isinf_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isinf_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isneginf_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isposinf_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isreal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_isreal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_2inputs_2outputs_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_4inputs_with_extra_args_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_binary_return_by_ref_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_jiterator_unary_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_kron_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_kthvalue_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ldexp_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ldexp_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_le_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_le_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_lerp_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_lgamma_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_det_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_diagonal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_diagonal_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_lstsq_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_lstsq_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_lu_factor_ex_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_matrix_power_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_pinv_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_solve_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_solve_triangular_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_tensorinv_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_tensorsolve_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linalg_vander_cuda_uint8, 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_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linspace_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linspace_tensor_overload_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_linspace_tensor_overload_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logaddexp2_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logical_and_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logical_and_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logical_or_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logical_or_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logit_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logspace_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_logsumexp_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mH_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_amax_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_amax_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_amin_cuda_float64, 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_int64, 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_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_cumsum_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_fill_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_logaddexp_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_mean_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_norm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_prod_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_scatter_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_softmin_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_std_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_std_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_sum_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_var_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_masked_var_cuda_int64, 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_no_dim_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_reduction_with_dim_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_max_reduction_with_dim_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_maximum_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_maximum_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_median_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_meshgrid_variadic_tensors_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_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_binary_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_no_dim_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_min_reduction_no_dim_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mode_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_movedim_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_movedim_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_msort_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_msort_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mul_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_1_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_mvlgamma_mvlgamma_p_3_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nanquantile_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nansum_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_narrow_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ne_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ne_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_neg_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_empty_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_empty_strided_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_new_full_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_avg_pool1d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_avg_pool2d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_avg_pool3d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_adaptive_max_pool1d_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_alpha_dropout_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_avg_pool1d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_avg_pool1d_cuda_float64, 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_channel_shuffle_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv1d_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv3d_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_conv_transpose2d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_cosine_similarity_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_cross_entropy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_embedding_bag_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_fractional_max_pool3d_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_group_norm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_hardsigmoid_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_interpolate_bilinear_cuda_float16, 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_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_max_pool3d_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_cuda_float32, 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_mish_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_mish_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_nll_loss_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_reflect_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pad_replicate_negative_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pairwise_distance_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_pixel_shuffle_cuda_float32, 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_poisson_nll_loss_cuda_int32, 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_silu_cuda_float32, 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_softshrink_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_threshold_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_threshold_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_threshold_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_loss_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_unfold_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nonzero_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_nonzero_static_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_norm_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_norm_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_norm_fro_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_norm_inf_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_normal_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_normal_in_place_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_outer_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_outer_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_permute_copy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_bool, 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_0_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_1_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_2_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_3_cuda_float16, 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_4_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_polygamma_polygamma_n_4_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_positive_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_positive_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_prod_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_prod_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_put_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_put_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ravel_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_ravel_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_real_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_real_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reciprocal_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reciprocal_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_remainder_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_interleave_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_interleave_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_repeat_interleave_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reshape_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_reshape_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resize_as__cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_conj_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_neg_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_resolve_neg_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_roll_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_roll_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rot90_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rsqrt_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rsqrt_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_rsub_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scalar_tensor_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scalar_tensor_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_add_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_scatter_cuda_int32, 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_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_searchsorted_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_scatter_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_select_scatter_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sgn_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_short_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_short_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sigmoid_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sign_cuda_int32, 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_general_cosine_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_signal_windows_hamming_cuda_float32, 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_sinh_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sinh_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_airy_ai_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_j0_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_y0_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_bessel_y1_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_chebyshev_polynomial_u_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_erfcx_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_erfcx_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_i0e_cuda_float32, 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_log_ndtr_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_k0_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_modified_bessel_k1_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_ndtr_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_scaled_modified_bessel_k0_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_scaled_modified_bessel_k0_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_u_cuda_uint8, 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_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_special_spherical_bessel_j0_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_list_args_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_split_with_sizes_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sqrt_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_square_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_squeeze_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_stack_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_std_mean_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_sub_cuda_int64, 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_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_t_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_take_along_dim_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tanh_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tensordot_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tile_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_to_sparse_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_trace_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_transpose_copy_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_transpose_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_trapezoid_cuda_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_triangular_solve_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_tril_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unbind_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unbind_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_copy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_copy_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_cuda_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unfold_cuda_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_uniform_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unique_cuda_uint16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsafe_split_cuda_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_unsqueeze_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_as_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_view_cuda_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_xlogy_cuda_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_xlogy_cuda_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCUDA::test_comprehensive_zeros_cuda_bool
2025-08-14T21:50:00.9684588Z 
2025-08-14T21:50:00.9684696Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T21:50:00.9684889Z Uploading artifacts took 0.00 seconds
2025-08-14T21:50:00.9685062Z Running inductor/test_cpu_repro 2/4 ... [2025-08-14 21:50:00.953606]
2025-08-14T21:50:00.9685279Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:50:00.9690899Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cpu_repro.py', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:00.953810]
2025-08-14T21:54:57.8198656Z 
2025-08-14T21:54:57.8199228Z inductor/test_cpu_repro 2/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_repro_2.4_08391fceb3afea35_.log
2025-08-14T21:54:57.8241821Z Running 178 items in this shard: test/inductor/test_cpu_repro.py::CPUReproTests::test_argmax_argmin_with_nan_value, test/inductor/test_cpu_repro.py::CPUReproTests::test_broadcast_scalar_cpp_tile_2d_kernel, test/inductor/test_cpu_repro.py::CPUReproTests::test_channels_last_view_as_complex, test/inductor/test_cpu_repro.py::CPUReproTests::test_complex_memory_overlap, test/inductor/test_cpu_repro.py::CPUReproTests::test_concat_inner_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_constant_store, test/inductor/test_cpu_repro.py::CPUReproTests::test_conv_stride_constraints, test/inductor/test_cpu_repro.py::CPUReproTests::test_convert_int32_to_int64_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_cpp_kernel_profile, test/inductor/test_cpu_repro.py::CPUReproTests::test_dequant_maxpool2d_lowering_int8, test/inductor/test_cpu_repro.py::CPUReproTests::test_dequant_quant_lowering_int8, test/inductor/test_cpu_repro.py::CPUReproTests::test_dequant_quant_lowering_uint8, test/inductor/test_cpu_repro.py::CPUReproTests::test_dequant_relu_quant_dequant_relu_quant_lowering_int8, test/inductor/test_cpu_repro.py::CPUReproTests::test_disabled_amp_is_inference_True, test/inductor/test_cpu_repro.py::CPUReproTests::test_dropout, test/inductor/test_cpu_repro.py::CPUReproTests::test_embedding_vec_bf16, test/inductor/test_cpu_repro.py::CPUReproTests::test_for_loop_collapsed, test/inductor/test_cpu_repro.py::CPUReproTests::test_fp8_cast_bfloat16_shape_15,3,13, test/inductor/test_cpu_repro.py::CPUReproTests::test_fp8_cast_bfloat16_shape_4,2048,4096, test/inductor/test_cpu_repro.py::CPUReproTests::test_frexp, test/inductor/test_cpu_repro.py::CPUReproTests::test_horizontal_fusion, test/inductor/test_cpu_repro.py::CPUReproTests::test_in_out_buffer, test/inductor/test_cpu_repro.py::CPUReproTests::test_index_propagation_issue_102065, test/inductor/test_cpu_repro.py::CPUReproTests::test_inplace_add_alpha, test/inductor/test_cpu_repro.py::CPUReproTests::test_invalid_index_of_empty_tensor, test/inductor/test_cpu_repro.py::CPUReproTests::test_ir_node_str, test/inductor/test_cpu_repro.py::CPUReproTests::test_issue122380, test/inductor/test_cpu_repro.py::CPUReproTests::test_load_half, test/inductor/test_cpu_repro.py::CPUReproTests::test_local_buffer_with_line_reuse, test/inductor/test_cpu_repro.py::CPUReproTests::test_logical_op_store_to_lowp_data_dtype, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_False_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_False_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_True_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_False_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_False_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_1_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_False_bias_True_empty_state_True_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_False_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_1_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_True_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_True_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_True_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_True_batch_first_False_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_False_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_True_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_1_hidden_size_7_num_layers_1_bidirectional_False_bias_False_empty_state_True_batch_first_True_batch_size_1_seq_len_7, 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test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_True_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_False_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_False_batch_size_1_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_False_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_False_empty_state_True_batch_first_True_batch_size_7_seq_len_7, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_False_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_False_batch_first_True_batch_size_1_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_lstm_packed_unbatched_True_input_size_7_hidden_size_7_num_layers_7_bidirectional_True_bias_True_empty_state_True_batch_first_False_batch_size_7_seq_len_1, test/inductor/test_cpu_repro.py::CPUReproTests::test_masked_load_int64_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_max_reduction_lowp_fp, test/inductor/test_cpu_repro.py::CPUReproTests::test_maxpool2d_with_pre_loop_collapse_cpu_only, test/inductor/test_cpu_repro.py::CPUReproTests::test_memory_copy_with_fusion, test/inductor/test_cpu_repro.py::CPUReproTests::test_meta_device, test/inductor/test_cpu_repro.py::CPUReproTests::test_mkl_linear, test/inductor/test_cpu_repro.py::CPUReproTests::test_multihead_attention_cpu, test/inductor/test_cpu_repro.py::CPUReproTests::test_new_vec_op_cpu_only, test/inductor/test_cpu_repro.py::CPUReproTests::test_nn_param_assign_wrapped, test/inductor/test_cpu_repro.py::CPUReproTests::test_non_contiguous_index_with_constant_stride, test/inductor/test_cpu_repro.py::CPUReproTests::test_non_contiguous_reduction_store, test/inductor/test_cpu_repro.py::CPUReproTests::test_parallel_num_threads, test/inductor/test_cpu_repro.py::CPUReproTests::test_pow_cos, test/inductor/test_cpu_repro.py::CPUReproTests::test_reduce_with_masked, test/inductor/test_cpu_repro.py::CPUReproTests::test_reduction_cpu_only, test/inductor/test_cpu_repro.py::CPUReproTests::test_redundant_to_node_elimination_lowp_fp, test/inductor/test_cpu_repro.py::CPUReproTests::test_relu_with_inf_value, test/inductor/test_cpu_repro.py::CPUReproTests::test_require_stride_order_non_owning, test/inductor/test_cpu_repro.py::CPUReproTests::test_scatter_using_atomic_add, test/inductor/test_cpu_repro.py::CPUReproTests::test_scatter_using_atomic_add_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_share_local_buffers_in_outer_loop_fusion, test/inductor/test_cpu_repro.py::CPUReproTests::test_sigmoid_with_reduction, test/inductor/test_cpu_repro.py::CPUReproTests::test_store_reduction, test/inductor/test_cpu_repro.py::CPUReproTests::test_tile2d_load_decomposed_dequant_add_relu_quant_int8, test/inductor/test_cpu_repro.py::CPUReproTests::test_to_dtype_float_bool, test/inductor/test_cpu_repro.py::CPUReproTests::test_torch_logit, test/inductor/test_cpu_repro.py::CPUReproTests::test_transpose_sum2d_cpu_only, test/inductor/test_cpu_repro.py::CPUReproTests::test_uint64_pointwise_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_uint64_reduction_vec, test/inductor/test_cpu_repro.py::CPUReproTests::test_vec_bitwise, test/inductor/test_cpu_repro.py::CPUReproTests::test_vec_indirect_load_cse_cache, test/inductor/test_cpu_repro.py::CPUReproTests::test_vec_logical, test/inductor/test_cpu_repro.py::CPUReproTests::test_view_dtype
2025-08-14T21:54:57.8282703Z 
2025-08-14T21:54:57.8282878Z Running dynamo/test_repros 1/1 ... [2025-08-14 21:54:57.819965]
2025-08-14T21:54:57.8283119Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:54:57.8283502Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_repros.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:54:57.820166]
2025-08-14T21:56:14.3436261Z 
2025-08-14T21:56:14.3438240Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_8318222bfb720e2e_.log
2025-08-14T21:56:14.3483547Z Running 329 items in this shard: test/dynamo/test_repros.py::LRUCacheWarningTests::test_lru_cache_warning_issued_during_tracing, test/dynamo/test_repros.py::ReproTests::test_Size, test/dynamo/test_repros.py::ReproTests::test_abc_setattr, test/dynamo/test_repros.py::ReproTests::test_add_complex_conj, test/dynamo/test_repros.py::ReproTests::test_add_sub_alpha_out, test/dynamo/test_repros.py::ReproTests::test_addr_alpha_beta_out, test/dynamo/test_repros.py::ReproTests::test_amp_foreach_fake_impl, test/dynamo/test_repros.py::ReproTests::test_ao_fake_quantize_tracing, test/dynamo/test_repros.py::ReproTests::test_aot_autograd_runtime_wrapper_prologue_profiled, test/dynamo/test_repros.py::ReproTests::test_as_strided_on_base_with_mutation_works, test/dynamo/test_repros.py::ReproTests::test_as_strided_on_existing_view_banned, test/dynamo/test_repros.py::ReproTests::test_attached_attribute_in_dir, test/dynamo/test_repros.py::ReproTests::test_autograd_function_graph_break, test/dynamo/test_repros.py::ReproTests::test_avoid_dupe_specialization, test/dynamo/test_repros.py::ReproTests::test_batch_encoding_clone_inputs, test/dynamo/test_repros.py::ReproTests::test_batch_norm_act, test/dynamo/test_repros.py::ReproTests::test_batchnorm_e2e, test/dynamo/test_repros.py::ReproTests::test_bigbird_unsqueeze_inplace, test/dynamo/test_repros.py::ReproTests::test_bitwise_op_guard, test/dynamo/test_repros.py::ReproTests::test_bitwise_print_precedence, test/dynamo/test_repros.py::ReproTests::test_boxes_len, test/dynamo/test_repros.py::ReproTests::test_build_map_unpack_with_call, test/dynamo/test_repros.py::ReproTests::test_c_defined_metaclass, test/dynamo/test_repros.py::ReproTests::test_changing_stride, test/dynamo/test_repros.py::ReproTests::test_chunk_reformer_ff, test/dynamo/test_repros.py::ReproTests::test_class_member, test/dynamo/test_repros.py::ReproTests::test_classmethod_with_slots, test/dynamo/test_repros.py::ReproTests::test_compilation_metrics_on_error, test/dynamo/test_repros.py::ReproTests::test_compile_complex_conj, test/dynamo/test_repros.py::ReproTests::test_compile_copy__int_overload, test/dynamo/test_repros.py::ReproTests::test_const_dict_keyerror, test/dynamo/test_repros.py::ReproTests::test_contains_range_constprop, test/dynamo/test_repros.py::ReproTests::test_convert_boxes_to_pooler_format, test/dynamo/test_repros.py::ReproTests::test_copy_weird_strides, test/dynamo/test_repros.py::ReproTests::test_create_rand_mask_from_inputs, test/dynamo/test_repros.py::ReproTests::test_dalle2_maybe, test/dynamo/test_repros.py::ReproTests::test_data_attr_mutation_after_saved_for_bw, test/dynamo/test_repros.py::ReproTests::test_dataclass_in_module, test/dynamo/test_repros.py::ReproTests::test_dataclass_init_with_default_factory_with_inputs, test/dynamo/test_repros.py::ReproTests::test_ddp_checkpoint, test/dynamo/test_repros.py::ReproTests::test_dedup_global, test/dynamo/test_repros.py::ReproTests::test_deferred_runtime_asserts, test/dynamo/test_repros.py::ReproTests::test_delattr, test/dynamo/test_repros.py::ReproTests::test_delattr_raises, test/dynamo/test_repros.py::ReproTests::test_delattr_return, test/dynamo/test_repros.py::ReproTests::test_delete_local_error, test/dynamo/test_repros.py::ReproTests::test_deleted_compile_wrapper_segfault, test/dynamo/test_repros.py::ReproTests::test_delsubscr, test/dynamo/test_repros.py::ReproTests::test_delsubscr_raises, test/dynamo/test_repros.py::ReproTests::test_detectron2_instances_cat, test/dynamo/test_repros.py::ReproTests::test_disabling_unpack_hooks_within_compiled_region, test/dynamo/test_repros.py::ReproTests::test_distributions_subclass, test/dynamo/test_repros.py::ReproTests::test_do_paste_mask, test/dynamo/test_repros.py::ReproTests::test_dont_aggressively_write_assert, test/dynamo/test_repros.py::ReproTests::test_dont_dce_rand, test/dynamo/test_repros.py::ReproTests::test_dropout_inline, test/dynamo/test_repros.py::ReproTests::test_dynamic_shape_disable_duck_size, test/dynamo/test_repros.py::ReproTests::test_dynamic_shapes_double_not_equal, test/dynamo/test_repros.py::ReproTests::test_dynamic_shapes_float_guard, test/dynamo/test_repros.py::ReproTests::test_dynamic_shapes_implicit_guard, test/dynamo/test_repros.py::ReproTests::test_dynamic_shapes_right_side, test/dynamo/test_repros.py::ReproTests::test_ellipsis, test/dynamo/test_repros.py::ReproTests::test_embedding_backward_broadcasting_decomp, test/dynamo/test_repros.py::ReproTests::test_empty_graph_nested_calls_fullgraph_False, test/dynamo/test_repros.py::ReproTests::test_empty_graph_nested_calls_fullgraph_True, test/dynamo/test_repros.py::ReproTests::test_empty_list_contains_with_jump, test/dynamo/test_repros.py::ReproTests::test_empty_out_dynamic, test/dynamo/test_repros.py::ReproTests::test_enum, test/dynamo/test_repros.py::ReproTests::test_ephemeral_module, test/dynamo/test_repros.py::ReproTests::test_error_return_without_exception_set, test/dynamo/test_repros.py::ReproTests::test_exception_in_dynamo_handling, test/dynamo/test_repros.py::ReproTests::test_exec_import, test/dynamo/test_repros.py::ReproTests::test_exec_wildcard_import, test/dynamo/test_repros.py::ReproTests::test_flip_bad_accuracy, test/dynamo/test_repros.py::ReproTests::test_for_loop_graph_break, test/dynamo/test_repros.py::ReproTests::test_for_loop_graph_break_before, test/dynamo/test_repros.py::ReproTests::test_foreach_decomp_arg_names, test/dynamo/test_repros.py::ReproTests::test_fsdp_set_input_mutation_applied_when_input_gets_no_gradients, test/dynamo/test_repros.py::ReproTests::test_function_in_skipfiles, test/dynamo/test_repros.py::ReproTests::test_functools_wraps, test/dynamo/test_repros.py::ReproTests::test_gan_repro_trying_to_backward_through_the_graph_a_second_time, test/dynamo/test_repros.py::ReproTests::test_generator_dealloc, test/dynamo/test_repros.py::ReproTests::test_get_parameter_dtype, test/dynamo/test_repros.py::ReproTests::test_global_fn_mutation, test/dynamo/test_repros.py::ReproTests::test_grad, test/dynamo/test_repros.py::ReproTests::test_grad_mode_carrying_correct_state_after_graph_break, test/dynamo/test_repros.py::ReproTests::test_grad_references_cleared, test/dynamo/test_repros.py::ReproTests::test_graph_break_on_jit_isinstance, test/dynamo/test_repros.py::ReproTests::test_graph_break_on_jit_isinstance_pep585, test/dynamo/test_repros.py::ReproTests::test_graph_break_unsupported_fake, test/dynamo/test_repros.py::ReproTests::test_guard_default_device, test/dynamo/test_repros.py::ReproTests::test_guard_fail_nested_tuple, test/dynamo/test_repros.py::ReproTests::test_guard_fail_tensor_bool, test/dynamo/test_repros.py::ReproTests::test_guard_ordering_shape_fail, test/dynamo/test_repros.py::ReproTests::test_guard_with_tuple_mutation, test/dynamo/test_repros.py::ReproTests::test_hasattr_builtin, test/dynamo/test_repros.py::ReproTests::test_hf_bigbird_unsqueeze, test/dynamo/test_repros.py::ReproTests::test_hf_classinstantier, test/dynamo/test_repros.py::ReproTests::test_hf_gelu_inline, test/dynamo/test_repros.py::ReproTests::test_hf_model_output, test/dynamo/test_repros.py::ReproTests::test_hf_t5_forward, test/dynamo/test_repros.py::ReproTests::test_hf_xsoftmax_inference, test/dynamo/test_repros.py::ReproTests::test_hf_xsoftmax_training, test/dynamo/test_repros.py::ReproTests::test_iadd_graph_break, test/dynamo/test_repros.py::ReproTests::test_incompatible_configs, test/dynamo/test_repros.py::ReproTests::test_indexing_with_list, test/dynamo/test_repros.py::ReproTests::test_inductor_dynamic_shapes_broadcasting, test/dynamo/test_repros.py::ReproTests::test_inductor_no_recursionerror_on_for_loops, test/dynamo/test_repros.py::ReproTests::test_inductor_rng_default_dtype, test/dynamo/test_repros.py::ReproTests::test_inference_mode_dynamic_shapes, test/dynamo/test_repros.py::ReproTests::test_inlining_cornercase, test/dynamo/test_repros.py::ReproTests::test_inplace_unsqueeze_input, test/dynamo/test_repros.py::ReproTests::test_int_format, test/dynamo/test_repros.py::ReproTests::test_intermediate_leaf_requires_grad, test/dynamo/test_repros.py::ReproTests::test_invalid_seq_unpack, test/dynamo/test_repros.py::ReproTests::test_is_make_fx_tracing, test/dynamo/test_repros.py::ReproTests::test_is_symbolic_tracing, test/dynamo/test_repros.py::ReproTests::test_isinstance_dtype, test/dynamo/test_repros.py::ReproTests::test_isinstance_storage, test/dynamo/test_repros.py::ReproTests::test_issue111522, test/dynamo/test_repros.py::ReproTests::test_issue111918, test/dynamo/test_repros.py::ReproTests::test_issue114171, test/dynamo/test_repros.py::ReproTests::test_issue126128, test/dynamo/test_repros.py::ReproTests::test_issue134451, test/dynamo/test_repros.py::ReproTests::test_issue1466_size_aot_autograd, test/dynamo/test_repros.py::ReproTests::test_issue175, test/dynamo/test_repros.py::ReproTests::test_jit_script_defaults, test/dynamo/test_repros.py::ReproTests::test_jit_trace_errors, test/dynamo/test_repros.py::ReproTests::test_kwargs_out_list_variable, test/dynamo/test_repros.py::ReproTests::test_list_aliasing, test/dynamo/test_repros.py::ReproTests::test_list_index, test/dynamo/test_repros.py::ReproTests::test_list_index_not_found, test/dynamo/test_repros.py::ReproTests::test_list_index_tensor_unsupported, test/dynamo/test_repros.py::ReproTests::test_list_reverse, test/dynamo/test_repros.py::ReproTests::test_list_self_reference, test/dynamo/test_repros.py::ReproTests::test_listcomp, test/dynamo/test_repros.py::ReproTests::test_longformer_chunk, test/dynamo/test_repros.py::ReproTests::test_longtensor_list, test/dynamo/test_repros.py::ReproTests::test_lru_cache_tracing, test/dynamo/test_repros.py::ReproTests::test_maml_item_capture, test/dynamo/test_repros.py::ReproTests::test_maml_no_item_capture, test/dynamo/test_repros.py::ReproTests::test_many_overlapping_inputs_does_not_explode_guards, test/dynamo/test_repros.py::ReproTests::test_many_views_with_mutation, test/dynamo/test_repros.py::ReproTests::test_map_with_multiple_args, test/dynamo/test_repros.py::ReproTests::test_maybe_multiply_symint, test/dynamo/test_repros.py::ReproTests::test_mem_leak_guards, test/dynamo/test_repros.py::ReproTests::test_merge_criteria_processor_list1, test/dynamo/test_repros.py::ReproTests::test_merge_criteria_processor_list2, test/dynamo/test_repros.py::ReproTests::test_method_overriding, test/dynamo/test_repros.py::ReproTests::test_module_in_skipfiles, test/dynamo/test_repros.py::ReproTests::test_modules, test/dynamo/test_repros.py::ReproTests::test_multi_dot_import, test/dynamo/test_repros.py::ReproTests::test_multi_import, test/dynamo/test_repros.py::ReproTests::test_named_buffers, test/dynamo/test_repros.py::ReproTests::test_nanmean_out, test/dynamo/test_repros.py::ReproTests::test_negative_floor_div_solve, test/dynamo/test_repros.py::ReproTests::test_negative_shape_guard, test/dynamo/test_repros.py::ReproTests::test_nested_while_loop_graph_break, test/dynamo/test_repros.py::ReproTests::test_nn_module_callable, test/dynamo/test_repros.py::ReproTests::test_nn_module_property_closure, test/dynamo/test_repros.py::ReproTests::test_nn_module_stack_bc, test/dynamo/test_repros.py::ReproTests::test_nn_param_freevar_codegen, test/dynamo/test_repros.py::ReproTests::test_nn_parameter, test/dynamo/test_repros.py::ReproTests::test_nn_parameter_ctor_graph_breaks, test/dynamo/test_repros.py::ReproTests::test_nn_parametrize, test/dynamo/test_repros.py::ReproTests::test_no_grad_inline, test/dynamo/test_repros.py::ReproTests::test_no_tracing_into_eval_frame, test/dynamo/test_repros.py::ReproTests::test_no_tracing_into_eval_frame_ctx_manager, test/dynamo/test_repros.py::ReproTests::test_nonconst_issubclass, test/dynamo/test_repros.py::ReproTests::test_not_rewrite_assert_for_other_errors, test/dynamo/test_repros.py::ReproTests::test_nullcontext1, test/dynamo/test_repros.py::ReproTests::test_nullcontext2, test/dynamo/test_repros.py::ReproTests::test_numpy_not_ndarray_recompiles, test/dynamo/test_repros.py::ReproTests::test_numpy_tobytes_no_error, test/dynamo/test_repros.py::ReproTests::test_odict_get_item_index_name, test/dynamo/test_repros.py::ReproTests::test_omegaconf_dictconfig, test/dynamo/test_repros.py::ReproTests::test_omegaconf_listconfig_contains, test/dynamo/test_repros.py::ReproTests::test_omegaconf_listconfig_iter, test/dynamo/test_repros.py::ReproTests::test_ones_out_dynamic, test/dynamo/test_repros.py::ReproTests::test_optim_state_references_cleared, test/dynamo/test_repros.py::ReproTests::test_optimized_deepcopy, test/dynamo/test_repros.py::ReproTests::test_optimized_module_patched_init, test/dynamo/test_repros.py::ReproTests::test_optimized_module_training, test/dynamo/test_repros.py::ReproTests::test_os_fspath, test/dynamo/test_repros.py::ReproTests::test_out_nested_cell_shape_change, test/dynamo/test_repros.py::ReproTests::test_out_nested_cell_tuple_shape_change, test/dynamo/test_repros.py::ReproTests::test_out_none, test/dynamo/test_repros.py::ReproTests::test_out_overload_non_contiguous, test/dynamo/test_repros.py::ReproTests::test_out_root_cell_shape_change, test/dynamo/test_repros.py::ReproTests::test_out_root_cell_tuple_shape_change, test/dynamo/test_repros.py::ReproTests::test_output_aliases_intermediate, test/dynamo/test_repros.py::ReproTests::test_overlapping_inputs_with_dynamic_shapes_error, test/dynamo/test_repros.py::ReproTests::test_overwriting_params, test/dynamo/test_repros.py::ReproTests::test_partially_initialized_module_property, test/dynamo/test_repros.py::ReproTests::test_partitioner_activation_memory_budget_with_unbacked_symints, test/dynamo/test_repros.py::ReproTests::test_partitioner_cse_respects_mutation_boundaries, test/dynamo/test_repros.py::ReproTests::test_pointless_graph_removal, test/dynamo/test_repros.py::ReproTests::test_primtorch, test/dynamo/test_repros.py::ReproTests::test_primtorch_no_graph_break, test/dynamo/test_repros.py::ReproTests::test_randint_out_dynamic, test/dynamo/test_repros.py::ReproTests::test_recursive_map, test/dynamo/test_repros.py::ReproTests::test_reformer_eval, test/dynamo/test_repros.py::ReproTests::test_reformer_min_chunk_len, test/dynamo/test_repros.py::ReproTests::test_reformer_sorting, test/dynamo/test_repros.py::ReproTests::test_reformer_train, test/dynamo/test_repros.py::ReproTests::test_reinplacing, test/dynamo/test_repros.py::ReproTests::test_relative_import, test/dynamo/test_repros.py::ReproTests::test_relative_import_no_modulename, test/dynamo/test_repros.py::ReproTests::test_requires_grad_guards_with_grad_mode1, test/dynamo/test_repros.py::ReproTests::test_requires_grad_guards_with_grad_mode2, test/dynamo/test_repros.py::ReproTests::test_restricted_list_subclass1, test/dynamo/test_repros.py::ReproTests::test_restricted_list_subclass2, test/dynamo/test_repros.py::ReproTests::test_restricted_list_subclass3, test/dynamo/test_repros.py::ReproTests::test_return_value_duplication_mixed_grad, test/dynamo/test_repros.py::ReproTests::test_return_value_duplication_scalar, test/dynamo/test_repros.py::ReproTests::test_return_value_duplication_tensor, test/dynamo/test_repros.py::ReproTests::test_return_weakref, test/dynamo/test_repros.py::ReproTests::test_rewrite_assert_dont_change_bytecode, test/dynamo/test_repros.py::ReproTests::test_rewrite_assert_noop, test/dynamo/test_repros.py::ReproTests::test_rewrite_assert_with_msg, test/dynamo/test_repros.py::ReproTests::test_rewrite_assert_with_non_string_msg, test/dynamo/test_repros.py::ReproTests::test_rewrite_assert_without_msg, test/dynamo/test_repros.py::ReproTests::test_rng_state, test/dynamo/test_repros.py::ReproTests::test_seq_append_list, test/dynamo/test_repros.py::ReproTests::test_setattr_requires_grad_graph_breaks, test/dynamo/test_repros.py::ReproTests::test_setitem_boolean_mask_diff, test/dynamo/test_repros.py::ReproTests::test_setitem_tuple_boolean_mask_diff, test/dynamo/test_repros.py::ReproTests::test_sigmoid_out, test/dynamo/test_repros.py::ReproTests::test_sigmoid_out2, test/dynamo/test_repros.py::ReproTests::test_size_typematch, test/dynamo/test_repros.py::ReproTests::test_slice_into_list_mutable, test/dynamo/test_repros.py::ReproTests::test_slicing_dynamic_shape, test/dynamo/test_repros.py::ReproTests::test_slicing_dynamic_shape_setitem, test/dynamo/test_repros.py::ReproTests::test_sort_out, test/dynamo/test_repros.py::ReproTests::test_sort_out2, test/dynamo/test_repros.py::ReproTests::test_specialized_stride, test/dynamo/test_repros.py::ReproTests::test_split_with_sizes_aot_autograd, test/dynamo/test_repros.py::ReproTests::test_staticmethod_allow_in_graph, test/dynamo/test_repros.py::ReproTests::test_stk_sdd_is_transposed, test/dynamo/test_repros.py::ReproTests::test_stop_iteration_reconstruct, test/dynamo/test_repros.py::ReproTests::test_str_isalnum, test/dynamo/test_repros.py::ReproTests::test_string_format, test/dynamo/test_repros.py::ReproTests::test_subclass_graph_output_repro, test/dynamo/test_repros.py::ReproTests::test_super_classmethod, test/dynamo/test_repros.py::ReproTests::test_super_classmethod_inheritance, test/dynamo/test_repros.py::ReproTests::test_super_diamond, test/dynamo/test_repros.py::ReproTests::test_super_in_staticmethod, test/dynamo/test_repros.py::ReproTests::test_super_staticmethod, test/dynamo/test_repros.py::ReproTests::test_swin_base_tensor_attr, test/dynamo/test_repros.py::ReproTests::test_symint_bitwise, test/dynamo/test_repros.py::ReproTests::test_symnode_is_not_op, test/dynamo/test_repros.py::ReproTests::test_symnode_is_op, test/dynamo/test_repros.py::ReproTests::test_sys_monitoring, test/dynamo/test_repros.py::ReproTests::test_tensor_data_kwarg, test/dynamo/test_repros.py::ReproTests::test_tensor_isinstance_tuple, test/dynamo/test_repros.py::ReproTests::test_tensor_item, test/dynamo/test_repros.py::ReproTests::test_tensor_random, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_aot_eager_func_name_func1, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_aot_eager_func_name_func2, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_aot_eager_func_name_func3, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_eager_func_name_func1, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_eager_func_name_func2, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_eager_func_name_func3, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_inductor_func_name_func1, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_inductor_func_name_func2, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_backend_inductor_func_name_func3, test/dynamo/test_repros.py::ReproTests::test_tensor_set_data_mismatched_dtype, test/dynamo/test_repros.py::ReproTests::test_tensor_split, test/dynamo/test_repros.py::ReproTests::test_tensor_split_within_device_cm, test/dynamo/test_repros.py::ReproTests::test_tensor_uniform, test/dynamo/test_repros.py::ReproTests::test_threading_local, test/dynamo/test_repros.py::ReproTests::test_tokenization, test/dynamo/test_repros.py::ReproTests::test_torch_compile_in_compile_frame, test/dynamo/test_repros.py::ReproTests::test_torch_ops_aten, test/dynamo/test_repros.py::ReproTests::test_torch_tensor_ops, test/dynamo/test_repros.py::ReproTests::test_torch_tensor_ops_no_graph_break, test/dynamo/test_repros.py::ReproTests::test_torch_variable_type, test/dynamo/test_repros.py::ReproTests::test_torchname, test/dynamo/test_repros.py::ReproTests::test_trace_functional_tensor_with, test/dynamo/test_repros.py::ReproTests::test_tuple_enum_as_key_dict, test/dynamo/test_repros.py::ReproTests::test_typed_dict, test/dynamo/test_repros.py::ReproTests::test_typed_dict_total, test/dynamo/test_repros.py::ReproTests::test_udf_classes_reconstruction, test/dynamo/test_repros.py::ReproTests::test_unbacked_arange_in_bounds, test/dynamo/test_repros.py::ReproTests::test_unbind_copy_out, test/dynamo/test_repros.py::ReproTests::test_unpack_hooks_can_be_disabled, test/dynamo/test_repros.py::ReproTests::test_unpack_hooks_dont_run_during_tracing, test/dynamo/test_repros.py::ReproTests::test_unspecialized_nn_module_with_torch_variable_attribute, test/dynamo/test_repros.py::ReproTests::test_unsqueeze_mul_strides, test/dynamo/test_repros.py::ReproTests::test_user_ctor_ctx_manager, test/dynamo/test_repros.py::ReproTests::test_user_ctor_ctx_manager_custom_init, test/dynamo/test_repros.py::ReproTests::test_user_ctor_ctx_manager_custom_init_graph_break, test/dynamo/test_repros.py::ReproTests::test_user_defined_iter, test/dynamo/test_repros.py::ReproTests::test_user_defined_object_callable, test/dynamo/test_repros.py::ReproTests::test_validate_model_kwargs, test/dynamo/test_repros.py::ReproTests::test_vc_bumped_in_inference_graph, test/dynamo/test_repros.py::ReproTests::test_vdd_duplicate_error, test/dynamo/test_repros.py::ReproTests::test_view_dtype_overload, test/dynamo/test_repros.py::ReproTests::test_weakref, test/dynamo/test_repros.py::ReproTests::test_weakref_callback, test/dynamo/test_repros.py::ReproTests::test_weakref_construction, test/dynamo/test_repros.py::ReproTests::test_weakref_del, test/dynamo/test_repros.py::ReproTests::test_weakref_reconstruct, test/dynamo/test_repros.py::ReproTests::test_while_loop_graph_break, test/dynamo/test_repros.py::ReproTests::test_while_loop_graph_break_inside_call_function, test/dynamo/test_repros.py::ReproTests::test_with_on_graph_break_inst, test/dynamo/test_repros.py::ReproTests::test_with_on_graph_break_nested, test/dynamo/test_repros.py::ReproTests::test_zeros_out_dynamic, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_data_dependent_error_log_no_print_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_deepcopy_constant_tensor_in_aot_bwd_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_filter_safe_grad_warning_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_filter_user_warnings_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_filter_warnings_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_flash_attn_backward_mixed_strides_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_getattr_return_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_guard_default_device_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_megablocks_moe_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_memleak_when_graph_input_has_tensor_attr_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_module_attribute_error_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_named_tuple_vt_clone_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_norm_dtype_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_partitioner_saves_weights_for_bw_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_sdpa_dynamic_shapes_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_sub_alpha_scalar_repro_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_tensor_size_hasattr_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_torch_cuda_is_initialized_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_truthiness_of_symints_no_recompiles_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_udf_class_source_cuda, test/dynamo/test_repros.py::ReproTestsDeviceCUDA::test_zero_dim_param_mixed_device_grad_cuda
2025-08-14T21:56:14.3513007Z 
2025-08-14T21:56:14.3513097Z Running inductor/test_perf 1/1 ... [2025-08-14 21:56:14.343945]
2025-08-14T21:56:14.3513265Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:56:14.3513637Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_perf.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:56:14.344173]
2025-08-14T21:56:51.5802695Z 
2025-08-14T21:56:51.5804129Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_db15b996a2ba4608_.log
2025-08-14T21:56:51.5812856Z Running 66 items in this shard: test/inductor/test_perf.py::NumBytesMetricTests::test_cat, test/inductor/test_perf.py::NumBytesMetricTests::test_cat_pointwise, test/inductor/test_perf.py::NumBytesMetricTests::test_cat_pointwise_config_option, test/inductor/test_perf.py::NumBytesMetricTests::test_cat_pointwise_many_complex_inputs, test/inductor/test_perf.py::NumBytesMetricTests::test_cat_pointwise_many_simple_inputs, test/inductor/test_perf.py::NumBytesMetricTests::test_extern, test/inductor/test_perf.py::NumBytesMetricTests::test_index, test/inductor/test_perf.py::NumBytesMetricTests::test_pointwise, test/inductor/test_perf.py::NumBytesMetricTests::test_reduction, test/inductor/test_perf.py::FusionTests::test_create_block_mask, test/inductor/test_perf.py::FusionTests::test_double_softmax, test/inductor/test_perf.py::FusionTests::test_factory_reduction, test/inductor/test_perf.py::FusionTests::test_horizontal_reduction_outer_pointwise, test/inductor/test_perf.py::FusionTests::test_horizontal_reduction_pointwise, test/inductor/test_perf.py::FusionTests::test_horizontal_reduction_pointwise2, test/inductor/test_perf.py::FusionTests::test_horizontal_reduction_reduction, test/inductor/test_perf.py::FusionTests::test_horizontal_sum_pw_broadcast, test/inductor/test_perf.py::FusionTests::test_index_pointwise, test/inductor/test_perf.py::FusionTests::test_index_reduction, test/inductor/test_perf.py::FusionTests::test_layer_norm, test/inductor/test_perf.py::FusionTests::test_mutation_fusion, test/inductor/test_perf.py::FusionTests::test_neighbor, test/inductor/test_perf.py::FusionTests::test_norm_chain, test/inductor/test_perf.py::FusionTests::test_pointwise_multi_level_reduction, test/inductor/test_perf.py::FusionTests::test_reduction_pointwise_multi_level_reduction, test/inductor/test_perf.py::FusionTests::test_softmax_backward, test/inductor/test_perf.py::FusionTests::test_softmax_inner, test/inductor/test_perf.py::FusionTests::test_vertical_sum_pw, test/inductor/test_perf.py::SchedulerFusionTests::test_fusion_choice1, test/inductor/test_perf.py::SchedulerFusionTests::test_fusion_choice2, test/inductor/test_perf.py::SchedulerFusionTests::test_fusion_choice3, test/inductor/test_perf.py::SchedulerFusionTests::test_fusion_choice4_cpu, test/inductor/test_perf.py::TilingTests::test_tiling_simple, test/inductor/test_perf.py::TilingTests::test_tiling_three, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_cat, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_dtype, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_full_remat, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_keops, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_long_chain_add, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_partial_remat, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_relu, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_unremat_bw, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_unremat_bw2, test/inductor/test_perf.py::MinCutPartitioningTests::test_partitioning_with_view, test/inductor/test_perf.py::NoopTests::test_noop_cat, test/inductor/test_perf.py::NoopTests::test_noop_clones, test/inductor/test_perf.py::NoopTests::test_noop_device_conversion, test/inductor/test_perf.py::NoopTests::test_noop_dtype_conversion, test/inductor/test_perf.py::NoopTests::test_noop_int_ops, test/inductor/test_perf.py::NoopTests::test_noop_slice_scatter, test/inductor/test_perf.py::InplacingTests::test_inplace_custom_op, test/inductor/test_perf.py::InplacingTests::test_inplace_custom_op_intermediate, test/inductor/test_perf.py::InplacingTests::test_inplace_custom_op_training, test/inductor/test_perf.py::InplacingTests::test_inplace_custom_op_training_two_mutated_inputs, test/inductor/test_perf.py::InplacingTests::test_inplace_custom_op_two_mutated_inputs, test/inductor/test_perf.py::InplacingTests::test_inplace_randperm_scatter, test/inductor/test_perf.py::InplacingTests::test_inplace_scatter, test/inductor/test_perf.py::InplacingTests::test_inplace_scatter_noop_view, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_training, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v1, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v2, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v3, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v4, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v5, test/inductor/test_perf.py::InplacingTests::test_inplace_triton_kernel_v6, test/inductor/test_perf.py::InplacingTests::test_triton_kernel_not_fusable_with_users
2025-08-14T21:56:51.5825546Z 
2025-08-14T21:56:51.5825642Z Running inductor/test_pattern_matcher 1/1 ... [2025-08-14 21:56:51.580523]
2025-08-14T21:56:51.5825838Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:56:51.5826234Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_pattern_matcher.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:56:51.580766]
2025-08-14T21:57:27.4676640Z 
2025-08-14T21:57:27.4677620Z inductor/test_pattern_matcher 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_pattern_matcher_1.1_4b7dbf4dbb0cf10d_.log
2025-08-14T21:57:27.4685740Z Running 47 items in this shard: test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_addmm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_addmm_activation, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_addmm_broadcasting_bias, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_addmm_dtype_mismatch, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_addmm_symbolic_scalar, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_bmm_to_mm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_cat_addmm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_cat_mm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_cat_slice_cat_cuda, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_cat_splitwithsizes, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_duplicate_search, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_fused_int_mm_mul, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_fused_int_mm_mul_epilogue, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_fused_int_mm_mul_gating, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_match_equivalent_function_invocations1, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_match_equivalent_function_invocations2, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_match_equivalent_function_invocations3, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_match_with_mutation, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm_bad_cases, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm_cpu, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm_epi_works, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm_exhaustive_dtypes, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mixed_mm_gating, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mm_plus_mm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_multioutput_register_replacement, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_mutation_op_matching, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_original_aten_preserved_split_addmm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_convert, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_cumsum, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_permute_pair, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_permute_pair_3d, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_view_pair, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_pointless_view_pair_dynamic_shapes, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_remove_pointless_clones, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_replace_mul_zero, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_scaled_softmax, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_serialized_patterns_up_to_date, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_splitwithsizes_cat, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_stable_topological_sort, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_successful_partial_reuse_case0, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_successful_partial_reuse_case1, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_successful_partial_reuse_case2, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_symint_pattern_matching, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_unfuse_bias_addmm, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_unsuccessful_partial_reuse_case0, test/inductor/test_pattern_matcher.py::TestPatternMatcher::test_unsuccessful_partial_reuse_case1
2025-08-14T21:57:27.4698456Z 
2025-08-14T21:57:27.4698572Z Running inductor/test_inductor_freezing 1/1 ... [2025-08-14 21:57:27.467471]
2025-08-14T21:57:27.4698759Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:57:27.4699180Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:57:27.467743]
2025-08-14T21:58:04.4133789Z 
2025-08-14T21:58:04.4136489Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_b5c9295f0d0e8695_.log
2025-08-14T21:58:04.4147856Z Running 48 items in this shard: test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_aliased_param_return_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_autocast_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_functional_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_layout_convert_with_view_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_multiple_uses_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_weight_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_with_as_strided_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_cpp_wrapper_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_dont_change_dtype_folding_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_error_on_eager_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_hardswish_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_functional_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mm_concat_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mutation_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_param_deallocated_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_redundant_clone_for_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_rng_op_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_static_indices_cudagraph_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_symint_not_folded_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unequal_bias_horizontal_addmm_fusion_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unfolded_bn_cpu, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_aliased_param_return_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_autocast_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_bn_with_multi_bn_share_conv_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_functional_bn_with_multi_bn_share_conv_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_layout_convert_with_view_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_multiple_uses_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_weight_layout_convert_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_conv_with_as_strided_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_cpp_wrapper_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_dont_change_dtype_folding_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_error_on_eager_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_folded_conv_bn_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_folded_conv_bn_hardswish_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_folded_conv_bn_with_module_sharing_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_folded_conv_functional_bn_with_module_sharing_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_mm_concat_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_mutation_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_param_deallocated_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_redundant_clone_for_layout_convert_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_rng_op_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_static_indices_cudagraph_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_symint_not_folded_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_unequal_bias_horizontal_addmm_fusion_cuda, test/inductor/test_inductor_freezing.py::FreezingGpuTests::test_unfolded_bn_cuda
2025-08-14T21:58:04.4155673Z 
2025-08-14T21:58:04.4161263Z Running dynamo/test_higher_order_ops 1/1 ... [2025-08-14 21:58:04.413225]
2025-08-14T21:58:04.4161455Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:58:04.4161853Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_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'] ... [2025-08-14 21:58:04.413509]
2025-08-14T21:58:37.9878648Z 
2025-08-14T21:58:37.9879707Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_af6bda77c393c602_.log
2025-08-14T21:58:37.9919782Z Running 227 items in this shard: test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_access_module_attr, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_allow_python_side_effects_utility, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_constants, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_global_num, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_global_num_adds_guard, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_input_num, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_numpy_number, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_tracked, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_tracked_nested, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_untracked_global, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_untracked_global_nested, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_untracked_nonlocal, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_capture_value_created_in_subgraph, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_concat_unbacked_shape_tensor, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_branches_no_arguments, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_branches_no_arguments_no_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_free_variable_in_both_branches, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_graph_break_in_one_branch, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_pytree_operands, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_pytree_operands_with_non_tensor_leaves, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_side_effect_in_one_branches, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_source_fn_stack, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_subgraph_name_is_valid, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_with_constant_pred, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_cond_with_empty_operands, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_dynamic_shapes_over_vmap_batch_size, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_enum_arg, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_error_message_sane, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_fallback_on_graph_break_complicated, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_fallback_on_graph_break_simple, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_fallback_on_python_primitives_output, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_flat_list_output, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_fn_with_kwargs_in_torch_ops, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_freevars_as_inputs_to_wrap, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_grad_source_fn_stack, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hints_wrapper, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hints_wrapper_incorrect_type, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hints_wrapper_no_hints, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hints_wrapper_pytree_inputs, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hooks, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_hopify_generic_wrap, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_inlined_functions, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_internal_nonlocal, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_lift_tensor_constant, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_lift_tensors_with_compound_expressions, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_lift_tensors_with_shared_symbols, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_make_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_example_value_metadata_consistent_with_eager, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_graph_break, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_kwargs, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_lowers_to_graph, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_multi_return, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_pytree_return, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_side_effect, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_source_fn_stack, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_subgraph_name_is_valid, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_map_symint_input, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_modules, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_nested_tuple_output, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_nested_wrap, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_no_freevars, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_output_with_dict, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_register_mode, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_register_subclass, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_return_captured_var, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_return_captured_var_used_multiple_times, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_return_captured_vars, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_same_freevar_twice, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_del_existing_attr_global_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_del_existing_attr_global_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_del_existing_attr_nonlocal_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_del_existing_attr_nonlocal_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_in_body, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_local_list_append_no_graph_break, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_global_list, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_global_num, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_global_num_builtin, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_global_tensor, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_global_tensor_builtin, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_nonlocal_num, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_nonlocal_num_builtin, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_nonlocal_tensor, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_mutate_nonlocal_tensor_builtin, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_nested_nonlocal_list_append_graph_break, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_nonlocal_list_append_graph_break, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_existing_attr_global_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_existing_attr_global_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_existing_attr_nonlocal_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_existing_attr_nonlocal_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_new_attr_global_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_new_attr_global_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_new_attr_nonlocal_module, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_side_effect_set_new_attr_nonlocal_obj, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_symint_in_slice, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_symint_input, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_tensor_and_unbacked_symbol_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_tensor_to_list_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_tensor_with_unbacked_shape_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_unbacked_symbol_closure, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_vmap_multiply_scalar, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_vmap_source_fn_stack, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_all_kwarg, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_allow_local_assign_in_body_fn, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_default, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_default_else_branch, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_default_if_branch, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_int, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_only, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_kwarg_recompile, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_pytree_args_nested, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_pytree_args_not_const_symint_tensor, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_pytree_args_with_symint_constant, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_pytree_kwargs, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_source_fn_stack, test/dynamo/test_higher_order_ops.py::HigherOrderOpTests::test_wrap_subgraph_name_is_valid, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_dual_level_guard, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_emit_functorch_guard_if_active, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_grad_guard_fail, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_jvp_guard_fail, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_linearize_recompiles, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_grad_guard_ok, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_grad_vmap_guard_fail, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_guard_fail, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_guard_fail_different_state, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_guard_ok, test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_recompile_different_states, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_functional_call, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_functional_call_disable_inline_nn_module, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_functional_call_sequential_params_and_buffers, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_call_compiled_backward_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_call_torch_compile_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_capture_tensor, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_closure_scalar, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_fn_with_kwargs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_freevar_python_scalar, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_freevar_tensor, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_non_tensor_input, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_over_grad, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_pytree, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_recompile, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_two_tensor_all_grad_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_two_tensor_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_with_graph_break, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_grad_with_side_effect, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_hessian, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_hessian_argnums, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacfwd, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacfwd_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacfwd_randomness, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacfwd_two_tensors_argnums, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacrev, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacrev_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jacrev_two_tensors_argnums, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_call_torch_compile_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_freevar_python_scalar, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_freevar_tensor, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_jvp, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_simple, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_two_tensors_disable_enable_disable_grad, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_two_tensors_disable_grad, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_jvp_two_tensors_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_linearize_jvp_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vjp, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vjp_call_compiled_backward_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vjp_has_aux, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vjp_multiple_outputs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vjp_multiple_outputs_python_struct, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_call_compiled_backward_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_call_torch_compile_fn, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_free_const, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_free_tensor, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_get_wrapped, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_kwargs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_multiple_invocation_in_dims, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_multiple_invocation_out_dims, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_multiple_outputs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_multiple_outputs_diff_dims, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_multiple_outputs_out_dims_tuple, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_new_tensor_implicit_via_op, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_new_tensor_in_body, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_new_tensor_unused_in_body, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_out_dims_None, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_over_vmap_captured, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_over_vmap_two_inputs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_previous_illegal_op_no_graph_break, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_pytree_inputs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_recompile, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_recompile_different_config, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_recompile_same_config, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_recompile_with_randomness, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_side_effects, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_side_effects_append_input, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_two_inputs, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_two_inputs_tuple_in_dims, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_with_conditional_graph_break, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_with_graph_break, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_with_graph_break_2, test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_vmap_with_graph_break_lambda, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_cond_with_invalid_kwargs, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_cond_with_kwargs, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_cond_with_mismatched_output, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_dropout, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_dropout_inductor, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_fallback, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_flop_counter_for_cond, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_flop_counter_for_cond_unbalanced_branches, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_flop_counter_for_nested_cond, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_function, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_function_with_kwargs, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_module, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_non_aliasing_util, test/dynamo/test_higher_order_ops.py::ActivationCheckpointingTests::test_override_fallthrough_dispatch_key, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_aot_eager_auto_functionalize_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_aot_eager_cond_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_aot_eager_invoke_quant_packed_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_aot_eager_invoke_quant_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_aot_eager_invoke_subgraph_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_inductor_auto_functionalize_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_inductor_cond_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_inductor_invoke_quant_packed_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_inductor_invoke_quant_simple_cuda_float32, test/dynamo/test_higher_order_ops.py::TestHigherOrderOpsOpInfoCUDA::test_hops_compile_backend_inductor_invoke_subgraph_simple_cuda_float32
2025-08-14T21:58:37.9953423Z 
2025-08-14T21:58:37.9953501Z Running dynamo/test_modules 1/1 ... [2025-08-14 21:58:37.988016]
2025-08-14T21:58:37.9953663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:58:37.9954036Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modules.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:58:37.988248]
2025-08-14T21:58:54.2429303Z 
2025-08-14T21:58:54.2430593Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_6858fc3e60d1b8e2_.log
2025-08-14T21:58:54.2448451Z Running 134 items in this shard: test/dynamo/test_modules.py::NNModuleTests::test_access_by_keys, test/dynamo/test_modules.py::NNModuleTests::test_basicmodule1, test/dynamo/test_modules.py::NNModuleTests::test_basicmodule2, test/dynamo/test_modules.py::NNModuleTests::test_call_fn_with_non_const_inputs_safe, test/dynamo/test_modules.py::NNModuleTests::test_cfgmod, test/dynamo/test_modules.py::NNModuleTests::test_children, test/dynamo/test_modules.py::NNModuleTests::test_constloop, test/dynamo/test_modules.py::NNModuleTests::test_conv_call_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_call_super_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_transpose_call_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_conv_transpose_call_super_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_densenet, test/dynamo/test_modules.py::NNModuleTests::test_enumvalues, test/dynamo/test_modules.py::NNModuleTests::test_fnmember, test/dynamo/test_modules.py::NNModuleTests::test_fnmembercmp1, test/dynamo/test_modules.py::NNModuleTests::test_fnmembercmp2, test/dynamo/test_modules.py::NNModuleTests::test_forward_directly, test/dynamo/test_modules.py::NNModuleTests::test_generation_tag, test/dynamo/test_modules.py::NNModuleTests::test_hasattr, test/dynamo/test_modules.py::NNModuleTests::test_inject_module_parameters, test/dynamo/test_modules.py::NNModuleTests::test_intarg, test/dynamo/test_modules.py::NNModuleTests::test_iseval1, test/dynamo/test_modules.py::NNModuleTests::test_iseval2, test/dynamo/test_modules.py::NNModuleTests::test_isnonelayer, test/dynamo/test_modules.py::NNModuleTests::test_istraining1, test/dynamo/test_modules.py::NNModuleTests::test_istraining2, test/dynamo/test_modules.py::NNModuleTests::test_layerlist, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module1, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module2, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module4, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module5, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module6, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module7, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_bad_params, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_bad_params_call_function, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_kwargs, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_no_cls_to_become, test/dynamo/test_modules.py::NNModuleTests::test_lazy_module_speculation_log_divergence, test/dynamo/test_modules.py::NNModuleTests::test_module_attribute_precedence, test/dynamo/test_modules.py::NNModuleTests::test_module_call_module_with_static_forward, test/dynamo/test_modules.py::NNModuleTests::test_module_class_method, test/dynamo/test_modules.py::NNModuleTests::test_module_comparison, test/dynamo/test_modules.py::NNModuleTests::test_module_forward_has_graph_break, test/dynamo/test_modules.py::NNModuleTests::test_module_guard_name_is_valid, test/dynamo/test_modules.py::NNModuleTests::test_module_name_string, test/dynamo/test_modules.py::NNModuleTests::test_module_property, test/dynamo/test_modules.py::NNModuleTests::test_module_static_method, test/dynamo/test_modules.py::NNModuleTests::test_moduledict, test/dynamo/test_modules.py::NNModuleTests::test_moduledict_custom, test/dynamo/test_modules.py::NNModuleTests::test_modulelist, test/dynamo/test_modules.py::NNModuleTests::test_modulelist_custom, test/dynamo/test_modules.py::NNModuleTests::test_modulelist_nested, test/dynamo/test_modules.py::NNModuleTests::test_modulemethod1, test/dynamo/test_modules.py::NNModuleTests::test_modulemethod2, test/dynamo/test_modules.py::NNModuleTests::test_named_children, test/dynamo/test_modules.py::NNModuleTests::test_nn_module_setattr, test/dynamo/test_modules.py::NNModuleTests::test_nn_module_unspec_int_attr, test/dynamo/test_modules.py::NNModuleTests::test_nn_moduledict_contains, test/dynamo/test_modules.py::NNModuleTests::test_parameterdict, test/dynamo/test_modules.py::NNModuleTests::test_parameterdict_custom, test/dynamo/test_modules.py::NNModuleTests::test_parameters1, test/dynamo/test_modules.py::NNModuleTests::test_parameters2, test/dynamo/test_modules.py::NNModuleTests::test_parameters3, test/dynamo/test_modules.py::NNModuleTests::test_parameters4, test/dynamo/test_modules.py::NNModuleTests::test_parameters5, test/dynamo/test_modules.py::NNModuleTests::test_self_mutating1, test/dynamo/test_modules.py::NNModuleTests::test_seq, test/dynamo/test_modules.py::NNModuleTests::test_sequential_with_duplicated_module, test/dynamo/test_modules.py::NNModuleTests::test_sequential_with_duplicated_module2, test/dynamo/test_modules.py::NNModuleTests::test_simple_torch_function, test/dynamo/test_modules.py::NNModuleTests::test_stringmember, test/dynamo/test_modules.py::NNModuleTests::test_submodules1, test/dynamo/test_modules.py::NNModuleTests::test_submodules2, test/dynamo/test_modules.py::NNModuleTests::test_super1, test/dynamo/test_modules.py::NNModuleTests::test_super2, test/dynamo/test_modules.py::NNModuleTests::test_super_class_method, test/dynamo/test_modules.py::NNModuleTests::test_tensorlist, test/dynamo/test_modules.py::NNModuleTests::test_torch_function_with_closure, test/dynamo/test_modules.py::NNModuleTests::test_torch_mangled_class_name, test/dynamo/test_modules.py::NNModuleTests::test_unsupportedmethod, test/dynamo/test_modules.py::NNModuleTests::test_unsupportedmodule, test/dynamo/test_modules.py::NNModuleTests::test_viamodulecall, test/dynamo/test_modules.py::OptimizedModuleTest::test_assign_does_not_exist, test/dynamo/test_modules.py::OptimizedModuleTest::test_attr, test/dynamo/test_modules.py::OptimizedModuleTest::test_attr_precedence, test/dynamo/test_modules.py::OptimizedModuleTest::test_backward_hooks, test/dynamo/test_modules.py::OptimizedModuleTest::test_branch_on_nn_module_custom_bool, test/dynamo/test_modules.py::OptimizedModuleTest::test_branch_on_nn_module_custom_len, test/dynamo/test_modules.py::OptimizedModuleTest::test_buffer_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_composition, test/dynamo/test_modules.py::OptimizedModuleTest::test_composition_with_opt_mod, test/dynamo/test_modules.py::OptimizedModuleTest::test_delattr_on_compiled_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_dir, test/dynamo/test_modules.py::OptimizedModuleTest::test_dunder_call_explicitly, test/dynamo/test_modules.py::OptimizedModuleTest::test_globals_change_in_other_file, test/dynamo/test_modules.py::OptimizedModuleTest::test_guard_on_torch_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules_compiles, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_allowed_modules_compiles_self_contained, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_inner, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_outer, test/dynamo/test_modules.py::OptimizedModuleTest::test_hooks_skip_guards, test/dynamo/test_modules.py::OptimizedModuleTest::test_inline_inbuilt_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_nn_module_tensor, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_previously_seen_tensor, test/dynamo/test_modules.py::OptimizedModuleTest::test_mark_static_with_freezing, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_keys, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_name, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_dict_iter_values, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_patch, test/dynamo/test_modules.py::OptimizedModuleTest::test_module_setattr, test/dynamo/test_modules.py::OptimizedModuleTest::test_monkeypatching_forward, test/dynamo/test_modules.py::OptimizedModuleTest::test_nn_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_no_op_assignment, test/dynamo/test_modules.py::OptimizedModuleTest::test_no_recompile_on_nn_guarded_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_overridden_call, test/dynamo/test_modules.py::OptimizedModuleTest::test_param_order, test/dynamo/test_modules.py::OptimizedModuleTest::test_param_requires_grad, test/dynamo/test_modules.py::OptimizedModuleTest::test_patch_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_recompile_limit_on_freed_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_recompile_limit_on_guarded_nn_modules, test/dynamo/test_modules.py::OptimizedModuleTest::test_recursion, test/dynamo/test_modules.py::OptimizedModuleTest::test_save_and_load_all_backends, test/dynamo/test_modules.py::OptimizedModuleTest::test_save_and_load_inductor, test/dynamo/test_modules.py::OptimizedModuleTest::test_setattr_on_compiled_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_to, test/dynamo/test_modules.py::OptimizedModuleTest::test_trace_delattr, test/dynamo/test_modules.py::OptimizedModuleTest::test_udo_instance_method_as_hook, test/dynamo/test_modules.py::OptimizedModuleTest::test_unhashable_nn_submodule, test/dynamo/test_modules.py::OptimizedModuleTest::test_unspec_non_inlinable_module, test/dynamo/test_modules.py::OptimizedModuleTest::test_unspecialized_seq, test/dynamo/test_modules.py::OptimizedModuleTest::test_user_defined_nn_module_dynamic, test/dynamo/test_modules.py::NNModuleTestsDeviceCUDA::test_lazy_module3_cuda
2025-08-14T21:58:54.2466857Z 
2025-08-14T21:58:54.2466969Z Running dynamo/test_backends 1/1 ... [2025-08-14 21:58:54.242917]
2025-08-14T21:58:54.2467132Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:58:54.2467516Z 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'] ... [2025-08-14 21:58:54.243133]
2025-08-14T21:59:04.0761145Z 
2025-08-14T21:59:04.0762796Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_8d0c63028abab758_.log
2025-08-14T21:59:04.0769558Z 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
2025-08-14T21:59:04.0774446Z 
2025-08-14T21:59:04.0781200Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-08-14 21:59:04.076288]
2025-08-14T21:59:04.0781563Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:59:04.0782171Z 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'] ... [2025-08-14 21:59:04.076539]
2025-08-14T21:59:20.4236697Z 
2025-08-14T21:59:20.4244589Z 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_03add6b6c3a5a1d8_.log
2025-08-14T21:59:20.4252311Z Running 31 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_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
2025-08-14T21:59:20.4258429Z 
2025-08-14T21:59:20.4258531Z Running inductor/test_select_algorithm 1/1 ... [2025-08-14 21:59:20.423636]
2025-08-14T21:59:20.4258721Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:59:20.4259141Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_select_algorithm.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:59:20.423904]
2025-08-14T21:59:55.6532703Z 
2025-08-14T21:59:55.6533639Z inductor/test_select_algorithm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_select_algorithm_1.1_566dcb2f51832741_.log
2025-08-14T21:59:55.6536974Z Running 21 items in this shard: test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_TritonTemplateCaller_str, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test__int_mm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_addmm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_addmm_fp16, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_baddbmm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_bmm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_convolution1, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_convolution2, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_convolution_as_mm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_linear_relu, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_dropout, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_dup_args, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_dup_args_view, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_not_even_k, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_plus_mm, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_plus_mm2, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_plus_mm3, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_mm_skip, test/inductor/test_select_algorithm.py::TestSelectAlgorithm::test_preprocessing_single_choice, test/inductor/test_select_algorithm.py::TestTemplateRender::test_finalized_subclass_hooks
2025-08-14T21:59:55.6544722Z 
2025-08-14T21:59:55.6544862Z Running inductor/test_compiled_autograd 1/2 ... [2025-08-14 21:59:55.653450]
2025-08-14T21:59:55.6545052Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T21:59:55.6545461Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_compiled_autograd.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:59:55.653743]
2025-08-14T22:06:01.1222591Z 
2025-08-14T22:06:01.1224701Z inductor/test_compiled_autograd 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compiled_autograd_1.2_dc223bc7e043d77e_.log
2025-08-14T22:06:01.1302460Z Running 423 items in this shard: test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_1_1, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_1_2, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_1_3, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_1_5_2, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_3_1, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_accumulate_grad_polyfill_case_3_2, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_anomaly_mode_already_nan, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_anomaly_mode_backward, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_anomaly_mode_grad, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_basic_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_data_dependent_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_id_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_non_traceable, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_saved_dynamic_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_saved_float_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_saved_int_is_traceable_False, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_autograd_cpp_node_saved_int_is_traceable_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_backward_hook_relative_ordering_partial, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cache_hit, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_checkpointing_sac, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_checkpointing_simple_reentrant_False, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_checkpointing_simple_reentrant_True, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_api_compile_backend_aot_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_api_compile_backend_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_api_compile_backend_inductor, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_api_optimize_backend_aot_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_disable_api_compile_backend_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compile_api_disable_api_compile_backend_inductor, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_compiled_autograd_does_not_specialize_on_bw_symints, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cpu_offloading, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cudagraphs_cpu_graph, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cudagraphs_cpu_scalar_used_in_cpp_custom_op, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cudagraphs_cpu_scalar_used_in_python_custom_op, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_cudagraphs_sdpa, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_bw_graph_break, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_compiled_fw_bw_graph_break, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_dynamically_defined_class, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_multiple_grads, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_saved_attr, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_saved_multiple_tensors, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_custom_fn_saved_tensors, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_ddp_cpp_reducer_error, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_ddp_python_reducer, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_disk_offloading, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_dynamic_shapes_annotations, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_dynamic_shapes_eager_node, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_dynamo_boxed, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_flex_attention, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_free_activation_memory_subclass, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_higher_order_gradients, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_inplace_grad_update, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_inputs_aliasing_bytecode_stack_restore, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_issue106555, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_keep_graph_usage_after_compiled, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_logging_tensor_flaky, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_optimize_assert_backend_aot_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_optimize_assert_backend_eager, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_optimize_assert_backend_inductor, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_output_nodes_all_leaves, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_reorder_multi_pre_hooks, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_reorder_multi_tensor_pre_hooks, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_reset, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_saved_tensor_unpack_hook_ordering, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_tensor_grad_hook1, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_tensor_grad_hook2, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_torch_compile_only_backward_call, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_torch_function_mode, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_trace_run_with_rng_state, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_verbose_logs_aot_dispatcher_nodes, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_verbose_logs_aot_dispatcher_nodes_hop, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_verbose_logs_cpp, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_verbose_logs_dynamic_shapes, test/inductor/test_compiled_autograd.py::TestCompiledAutograd::test_verbose_logs_snapshot, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_access_saved_tensor_twice_without_recomputation_works, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_accumulate_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_accumulate_grad_posthooks_can_observe_tensor_prehook, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_accumulate_grad_posthooks_should_not_execute, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_accumulate_grad_with_zero_numel_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_anomaly_assign_parent_cleanup, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_anomaly_detect_nan, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_anomaly_mode_no_check_nan, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_inplace_view_of_view, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_inplace_views_creation_meta, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_inplace_views_cross_dtype, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_multiple_views_python, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_simple_views_python, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_views_codegen, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_badcalls, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_copy, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_create_graph_warns, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_hook_relative_ordering, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_no_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_twice_retained_graph_with_saved_values, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_twice_with_saved_values, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_backward_with_inputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_calculate_shape_util, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_callback_adds_callback, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_cant_create_saved_tensors, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpoint_detects_non_determinism, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpoint_valid_reset_on_error, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_correct_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_custom_function_works, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_dataparallel, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_detached_tensor_use_reentrant_False, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_input_requires_grad_False, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_input_requires_grad_True, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_checkpointing_without_reentrant_memory_savings, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_create_graph_and_full_backward_hook_cycle, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_current_graph_task_execution_order, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_autograd_no_early_free, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_autograd_repeated_grad_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_cycle, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_error, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_exception, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_forward_mode_non_differentiable, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_forward_mode_non_tensor_before_tensor_args, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_forward_mode_wrong_formula, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_mark_dirty_not_differentiable, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_preserve_torch_function_when_return_as_is, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_saved_tensors, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_setup_context_simple, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_custom_function_vmap_defaults, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_deep_reentrant, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_dep_nograd, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_dependent_backward, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_detach_base, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_detach_then_inplace_raises_in_autograd, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_disabling_saved_tensor_hooks, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_disabling_saved_tensor_hooks_nested, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_duplicate_backward_root, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_enable_grad_decorator_no_paren, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_first_grad_fn_access_in_no_grad_mode, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_free_deep_graph_complicated, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_free_deep_graph_pyfunction, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_function, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_batched_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_empty_inputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_fn_badcalls, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_fn_input_metadata, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_fn_prehooks, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_fn_prehooks_multiple_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_nonleaf, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_nonleaf_register_hook, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_to_node, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_to_node_inplace, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_to_node_materialize, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_grad_unreachable_discovery, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_check_batched_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_check_forward_or_backward_only, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_complex_non_complex_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_custom_error, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_dense_and_sparse_inputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_forward_ad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_forward_ad_respects_requires_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_forward_ad_runs_with_no_requires_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_input_layout2, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_input_layout4, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_output_shape_or_dtype_depend_on_values, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_test_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_gradcheck_validates_inputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_graph_save_on_cpu, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_hook_edge_case_when_called_with_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_hook_none, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_hooks_cpp, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_indexing, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_not_requires_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_on_view_backward, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_on_view_leaf_errors, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_on_view_weak_grad_fn, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_integer_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_legacy_function_deprecation_exception, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_lobpcg, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_mark_non_differentiable, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_materialize_grads, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_multi_backward, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_multi_backward_no_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_named_tensor_for_complex_views, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_naughty_anomaly_access, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_naughty_autograd_function_stashing_ctx, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_nested_anomaly_printstack_cleanup, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_next_functions, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_no_grad_python_function, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_no_requires_grad_inplace, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_no_unnecessary_save, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_not_implemented_fwad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_pickle, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_post_accumulate_grad_hook_gets_cleaned_up, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_post_accumulate_grad_hook_returns_not_None, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_pow_zero_tensor_gradient, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_power_function, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_prehook_ordering, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_profiler, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_profiler_aggregation_table, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_profiler_function_event_avg, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_profiler_seq_nr, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_profiler_shapes, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_record_function, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_reentrant_child_error, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_reentrant_with_callbacks_depth_0, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_reentrant_with_leaf_variable_hook, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_requires_grad_, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_retain_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_retain_grad_cycle, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_retains_grad_inplace_multiple_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_return_duplicate, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_return_duplicate_inplace, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_return_leaf, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_save_none_for_backward, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_save_on_cpu_and_checkpoint, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_save_output_nr, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_saved_tensor_hooks_custom_function_intermediates, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_saved_tensor_hooks_extra_enter_during_bw_no_leak, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_saved_variable_packing_unpacking_did_not_save_original_with_hooks, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_saved_variable_packing_unpacking_saved_original_with_default_hooks, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_saved_variable_version_counter, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_scalar_grad_mixed_device, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_select_expanded_v, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_set_data_tensorimpl_type, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_set_grad_coroutines_benign_exceptions, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_set_grad_enabled_wraps, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_set_grad_generator_functions, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_set_materialize_non_diff_grads, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_shape, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_sharded_grad, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_sparse_gather_both_scalar, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_sparse_gather_dim_neg, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_sparse_gather_ind_scalar, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_tensor_grad_warnings, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_tensor_hooks_inplace_multiple_outputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_thread_shutdown, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_too_many_grads, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_unrelated_inputs, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_unused_output, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_var_mean_differentiable, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_version_counter, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_view_func_replay_with_modified_state, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_volatile_deprecated, test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_will_engine_execute_node, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_early_stop_False, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_early_stop_True, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_kwargs_early_stop_True, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_non_tensor_inputs_and_outputs_early_stop_True, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_reentrant_backwards_early_stop_False, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_reentrant_backwards_early_stop_True, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_same_graph_early_stop_True, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_two_children_early_stop_False, test/inductor/test_compiled_autograd.py::TestNestedCheckpointWithCompiledAutograd::test_nested_checkpoint_two_children_early_stop_True, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_abstract_impl_on_existing_op, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_abstract_impl_on_existing_op_with_CompositeExplicitAutograd, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_dict_grad_for_nontensor, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_impl_on_existing_op_incorrect_schema_mutable, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_impl_on_existing_op_incorrect_schema_no_output, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_impl_on_existing_op_with_key_key_AutogradCUDA, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_output_differentiability_tensorlist, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_backward_tensorlist_input_requires_list_grads_with_same_numel, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_basic_make_fx, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_data_dependent_basic, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_data_dependent_nms_dynamic_compile, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_defined_in_python, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_duplicate_impl, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_abstract_overload, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_device_cpu, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_invalid_devices, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_multiple, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_on_existing_op_with_cpu_registration_key_CPU, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_on_existing_op_with_cpu_registration_key_CompositeImplicitAutograd, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_impl_separate, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_infer_schema_supported, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_infer_schema_unsupported, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_invalid_qualname, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_invalid_schemas, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_is_functional_schema, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_is_tensorlist_like_type, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_legacy_define, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_legacy_impl, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_meta_for_data_dependent_shape_operation, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_name_must_match, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_new_data_dependent_symint, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_override_impl, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_override_meta, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_private_ctor, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_supported_param_types, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_symints, test/inductor/test_compiled_autograd.py::TestCustomOpWithCompiledAutograd::test_unsupported_schemas, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_allow_python_side_effects_utility, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_capture_constants, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_capture_input_num, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_capture_numpy_number, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_capture_tracked, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_capture_untracked_global_nested, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_branches_no_arguments, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_free_variable_in_both_branches, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_graph_break_in_one_branch, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_pytree_operands, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_side_effect_in_one_branches, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_source_fn_stack, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_cond_with_constant_pred, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_fallback_on_graph_break_simple, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_freevars_as_inputs_to_wrap, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_grad_source_fn_stack, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_hints_wrapper_no_hints, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_hopify_generic_wrap, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_internal_nonlocal, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_lift_tensors_with_compound_expressions, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_kwargs, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_lowers_to_graph, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_multi_return, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_pytree_return, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_source_fn_stack, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_map_subgraph_name_is_valid, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_nested_tuple_output, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_nested_wrap, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_no_freevars, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_output_with_dict, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_register_subclass, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_return_captured_var, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_return_captured_var_used_multiple_times, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_return_captured_vars, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_del_existing_attr_global_obj, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_del_existing_attr_nonlocal_obj, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_local_list_append_no_graph_break, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_global_list, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_global_num, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_global_num_builtin, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_global_tensor, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_nonlocal_num, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_nonlocal_num_builtin, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_mutate_nonlocal_tensor_builtin, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_nested_nonlocal_list_append_graph_break, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_nonlocal_list_append_graph_break, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_set_existing_attr_global_module, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_set_existing_attr_global_obj, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_set_existing_attr_nonlocal_module, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_side_effect_set_new_attr_global_module, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_symint_in_slice, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_unbacked_symbol_closure, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_vmap_multiply_scalar, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_vmap_source_fn_stack, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_allow_local_assign_in_body_fn, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_kwarg, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_kwarg_default_else_branch, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_kwarg_only, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_kwarg_recompile, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_pytree_kwargs, test/inductor/test_compiled_autograd.py::HigherOrderOpTestsWithCompiledAutograd::test_wrap_source_fn_stack, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_functional_call_sequential_params_and_buffers, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_call_compiled_backward_fn, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_call_torch_compile_fn, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_fn_with_kwargs, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_freevar_python_scalar, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_freevar_tensor, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_has_aux, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_pytree, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_recompile, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_with_graph_break, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_grad_with_side_effect, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_hessian, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_hessian_argnums, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jacfwd, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jacfwd_has_aux, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jacrev_has_aux, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jacrev_two_tensors_argnums, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jvp_call_torch_compile_fn, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jvp_freevar_tensor, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jvp_has_aux, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jvp_simple, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_jvp_two_tensors_has_aux, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vjp_call_compiled_backward_fn, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vjp_multiple_outputs, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vjp_multiple_outputs_python_struct, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_call_torch_compile_fn, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_free_const, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_multiple_invocation_in_dims, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_multiple_invocation_out_dims, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_multiple_outputs_diff_dims, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_over_vmap_captured, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_pytree_inputs, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_recompile, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_recompile_different_config, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_recompile_same_config, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_side_effects, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_side_effects_append_input, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_two_inputs, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_two_inputs_tuple_in_dims, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_with_conditional_graph_break, test/inductor/test_compiled_autograd.py::FuncTorchHigherOrderOpTestsWithCompiledAutograd::test_vmap_with_graph_break, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_cond_with_invalid_kwargs, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_dropout_inductor, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_flop_counter_for_cond, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_flop_counter_for_cond_unbalanced_branches, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_function, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_module, test/inductor/test_compiled_autograd.py::ActivationCheckpointingTestsWithCompiledAutograd::test_non_aliasing_util, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_device_mesh_compile, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_basic_export, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_constructor_w_dynamo_disable, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_constructor_w_graph_break, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_different_gradient_placement, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_dont_recompile_on_same_placement_devicemesh, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_dynamic, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_dynamic_slice, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_dynamo_device_mesh_attrs, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_partial_placement_graph_output, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dtensor_partial_placement_redistribute_unbalanced_correct_strides, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_dtensor, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_dtensor_from_local_dynamic_shapes, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_dtensor_from_local_redistribute, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_dtensor_from_local_redistribute_async, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_dtensor_recompile, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_to_local_kwargs, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_dynamo_to_local_kwargs_forward_hook, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_fakify_dtensor, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_graph_input_is_async, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_placement_compile, test/inductor/test_compiled_autograd.py::TestDTensorCompileWithCompiledAutograd::test_unwrap_async_collective_tensor_tangent, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_cond_simple_cuda_float32, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_invoke_quant_packed_simple_cuda_float32, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_invoke_subgraph_simple_cuda_float32, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_map_nested_cuda_float32, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_map_simple_cuda_float32, test/inductor/test_compiled_autograd.py::TestCompiledAutogradOpInfoCUDA::test_hops_in_bwd_while_loop_simple_cuda_float32
2025-08-14T22:06:01.1368208Z 
2025-08-14T22:06:01.1368309Z Running dynamo/test_cudagraphs 1/1 ... [2025-08-14 22:06:01.122754]
2025-08-14T22:06:01.1368484Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:06:01.1374003Z 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'] ... [2025-08-14 22:06:01.123009]
2025-08-14T22:06:04.7449861Z 
2025-08-14T22:06:04.7450453Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_4aba5405a66161a8_.log
2025-08-14T22:06:04.7451512Z 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
2025-08-14T22:06:04.7452328Z 
2025-08-14T22:06:04.7452411Z Running dynamo/test_exc 1/1 ... [2025-08-14 22:06:04.745014]
2025-08-14T22:06:04.7454352Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:06:04.7455069Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:06:04.745227]
2025-08-14T22:06:08.5670696Z 
2025-08-14T22:06:08.5671834Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_3955cfa0fc295fc9_.log
2025-08-14T22:06:08.5675007Z Running 10 items in this shard: test/dynamo/test_exc.py::ExcTests::test_backend_suppress_line, test/dynamo/test_exc.py::ExcTests::test_graph_break_log, test/dynamo/test_exc.py::ExcTests::test_graph_break_log_generic_jump, test/dynamo/test_exc.py::ExcTests::test_internal_error_no_suppress, test/dynamo/test_exc.py::ExcTests::test_internal_error_suppress_errors, test/dynamo/test_exc.py::ExcTests::test_not_implemented_error, test/dynamo/test_exc.py::ExcTests::test_trigger_bisect_on_error, test/dynamo/test_exc.py::ExcTests::test_trigger_on_error, test/dynamo/test_exc.py::ExcTests::test_unsupported_error, test/dynamo/test_exc.py::ExcTests::test_unsupported_real_stack
2025-08-14T22:06:08.5677403Z 
2025-08-14T22:06:08.5677657Z Running inductor/test_binary_folding 1/1 ... [2025-08-14 22:06:08.567011]
2025-08-14T22:06:08.5678128Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:06:08.5679122Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_binary_folding.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:06:08.567256]
2025-08-14T22:06:50.9801317Z 
2025-08-14T22:06:50.9807090Z inductor/test_binary_folding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_binary_folding_1.1_2703a1b4645c48cd_.log
2025-08-14T22:06:50.9809989Z Running 6 items in this shard: test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_binary_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_bn_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_linear_binary_folding_cpu, test/inductor/test_binary_folding.py::FreezingGpuTests::test_conv_binary_folding_cuda, test/inductor/test_binary_folding.py::FreezingGpuTests::test_conv_bn_folding_cuda, test/inductor/test_binary_folding.py::FreezingGpuTests::test_linear_binary_folding_cuda
2025-08-14T22:06:50.9812019Z 
2025-08-14T22:06:50.9812247Z Running inductor/test_minifier 1/1 ... [2025-08-14 22:06:50.979920]
2025-08-14T22:06:50.9812695Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:06:50.9813914Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_minifier.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:06:50.980171]
2025-08-14T22:07:58.6743166Z 
2025-08-14T22:07:58.6744691Z inductor/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_1.1_bfc00295285f594b_.log
2025-08-14T22:07:58.6749682Z Running 14 items in this shard: test/inductor/test_minifier.py::MinifierTests::test_accuracy_vs_strict_accuracy, test/inductor/test_minifier.py::MinifierTests::test_after_aot_cpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_cpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_gpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_after_aot_gpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_cpu_compile_error_unflatten, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_accuracy_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_compile_error, test/inductor/test_minifier.py::MinifierTests::test_aoti_gpu_compile_error_unflatten, test/inductor/test_minifier.py::MinifierTests::test_constant_in_graph, test/inductor/test_minifier.py::MinifierTests::test_offload_to_disk, test/inductor/test_minifier.py::MinifierTests::test_rmse_improves_over_atol
2025-08-14T22:07:58.6752217Z 
2025-08-14T22:07:58.6752370Z Running inductor/test_flex_attention 4/4 ... [2025-08-14 22:07:58.673439]
2025-08-14T22:07:58.6752648Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:07:58.6753426Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:07:58.673669]
2025-08-14T22:17:09.8216566Z 
2025-08-14T22:17:09.8217395Z inductor/test_flex_attention 4/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_4.4_c1c87b6d7b3056c4_.log
2025-08-14T22:17:09.8257734Z Running 190 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod2_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod3_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_autograd_function_in_score_mod_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_256_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_256_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_256_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_256_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE_256_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_256_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_seqlen_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_dynamic_score_mask_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_dynamic_score_mask_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod0_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod0_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod2_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod5_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod6_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod7_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_score_mod7_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_captured_buffers_all_dims_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_captured_buffers_all_dims_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_captured_score_mod_aot_eager_gradcheck_score_mod_name__head_offset_mode_eager_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_captured_wrong_device_error_message_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_causal_block_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_causal_block_non_divisible_with_captured_buffer_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_epilogue_fused_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_eager_permute_order0_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_eager_permute_order1_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_eager_permute_order3_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order0_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order3_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_eager_permute_order0_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_eager_permute_order3_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_inductor_permute_order0_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_inductor_permute_order4_shape1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order0_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order2_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order3_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_flex_attention_stride_ordering_mode_paged_attention_permute_order4_shape0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_fully_masked_out_rows_0_check_compile_True_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_function_composition_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_index_weird1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kernel_options_argument_is_respected_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims1_head_dims0_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims0_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_load_from_view_buffer_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_logsumexp_correctness_score_mod1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_logsumexp_correctness_score_mod1_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_lse_masked_output_backend_eager_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_multiple_score_mod_calls2_paged_attention_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_multiple_score_mod_calls_paged_attention_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_njt_causal_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod0_head_dims0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod1_head_dims0_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod1_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod2_head_dims0_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod2_head_dims1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod3_head_dims0_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod3_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod4_head_dims1_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod5_head_dims0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod5_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod6_head_dims0_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod6_head_dims1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod7_head_dims0_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_equal_head_dims_score_mod7_head_dims1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_non_pow_2_headdim_head_dim_24_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_num_warps_8_error_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_silu_on_score_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_small_block_mask_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s0_k_s0_v_s0_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s0_k_s2_v_s2_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s0_v_s0_do_s1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s1_v_s1_do_s1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s1_v_s1_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_strided_inputs_q_s1_k_s3_v_s3_do_s2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_subgraph_respect_decompostion_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_symbol_closure_in_score_mod_cuda, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_convert_logical_block_mask_cuda, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod1_cuda_bfloat16, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod1_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod5_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod7_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_update_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_mask_operations_with_none_q_indices_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_mask_vs_sequence_lengths_compile_True_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_size_changes_BLOCK_SIZE4_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_compiling_create_block_mask_no_recompile_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_doc_mask_clamped_repro_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_eager_tracing_correctness_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_from_kv_blocks_without_q_computation_full_indices_False_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_upcast_appropriately_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_absolute_2d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_absolute_2d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_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_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_local_window_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:37_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:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda
2025-08-14T22:17:09.8295705Z 
2025-08-14T22:17:09.8295809Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T22:17:09.8295992Z Uploading artifacts took 0.00 seconds
2025-08-14T22:17:09.8296157Z Running inductor/test_cpu_select_algorithm 1/1 ... [2025-08-14 22:17:09.821768]
2025-08-14T22:17:09.8296344Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:17:09.8296751Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:17:09.822037]
2025-08-14T22:17:14.6336027Z 
2025-08-14T22:17:14.6337419Z inductor/test_cpu_select_algorithm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_1.1_e4968f487e784a93_.log
2025-08-14T22:17:14.6338320Z Running 0 items in this shard:
2025-08-14T22:17:14.6338531Z 
2025-08-14T22:17:14.6338780Z Running inductor/test_flex_decoding 1/2 ... [2025-08-14 22:17:14.633313]
2025-08-14T22:17:14.6339236Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:17:14.6340300Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_decoding.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:17:14.633514]
2025-08-14T22:25:27.2028105Z 
2025-08-14T22:25:27.2032799Z inductor/test_flex_decoding 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_decoding_1.2_21adb07cb7cb71c2_.log
2025-08-14T22:25:27.2091666Z Running 280 items in this shard: test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod0_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod0_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod0_head_dims2_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod1_head_dims2_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod2_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod3_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod3_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod4_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod5_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod6_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod6_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_bfloat16_score_mod8_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE_64_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE_64_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE_64_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod8_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod8_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_bfloat16_score_mod8_BLOCK_SIZE_128_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod0_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod1_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod1_BLOCK_SIZE_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod2_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod2_BLOCK_SIZE_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod2_BLOCK_SIZE_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod3_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod4_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod4_BLOCK_SIZE_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod5_BLOCK_SIZE_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod6_BLOCK_SIZE_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod6_BLOCK_SIZE_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod7_BLOCK_SIZE_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod8_BLOCK_SIZE2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float16_score_mod8_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod0_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod4_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod4_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod5_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod6_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod7_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod7_BLOCK_SIZE3_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod7_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod7_BLOCK_SIZE_64_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_different_block_size_float32_score_mod8_BLOCK_SIZE_64_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod0_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod0_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod0_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod1_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod2_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod3_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod4_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod4_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod5_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod5_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod6_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod6_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod7_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod7_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod8_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod8_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float16_score_mod8_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod0_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod0_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod1_head_dims2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod2_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod2_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod3_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod5_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod6_head_dims2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod7_head_dims2_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod8_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_builtin_score_mods_float32_score_mod8_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_captured_buffers_all_dims_bfloat16_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_captured_buffers_all_dims_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_captured_buffers_all_dims_float32_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_captured_buffers_bfloat16_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_captured_buffers_float32_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_decode_at_different_input_position_float16_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_decode_at_different_input_position_float16_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_decode_at_different_input_position_float16_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_do_not_trigger_dynamic_shapes_on_empty_block_mask_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_fully_masked_out_rows_0_check_gqa_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_function_composition_float32_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod0_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod1_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod2_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod2_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod2_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod3_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod4_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod4_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod5_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod5_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod6_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod7_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_head_dependent_mask_mod_float16_score_mod7_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims0_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims0_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims0_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims0_score_mod5_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims0_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod5_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims1_score_mod8_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims2_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims2_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims2_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims2_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims2_score_mod8_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod5_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod6_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims0_batch_dims3_score_mod8_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims0_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims0_score_mod5_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims1_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims1_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims1_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims1_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims2_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims2_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims2_score_mod6_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims2_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims1_batch_dims3_score_mod8_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims0_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims0_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims0_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims0_score_mod5_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims0_score_mod6_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims1_score_mod3_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims1_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims1_score_mod6_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims1_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims2_score_mod4_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims3_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims3_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims3_score_mod6_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims3_score_mod7_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_kv_batch_broadcast_float16_head_dims2_batch_dims3_score_mod8_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_larger_block_mask_bug_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_load_from_bias_seq_only_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_logsumexp_correctness_bfloat16_score_mod0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_logsumexp_correctness_float16_score_mod0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_logsumexp_correctness_float16_score_mod1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_logsumexp_correctness_float32_score_mod0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_logsumexp_correctness_float32_score_mod1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_multiple_score_mod_calls2_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_multiple_score_mod_calls_paged_attention2_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_multiple_score_mod_calls_paged_attention_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_njt_causal_float32_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_divisible_multi_token_offset_mask_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_divisible_offset_mask_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_divisible_offset_mask_with_captured_buffer_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod0_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod0_float32_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod0_float32_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod1_float16_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod2_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod2_float16_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod2_float32_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod3_float16_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod3_float32_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod4_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod4_bfloat16_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod4_float32_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod5_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod5_bfloat16_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod5_float16_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod5_float32_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod5_float32_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod6_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod6_float16_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod7_float32_head_dims0_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod8_bfloat16_head_dims0_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod8_bfloat16_head_dims1_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod8_float16_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod8_float16_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_equal_head_dims_score_mod8_float32_head_dims1_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_pow_2_headdim_head_dim_121_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_non_pow_2_headdim_head_dim_24_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_padded_dense_causal_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod0_head_dims0_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod0_head_dims1_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod0_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod0_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod0_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod1_head_dims0_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod1_head_dims1_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod1_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod1_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod2_head_dims0_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod2_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod2_head_dims2_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod2_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod3_head_dims0_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod3_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod3_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod3_head_dims2_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod3_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod4_head_dims0_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod4_head_dims0_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod4_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod4_head_dims2_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod4_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod5_head_dims0_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod5_head_dims1_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod5_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod5_head_dims2_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod6_head_dims0_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod6_head_dims0_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod6_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod6_head_dims2_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod6_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod7_head_dims0_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod7_head_dims1_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod7_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod7_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims0_page_size_128_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims0_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims0_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims1_page_size_256_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims1_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_paged_attention_page_size_float16_score_mod8_head_dims2_page_size_64_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_recompile_changed_score_mod_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_skip_odd_keys_bfloat16_cuda_bfloat16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_skip_odd_keys_float32_cuda_float32, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s0_v_s0_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s0_v_s0_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s0_v_s2_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s0_v_s3_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s0_v_s3_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s0_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s0_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s1_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s2_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s2_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s3_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s1_v_s3_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s1_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s1_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s1_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s2_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s2_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s3_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s3_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s2_v_s3_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s3_v_s0_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s3_v_s1_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s3_v_s1_head_dims1_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s3_v_s2_head_dims0_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_strided_inputs_float16_k_s3_v_s3_head_dims2_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_subgraph_respect_decompostion_float16_cuda_float16, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_windowed_full_mask_vs_sdpa_cuda, test/inductor/test_flex_decoding.py::TestFlexDecodingCUDA::test_windowed_partial_block_vs_sdpa_paged_attention_cuda
2025-08-14T22:25:27.2139654Z 
2025-08-14T22:25:27.2139806Z Running test_testing 1/1 ... [2025-08-14 22:25:27.203450]
2025-08-14T22:25:27.2139979Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:25:27.2140393Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_testing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:25:27.203720]
2025-08-14T22:25:52.8721900Z 
2025-08-14T22:25:52.8722775Z test_testing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_testing_1.1_307fe6b8ca9a9194_.log
2025-08-14T22:25:52.9013085Z Running 2072 items in this shard: test/test_testing.py::TestTestingCUDA::test_assertEqual_longMessage_cuda, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_bool, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_complex128, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_complex64, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_float16, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_float32, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_float64, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_int16, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_int32, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_int64, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_int8, test/test_testing.py::TestTestingCUDA::test_assertEqual_numpy_cuda_uint8, test/test_testing.py::TestTestingCUDA::test_cuda_assert_should_not_stop_common_distributed_test_suite_cuda, test/test_testing.py::TestTestingCUDA::test_cuda_assert_should_stop_common_device_type_test_suite_cuda, test/test_testing.py::TestTestingCUDA::test_cuda_assert_should_stop_common_utils_test_suite_cuda, test/test_testing.py::TestTestingCUDA::test_get_supported_dtypes_cuda, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_bool, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_float16, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_float32, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_float64, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_int16, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_int32, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_int64, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_int8, test/test_testing.py::TestTestingCUDA::test_isclose_atol_rtol_greater_than_zero_cuda_uint8, test/test_testing.py::TestTestingCUDA::test_isclose_bool_cuda, test/test_testing.py::TestTestingCUDA::test_isclose_complex_cuda_complex128, test/test_testing.py::TestTestingCUDA::test_isclose_complex_cuda_complex64, test/test_testing.py::TestTestingCUDA::test_isclose_equality_shortcut_cuda, test/test_testing.py::TestTestingCUDA::test_isclose_float_cuda_float16, test/test_testing.py::TestTestingCUDA::test_isclose_float_cuda_float32, test/test_testing.py::TestTestingCUDA::test_isclose_float_cuda_float64, test/test_testing.py::TestTestingCUDA::test_isclose_integer_cuda_int16, test/test_testing.py::TestTestingCUDA::test_isclose_integer_cuda_int32, test/test_testing.py::TestTestingCUDA::test_isclose_integer_cuda_int64, test/test_testing.py::TestTestingCUDA::test_isclose_integer_cuda_int8, test/test_testing.py::TestTestingCUDA::test_isclose_integer_cuda_uint8, test/test_testing.py::TestTestingCUDA::test_isclose_nan_equality_shortcut_cuda_complex128, test/test_testing.py::TestTestingCUDA::test_isclose_nan_equality_shortcut_cuda_complex64, test/test_testing.py::TestTestingCUDA::test_isclose_nan_equality_shortcut_cuda_float16, test/test_testing.py::TestTestingCUDA::test_isclose_nan_equality_shortcut_cuda_float32, test/test_testing.py::TestTestingCUDA::test_isclose_nan_equality_shortcut_cuda_float64, test/test_testing.py::TestTestingCUDA::test_setup_and_teardown_run_for_device_specific_tests_cuda, test/test_testing.py::TestTestingCUDA::test_supported_dtypes_abs_cuda, test/test_testing.py::TestFrameworkUtils::test_filtering_env_var, test/test_testing.py::TestAssertClose::test_bool, test/test_testing.py::TestAssertClose::test_default_tolerance_selection_mismatching_dtypes, test/test_testing.py::TestAssertClose::test_docstring_examples, test/test_testing.py::TestAssertClose::test_matching, test/test_testing.py::TestAssertClose::test_matching_atol, test/test_testing.py::TestAssertClose::test_matching_conjugate_bit, test/test_testing.py::TestAssertClose::test_matching_nan, test/test_testing.py::TestAssertClose::test_matching_nan_with_equal_nan, test/test_testing.py::TestAssertClose::test_matching_rtol, test/test_testing.py::TestAssertClose::test_meta, test/test_testing.py::TestAssertClose::test_mismatching_dtype, test/test_testing.py::TestAssertClose::test_mismatching_dtype_no_check, test/test_testing.py::TestAssertClose::test_mismatching_layout, test/test_testing.py::TestAssertClose::test_mismatching_layout_no_check, test/test_testing.py::TestAssertClose::test_mismatching_shape, test/test_testing.py::TestAssertClose::test_mismatching_stride, test/test_testing.py::TestAssertClose::test_mismatching_stride_no_check, test/test_testing.py::TestAssertClose::test_mismatching_types, test/test_testing.py::TestAssertClose::test_mismatching_types_subclasses, test/test_testing.py::TestAssertClose::test_mismatching_types_type_equality, test/test_testing.py::TestAssertClose::test_mismatching_values, test/test_testing.py::TestAssertClose::test_mismatching_values_atol, test/test_testing.py::TestAssertClose::test_mismatching_values_rtol, test/test_testing.py::TestAssertClose::test_none, test/test_testing.py::TestAssertClose::test_none_mismatch, test/test_testing.py::TestAssertClose::test_numpy, test/test_testing.py::TestAssertClose::test_only_atol, test/test_testing.py::TestAssertClose::test_only_rtol, test/test_testing.py::TestAssertClose::test_scalar, test/test_testing.py::TestAssertClose::test_unexpected_error_compare, test/test_testing.py::TestAssertClose::test_unexpected_error_originate, test/test_testing.py::TestAssertClose::test_unknown_layout, test/test_testing.py::TestAssertClose::test_unknown_type, test/test_testing.py::TestAssertCloseMultiDeviceCUDA::test_mismatching_device_cuda, test/test_testing.py::TestAssertCloseMultiDeviceCUDA::test_mismatching_device_no_check_cuda, test/test_testing.py::TestAssertCloseErrorMessage::test_abs_diff, test/test_testing.py::TestAssertCloseErrorMessage::test_abs_diff_scalar, test/test_testing.py::TestAssertCloseErrorMessage::test_atol, test/test_testing.py::TestAssertCloseErrorMessage::test_identifier_scalars, test/test_testing.py::TestAssertCloseErrorMessage::test_identifier_tensor_likes, test/test_testing.py::TestAssertCloseErrorMessage::test_mismatched_elements, test/test_testing.py::TestAssertCloseErrorMessage::test_msg_callable, test/test_testing.py::TestAssertCloseErrorMessage::test_msg_str, test/test_testing.py::TestAssertCloseErrorMessage::test_not_close, test/test_testing.py::TestAssertCloseErrorMessage::test_not_equal, test/test_testing.py::TestAssertCloseErrorMessage::test_rel_diff, test/test_testing.py::TestAssertCloseErrorMessage::test_rel_diff_scalar, test/test_testing.py::TestAssertCloseErrorMessage::test_rtol, test/test_testing.py::TestAssertCloseErrorMessage::test_small_float_dtype, test/test_testing.py::TestAssertCloseErrorMessage::test_zero_div_zero, test/test_testing.py::TestAssertCloseContainer::test_mapping_mismatching_keys, test/test_testing.py::TestAssertCloseContainer::test_mapping_mismatching_values_msg, test/test_testing.py::TestAssertCloseContainer::test_sequence_mismatching_len, test/test_testing.py::TestAssertCloseContainer::test_sequence_mismatching_values_msg, test/test_testing.py::TestAssertCloseSparseCOO::test_matching_coalesced, test/test_testing.py::TestAssertCloseSparseCOO::test_matching_uncoalesced, test/test_testing.py::TestAssertCloseSparseCOO::test_mismatching_indices_msg, test/test_testing.py::TestAssertCloseSparseCOO::test_mismatching_nnz, test/test_testing.py::TestAssertCloseSparseCOO::test_mismatching_sparse_dims, test/test_testing.py::TestAssertCloseSparseCOO::test_mismatching_values_msg, test/test_testing.py::TestAssertCloseSparseCSR::test_matching, test/test_testing.py::TestAssertCloseSparseCSR::test_mismatching_col_indices_msg, test/test_testing.py::TestAssertCloseSparseCSR::test_mismatching_crow_indices_msg, test/test_testing.py::TestAssertCloseSparseCSR::test_mismatching_values_msg, test/test_testing.py::TestAssertCloseSparseCSC::test_matching, test/test_testing.py::TestAssertCloseSparseCSC::test_mismatching_ccol_indices_msg, test/test_testing.py::TestAssertCloseSparseCSC::test_mismatching_row_indices_msg, test/test_testing.py::TestAssertCloseSparseCSC::test_mismatching_values_msg, test/test_testing.py::TestAssertCloseSparseBSR::test_matching, test/test_testing.py::TestAssertCloseSparseBSR::test_mismatching_col_indices_msg, test/test_testing.py::TestAssertCloseSparseBSR::test_mismatching_crow_indices_msg, test/test_testing.py::TestAssertCloseSparseBSR::test_mismatching_values_msg, test/test_testing.py::TestAssertCloseSparseBSC::test_matching, test/test_testing.py::TestAssertCloseSparseBSC::test_mismatching_ccol_indices_msg, test/test_testing.py::TestAssertCloseSparseBSC::test_mismatching_row_indices_msg, test/test_testing.py::TestAssertCloseSparseBSC::test_mismatching_values_msg, test/test_testing.py::TestAssertCloseQuantized::test_matching_per_channel, test/test_testing.py::TestAssertCloseQuantized::test_matching_per_tensor, test/test_testing.py::TestAssertCloseQuantized::test_mismatching_is_quantized, test/test_testing.py::TestAssertCloseQuantized::test_mismatching_qscheme, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_exclude_zero_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types0_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types1_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types2_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high0_value_types3_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types0_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types1_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types2_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_int32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_int64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_int8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high1_value_types3_cuda_uint8, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_bfloat16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_bool, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_complex128, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_complex32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_complex64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_float16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_float32, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_float64, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_int16, test/test_testing.py::TestMakeTensorCUDA::test_low_ge_high_low_high2_value_types0_cuda_int32, 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test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_digamma_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_dist_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_div_floor_rounding_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_div_no_rounding_mode_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_div_trunc_rounding_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_dot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_double_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_dsplit_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_dstack_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_einsum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_empty_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_empty_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_empty_permuted_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_empty_strided_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_eq_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_equal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_erf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_erfc_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_erfinv_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_exp2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_exp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_expand_as_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_expand_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_expand_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_expm1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_exponential_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_eye_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_fft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_fft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_fftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_fftshift_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_hfft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_hfft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_hfftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ifft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ifft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ifftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ifftshift_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ihfft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ihfft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_ihfftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_irfft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_irfft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_irfftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_rfft2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_rfft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fft_rfftn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fill_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_flatten_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_flip_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fliplr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_flipud_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_float_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_float_power_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_floor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_floor_divide_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fmin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_fmod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_frac_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_frexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_full_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_full_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_gather_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_gcd_cuda_int64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ge_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_geometric_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_geqrf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_gradient_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_grid_sampler_2d_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_gt_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_half_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_hash_tensor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_heaviside_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_histc_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_hsplit_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_hstack_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_hypot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_i0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_igamma_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_igammac_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_imag_cuda_complex64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_add_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_fill_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_put_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_reduce_amax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_reduce_amin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_reduce_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_reduce_prod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_index_select_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_inner_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_int_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isclose_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isfinite_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isinf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isnan_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isneginf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isposinf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_isreal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_istft_cuda_complex64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_item_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_jiterator_2inputs_2outputs_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_jiterator_4inputs_with_extra_args_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_jiterator_binary_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_jiterator_binary_return_by_ref_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_jiterator_unary_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_kron_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_kthvalue_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lcm_cuda_int64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ldexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_le_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lerp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lgamma_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_cholesky_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_cholesky_ex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_cond_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_cross_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_det_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_diagonal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_eig_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_eigh_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_eigvals_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_eigvalsh_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_householder_product_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_inv_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_inv_ex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_ldl_factor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_ldl_factor_ex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_ldl_solve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lstsq_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lstsq_grad_oriented_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lu_factor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lu_factor_ex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_lu_solve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_matrix_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_matrix_power_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_matrix_rank_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_matrix_rank_hermitian_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_multi_dot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_norm_subgradients_at_zero_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_pinv_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_pinv_hermitian_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_pinv_singular_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_qr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_slogdet_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_solve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_solve_ex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_solve_triangular_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_svd_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_svdvals_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_tensorinv_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_tensorsolve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_vander_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_vecdot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linalg_vector_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linspace_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_linspace_tensor_overload_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log10_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log1p_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log_normal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log_softmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_log_softmax_with_dtype_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logaddexp2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logaddexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logcumsumexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logdet_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logical_and_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logical_not_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logical_or_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logical_xor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logit_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logspace_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logspace_tensor_overload_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_logsumexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_long_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lt_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lu_solve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_lu_unpack_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mH_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mT_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_amax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_amin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_argmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_argmin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_cumprod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_cumsum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_fill_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_log_softmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_logaddexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_logsumexp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_median_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_normalize_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_prod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_scatter_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_select_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_softmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_softmin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_std_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_sum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_masked_var_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_matmul_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_matrix_exp_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_max_binary_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_max_pool2d_with_indices_backward_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_max_reduction_no_dim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_max_reduction_with_dim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_maximum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_median_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_meshgrid_list_of_tensors_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_meshgrid_variadic_tensors_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_min_binary_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_min_reduction_no_dim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_min_reduction_with_dim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_minimum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mode_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_movedim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_msort_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mul_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_multinomial_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mv_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nan_to_num_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nanmean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nanmedian_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nanquantile_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nansum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_narrow_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_narrow_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_native_batch_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_native_dropout_backward_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_native_layer_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ne_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_neg_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_new_empty_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_new_empty_strided_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_new_full_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_new_ones_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_new_zeros_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nextafter_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_adaptive_avg_pool1d_cuda_float32, 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test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_interpolate_nearest_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_interpolate_trilinear_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_kl_div_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_l1_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_layer_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_leaky_relu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_linear_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_local_response_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_logsigmoid_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_margin_ranking_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_max_pool1d_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_max_pool2d_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_max_pool3d_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_max_unpool1d_cuda_float32, 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test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_multi_head_attention_forward_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_multi_margin_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_multilabel_margin_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_nll_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_normalize_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_one_hot_cuda_int64, 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test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_pixel_unshuffle_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_poisson_nll_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_prelu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_relu6_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_relu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_rms_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_rrelu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_selu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_silu_complex_cuda_complex64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_silu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_smooth_l1_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_soft_margin_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_softmin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_softmin_with_dtype_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_softplus_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_softshrink_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_softsign_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_tanhshrink_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_threshold_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_triplet_margin_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_unfold_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_upsample_bilinear_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nn_functional_upsample_nearest_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nonzero_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_nonzero_static_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_norm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_norm_fro_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_norm_inf_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_norm_nuc_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_normal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_normal_in_place_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_normal_number_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ones_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ones_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ormqr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_outer_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_pca_lowrank_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_permute_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_permute_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_pinverse_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polar_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polygamma_polygamma_n_0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polygamma_polygamma_n_1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polygamma_polygamma_n_2_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polygamma_polygamma_n_3_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_polygamma_polygamma_n_4_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_positive_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_pow_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_prod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_put_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_qr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_quantile_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_rad2deg_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_rand_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_randint_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_randint_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_randn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_randn_like_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_ravel_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_real_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_reciprocal_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_remainder_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_renorm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_repeat_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_repeat_interleave_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_reshape_as_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_reshape_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_resize__cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_resize_as__cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_resolve_conj_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_resolve_neg_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_roll_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_rot90_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_round_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_round_decimals_0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_round_decimals_3_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_round_decimals_neg_3_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_rsqrt_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_rsub_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scalar_tensor_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_add_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_reduce_amax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_reduce_amin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_reduce_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_reduce_prod_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_scatter_reduce_sum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_searchsorted_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_select_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_select_scatter_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sgn_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_short_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sigmoid_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sign_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_bartlett_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_blackman_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_cosine_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_exponential_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_gaussian_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_general_cosine_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_general_hamming_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_hamming_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_hann_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_kaiser_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signal_windows_nuttall_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_signbit_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sin_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sinc_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sinh_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_slice_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_slice_scatter_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_softmax_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_softmax_with_dtype_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sort_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sparse_mm_reduce_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sparse_sampled_addmm_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_airy_ai_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_bessel_j0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_bessel_j1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_bessel_y0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_bessel_y1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_chebyshev_polynomial_t_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_chebyshev_polynomial_u_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_chebyshev_polynomial_v_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_chebyshev_polynomial_w_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_entr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_erfcx_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_hermite_polynomial_h_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_hermite_polynomial_he_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_i0e_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_i1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_i1e_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_laguerre_polynomial_l_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_legendre_polynomial_p_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_log_ndtr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_modified_bessel_i0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_modified_bessel_i1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_modified_bessel_k0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_modified_bessel_k1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_ndtr_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_ndtri_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_scaled_modified_bessel_k0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_scaled_modified_bessel_k1_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_shifted_chebyshev_polynomial_t_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_shifted_chebyshev_polynomial_w_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_spherical_bessel_j0_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_xlog1py_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_special_zeta_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_split_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_split_list_args_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_split_with_sizes_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_split_with_sizes_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sqrt_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_square_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_squeeze_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_squeeze_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_squeeze_multiple_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_stack_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_std_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_std_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_std_mean_unbiased_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_std_unbiased_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_stft_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sub_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sum_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_sum_to_size_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_svd_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_svd_lowrank_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_t_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_t_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_take_along_dim_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_take_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tan_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tanh_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tensor_split_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tensordot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tile_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_to_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_to_sparse_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_topk_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_torch__scaled_mm_cuda_float8_e4m3fn, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_torch_ops_aten__flash_attention_forward_cuda_float16, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_torch_ops_aten__safe_softmax_default_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_trace_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_transpose_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_transpose_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_trapezoid_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_trapz_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_triangular_solve_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tril_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_tril_indices_cuda_int64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_triu_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_triu_indices_cuda_int64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_true_divide_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_trunc_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unbind_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unbind_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unflatten_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unfold_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unfold_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_uniform_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unique_consecutive_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unique_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unravel_index_cuda_int64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unsafe_chunk_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unsafe_split_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unsqueeze_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_unsqueeze_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_var_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_var_mean_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_var_mean_unbiased_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_var_unbiased_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_vdot_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_view_as_complex_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_view_as_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_view_as_real_cuda_complex64, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_view_copy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_view_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_vsplit_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_vstack_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_where_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_xlogy_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_zero__cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_zeros_cuda_float32, test/test_testing.py::TestOpInfoSampleFunctionsCUDA::test_opinfo_sample_generators_zeros_like_cuda_float32
2025-08-14T22:25:52.9292509Z 
2025-08-14T22:25:52.9292630Z Running export/test_export_training_ir_to_run_decomp 1/1 ... [2025-08-14 22:25:52.873741]
2025-08-14T22:25:52.9292823Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:25:52.9293294Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_export_training_ir_to_run_decomp.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:25:52.873959]
2025-08-14T22:30:12.5125532Z 
2025-08-14T22:30:12.5129855Z export/test_export_training_ir_to_run_decomp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_export_training_ir_to_run_decomp_1.1_37e022b3d039fa36_.log
2025-08-14T22:30:12.5316371Z Running 818 items in this shard: test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_assume_static_by_default_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_constraints_error_not_in_range_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_constraints_error_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_inline_constraints_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_slice_maxsize_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_slice_unbacked_dim1_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_export_strict_narrow_unbacked_expr_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_no_grad_param_inplace_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestDynamismExpression::test_reshape_view_backed_size_oblivious_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestDynamismExpression::test_export_assume_static_by_default_training_ir_to_decomp_nonstrict, 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test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_cond_int_closure_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_cond_unflatten_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_cond_with_module_stack_export_with_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_cond_with_module_stack_export_with_unflatten_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_aliasing_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_input_naming_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_no_user_inp_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_output_dup_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_output_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_requires_grad_const_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_return_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_constant_tensor_mutation_training_ir_to_decomp_strict, 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test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_exception_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_api_with_dynamic_shapes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_as_backend_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_associative_scan_lifted_buffers_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_associative_scan_symbol_dim_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_associative_scan_symbol_scandim_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_aten_to_unflatten_subclass_pre_dispatch_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_aten_to_unflatten_subclass_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_aten_to_unflatten_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_cond_preserve_torch_fn_for_subgraphs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_cond_symbool_pred_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_cond_warns_constant_pred_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_custom_decomp_table_basic_pop_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_custom_decomp_table_container_methods_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_custom_op_lib_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_custom_triton_kernel_mutable_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_custom_triton_kernel_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_decomp_torture_case_1_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_decomp_torture_case_2_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_decomps_dynamic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_decomps_simple_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_dynamo_config_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_for_training_run_decomp_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_for_training_with_container_type_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_for_training_with_dynamic_shapes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_for_training_with_mutation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_for_training_with_state_dict_hooks_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_default_kwargs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_keyword_only_args_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_kwargs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_pytree_kwargs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_var_keyword_args_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_var_keyword_pytree_args_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_func_with_var_postional_args_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_function_schema_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_graph_with_no_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_input_mutation_bug_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_input_mutation_dynamic_shape_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_input_mutation_static_shape_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_linear_preserve_dynamic_shape_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_max_nonstrict_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_max_onnx_reported_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_method_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_mod_constraints_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_preserve_linear_at_aot_level_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_preserve_linear_but_not_custom_op_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_scan_pytree_output_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_script_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_statically_known_true_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_then_compile_tensor_ctor_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_autocast_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_fake_tensor_inputs_on_cuda_devices_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_fake_tensor_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_inline_constraints_complex_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_inline_constraints_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_set_grad_enabled_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_export_with_wrong_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_external_call_non_strict_real_tensor_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_fake_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_fake_weights_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_filter_traceback_frames_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_float_conversion_from_int_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_float_conversion_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_fqn_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_from_node_metadata_export_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_full_on_scalar_tensor_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_hints_wrapper_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_hoo_inline_users_issue_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_if_functional_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_if_post_autograd_op_preserved_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_inline_script_class_method_recursive_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_inline_script_class_method_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_inline_script_function_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_inline_script_method_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_int_shape_specialization_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_intermediate_shape_comp_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_is_exporting_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_is_non_negative_check_function_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_is_nonzero_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_isnonzero_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_issue_113041_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_issue_157289_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_istft_op_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_keep_composite_ops_invalid_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_keep_composite_ops_linear_convd_for_training_ir_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_keep_composite_ops_linear_convd_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_kwarg_dynamic_shapes_diff_order_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_kwargs_reorder_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_layer_sharing_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_lazy_module_kwargs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_lifted_constants_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_linear_conv_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_malformed_fqn_from_source_name_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_map_buffers_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_map_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_mask_nonzero_static_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_masked_select_dynamic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_math_pow_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_mismatched_dynamic_shapes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_mixed_input_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_dict_key_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_input_subclasses_parameterization_nested_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_input_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_list_slice_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_module_with_dict_container_inp_out_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_modules_access_for_deleted_submodule_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_more_multidimensional_slicing_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_multidimensional_slicing_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_multinomial_dynamic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_multiple_definitions_same_name_dim_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nested_dynamic_shapes_spec_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nested_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nested_module_with_constant_buffer_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nested_module_with_init_buffer_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nested_module_with_parameter_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nn_module_stack_shared_submodule_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nn_module_stack_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_check_is_size_error_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_suggested_fixes_for_data_dependent_errors_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_tensor_computation_2_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_tensor_computation_3_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_tensor_computation_4_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_no_tensor_computation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_arg_name_dynamic_shapes_api_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_arg_name_dynamic_shapes_api_with_container_type_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_arg_name_dynamic_shapes_api_with_kwarg_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_persistent_buffer_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_strict_dynamic_shapes_suggested_fixes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_non_strict_dynamic_shapes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_none_buffers_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nonstrict_retrace_preserves_metadata_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nonzero_2_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_nonzero_dynamic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_not_registered_parameter_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_operator_aten_tensor_mode_variant_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_output_node_name_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_pad_sequence_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_param_util_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_partial_patched_forward_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_placeholder_naming_collisions_hoo_subgraphs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_placeholder_naming_collisions_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_placeholder_naming_order_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_placeholder_naming_order_variadic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_placeholder_update_preserving_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_predispatch_cond_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_predispatch_grad_wrappers_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_preserve_module_call_signature_unflatten_specialization_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_preserve_requires_grad_placeholders_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_preserve_shape_dynamism_for_unused_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_profiling_code_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_python_asserts_with_sym_int_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_pytree_register_data_class_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_pytree_register_nested_data_class_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_raise_user_error_when_guard_on_data_dependent_operation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_range_constraints_with_replacement_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_real_tensor_alias_dtype_mismatch_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_real_tensor_bool_cast_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_real_tensor_errors_on_aliasing_custom_op_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_real_tensor_for_max_op_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_real_tensor_size_mismatch_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_redundant_assert_max_upper_bound_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_redundant_asserts_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_refine_dynamic_shapes_from_suggested_fixes_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_register_constant_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_repeat_interleave_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_replace_unbacked_with_very_large_upperbound_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_replaced_unbacked_bindings_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_reshape_view_helper_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_retracable_ep_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_retrace_pre_autograd_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_run_decomposition_supports_user_input_mutation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_run_decompositions_keep_metadata_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_run_decompositions_keep_tensor_constant_metadata_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_runtime_assert_for_prim_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_runtime_assert_for_prm_str_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_runtime_assert_with_size_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_sdpa_gqa_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_sequential_slicing_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_set_example_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_set_grad_as_side_effect_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_set_grad_empty_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_set_grad_unflatten_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_setgrad_lifted_tensor_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_shared_submodule_nn_module_stack_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_simple_export_for_training_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_simple_unbacked_view_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_size_input_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_slice_nn_module_stack_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_solver_unsupported_sympy_function_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_specialize_derived_dim_roots_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_split_const_gm_with_lifted_constants_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_stack_trace_make_fx_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_stack_trace_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_state_primitives_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_state_shape_attribute_assignment_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_state_tensors_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_static_dim_constraints_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclass_nested_attr_access_complicated_metadata_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclass_nested_attr_access_const_metadata_not_top_level_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclass_nested_attr_access_const_metadata_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclass_nested_attr_access_submodule_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclass_nested_attr_access_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclasses_parameterization_nested_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_subclasses_parameterization_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_suggest_torch_checks_with_non_negative_check_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_suggest_torch_checks_with_regular_check_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_suggested_fixes_for_data_dependent_errors_basic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_suggested_fixes_for_data_dependent_errors_puzzlers_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_suggested_fixes_new_roots_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_sym_float_operators_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_sym_or_sym_and_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_sym_sqrt_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symbool_item_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symfloat_item_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_input_additional_inputs_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_input_basic_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_input_ranges_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_input_shapes_collection_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_input_specialization_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_item_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_output_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_symint_tensor_return_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_tensor_attribute_zero_args_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_tensor_constant_aten_to_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_tensor_constant_with_wrapped_method_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_to_module_with_mutated_buffer_multiple_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_to_module_with_mutated_buffer_multiple_update_sub_later_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_to_module_with_mutated_buffer_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_tolist_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_torch_check_eq_commutativity_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_torch_fn_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_trace_under_fake_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_train_eval_on_exported_preautograd_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_3d_matmul_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_bincount_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_bindings_for_divisible_u_symint_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_deferred_runtime_retrace_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_expand_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_infer_size_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_kth_value_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_linear_layer_norm_input_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_noncontig_lin_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_pad_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_scalar_constructor_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_slice_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_to_cond_passthrough_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_to_cond_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unbacked_unsqueeze_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_asserts_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_buffer_update_child2parent_swap_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_closure_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_isinstance_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_multiple_graphs_dispatch_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_multiple_graphs_preserve_signature_no_error_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_multiple_graphs_shared_submodule_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_multiple_graphs_state_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_no_unroll_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_placeholder_update_child2parent_swap_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_placeholder_update_grandchild2cousin_swap_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_5_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_6_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_buf_8_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_const_preserving_3_1_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_const_preserving_3_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_4_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_6_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_9_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_10_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_1_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_5_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_mutating_buf_preserving_7_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unflatten_random_dag_preserving_4_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unused_aliases_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_unused_constant_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_use_embedding_twice_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_user_input_and_buffer_mutation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_while_loop_assert_separation_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_while_loop_index_assertions_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_while_loop_simple_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_while_loop_tensor_constant_idx_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportTestExport::test_wrapper_module_training_ir_to_decomp_strict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test__scaled_dot_product_flash_attention_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_additional_inputs_constants_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_allow_explicit_guards_as_runtime_asserts_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_args_type_checked_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_aten_lift_fresh_copy_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_attention_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_attr_assignment_extra_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_automatic_constrain_size_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_automatic_dynamic_shapes_constant_relation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_automatic_dynamic_shapes_linear_relation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_automatic_dynamic_shapes_simple_equality_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_baddbmm_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_basic_non_strict_fake_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_basic_non_strict_real_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_bincount_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_buffer_util_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_capture_subclass_constructor_torch_ir_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_capture_subclass_constructor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_capture_subclass_wrong_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_ccode_python_mod_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_check_specialized_int_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_checks_to_constrain_range_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cleanup_dynamic_markers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_colin_unbacked_backed_vr_sub_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_colon_parameter_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_compiling_state_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_access_identical_symint_closure_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_branches_return_constant_int_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_branches_return_same_int_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_buffers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_contains_unbacked_no_escape_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_int_closure_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_unflatten_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_with_module_stack_export_with_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cond_with_module_stack_export_with_unflatten_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_aliasing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_input_naming_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_no_user_inp_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_output_dup_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_output_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_requires_grad_const_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_return_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_tensor_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_tensor_with_non_functional_nested_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constant_tensor_with_non_functional_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constrain_decomp_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constrain_size_in_eager_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constrain_size_with_constrain_value_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_constrain_size_with_various_cases_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_conv_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_crop_like_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_cse_for_symint_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_op_auto_functionalize_pre_dispatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_op_auto_functionalize_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_op_auto_warn_pre_dispatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_op_preserve_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_pytree_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_custom_tag_metadata_re_export_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_decomp_batch_norm_functional_predispatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_decomp_item_in_prim_after_decomposition_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_decomp_item_in_prim_before_decomposition_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_default_decomposition_core_cia_ops_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_1_2_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_integer_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_nested_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_out_of_order_repeat_derived_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_out_of_order_simplified_repeat_non_derived_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_out_of_order_simplified_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_out_of_order_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_derived_dim_repeat_derived_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_detect_leak_strict_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_device_to_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_device_to_gpu_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_device_to_mutation_float_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_device_to_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_device_to_static_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_1_2_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_auto_and_dim_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_dynamic_divisibility_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_dynamic_specialization_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_hint_range_violations_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dim_hint_ranges_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_disable_forced_specializations_errors_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_disable_forced_specializations_ok_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_distributed_all_gather_into_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_distributed_all_gather_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_distributed_all_reduce_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_distributed_all_to_all_single_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_distributed_reduce_scatter_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dont_duck_size_for_auto_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_double_lifted_constants_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_checks_aliasing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_checks_mutation_list_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_checks_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_checks_mutation_with_nan_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_fake_kernel_inference_errors_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_draft_export_infers_fake_kernel_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_duplicate_modules_with_non_persistent_buffers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_lr_shift_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_bounds_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_builder_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_builder_kwargs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_builder_pytree_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_dataclass_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_inferred_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_serdes_generic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_serdes_user_errors_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_serdes_various_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_shapes_spec_with_pytree_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_dynamic_sym_round_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_ends_of_bounds_oblivious_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_error_does_not_reference_eager_fallback_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_error_when_passing_mutating_primitive_op_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_exception_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_api_with_dynamic_shapes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_as_backend_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_associative_scan_lifted_buffers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_associative_scan_symbol_dim_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_associative_scan_symbol_scandim_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_aten_to_unflatten_subclass_pre_dispatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_aten_to_unflatten_subclass_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_aten_to_unflatten_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_cond_preserve_torch_fn_for_subgraphs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_cond_symbool_pred_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_cond_warns_constant_pred_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_custom_decomp_table_basic_pop_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_custom_decomp_table_container_methods_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_custom_op_lib_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_custom_triton_kernel_mutable_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_custom_triton_kernel_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_decomp_torture_case_1_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_decomp_torture_case_2_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_decomps_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_decomps_simple_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_dynamo_config_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_for_training_run_decomp_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_for_training_with_container_type_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_for_training_with_dynamic_shapes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_for_training_with_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_for_training_with_state_dict_hooks_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_default_kwargs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_keyword_only_args_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_kwargs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_pytree_kwargs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_var_keyword_args_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_var_keyword_pytree_args_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_func_with_var_postional_args_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_function_schema_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_graph_with_no_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_input_mutation_bug_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_input_mutation_dynamic_shape_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_input_mutation_static_shape_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_linear_preserve_dynamic_shape_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_max_nonstrict_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_max_onnx_reported_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_method_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_mod_constraints_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_module_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_preserve_linear_at_aot_level_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_preserve_linear_but_not_custom_op_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_scan_pytree_output_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_script_module_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_statically_known_true_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_then_compile_tensor_ctor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_autocast_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_fake_tensor_inputs_on_cuda_devices_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_fake_tensor_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_inline_constraints_complex_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_inline_constraints_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_set_grad_enabled_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_export_with_wrong_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_external_call_non_strict_real_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_fake_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_fake_weights_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_filter_traceback_frames_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_float_conversion_from_int_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_float_conversion_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_fqn_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_from_node_metadata_export_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_full_on_scalar_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_hints_wrapper_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_hoo_inline_users_issue_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_if_functional_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_if_post_autograd_op_preserved_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_inline_script_class_method_recursive_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_inline_script_class_method_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_inline_script_function_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_inline_script_method_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_int_shape_specialization_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_intermediate_shape_comp_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_is_exporting_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_is_non_negative_check_function_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_is_nonzero_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_isnonzero_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_issue_113041_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_issue_157289_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_istft_op_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_keep_composite_ops_invalid_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_keep_composite_ops_linear_convd_for_training_ir_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_keep_composite_ops_linear_convd_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_kwarg_dynamic_shapes_diff_order_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_kwargs_reorder_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_layer_sharing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_lazy_module_kwargs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_lifted_constants_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_linear_conv_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_malformed_fqn_from_source_name_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_map_buffers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_map_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_mask_nonzero_static_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_masked_select_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_math_pow_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_mismatched_dynamic_shapes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_mixed_input_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_dict_key_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_input_subclasses_parameterization_nested_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_input_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_list_slice_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_module_with_dict_container_inp_out_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_modules_access_for_deleted_submodule_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_more_multidimensional_slicing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_multidimensional_slicing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_multinomial_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_multiple_definitions_same_name_dim_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nested_dynamic_shapes_spec_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nested_module_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nested_module_with_constant_buffer_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nested_module_with_init_buffer_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nested_module_with_parameter_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nn_module_stack_shared_submodule_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nn_module_stack_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_check_is_size_error_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_suggested_fixes_for_data_dependent_errors_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_tensor_computation_2_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_tensor_computation_3_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_tensor_computation_4_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_no_tensor_computation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_with_container_type_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_arg_name_dynamic_shapes_api_with_kwarg_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_persistent_buffer_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_strict_dynamic_shapes_suggested_fixes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_non_strict_dynamic_shapes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_none_buffers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nonstrict_retrace_preserves_metadata_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nonzero_2_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_nonzero_dynamic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_not_registered_parameter_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_operator_aten_tensor_mode_variant_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_output_node_name_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_pad_sequence_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_param_util_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_partial_patched_forward_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_placeholder_naming_collisions_hoo_subgraphs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_placeholder_naming_collisions_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_placeholder_naming_order_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_placeholder_naming_order_variadic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_placeholder_update_preserving_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_predispatch_cond_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_predispatch_grad_wrappers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_preserve_module_call_signature_unflatten_specialization_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_preserve_requires_grad_placeholders_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_preserve_shape_dynamism_for_unused_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_profiling_code_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_python_asserts_with_sym_int_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_pytree_register_data_class_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_pytree_register_nested_data_class_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_raise_user_error_when_guard_on_data_dependent_operation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_range_constraints_with_replacement_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_real_tensor_alias_dtype_mismatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_real_tensor_bool_cast_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_real_tensor_errors_on_aliasing_custom_op_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_real_tensor_for_max_op_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_real_tensor_size_mismatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_redundant_assert_max_upper_bound_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_redundant_asserts_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_refine_dynamic_shapes_from_suggested_fixes_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_register_constant_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_repeat_interleave_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_replace_unbacked_with_very_large_upperbound_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_replaced_unbacked_bindings_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_reshape_view_helper_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_retracable_ep_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_retrace_pre_autograd_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_run_decomposition_supports_user_input_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_run_decompositions_keep_metadata_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_run_decompositions_keep_tensor_constant_metadata_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_runtime_assert_for_prim_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_runtime_assert_for_prm_str_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_runtime_assert_with_size_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_sdpa_gqa_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_sequential_slicing_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_set_example_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_set_grad_as_side_effect_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_set_grad_empty_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_set_grad_unflatten_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_setgrad_lifted_tensor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_shared_submodule_nn_module_stack_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_simple_export_for_training_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_simple_unbacked_view_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_size_input_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_slice_nn_module_stack_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_solver_unsupported_sympy_function_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_specialize_derived_dim_roots_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_split_const_gm_with_lifted_constants_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_stack_trace_make_fx_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_stack_trace_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_state_primitives_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_state_shape_attribute_assignment_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_state_tensors_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_static_dim_constraints_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclass_nested_attr_access_complicated_metadata_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclass_nested_attr_access_const_metadata_not_top_level_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclass_nested_attr_access_const_metadata_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclass_nested_attr_access_submodule_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclass_nested_attr_access_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclasses_parameterization_nested_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_subclasses_parameterization_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_suggest_torch_checks_with_non_negative_check_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_suggest_torch_checks_with_regular_check_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_suggested_fixes_for_data_dependent_errors_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_suggested_fixes_for_data_dependent_errors_puzzlers_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_suggested_fixes_new_roots_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_sym_float_operators_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_sym_or_sym_and_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_sym_sqrt_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symbool_item_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symfloat_item_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_input_additional_inputs_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_input_basic_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_input_ranges_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_input_shapes_collection_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_input_specialization_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_item_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_output_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_symint_tensor_return_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_tensor_attribute_zero_args_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_tensor_constant_aten_to_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_tensor_constant_with_wrapped_method_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_to_module_with_mutated_buffer_multiple_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_to_module_with_mutated_buffer_multiple_update_sub_later_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_to_module_with_mutated_buffer_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_tolist_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_torch_check_eq_commutativity_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_torch_fn_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_trace_under_fake_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_train_eval_on_exported_preautograd_module_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_3d_matmul_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_bincount_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_bindings_for_divisible_u_symint_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_deferred_runtime_retrace_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_expand_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_infer_size_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_kth_value_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_linear_layer_norm_input_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_noncontig_lin_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_pad_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_scalar_constructor_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_slice_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_to_cond_passthrough_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_to_cond_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unbacked_unsqueeze_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_asserts_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_buffer_update_child2parent_swap_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_closure_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_isinstance_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_multiple_graphs_dispatch_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_multiple_graphs_preserve_signature_no_error_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_multiple_graphs_shared_submodule_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_multiple_graphs_state_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_no_unroll_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_placeholder_update_child2parent_swap_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_placeholder_update_grandchild2cousin_swap_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_5_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_6_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_buf_8_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_const_preserving_3_1_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_const_preserving_3_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_4_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_6_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_9_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_10_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_1_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_4_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_5_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_mutating_buf_preserving_7_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unflatten_random_dag_preserving_4_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unused_aliases_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_unused_constant_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_use_embedding_twice_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_user_input_and_buffer_mutation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_while_loop_assert_separation_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_while_loop_index_assertions_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_while_loop_simple_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_while_loop_tensor_constant_idx_training_ir_to_decomp_nonstrict, test/export/test_export_training_ir_to_run_decomp.py::TrainingIRToRunDecompExportNonStrictTestExport::test_wrapper_module_training_ir_to_decomp_nonstrict
2025-08-14T22:30:12.5495363Z 
2025-08-14T22:30:12.5495444Z Running test_quantization 3/5 ... [2025-08-14 22:30:12.513635]
2025-08-14T22:30:12.5500656Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:30:12.5501044Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:30:12.513859]
2025-08-14T22:34:25.5630868Z 
2025-08-14T22:34:25.5637327Z test_quantization 3/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.5_1582826a01546516_.log
2025-08-14T22:34:25.5669002Z Running 256 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool, test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_batch_norm_relu, test/test_quantization.py::TestQuantizedOps::test_custom_module_multi_head_attention, test/test_quantization.py::TestQuantizedOps::test_hardswish, test/test_quantization.py::TestQuantizedOps::test_int8_add_onednn, test/test_quantization.py::TestQuantizedOps::test_int8_batch_norm_onednn, test/test_quantization.py::TestQuantizedOps::test_interpolate3d, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_pt2e, test/test_quantization.py::TestQuantizedOps::test_qadd_broadcast, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_cudnn, test/test_quantization.py::TestQuantizedOps::test_qelu, test/test_quantization.py::TestQuantizedOps::test_qprelu, test/test_quantization.py::TestQuantizedOps::test_qrelu6, test/test_quantization.py::TestQuantizedOps::test_quantized_equal, test/test_quantization.py::TestQuantizedOps::test_sigmoid_dequantize_rounding_error, test/test_quantization.py::TestQuantizedOps::test_sigmoid_non_observed, test/test_quantization.py::TestQNNPackOps::test_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_sigmoid, test/test_quantization.py::TestQuantizedLinear::test_qlinear_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_gelu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_qnnpack_free_memory_and_unpack, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_conv_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_conv_transpose_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv1d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_add_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardswish_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv3d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose3d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear, test/test_quantization.py::TestDynamicQuantizedOps::test_qrnncell, test/test_quantization.py::TestDynamicQuantizedOps::test_wrapped_fbgemm_linear_fp16, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_backward_per_tensor_with_amp, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_fq_serializable_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_channel_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cpu, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_backward_op_fake_quant_off, test/test_quantization.py::TestQuantizedTensor::test_decomposed_choose_qparams_per_token_asymmetric_backward, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cuda, test/test_quantization.py::TestQuantizedTensor::test_jit_serialization, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cuda, test/test_quantization.py::TestQuantizedTensor::test_per_tensor_to_device, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor_nhwc, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_int_repr, test/test_quantization.py::TestQuantizedTensor::test_qtensor_masked_fill_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_permute, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_quantize_per_channel_float_qparams, test/test_quantization.py::TestQuantizedTensor::test_torch_qtensor_deepcopy, test/test_quantization.py::TestObserver::test_histogram_observer_handle_OOM_due_to_close_min_max_value, test/test_quantization.py::TestObserver::test_histogram_observer_handle_close_to_infinity, test/test_quantization.py::TestObserver::test_histogram_observer_save_load_state_dict, test/test_quantization.py::TestObserver::test_zero_numel, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm3d, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add_relu, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv3d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_quant_dequant_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv1d, test/test_quantization.py::TestReferenceQuantizedModule::test_sparse, test/test_quantization.py::TestHistogramObserver::test_histogram_observer, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_extreme_inputs, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_same_inputs, test/test_quantization.py::TestHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_reduce_range, test/test_quantization.py::TestBackendConfig::test_backend_config_from_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_fused_module, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_reference_quantized_module, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_channel, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_activations, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_activations_in_non_leaf_module_list, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested3, test/test_quantization.py::TestQuantizeEagerQAT::test_dropout, test/test_quantization.py::TestQuantizeEagerQAT::test_qat_embedding_bag_errors, test/test_quantization.py::TestQuantizeEagerQAT::test_train_save_load_eval, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_numerics, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestModelNumericsEager::test_weight_only_activation_only_fakequant, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_functional_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_conv_static, test/test_quantization.py::TestEqualizeEager::test_converged, test/test_quantization.py::TestEqualizeEager::test_equalize, test/test_quantization.py::TestBiasCorrectionEager::test_conv_chain, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_relu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_multiple_inputs, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_dynamic_quant, test/test_quantization.py::TestQuantizeFx::test_attention, test/test_quantization.py::TestQuantizeFx::test_backend_config_check_for_weight_and_bias, test/test_quantization.py::TestQuantizeFx::test_backend_config_quantization_range, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_lowering, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_not_reference, test/test_quantization.py::TestQuantizeFx::test_convtranspose_per_channel_fails_early, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_multiple_users, test/test_quantization.py::TestQuantizeFx::test_deepcopy_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_fp32_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_leaky_relu_lowering, test/test_quantization.py::TestQuantizeFx::test_linear_tanh_lowering, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_mul_add_fp16_config, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_float_to_observed_mapping, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_prepared_model_deepcopy, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_type, test/test_quantization.py::TestQuantizeFx::test_qnnpack_backend_config, test/test_quantization.py::TestQuantizeFx::test_quantized_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_ref_conv_module, test/test_quantization.py::TestQuantizeFx::test_reuse_input_qconfig, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_torch_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_boolean_tensor, test/test_quantization.py::TestQuantizeFxOps::test_chunk, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_fp16, test/test_quantization.py::TestQuantizeFxOps::test_general_value_ops, test/test_quantization.py::TestQuantizeFxOps::test_leaky_relu, test/test_quantization.py::TestQuantizeFxOps::test_linear_static_fp16, test/test_quantization.py::TestQuantizeFxOps::test_quantized_mul_qat, test/test_quantization.py::TestQuantizeFxModels::test_prepare_serialize_switch_device_convert, test/test_quantization.py::TestQuantizeFxModels::test_qat_embedding_linear, test/test_quantization.py::TestQuantizeFxModels::test_switch_device_prepare_convert, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_quantization.py::TestGraphUtils::test_conv_bn_conv_relu, test/test_quantization.py::TestDuplicateDQPass::test_simple_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestNumericDebugger::test_prepare_for_propagation_comparison, test/test_quantization.py::TestQuantizePT2E::test_allow_implicit_sharing, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_throw, test/test_quantization.py::TestQuantizePT2E::test_disallow_eval_train, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_observer_dedup, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_qat, test/test_quantization.py::TestQuantizePT2E::test_groupwise_per_channel_quant, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_int16, test/test_quantization.py::TestXNNPACKQuantizer::test_add_mul_long, test/test_quantization.py::TestXNNPACKQuantizer::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_float32_max, test/test_quantization.py::TestXNNPACKQuantizer::test_qat_dynamic_linear, test/test_quantization.py::TestXNNPACKQuantizerModels::test_resnet18, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_lowering_to_x86, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_with_underscores, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu, test/test_quantization.py::TestFXGraphMatcher::test_dict_return_type, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_either_fp32_or_int8_input, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_only_kwargs_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_module, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_end_to_end, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_insert_padding, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_mobilenet_v2, test/test_quantization.py::TestFxModelReportDetector::test_conv_sub_class_considered, test/test_quantization.py::TestFxModelReportDetector::test_multiple_q_config_options, test/test_quantization.py::TestFxModelReportDetector::test_simple_conv, test/test_quantization.py::TestFxModelReportClass::test_equalization_mapping_generation, test/test_quantization.py::TestFxDetectOutliers::test_all_outlier_report_gen, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_prepare, test/test_quantization.py::TestEqualizeFx::test_selective_equalization, test/test_quantization.py::TestSerialization::test_linear_relu, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_debug, test/test_quantization.py::TestQuantizeJitPasses::test_fuse_linear, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_nested_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant_shared_class_type, test/test_quantization.py::TestQuantizeJitOps::test_cat_linear, test/test_quantization.py::TestQuantizeJitOps::test_clamp, test/test_quantization.py::TestQuantizeJitOps::test_elu, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_insert_quant_dequant_linear_dynamic, test/test_quantization.py::TestFusionPasses::test_quantized_add_relu_fusion, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_rnn, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_embedding_ops, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_bn_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_equalize, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fuse, test/test_quantization.py::TestFloat8DtypeCUDA::test_cast_round_trip_extremes_cuda_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCUDA::test_cast_round_trip_subnormals_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_cat_cuda_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCUDA::test_cat_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_creation_with_zeros_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_creation_with_zeros_cuda_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCUDA::test_finfo_cuda_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCUDA::test_save_load_cuda_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCUDA::test_special_numbers_cuda_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCUDA::test_to_string_cuda_float8_e8m0fnu
2025-08-14T22:34:25.5696589Z 
2025-08-14T22:34:25.5696730Z Running dynamo/test_package 1/1 ... [2025-08-14 22:34:25.563931]
2025-08-14T22:34:25.5696931Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:34:25.5697341Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/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'] ... [2025-08-14 22:34:25.564199]
2025-08-14T22:34:56.7359079Z 
2025-08-14T22:34:56.7363415Z dynamo/test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_package_1.1_d649de4aa02a4382_.log
2025-08-14T22:34:56.7376358Z Running 46 items in this shard: test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_autotune_cache_device_cuda, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_autotune_cache_device_xpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_graph_breaks_device_cpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_graph_breaks_device_cuda, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_graph_breaks_device_xpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_lazy_backward_device_cpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_lazy_backward_device_cuda, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_lazy_backward_device_xpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_recompiles_device_cpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_recompiles_device_cuda, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_recompiles_device_xpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_serialize_device_cpu, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_serialize_device_cuda, test/dynamo/test_package.py::TestPackage::test_automatic_dynamo_serialize_device_xpu, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_eager_device_cpu, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_eager_device_cuda, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_eager_device_xpu, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_inductor_device_cpu, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_inductor_device_cuda, test/dynamo/test_package.py::TestPackage::test_basic_fn_backend_inductor_device_xpu, test/dynamo/test_package.py::TestPackage::test_call_function_from_resume_device_cpu, test/dynamo/test_package.py::TestPackage::test_call_function_from_resume_device_cuda, test/dynamo/test_package.py::TestPackage::test_call_function_from_resume_device_xpu, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_eager_device_cpu, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_eager_device_cuda, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_eager_device_xpu, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_inductor_device_cpu, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_inductor_device_cuda, test/dynamo/test_package.py::TestPackage::test_dynamic_shape_backend_inductor_device_xpu, test/dynamo/test_package.py::TestPackage::test_dynamo_cache_manual_load_device_cpu, test/dynamo/test_package.py::TestPackage::test_dynamo_cache_manual_load_device_cuda, test/dynamo/test_package.py::TestPackage::test_dynamo_cache_manual_load_device_xpu, test/dynamo/test_package.py::TestPackage::test_file_change, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_eager_device_cpu, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_eager_device_cuda, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_eager_device_xpu, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_inductor_device_cpu, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_inductor_device_cuda, test/dynamo/test_package.py::TestPackage::test_graph_break_bomb_backend_inductor_device_xpu, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_eager_device_cpu, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_eager_device_cuda, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_eager_device_xpu, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_inductor_device_cpu, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_inductor_device_cuda, test/dynamo/test_package.py::TestPackage::test_lazy_backward_backend_inductor_device_xpu, test/dynamo/test_package.py::TestPackage::test_nn_module
2025-08-14T22:34:56.7383743Z 
2025-08-14T22:34:56.7383835Z Running export/test_db 1/1 ... [2025-08-14 22:34:56.735863]
2025-08-14T22:34:56.7384028Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:34:56.7384490Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_db.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:56.736146]
2025-08-14T22:35:08.3235688Z 
2025-08-14T22:35:08.3236319Z export/test_db 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_db_1.1_0ded1dc338a163a5_.log
2025-08-14T22:35:08.3244194Z Running 36 items in this shard: test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_dynamic_shape_round, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_model_attr_mutation, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_optional_input, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_unsupported_operator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_assume_constant_result, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_autograd_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nonlocal_variables, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_closed_over_variable, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_operands, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_predicate, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_size_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_value_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_decorator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dictionary, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_assert, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_constructor, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_if_guard, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_map, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_slicing, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_view, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_fn_with_kwargs, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_contains, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_unpack, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_null_context_manager, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_pytree_flatten, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_scalar_output, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_specialized_attribute, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_for_loop, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_if, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_tensor_setattr, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_type_reflection_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_user_input_mutation
2025-08-14T22:35:08.3251054Z 
2025-08-14T22:35:08.3251162Z Running export/test_schema 1/1 ... [2025-08-14 22:35:08.323444]
2025-08-14T22:35:08.3257229Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:08.3257731Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_schema.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:08.323683]
2025-08-14T22:35:10.8429332Z 
2025-08-14T22:35:10.8430661Z export/test_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_schema_1.1_6c6931b1caf4dc7b_.log
2025-08-14T22:35:10.8432621Z Running 5 items in this shard: test/export/test_schema.py::TestSchema::test_schema_check, test/export/test_schema.py::TestSchema::test_schema_comparison, test/export/test_schema.py::TestSchema::test_schema_compatibility, test/export/test_schema.py::TestSchema::test_schema_diff, test/export/test_schema.py::TestSchema::test_thrift_schema_unchanged
2025-08-14T22:35:10.8434871Z 
2025-08-14T22:35:10.8435091Z Running export/test_sparse 1/1 ... [2025-08-14 22:35:10.842877]
2025-08-14T22:35:10.8435532Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:10.8436556Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_sparse.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:10.843104]
2025-08-14T22:35:13.0125603Z 
2025-08-14T22:35:13.0135482Z export/test_sparse 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_sparse_1.1_d5d830d4318ef191_.log
2025-08-14T22:35:13.0169149Z Running 203 items in this shard: test/export/test_sparse.py::TestSparseProp::test_activation_coo, test/export/test_sparse.py::TestSparseProp::test_activation_csr, test/export/test_sparse.py::TestSparseProp::test_add, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_bfloat16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float32_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_float64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_eltwisenet_int64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_bfloat16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float32_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_float64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_idnet_int64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_bfloat16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float32_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_float64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_sumnet_int64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_bfloat16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float16_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float32_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_float64_int64_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int32_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int32_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int32_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int32_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int32_SparseCSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int64_SparseBSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int64_SparseBSR, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int64_SparseCOO, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int64_SparseCSC, test/export/test_sparse.py::TestSparseProp::test_todensenet_int64_int64_SparseCSR
2025-08-14T22:35:13.0191387Z 
2025-08-14T22:35:13.0191470Z Running export/test_swap 1/1 ... [2025-08-14 22:35:13.012881]
2025-08-14T22:35:13.0191637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:13.0192016Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'export/test_swap.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:13.013167]
2025-08-14T22:35:19.8915215Z 
2025-08-14T22:35:19.8916929Z export/test_swap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_swap_1.1_46bfc83da99fd02a_.log
2025-08-14T22:35:19.8922519Z Running 18 items in this shard: test/export/test_swap.py::TestSwap_nonstrict::test_custom_input_args, test/export/test_swap.py::TestSwap_nonstrict::test_custom_input_kwargs, test/export/test_swap.py::TestSwap_nonstrict::test_custom_output, test/export/test_swap.py::TestSwap_nonstrict::test_dedup_sym_size, test/export/test_swap.py::TestSwap_nonstrict::test_nested_leaf, test/export/test_swap.py::TestSwap_nonstrict::test_remove_duplicate_pytree_different_order, test/export/test_swap.py::TestSwap_nonstrict::test_remove_duplicate_pytree_simple, test/export/test_swap.py::TestSwap_nonstrict::test_unflatten_preserve_signature, test/export/test_swap.py::TestSwap_nonstrict::test_unflatten_preserve_with_unused_input, test/export/test_swap.py::TestSwap_strict::test_custom_input_args, test/export/test_swap.py::TestSwap_strict::test_custom_input_kwargs, test/export/test_swap.py::TestSwap_strict::test_custom_output, test/export/test_swap.py::TestSwap_strict::test_dedup_sym_size, test/export/test_swap.py::TestSwap_strict::test_nested_leaf, test/export/test_swap.py::TestSwap_strict::test_remove_duplicate_pytree_different_order, test/export/test_swap.py::TestSwap_strict::test_remove_duplicate_pytree_simple, test/export/test_swap.py::TestSwap_strict::test_unflatten_preserve_signature, test/export/test_swap.py::TestSwap_strict::test_unflatten_preserve_with_unused_input
2025-08-14T22:35:19.8928053Z 
2025-08-14T22:35:19.8928218Z Running inductor/test_autoheuristic 1/1 ... [2025-08-14 22:35:19.891420]
2025-08-14T22:35:19.8928500Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:19.8929081Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_autoheuristic.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:19.891721]
2025-08-14T22:35:24.4314777Z 
2025-08-14T22:35:24.4316364Z inductor/test_autoheuristic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_autoheuristic_1.1_970e4531016a3e54_.log
2025-08-14T22:35:24.4324117Z Running 0 items in this shard:
2025-08-14T22:35:24.4324257Z 
2025-08-14T22:35:24.4324917Z Running inductor/test_benchmarking 1/1 ... [2025-08-14 22:35:24.431446]
2025-08-14T22:35:24.4325195Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:24.4325843Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_benchmarking.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:24.431802]
2025-08-14T22:35:30.0577798Z 
2025-08-14T22:35:30.0578916Z inductor/test_benchmarking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmarking_1.1_b130edd684eee608_.log
2025-08-14T22:35:30.0584750Z Running 12 items in this shard: test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_cpu_smoke_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_cpu_smoke_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_gpu_smoke_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_gpu_smoke_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_many_devices_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_many_devices_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_no_devices_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_no_devices_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls0_device_cpu, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls0_device_cuda, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls1_device_cpu, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls1_device_cuda
2025-08-14T22:35:30.0595965Z 
2025-08-14T22:35:30.0596179Z Running inductor/test_block_analysis 1/1 ... [2025-08-14 22:35:30.057854]
2025-08-14T22:35:30.0596510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:30.0597264Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_block_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'] ... [2025-08-14 22:35:30.058110]
2025-08-14T22:35:34.7321277Z 
2025-08-14T22:35:34.7322639Z inductor/test_block_analysis 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_block_analysis_1.1_04736394fa71cec6_.log
2025-08-14T22:35:34.7327628Z Running 10 items in this shard: test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_3_symbol2_expr2, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_4_symbol1_expr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_affine_identity_stride_5_symbol0_expr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_index_with_dynamic_shapes, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims0_strides0_symbol0_expr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims1_strides1_symbol1_expr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_mod_div_identity_dims2_strides2_symbol2_expr2, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol0_expr0_subexpr0, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol1_expr1_subexpr1, test/inductor/test_block_analysis.py::BlockAnalysisTest::test_subexpr_identity_symbol2_expr2_subexpr2
2025-08-14T22:35:34.7331060Z 
2025-08-14T22:35:34.7331282Z Running inductor/test_codegen_triton 1/1 ... [2025-08-14 22:35:34.732019]
2025-08-14T22:35:34.7331681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:34.7339696Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_codegen_triton.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:34.732278]
2025-08-14T22:35:39.8065774Z 
2025-08-14T22:35:39.8067300Z inductor/test_codegen_triton 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_codegen_triton_1.1_bbe083698ec779f5_.log
2025-08-14T22:35:39.8068456Z Running 1 items in this shard: test/inductor/test_codegen_triton.py::TestCodegenTriton::test_config_of_sizearg
2025-08-14T22:35:39.8068929Z 
2025-08-14T22:35:39.8069278Z Running inductor/test_compile 1/1 ... [2025-08-14 22:35:39.806553]
2025-08-14T22:35:39.8069736Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:39.8071826Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/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'] ... [2025-08-14 22:35:39.806852]
2025-08-14T22:35:50.1419794Z 
2025-08-14T22:35:50.1420633Z inductor/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compile_1.1_39ef5a6d9f0742fe_.log
2025-08-14T22:35:50.1424441Z Running 9 items in this shard: test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_generate_debug_symbol, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_bare_module, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_export1, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_export2, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_fx, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_fx_dict_input, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_fx_tensor_return, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_make_fx, test/inductor/test_compile.py::TestStandaloneInductor::test_inductor_via_op_with_multiple_outputs
2025-08-14T22:35:50.1427242Z 
2025-08-14T22:35:50.1427366Z Running inductor/test_custom_post_grad_passes 1/1 ... [2025-08-14 22:35:50.141961]
2025-08-14T22:35:50.1427579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:50.1428043Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_custom_post_grad_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'] ... [2025-08-14 22:35:50.142170]
2025-08-14T22:35:58.2724919Z 
2025-08-14T22:35:58.2725802Z inductor/test_custom_post_grad_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_custom_post_grad_passes_1.1_8b78c34d485bc6d0_.log
2025-08-14T22:35:58.2727239Z Running 6 items in this shard: test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_backend_pass, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_joint_pass_post, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_joint_pass_pre, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_post_pass, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_pre_grad_pass, test/inductor/test_custom_post_grad_passes.py::TestPostGradCustomPrePostPass::test_custom_pre_pass
2025-08-14T22:35:58.2728273Z 
2025-08-14T22:35:58.2728391Z Running inductor/test_distributed_patterns 1/1 ... [2025-08-14 22:35:58.272501]
2025-08-14T22:35:58.2728612Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:35:58.2729312Z 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'] ... [2025-08-14 22:35:58.272728]
2025-08-14T22:36:12.1660798Z 
2025-08-14T22:36:12.1661540Z 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_e11fbb00a3bad2a8_.log
2025-08-14T22:36:12.1666135Z 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
2025-08-14T22:36:12.1669934Z 
2025-08-14T22:36:12.1670060Z Running inductor/test_kernel_optimization 1/1 ... [2025-08-14 22:36:12.166276]
2025-08-14T22:36:12.1670275Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:12.1670739Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_kernel_optimization.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:12.166520]
2025-08-14T22:36:20.3968214Z 
2025-08-14T22:36:20.3970282Z inductor/test_kernel_optimization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_kernel_optimization_1.1_c5c33e61c4cb7a1a_.log
2025-08-14T22:36:20.3971609Z Running 1 items in this shard: test/inductor/test_kernel_optimization.py::TestKernelOptimization::test_einsum_to_pointwise
2025-08-14T22:36:20.3972134Z 
2025-08-14T22:36:20.3972386Z Running inductor/test_minifier_utils 1/1 ... [2025-08-14 22:36:20.396561]
2025-08-14T22:36:20.3972853Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:20.3973774Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_minifier_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'] ... [2025-08-14 22:36:20.396807]
2025-08-14T22:36:25.1704759Z 
2025-08-14T22:36:25.1706059Z inductor/test_minifier_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_utils_1.1_beb92af1c49bd15b_.log
2025-08-14T22:36:25.1707880Z Running 3 items in this shard: test/inductor/test_minifier_utils.py::MinifierUtilsTests::test_convert_module_to_string, test/inductor/test_minifier_utils.py::MinifierUtilsTests::test_invalid_output, test/inductor/test_minifier_utils.py::MinifierUtilsTests::test_non_exportable
2025-08-14T22:36:25.1709516Z 
2025-08-14T22:36:25.1709781Z Running inductor/test_op_completeness 1/1 ... [2025-08-14 22:36:25.170309]
2025-08-14T22:36:25.1718018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:25.1718886Z 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'] ... [2025-08-14 22:36:25.170551]
2025-08-14T22:36:27.7402064Z 
2025-08-14T22:36:27.7402853Z 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_70174d11d3327b36_.log
2025-08-14T22:36:27.7405782Z 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
2025-08-14T22:36:27.7407616Z 
2025-08-14T22:36:27.7407850Z Running inductor/test_ordered_set 1/1 ... [2025-08-14 22:36:27.740233]
2025-08-14T22:36:27.7408302Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:27.7409356Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_ordered_set.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:27.740512]
2025-08-14T22:36:31.0621106Z 
2025-08-14T22:36:31.0621738Z inductor/test_ordered_set 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_ordered_set_1.1_4a3035a0d796d9e5_.log
2025-08-14T22:36:31.0668439Z Running 401 items in this shard: test/inductor/test_ordered_set.py::TestJointOps::test_and, test/inductor/test_ordered_set.py::TestJointOps::test_badcmp, test/inductor/test_ordered_set.py::TestJointOps::test_container_iterator, test/inductor/test_ordered_set.py::TestJointOps::test_contains, test/inductor/test_ordered_set.py::TestJointOps::test_cyclical_repr, test/inductor/test_ordered_set.py::TestJointOps::test_deepcopy, test/inductor/test_ordered_set.py::TestJointOps::test_difference, test/inductor/test_ordered_set.py::TestJointOps::test_do_not_rehash_dict_keys, test/inductor/test_ordered_set.py::TestJointOps::test_equality, test/inductor/test_ordered_set.py::TestJointOps::test_free_after_iterating, test/inductor/test_ordered_set.py::TestJointOps::test_gc, test/inductor/test_ordered_set.py::TestJointOps::test_intersection, test/inductor/test_ordered_set.py::TestJointOps::test_isdisjoint, test/inductor/test_ordered_set.py::TestJointOps::test_iterator_pickling, test/inductor/test_ordered_set.py::TestJointOps::test_len, test/inductor/test_ordered_set.py::TestJointOps::test_new_or_init, test/inductor/test_ordered_set.py::TestJointOps::test_or, test/inductor/test_ordered_set.py::TestJointOps::test_pickling, test/inductor/test_ordered_set.py::TestJointOps::test_setOfFrozensets, test/inductor/test_ordered_set.py::TestJointOps::test_sub, test/inductor/test_ordered_set.py::TestJointOps::test_sub_and_super, test/inductor/test_ordered_set.py::TestJointOps::test_subclass_with_custom_hash, test/inductor/test_ordered_set.py::TestJointOps::test_symmetric_difference, test/inductor/test_ordered_set.py::TestJointOps::test_union, test/inductor/test_ordered_set.py::TestJointOps::test_uniquification, test/inductor/test_ordered_set.py::TestJointOps::test_xor, test/inductor/test_ordered_set.py::TestSet::test_add, test/inductor/test_ordered_set.py::TestSet::test_and, test/inductor/test_ordered_set.py::TestSet::test_badcmp, test/inductor/test_ordered_set.py::TestSet::test_clear, test/inductor/test_ordered_set.py::TestSet::test_constructor_identity, test/inductor/test_ordered_set.py::TestSet::test_container_iterator, test/inductor/test_ordered_set.py::TestSet::test_contains, test/inductor/test_ordered_set.py::TestSet::test_copy, test/inductor/test_ordered_set.py::TestSet::test_cyclical_repr, test/inductor/test_ordered_set.py::TestSet::test_deepcopy, test/inductor/test_ordered_set.py::TestSet::test_difference, test/inductor/test_ordered_set.py::TestSet::test_difference_update, test/inductor/test_ordered_set.py::TestSet::test_discard, test/inductor/test_ordered_set.py::TestSet::test_do_not_rehash_dict_keys, test/inductor/test_ordered_set.py::TestSet::test_equality, test/inductor/test_ordered_set.py::TestSet::test_free_after_iterating, test/inductor/test_ordered_set.py::TestSet::test_gc, test/inductor/test_ordered_set.py::TestSet::test_hash, test/inductor/test_ordered_set.py::TestSet::test_iand, test/inductor/test_ordered_set.py::TestSet::test_init, test/inductor/test_ordered_set.py::TestSet::test_inplace_on_self, test/inductor/test_ordered_set.py::TestSet::test_intersection, test/inductor/test_ordered_set.py::TestSet::test_intersection_update, test/inductor/test_ordered_set.py::TestSet::test_ior, test/inductor/test_ordered_set.py::TestSet::test_isdisjoint, test/inductor/test_ordered_set.py::TestSet::test_isub, test/inductor/test_ordered_set.py::TestSet::test_iterator_pickling, test/inductor/test_ordered_set.py::TestSet::test_ixor, test/inductor/test_ordered_set.py::TestSet::test_len, test/inductor/test_ordered_set.py::TestSet::test_new_or_init, test/inductor/test_ordered_set.py::TestSet::test_or, test/inductor/test_ordered_set.py::TestSet::test_pickling, test/inductor/test_ordered_set.py::TestSet::test_pop, test/inductor/test_ordered_set.py::TestSet::test_remove, test/inductor/test_ordered_set.py::TestSet::test_remove_keyerror_set, test/inductor/test_ordered_set.py::TestSet::test_remove_keyerror_unpacking, test/inductor/test_ordered_set.py::TestSet::test_rich_compare, test/inductor/test_ordered_set.py::TestSet::test_setOfFrozensets, test/inductor/test_ordered_set.py::TestSet::test_set_literal, test/inductor/test_ordered_set.py::TestSet::test_set_literal_evaluation_order, test/inductor/test_ordered_set.py::TestSet::test_set_literal_insertion_order, test/inductor/test_ordered_set.py::TestSet::test_sub, test/inductor/test_ordered_set.py::TestSet::test_sub_and_super, test/inductor/test_ordered_set.py::TestSet::test_subclass_with_custom_hash, test/inductor/test_ordered_set.py::TestSet::test_symmetric_difference, test/inductor/test_ordered_set.py::TestSet::test_symmetric_difference_update, test/inductor/test_ordered_set.py::TestSet::test_union, test/inductor/test_ordered_set.py::TestSet::test_uniquification, test/inductor/test_ordered_set.py::TestSet::test_update, test/inductor/test_ordered_set.py::TestSet::test_weakref, test/inductor/test_ordered_set.py::TestSet::test_xor, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_length, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsEmpty::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_in, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_length, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_not_in, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsSingleton::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_in, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_length, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_not_in, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsTuple::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_length, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsTriple::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsString::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsString::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsString::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsString::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsString::test_length, test/inductor/test_ordered_set.py::TestBasicOpsString::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsString::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsString::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsString::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_length, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsBytes::test_union_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_copy, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_difference_rev, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_intersection, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_empty_union, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_equivalent_equality, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_intersection_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_isdisjoint_empty, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_issue_37219, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_iteration, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_length, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_pickling, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_repr, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_equality, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_intersection, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_isdisjoint, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_symmetric_difference, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_self_union, test/inductor/test_ordered_set.py::TestBasicOpsMixedStringBytes::test_union_empty, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_changingSizeWhileIterating, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_instanceWithException, test/inductor/test_ordered_set.py::TestExceptionPropagation::test_instancesWithoutException, test/inductor/test_ordered_set.py::TestSetOfSets::test_constructor, test/inductor/test_ordered_set.py::TestBinaryOps::test_eq, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_intersection_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_isdisjoint_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_sym_difference_superset, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_non_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_overlap, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_subset, test/inductor/test_ordered_set.py::TestBinaryOps::test_union_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_difference_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_intersection_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_sym_difference_superset, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_method_call, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_non_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_overlap, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_subset, test/inductor/test_ordered_set.py::TestUpdateOps::test_union_superset, test/inductor/test_ordered_set.py::TestMutate::test_add_absent, test/inductor/test_ordered_set.py::TestMutate::test_add_present, test/inductor/test_ordered_set.py::TestMutate::test_add_until_full, test/inductor/test_ordered_set.py::TestMutate::test_clear, test/inductor/test_ordered_set.py::TestMutate::test_discard_absent, test/inductor/test_ordered_set.py::TestMutate::test_discard_present, test/inductor/test_ordered_set.py::TestMutate::test_pop, test/inductor/test_ordered_set.py::TestMutate::test_remove_absent, test/inductor/test_ordered_set.py::TestMutate::test_remove_present, test/inductor/test_ordered_set.py::TestMutate::test_remove_until_empty, test/inductor/test_ordered_set.py::TestMutate::test_update_empty_tuple, test/inductor/test_ordered_set.py::TestMutate::test_update_unit_tuple_non_overlap, test/inductor/test_ordered_set.py::TestMutate::test_update_unit_tuple_overlap, test/inductor/test_ordered_set.py::TestSubsets::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEqualEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEqualNonEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetEmptyNonEmpty::test_issubset, test/inductor/test_ordered_set.py::TestSubsetPartial::test_issubset, test/inductor/test_ordered_set.py::TestSubsetNonOverlap::test_issubset, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_union, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_update, test/inductor/test_ordered_set.py::TestOnlySetsNumeric::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_union, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_update, test/inductor/test_ordered_set.py::TestOnlySetsDict::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_union, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_update, test/inductor/test_ordered_set.py::TestOnlySetsOperator::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_union, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_update, test/inductor/test_ordered_set.py::TestOnlySetsTuple::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsString::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsString::test_union, test/inductor/test_ordered_set.py::TestOnlySetsString::test_update, test/inductor/test_ordered_set.py::TestOnlySetsString::test_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_eq_ne, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_ge_gt_le_lt, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_intersection_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_sym_difference_update_operator, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_union, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_update, test/inductor/test_ordered_set.py::TestOnlySetsGenerator::test_update_operator, test/inductor/test_ordered_set.py::TestCopyingEmpty::test_copy, test/inductor/test_ordered_set.py::TestCopyingEmpty::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingSingleton::test_copy, test/inductor/test_ordered_set.py::TestCopyingSingleton::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingTriple::test_copy, test/inductor/test_ordered_set.py::TestCopyingTriple::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingTuple::test_copy, test/inductor/test_ordered_set.py::TestCopyingTuple::test_deep_copy, test/inductor/test_ordered_set.py::TestCopyingNested::test_copy, test/inductor/test_ordered_set.py::TestCopyingNested::test_deep_copy, test/inductor/test_ordered_set.py::TestIdentities::test_binopsVsSubsets, test/inductor/test_ordered_set.py::TestIdentities::test_commutativity, test/inductor/test_ordered_set.py::TestIdentities::test_exclusion, test/inductor/test_ordered_set.py::TestIdentities::test_summations, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_constructor, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_inline_methods, test/inductor/test_ordered_set.py::TestVariousIteratorArgs::test_inplace_methods, test/inductor/test_ordered_set.py::TestWeirdBugs::test_8420_set_merge, test/inductor/test_ordered_set.py::TestWeirdBugs::test_iter_and_mutate, test/inductor/test_ordered_set.py::TestWeirdBugs::test_merge_and_mutate, test/inductor/test_ordered_set.py::TestGraphs::test_cube, test/inductor/test_ordered_set.py::TestGraphs::test_cuboctahedron
2025-08-14T22:36:31.0710768Z 
2025-08-14T22:36:31.0710873Z Running inductor/test_split_cat_fx_aten_passes 1/1 ... [2025-08-14 22:36:31.062197]
2025-08-14T22:36:31.0711055Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:31.0711449Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_split_cat_fx_aten_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'] ... [2025-08-14 22:36:31.062420]
2025-08-14T22:36:40.1450252Z 
2025-08-14T22:36:40.1451700Z inductor/test_split_cat_fx_aten_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_d5a352c4d85c99ea_.log
2025-08-14T22:36:40.1454544Z Running 5 items in this shard: test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_move_view_after_cat_aten, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_select_cat_post_grad, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_split_cat_post_grad, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_split_cat_post_grad_singular, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAtenNormalizationPasses::test_split_aten_normalization
2025-08-14T22:36:40.1457117Z 
2025-08-14T22:36:40.1457352Z Running test_sparse_semi_structured 1/1 ... [2025-08-14 22:36:40.144391]
2025-08-14T22:36:40.1457814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:40.1458882Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_semi_structured.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:40.144642]
2025-08-14T22:36:44.7690967Z 
2025-08-14T22:36:44.7691996Z test_sparse_semi_structured 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_semi_structured_1.1_106b7f714c243cf3_.log
2025-08-14T22:36:44.7705981Z Running 42 items in this shard: test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_mlp_contiguous_relu_compile_cusparselt, test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_mlp_contiguous_relu_compile_cutlass, test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_sp24_compile, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_indices, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_linear, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_min_sparse_shape, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mlp, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mm_sparse_first_NN, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mm_sparse_first_NT, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mm_sparse_first_TN, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mm_sparse_second_NN, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_mm_sparse_second_NT, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_to_sparse_semi_structured, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_unsupported_dim, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_unsupported_dtype, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_unsupported_shape, test/test_sparse_semi_structured.py::TestSparseSemiStructured::test_values, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_gemm, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_pack_both_ways_edge_case1, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_pack_both_ways_id, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_pack_both_ways_meta_correctness, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_prune_dense_static_sort, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_pruning_algo_largest_abs_values_greedy, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_sp24_apply, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_sp24_apply_dense, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_sp24_matmuls, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_sp24_matmuls_bmm, test/test_sparse_semi_structured.py::TestSparseSemiStructuredTraining::test_sp24_matmuls_mat_vec, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUTLASS::test_conversions, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUTLASS::test_conversions_all_patterns, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUTLASS::test_linear_cutlass, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUTLASS::test_sparse_semi_structured_ops_cutlass, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cslt_sparse_mm_alpha, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cslt_sparse_mm_alpha_compile_autotune, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cslt_sparse_mm_alpha_mixed_dtype, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cslt_sparse_mm_mixed_dtype, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cslt_sparse_mm_search, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_csrc_cslt_sparse_mm_search, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_cusparselt_backend, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_sparse_fp8fp8_mm, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_sparse_semi_structured_scaled_mm, test/test_sparse_semi_structured.py::TestSparseSemiStructuredCUSPARSELT::test_sparse_semi_structured_scaled_mm_fp8
2025-08-14T22:36:44.7716957Z 
2025-08-14T22:36:44.7717039Z Running test_dynamic_shapes 1/1 ... [2025-08-14 22:36:44.769117]
2025-08-14T22:36:44.7717201Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:36:44.7717571Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_dynamic_shapes.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:44.769501]
2025-08-14T22:37:07.0271744Z 
2025-08-14T22:37:07.0272418Z test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dynamic_shapes_1.1_5005d34ea6f7b427_.log
2025-08-14T22:37:07.0316143Z Running 331 items in this shard: test/test_dynamic_shapes.py::TestPySymInt::test_arith_ops, test/test_dynamic_shapes.py::TestPySymInt::test_aten_ops, test/test_dynamic_shapes.py::TestPySymInt::test_avoid_unbacked_substitution, test/test_dynamic_shapes.py::TestPySymInt::test_backed_size_oblivious_01_spec, test/test_dynamic_shapes.py::TestPySymInt::test_baddbmm_symint, test/test_dynamic_shapes.py::TestPySymInt::test_binary, test/test_dynamic_shapes.py::TestPySymInt::test_data_dependent_guard, test/test_dynamic_shapes.py::TestPySymInt::test_data_dependent_guard_propagate_real_tensors, test/test_dynamic_shapes.py::TestPySymInt::test_debug_has_internal_overlap_unbacked, test/test_dynamic_shapes.py::TestPySymInt::test_deepcopy, test/test_dynamic_shapes.py::TestPySymInt::test_duck_shape, test/test_dynamic_shapes.py::TestPySymInt::test_ephemeral_source_simplification, test/test_dynamic_shapes.py::TestPySymInt::test_ephemeral_source_unified_with_non_ephemeral_source, test/test_dynamic_shapes.py::TestPySymInt::test_expect_true_basic, test/test_dynamic_shapes.py::TestPySymInt::test_expect_true_double_digits, test/test_dynamic_shapes.py::TestPySymInt::test_expect_true_prefer_later, test/test_dynamic_shapes.py::TestPySymInt::test_expect_true_refine_range, test/test_dynamic_shapes.py::TestPySymInt::test_expect_true_with_s0, test/test_dynamic_shapes.py::TestPySymInt::test_floor_clean_div_axioms, test/test_dynamic_shapes.py::TestPySymInt::test_floordiv_static, test/test_dynamic_shapes.py::TestPySymInt::test_fx_trace_intlist, test/test_dynamic_shapes.py::TestPySymInt::test_guard_int, test/test_dynamic_shapes.py::TestPySymInt::test_guard_refine_range, test/test_dynamic_shapes.py::TestPySymInt::test_int_bool, test/test_dynamic_shapes.py::TestPySymInt::test_int_conversion, test/test_dynamic_shapes.py::TestPySymInt::test_int_to_float, test/test_dynamic_shapes.py::TestPySymInt::test_max_of_unique_summation_opt, test/test_dynamic_shapes.py::TestPySymInt::test_meta_symint, test/test_dynamic_shapes.py::TestPySymInt::test_mul_int_oo_nan, test/test_dynamic_shapes.py::TestPySymInt::test_non_overlapping_and_dense, test/test_dynamic_shapes.py::TestPySymInt::test_non_overlapping_and_dense_unbacked, test/test_dynamic_shapes.py::TestPySymInt::test_numel, test/test_dynamic_shapes.py::TestPySymInt::test_numpy_sym_max, test/test_dynamic_shapes.py::TestPySymInt::test_numpy_sym_min, test/test_dynamic_shapes.py::TestPySymInt::test_prefer_deferred_runtime_assertions_over_guards, test/test_dynamic_shapes.py::TestPySymInt::test_print_readable_with_symints, test/test_dynamic_shapes.py::TestPySymInt::test_reverse_arith_ops, test/test_dynamic_shapes.py::TestPySymInt::test_roundtrip, test/test_dynamic_shapes.py::TestPySymInt::test_size_expressions, test/test_dynamic_shapes.py::TestPySymInt::test_specialize_zero_one, test/test_dynamic_shapes.py::TestPySymInt::test_statically_known_false, test/test_dynamic_shapes.py::TestPySymInt::test_statically_known_true, test/test_dynamic_shapes.py::TestPySymInt::test_stride, test/test_dynamic_shapes.py::TestPySymInt::test_sym_ceil, test/test_dynamic_shapes.py::TestPySymInt::test_sym_floor, test/test_dynamic_shapes.py::TestPySymInt::test_sym_int, test/test_dynamic_shapes.py::TestPySymInt::test_sym_ite, test/test_dynamic_shapes.py::TestPySymInt::test_sym_log2, test/test_dynamic_shapes.py::TestPySymInt::test_sym_max_multi_max_simplify, test/test_dynamic_shapes.py::TestPySymInt::test_sym_sqrt, test/test_dynamic_shapes.py::TestPySymInt::test_sym_sum, test/test_dynamic_shapes.py::TestPySymInt::test_sym_trunc, test/test_dynamic_shapes.py::TestPySymInt::test_symint_args, test/test_dynamic_shapes.py::TestPySymInt::test_symint_as_scalar, test/test_dynamic_shapes.py::TestPySymInt::test_symint_bitwise_and, test/test_dynamic_shapes.py::TestPySymInt::test_symint_bitwise_or, test/test_dynamic_shapes.py::TestPySymInt::test_symint_vargs, test/test_dynamic_shapes.py::TestPySymInt::test_sympify_symint, test/test_dynamic_shapes.py::TestPySymInt::test_sympy_optimized_add, test/test_dynamic_shapes.py::TestPySymInt::test_sympy_optimized_add_binary_search, test/test_dynamic_shapes.py::TestPySymInt::test_tensor_factory_with_symint, test/test_dynamic_shapes.py::TestPySymInt::test_tracing_sym_ite, test/test_dynamic_shapes.py::TestPySymInt::test_unbacked_substitution, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_abs, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_add, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_and, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_bitwise_and, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_bitwise_or, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_ceil, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_eq, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_float_pow, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_float_truediv, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_floor, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_ge, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_gt, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_int_floordiv, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_int_truediv, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_is_integer, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_le, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_lshift, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_lt, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_mod, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_mul, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_ne, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_neg, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_or, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_pos, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_pow_by_natural, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_round, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_rshift, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sub, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_acos, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_asin, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_atan, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_cos, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_cosh, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_ite, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_log2, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_max, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_min, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_not, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_sin, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_sinh, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_sqrt, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_tan, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_sym_tanh, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_bool_method_fn_trunc, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_abs_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_abs_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_abs_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_abs_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_add_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_add_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_add_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_add_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_and_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_and_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_and_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_and_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_and_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_and_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_and_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_and_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_or_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_or_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_or_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_bitwise_or_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ceil_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ceil_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ceil_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ceil_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_eq_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_eq_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_eq_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_eq_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_pow_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_pow_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_pow_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_pow_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_truediv_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_truediv_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_truediv_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_float_truediv_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_floor_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_floor_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_floor_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_floor_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ge_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ge_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ge_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ge_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_gt_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_gt_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_gt_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_gt_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_floordiv_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_floordiv_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_floordiv_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_floordiv_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_truediv_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_truediv_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_truediv_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_int_truediv_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_is_integer_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_is_integer_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_is_integer_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_is_integer_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_le_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_le_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_le_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_le_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lshift_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lshift_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lshift_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lshift_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lt_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lt_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lt_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_lt_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mod_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mod_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mod_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mod_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mul_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mul_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mul_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_mul_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ne_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ne_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ne_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_ne_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_neg_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_neg_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_neg_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_neg_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_or_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_or_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_or_first_type_int_second_type_float, 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test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_pow_by_natural_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_round_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_round_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_round_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_round_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_rshift_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_rshift_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_rshift_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_rshift_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sub_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sub_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sub_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sub_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_acos_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_acos_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_acos_first_type_int_second_type_float, 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test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_max_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_min_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_min_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_min_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_min_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_not_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_not_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_not_first_type_int_second_type_float, 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test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_sinh_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_sqrt_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_sqrt_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_sqrt_first_type_int_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_sqrt_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_tan_first_type_float_second_type_float, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_tan_first_type_float_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_sym_tan_first_type_int_second_type_float, 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test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_method_fn_trunc_first_type_int_second_type_int, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_non_symbolic_symnode, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_stride_symnode, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_symint_deepcopy, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_symint_hashing, test/test_dynamic_shapes.py::TestSymNumberMagicMethods::test_symnode_hashing, test/test_dynamic_shapes.py::TestFloorDiv::test_floordiv_assumptions, test/test_dynamic_shapes.py::TestFloorDiv::test_floordiv_div_by_one, test/test_dynamic_shapes.py::TestFloorDiv::test_floordiv_float_int, test/test_dynamic_shapes.py::TestFloorDiv::test_floordiv_simplify, test/test_dynamic_shapes.py::TestDimConstraints::test_dim_constraints_reduce_congruences_simple, test/test_dynamic_shapes.py::TestDimConstraints::test_dim_constraints_reduce_inequalities_error, test/test_dynamic_shapes.py::TestDimConstraints::test_dim_constraints_reduce_inequalities_simple, test/test_dynamic_shapes.py::TestDimConstraints::test_dim_constraints_solve_full, test/test_dynamic_shapes.py::TestDimConstraints::test_simplify_max_1_0, test/test_dynamic_shapes.py::TestGuardsExpressions::test_guard_or_false, test/test_dynamic_shapes.py::TestGuardsExpressions::test_guard_or_true, test/test_dynamic_shapes.py::TestGuardsExpressions::test_guards_float_div, test/test_dynamic_shapes.py::TestGuardsExpressions::test_guards_float_print, test/test_dynamic_shapes.py::TestGuardsExpressions::test_guards_gt_lt, test/test_dynamic_shapes.py::TestGuardsExpressions::test_remove_symbols_without_guarding, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_neq_assert_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_neq_assert_backend_inductor, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_sym_eq_assert_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_sym_eq_assert_backend_inductor, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_sym_or_assert_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_sym_or_assert_backend_inductor, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_with_unbacked_input_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_deferred_with_unbacked_input_backend_inductor, test/test_dynamic_shapes.py::TestUnbacked::test_has_free_symbols, test/test_dynamic_shapes.py::TestUnbacked::test_post_specialize_runtime_assert1_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_post_specialize_runtime_assert1_backend_inductor, test/test_dynamic_shapes.py::TestUnbacked::test_post_specialize_runtime_assert2_backend_eager, test/test_dynamic_shapes.py::TestUnbacked::test_post_specialize_runtime_assert2_backend_inductor, test/test_dynamic_shapes.py::TestUbackedOps::test_invalid_view_unbacked_view, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_contiguous, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_non_contigious_reshape_failing, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_reshape1, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_reshape2, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_select2, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_select_index, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_select_index_cpp_wrapper, test/test_dynamic_shapes.py::TestUbackedOps::test_unbacked_select_index_with_check, test/test_dynamic_shapes.py::TestUbackedOps::test_unbind_not_dynamic
2025-08-14T22:37:07.0359706Z 
2025-08-14T22:37:07.0359789Z Running test_reductions 1/1 ... [2025-08-14 22:37:07.027345]
2025-08-14T22:37:07.0359941Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:37:07.0360312Z 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'] ... [2025-08-14 22:37:07.027560]
2025-08-14T22:38:14.9767129Z 
2025-08-14T22:38:14.9768084Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_812e87ff430ead73_.log
2025-08-14T22:38:15.0343627Z 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, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_int64, 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2025-08-14T22:38:15.0912492Z 
2025-08-14T22:38:15.0912611Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T22:38:15.0912803Z Uploading artifacts took 0.00 seconds
2025-08-14T22:38:15.0913001Z Running test_ops 4/4 ... [2025-08-14 22:38:14.979816]
2025-08-14T22:38:15.0913158Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:38:15.0913528Z 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'] ... [2025-08-14 22:38:14.980036]
2025-08-14T22:47:48.0490171Z 
2025-08-14T22:47:48.0490836Z test_ops 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_4.4_563c04f7aa389b3b_.log
2025-08-14T22:47:48.1432476Z Running 8253 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, 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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_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, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_shifted_chebyshev_polynomial_u_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_zeta_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_special_zeta_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_split_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_split_list_args_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_sqrt_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_squeeze_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_std_mean_unbiased_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_sub_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_sum_to_size_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_t_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_t_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_tan_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_tensor_split_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_tile_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_topk_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_topk_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_trace_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_tril_indices_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_triu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_triu_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_trunc_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_unfold_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_unsafe_chunk_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_var_mean_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_vdot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_view_as_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_view_copy_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_view_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_view_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_vstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_vstack_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_where_cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_zero__cuda_float32, test/test_ops.py::TestCommonCUDA::test_multiple_devices_zero__cuda_int64, test/test_ops.py::TestCommonCUDA::test_multiple_devices_zeros_like_cuda_float32, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_H_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_T_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values___rdiv___cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values___ror___cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values__unsafe_masked_index_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_acosh_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_addr_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_alias_copy_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_amax_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_amin_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_argsort_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_as_strided_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_asinh_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_atanh_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_bitwise_or_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_bool_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_broadcast_tensors_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_cat_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_chunk_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_conj_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_cummax_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_diag_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_dstack_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_empty_permuted_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_erfc_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_exp2_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_expand_copy_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_fft_ihfft_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_fft_rfft2_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_fft_rfft_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_fill_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_flatten_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_flipud_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_fmax_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_hash_tensor_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_index_copy_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_isposinf_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_jiterator_4inputs_with_extra_args_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_le_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_lgamma_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_linalg_diagonal_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_log10_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_log2_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_log_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_logical_not_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_logical_xor_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_mT_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_masked_sum_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_max_reduction_with_dim_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_maximum_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_movedim_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_mul_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_narrow_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_new_empty_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_new_full_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_new_zeros_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_nn_functional_channel_shuffle_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_nn_functional_pad_circular_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_polygamma_polygamma_n_3_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_reciprocal_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_scalar_tensor_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_scatter_reduce_sum_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_select_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_signbit_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_sinh_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_sort_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_airy_ai_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_bessel_j0_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_chebyshev_polynomial_u_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_modified_bessel_k0_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_scaled_modified_bessel_k1_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_t_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_split_list_args_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_split_with_sizes_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_squeeze_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_t_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_take_along_dim_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_take_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_tanh_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_tile_cuda_bool, test/test_ops.py::TestCommonCUDA::test_non_standard_bool_values_to_sparse_cuda_bool, 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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, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_flip_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_fliplr_cuda_int64, test/test_ops.py::TestCommonCUDA::test_noncontiguous_samples_float_cuda_complex64, 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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, 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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, 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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, test/test_ops.py::TestCommonCUDA::test_out_linalg_norm_subgradients_at_zero_cuda_float32, 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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, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_avg_pool3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_binary_cross_entropy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_conv_transpose1d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_conv_transpose3d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_embedding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_instance_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_interpolate_trilinear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_linear_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_local_response_norm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_max_pool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_max_unpool1d_grad_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_max_unpool2d_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_max_unpool2d_grad_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_mish_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_mse_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_multilabel_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_nll_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_normalize_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_pad_constant_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_pad_replicate_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_pad_replicate_negative_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_poisson_nll_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_selu_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_smooth_l1_loss_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_nn_functional_unfold_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_norm_fro_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_ones_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_ormqr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_permute_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_prod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_put_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_rad2deg_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_randint_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_ravel_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_acosh_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_add_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_addcdiv_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_addmm_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_addmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_alias_copy_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_angle_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_asinh_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_atan_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_baddbmm_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_baddbmm_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_ceil_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_cross_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_cumprod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_cumsum_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_deg2rad_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_digamma_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_div_floor_rounding_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_exp2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_expm1_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_fft2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_ifft2_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_ifft2_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_ifft_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_irfftn_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fft_irfftn_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_float_power_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fmax_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_fmod_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_frac_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_hstack_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_hypot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_index_copy_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_index_select_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_kthvalue_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_lerp_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_cross_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_det_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_eigh_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_eigvalsh_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_inv_ex_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_lu_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_matrix_power_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_multi_dot_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_multi_dot_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_pinv_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_pinv_hermitian_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_pinv_hermitian_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_qr_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_slogdet_cuda_complex64, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_slogdet_cuda_float32, test/test_ops.py::TestCommonCUDA::test_out_requires_grad_error_linalg_solve_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_softmax_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_sparse_mm_reduce_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_sparse_sampled_addmm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_bessel_j1_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_chebyshev_polynomial_t_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_chebyshev_polynomial_w_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_entr_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_legendre_polynomial_p_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_modified_bessel_i0_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_forward_ad_special_modified_bessel_k1_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_operator_isreal_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_jiterator_binary_return_by_ref_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_kron_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_lerp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_lgamma_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_diagonal_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_eigvals_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_ldl_factor_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_lstsq_grad_oriented_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_lu_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_linalg_lu_factor_ex_cuda_float32, 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test/test_ops.py::TestCompositeComplianceCUDA::test_operator_mH_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_amax_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_cumsum_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_fill_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_logaddexp_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_norm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_masked_softmax_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_max_reduction_with_dim_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_mean_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_meshgrid_list_of_tensors_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_meshgrid_variadic_tensors_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_minimum_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nanmean_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_native_dropout_backward_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_new_empty_strided_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_adaptive_max_pool3d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_alpha_dropout_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_avg_pool1d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_avg_pool2d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_batch_norm_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_bilinear_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_fractional_max_pool3d_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_gelu_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_hardshrink_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_hardsigmoid_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_hardswish_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_interpolate_linear_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_linear_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_logsigmoid_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_margin_ranking_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_max_unpool1d_grad_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_pad_constant_cuda_float32, test/test_ops.py::TestCompositeComplianceCUDA::test_operator_nn_functional_pad_replicate_cuda_float32, 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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_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_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
2025-08-14T22:47:48.2317848Z 
2025-08-14T22:47:48.2317935Z Running test_hop_infra 1/1 ... [2025-08-14 22:47:48.054734]
2025-08-14T22:47:48.2318094Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:47:48.2318464Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_hop_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'] ... [2025-08-14 22:47:48.054956]
2025-08-14T22:47:50.7254310Z 
2025-08-14T22:47:50.7255128Z test_hop_infra 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hop_infra_1.1_44b9f87c3c024fa4_.log
2025-08-14T22:47:50.7256469Z Running 3 items in this shard: test/test_hop_infra.py::TestHOPInfra::test_all_hops_are_imported, test/test_hop_infra.py::TestHOPInfra::test_all_hops_have_opinfo, test/test_hop_infra.py::TestHOPInfra::test_imports_from_all_work
2025-08-14T22:47:50.7257035Z 
2025-08-14T22:47:50.7258387Z Running test_fx 1/2 ... [2025-08-14 22:47:50.725451]
2025-08-14T22:47:50.7258730Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:47:50.7267698Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:47:50.725790]
2025-08-14T22:48:58.6926780Z 
2025-08-14T22:48:58.6934785Z test_fx 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.2_b76c3a3eb145d7a7_.log
2025-08-14T22:48:58.7022629Z Running 646 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cuda, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_true, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TypeCheckerTest::test_typecheck_basicblock, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_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::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_nodes_with_kwargs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_duplicated_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, 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_fx_create_arg, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, 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_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_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_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___radd___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rdiv___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmod___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmul___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__chunk_cat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_abs_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_acos_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_acosh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_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_addmv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_alias_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_all_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_amax_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_argwhere_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_asinh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atan2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atan_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bernoulli_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_broadcast_shapes_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_broadcast_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bucketize_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cartesian_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cauchy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cdist_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ceil_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_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_max_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_min_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_column_stack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_constant_pad_nd_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_copysign_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_count_nonzero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cov_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cummax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cummin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diag_embed_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagflat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagonal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagonal_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diff_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_digamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_div_floor_rounding_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_einsum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_permuted_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_strided_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_eq_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_exp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expand_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expand_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expm1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_eye_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fftshift_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_hfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_hfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ihfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ihfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_irfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_rfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_rfftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_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_floor_divide_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_frac_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_frexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ge_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_geometric_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_geqrf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_gradient_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_heaviside_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_hstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_i0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_igamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_igammac_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_fill_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_put_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_reduce_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_inner_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_isreal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_item_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_unary_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lerp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lgamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cholesky_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cond_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cross_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_diagonal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_householder_product_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lstsq_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_pinv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_pinv_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_linalg_svd_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_svdvals_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vander_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vecdot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log10_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log1p_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logaddexp2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logcumsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logdet_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_not_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_or_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_xor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_unpack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mT_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_cumprod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_fill_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_log_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_logaddexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_logsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_median_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_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_std_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_var_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_matrix_exp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_maximum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_minimum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_msort_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mul_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nan_to_num_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nanmean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_narrow_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_narrow_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_native_batch_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_native_layer_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ne_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_neg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_full_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_ones_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_celu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_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_group_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_linear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_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_constant_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_selu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_silu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nonzero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_fro_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_normal_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_outer_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_pca_lowrank_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_permute_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_permute_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_pinverse_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_quantile_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rad2deg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rand_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randint_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randint_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ravel_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_resolve_conj_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_resolve_neg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rot90_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rsqrt_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rsub_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scalar_tensor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_select_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_short_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sign_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_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_slice_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_slice_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sort_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_erfcx_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_i1e_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_log_ndtr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_zeta_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_split_with_sizes_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_square_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_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_sub_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sum_to_size_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_svd_lowrank_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_take_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tan_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_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_transpose_copy_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_unbind_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unflatten_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unfold_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_uniform_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unsqueeze_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_vdot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_as_complex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_xlogy_cuda_float32, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet161, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet169, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b1, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b3, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b5, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b6, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b7, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_l, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_m, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_75, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_128gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet18, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext50_32x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2
2025-08-14T22:48:58.7097872Z 
2025-08-14T22:48:58.7097977Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2025-08-14 22:48:58.694536]
2025-08-14T22:48:58.7098161Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:48:58.7098561Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'benchmark_utils/test_benchmark_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'] ... [2025-08-14 22:48:58.694761]
2025-08-14T22:49:01.4653757Z 
2025-08-14T22:49:01.4654739Z benchmark_utils/test_benchmark_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/benchmark_utils.test_benchmark_utils_1.1_7980c522f29d62ee_.log
2025-08-14T22:49:01.4658900Z Running 9 items in this shard: test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_adaptive_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_cpp_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_compare, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_cpp_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_fuzzer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_manipulate_callgrind_stats, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer_tiny_fast_snippet
2025-08-14T22:49:01.4662042Z 
2025-08-14T22:49:01.4662248Z Running test_decomp 1/12 ... [2025-08-14 22:49:01.465354]
2025-08-14T22:49:01.4662497Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:49:01.4663221Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_decomp.py', '--shard-id=1', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:49:01.465621]
2025-08-14T22:59:25.7636946Z 
2025-08-14T22:59:25.7637846Z test_decomp 1/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.12_0ac2c50acc31304a_.log
2025-08-14T22:59:25.7715960Z Running 743 items in this shard: test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive___radd___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmod___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rpow___cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rpow___cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rpow___cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rsub___cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive__softmax_backward_data_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive__unsafe_masked_index_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive__upsample_bilinear2d_aa_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_abs_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_acosh_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_addmm_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_addr_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_alias_copy_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_alias_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_all_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_all_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_all_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_amin_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_amin_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_aminmax_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_angle_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_any_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argmin_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argmin_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_argsort_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_partial_views_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_as_strided_scatter_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atan2_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atan_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atanh_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_atleast_1d_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_baddbmm_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_baddbmm_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bfloat16_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bincount_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bincount_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bitwise_left_shift_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bitwise_right_shift_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_block_diag_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bmm_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_broadcast_tensors_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_broadcast_tensors_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_broadcast_to_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_bucketize_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_byte_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cartesian_prod_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cartesian_prod_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cartesian_prod_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cat_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cauchy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cdouble_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cdouble_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ceil_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ceil_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_chalf_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_chalf_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_chalf_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cholesky_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cholesky_inverse_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_chunk_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_clamp_max_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_column_stack_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_combinations_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_complex_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_contiguous_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_contiguous_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_copysign_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cov_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cov_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cross_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_cummax_cuda_float16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_scatter_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_select_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_softmin_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_sum_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_matmul_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_binary_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_binary_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_binary_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_reduction_no_dim_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_reduction_with_dim_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_reduction_with_dim_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_reduction_no_dim_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_reduction_with_dim_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_reduction_with_dim_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_minimum_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_minimum_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_movedim_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mul_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nan_to_num_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_narrow_cuda_bfloat16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_linear_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_nearest_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_layer_norm_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_logsigmoid_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_margin_ranking_loss_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_pool3d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_unpool3d_grad_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_mish_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_mse_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_multi_margin_loss_cuda_bfloat16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_rrelu_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_silu_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_soft_margin_loss_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softshrink_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_upsample_bilinear_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nonzero_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nonzero_static_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nonzero_static_cuda_float16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_hermite_polynomial_h_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_i1e_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_laguerre_polynomial_l_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_laguerre_polynomial_l_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_i0_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_i0_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_i1_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtr_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_ndtri_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_scaled_modified_bessel_k0_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_scaled_modified_bessel_k1_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_v_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_w_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_spherical_bessel_j0_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_spherical_bessel_j0_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_list_args_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sqrt_cuda_float16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_topk_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_torch_ops_aten__flash_attention_forward_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trace_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_transpose_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_transpose_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trapz_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_true_divide_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_true_divide_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_trunc_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unbind_copy_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unbind_copy_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unbind_copy_cuda_int8, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_mean_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_unbiased_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_vdot_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_as_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_as_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_as_real_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_where_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_xlogy_cuda_uint8, 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test/test_decomp.py::TestDecompCUDA::test_quick_slice_scatter_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_slice_scatter_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_softmax_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_special_erfcx_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_special_i0e_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1e_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_special_log_ndtr_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_special_xlog1py_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_special_zeta_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_split_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_split_list_args_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_split_with_sizes_copy_cuda_int16, 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test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_uint8, test/test_decomp.py::DecompOneOffTestsCUDA::test_weight_norm_interface_cuda
2025-08-14T22:59:25.7795672Z 
2025-08-14T22:59:25.7795877Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T22:59:25.7796112Z Uploading artifacts took 0.00 seconds
2025-08-14T22:59:25.7796267Z Running test_decomp 7/12 ... [2025-08-14 22:59:25.763857]
2025-08-14T22:59:25.7796421Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T22:59:25.7796800Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_decomp.py', '--shard-id=7', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:59:25.764069]
2025-08-14T23:08:43.0250314Z 
2025-08-14T23:08:43.0254685Z test_decomp 7/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_7.12_d842b52d15d8ded2_.log
2025-08-14T23:08:43.0339102Z Running 748 items in this shard: test/test_decomp.py::TestDecompCUDA::test_batch_norm_unflatten_weight_bias_cuda, test/test_decomp.py::TestDecompCUDA::test_comprehensive_H_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___getitem___cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___getitem___cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___radd___cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rand___cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmod___cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmod___cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rmul___cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rpow___cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rxor___cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive___rxor___cuda_int16, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_mH_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mH_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mH_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mH_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_amax_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_amin_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_amin_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_argmax_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumprod_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumprod_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumprod_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_cumprod_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_fill_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_logaddexp_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_logsumexp_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_prod_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_select_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_softmax_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_std_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_std_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_masked_var_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_matmul_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_matrix_exp_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_pool2d_with_indices_backward_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_pool2d_with_indices_backward_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_max_reduction_no_dim_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_list_of_tensors_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_meshgrid_variadic_tensors_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_binary_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_binary_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_min_binary_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mm_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mode_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_mode_cuda_uint8, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_celu_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_celu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv1d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv2d_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv3d_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv_transpose1d_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_conv_transpose2d_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_dropout2d_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_dropout2d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_dropout_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_embedding_bag_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_bilinear_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_nearest_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_interpolate_nearest_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_leaky_relu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_linear_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_pool1d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_max_pool3d_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_multi_margin_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_nll_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_circular_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_circular_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_replicate_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pad_replicate_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pairwise_distance_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pairwise_distance_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pdist_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pixel_shuffle_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pixel_unshuffle_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pixel_unshuffle_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_pixel_unshuffle_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_poisson_nll_loss_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_relu_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_relu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_selu_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_silu_complex_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_smooth_l1_loss_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softmin_with_dtype_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_softshrink_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_tanhshrink_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_threshold_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_triplet_margin_loss_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_nn_functional_upsample_nearest_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_norm_fro_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_norm_nuc_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_norm_nuc_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ones_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ones_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ones_like_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ones_like_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_outer_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_outer_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_permute_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_permute_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_0_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_polygamma_polygamma_n_4_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_positive_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_pow_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_prod_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_randint_like_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_randn_like_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ravel_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_ravel_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_real_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_repeat_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_repeat_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_repeat_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_repeat_interleave_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_reshape_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_resize__cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_roll_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rot90_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_round_decimals_3_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_round_decimals_neg_3_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_round_decimals_neg_3_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_round_decimals_neg_3_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rsub_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_rsub_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_add_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_add_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_amax_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_scatter_reduce_amin_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_searchsorted_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_scatter_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_select_scatter_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sgn_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_short_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sigmoid_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sign_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_signal_windows_general_cosine_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_signal_windows_kaiser_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_signal_windows_nuttall_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_signbit_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sin_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_slice_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_slice_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_slice_scatter_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_slice_scatter_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_slice_scatter_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_softmax_with_dtype_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sort_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sort_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sparse_mm_reduce_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_bessel_y0_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_bessel_y0_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_bessel_y1_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_chebyshev_polynomial_t_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_chebyshev_polynomial_u_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_chebyshev_polynomial_u_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_chebyshev_polynomial_w_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_erfcx_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_hermite_polynomial_h_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_laguerre_polynomial_l_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_log_ndtr_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_k0_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_modified_bessel_k1_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_polygamma_special_polygamma_n_0_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_scaled_modified_bessel_k1_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_u_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_shifted_chebyshev_polynomial_u_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_spherical_bessel_j0_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_xlog1py_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_special_xlog1py_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_split_with_sizes_copy_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_square_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_square_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_copy_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_copy_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_squeeze_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_mean_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_mean_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_mean_unbiased_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_std_mean_unbiased_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sub_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sub_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_sum_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_t_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_along_dim_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_take_cuda_bool, 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test/test_decomp.py::TestDecompCUDA::test_comprehensive_unique_cuda_uint64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unravel_index_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_chunk_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_chunk_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsafe_split_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_unsqueeze_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_mean_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_comprehensive_var_mean_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_comprehensive_view_as_complex_cuda_float64, 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test/test_decomp.py::TestDecompCUDA::test_quick_floor_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_fmin_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_full_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_ge_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ge_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_hypot_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_index_add_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_index_copy_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_index_copy_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_index_copy_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_index_copy_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_isinf_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_item_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_item_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_le_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_le_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_lerp_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_lgamma_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_diagonal_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_diagonal_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_linalg_vector_norm_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_linspace_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_log1p_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_log1p_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_log2_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_log_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_logaddexp2_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_logical_and_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_logical_or_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_logical_xor_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_tensor_overload_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_logspace_tensor_overload_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_lt_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_maximum_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_mean_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_list_of_tensors_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_list_of_tensors_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_variadic_tensors_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_meshgrid_variadic_tensors_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_mvlgamma_mvlgamma_p_3_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_nansum_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_nansum_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_narrow_copy_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_native_layer_norm_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ne_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_ne_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_neg_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_neg_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_new_zeros_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_hardtanh_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_leaky_relu_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_logsigmoid_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_mse_loss_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_softshrink_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_nn_functional_unfold_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_norm_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_norm_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_norm_fro_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_permute_copy_cuda_bool, test/test_decomp.py::TestDecompCUDA::test_quick_permute_copy_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_permute_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_permute_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_pow_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_prod_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_prod_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_reciprocal_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_repeat_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_roll_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_rot90_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_round_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_round_decimals_0_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_round_decimals_3_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_rsqrt_cuda_int8, test/test_decomp.py::TestDecompCUDA::test_quick_select_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_select_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_sgn_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_sgn_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_sinc_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_sinc_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_sinh_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_sinh_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_sinh_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_slice_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_slice_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_slice_scatter_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_special_entr_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_special_i0e_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_special_i0e_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_special_i1_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_special_ndtr_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_special_ndtri_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_special_xlog1py_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_special_zeta_cuda_int32, test/test_decomp.py::TestDecompCUDA::test_quick_split_cuda_uint8, test/test_decomp.py::TestDecompCUDA::test_quick_split_list_args_cuda_int64, test/test_decomp.py::TestDecompCUDA::test_quick_sqrt_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_squeeze_multiple_cuda_int16, 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test/test_decomp.py::TestDecompCUDA::test_quick_unfold_copy_cuda_int16, test/test_decomp.py::TestDecompCUDA::test_quick_unfold_cuda_float32, test/test_decomp.py::TestDecompCUDA::test_quick_unsafe_split_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_copy_cuda_complex32, test/test_decomp.py::TestDecompCUDA::test_quick_unsqueeze_cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_var_cuda_float16, test/test_decomp.py::TestDecompCUDA::test_quick_vdot_cuda_complex64, test/test_decomp.py::TestDecompCUDA::test_quick_view_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_view_cuda_complex128, test/test_decomp.py::TestDecompCUDA::test_quick_where_cuda_bfloat16, test/test_decomp.py::TestDecompCUDA::test_quick_zero__cuda_float64, test/test_decomp.py::TestDecompCUDA::test_quick_zeros_like_cuda_int32, test/test_decomp.py::DecompOneOffTestsCUDA::test_elu_backward_cuda, test/test_decomp.py::DecompOneOffTestsCUDA::test_rms_norm_decomp_cuda_cuda
2025-08-14T23:08:43.0414674Z 
2025-08-14T23:08:43.0414766Z Running test_expanded_weights 1/1 ... [2025-08-14 23:08:43.026093]
2025-08-14T23:08:43.0414944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:08:43.0415338Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_expanded_weights.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:08:43.026401]
2025-08-14T23:09:08.6054451Z 
2025-08-14T23:09:08.6064520Z test_expanded_weights 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_expanded_weights_1.1_83de6039c2930afd_.log
2025-08-14T23:09:08.6105696Z Running 220 items in this shard: test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_forward_helper_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_forward_helper_failure_args_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_set_grad_sample_if_exists_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_set_grad_sample_if_exists_failure_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_sum_over_all_but_batch_and_last_n_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_unpack_expanded_weight_or_tensor_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_unpack_expanded_weight_or_tensor_failure_cuda, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCUDA::test_unpack_expanded_weight_or_tensor_with_custom_function_cuda, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_cnn_model_mean_cuda, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_cnn_model_sum_cuda, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_embedding_model_cuda, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_error_cuda, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_complex32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv1d_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_complex32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv2d_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_complex32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_conv3d_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_embedding_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_embedding_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_embedding_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_embedding_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_group_norm_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_group_norm_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_group_norm_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_group_norm_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_instance_norm_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_instance_norm_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_instance_norm_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_instance_norm_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_layer_norm_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_layer_norm_cuda_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_layer_norm_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_layer_norm_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_linear_cuda_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCUDA::test_expanded_weight_forward_nn_functional_linear_cuda_complex128, 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test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_replicate_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_replicate_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_replicate_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_stride_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_stride_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_stride_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zero_batch_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zero_batch_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zero_batch_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zeros_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zeros_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv1d_zeros_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_circular_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_circular_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_circular_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_dilated_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_dilated_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_dilated_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_no_bias_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_no_bias_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_no_bias_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_padding_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_padding_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_padding_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_reflect_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_reflect_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_reflect_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_replicate_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_replicate_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_replicate_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_strided_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_strided_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_strided_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zero_batch_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zero_batch_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zero_batch_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zeros_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zeros_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv2d_zeros_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_1x1x1_no_bias_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_1x1x1_no_bias_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_1x1x1_no_bias_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_circular_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_circular_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_circular_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_no_bias_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_no_bias_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_no_bias_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_replicate_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_replicate_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_replicate_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_padding_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_padding_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_stride_padding_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zero_batch_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zero_batch_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zero_batch_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zeros_stride2_pad2_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zeros_stride2_pad2_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Conv3d_zeros_stride2_pad2_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_discontiguous_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_discontiguous_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_discontiguous_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Embedding_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_LayerNorm_3d_no_affine_large_feature_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_LayerNorm_3d_no_affine_large_feature_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_batch_dim_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_batch_dim_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_batch_dim_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_bias_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_bias_cuda_double_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_Linear_no_bias_multiple_inputs_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_GRU_eval_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_GRU_eval_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_GRU_train_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_GRU_train_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_LSTM_eval_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_LSTM_eval_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_LSTM_train_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_LSTM_train_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_RNN_eval_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_RNN_eval_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_RNN_train_mode_cuda_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_module_nn_RNN_train_mode_cuda_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_per_sample_api_compute_batch_size_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_per_sample_api_compute_batch_size_not_pytreeable_cuda, test/test_expanded_weights.py::TestExpandedWeightModuleCUDA::test_per_sample_api_failing_cuda
2025-08-14T23:09:08.6139460Z 
2025-08-14T23:09:08.6139529Z Running doctests 1/1 ... [2025-08-14 23:09:08.605572]
2025-08-14T23:09:08.6226733Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch')
2025-08-14T23:09:08.6227012Z Listing tests
2025-08-14T23:09:08.6720494Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=565.
2025-08-14T23:09:08.6721584Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.6721954Z 
2025-08-14T23:09:08.6722142Z Load a model from a github repo or a local directory.
2025-08-14T23:09:08.6722412Z 
2025-08-14T23:09:08.6722644Z Note: Loading a model is the typical use case, but this can also be used to
2025-08-14T23:09:08.6723311Z for loading other objects such as tokenizers, loss functions, etc.
2025-08-14T23:09:08.6723625Z 
2025-08-14T23:09:08.6723816Z If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-08-14T23:09:08.6724275Z of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-08-14T23:09:08.6724661Z ref (a tag or a branch).
2025-08-14T23:09:08.6724847Z 
2025-08-14T23:09:08.6725031Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-08-14T23:09:08.6725421Z path to a local directory.
2025-08-14T23:09:08.6725608Z 
2025-08-14T23:09:08.6725699Z Args:
2025-08-14T23:09:08.6726134Z     repo_or_dir (str): If ``source`` is 'github',
2025-08-14T23:09:08.6726675Z         this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-08-14T23:09:08.6727357Z         an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-08-14T23:09:08.6728051Z         the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-08-14T23:09:08.6728608Z         If ``source`` is 'local'  then it should be a path to a local directory.
2025-08-14T23:09:08.6729120Z     model (str): the name of a callable (entrypoint) defined in the
2025-08-14T23:09:08.6729547Z         repo/dir's ``hubconf.py``.
2025-08-14T23:09:08.6729961Z     *args (optional): the corresponding args for callable ``model``.
2025-08-14T23:09:08.6730448Z     source (str, optional): 'github' or 'local'. Specifies how
2025-08-14T23:09:08.6730904Z         ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-08-14T23:09:08.6731391Z     trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-08-14T23:09:08.6731834Z         This parameter was introduced in v1.12 and helps ensuring that users
2025-08-14T23:09:08.6732004Z         only run code from repos that they trust.
2025-08-14T23:09:08.6732093Z 
2025-08-14T23:09:08.6732170Z         - If ``False``, a prompt will ask the user whether the repo should
2025-08-14T23:09:08.6732315Z           be trusted.
2025-08-14T23:09:08.6732446Z         - If ``True``, the repo will be added to the trusted list and loaded
2025-08-14T23:09:08.6732604Z           without requiring explicit confirmation.
2025-08-14T23:09:08.6732759Z         - If ``"check"``, the repo will be checked against the list of
2025-08-14T23:09:08.6733023Z           trusted repos in the cache. If it is not present in that list, the
2025-08-14T23:09:08.6733209Z           behaviour will fall back onto the ``trust_repo=False`` option.
2025-08-14T23:09:08.6733382Z         - If ``None``: this will raise a warning, inviting the user to set
2025-08-14T23:09:08.6733558Z           ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-08-14T23:09:08.6733741Z           is only present for backward compatibility and will be removed in
2025-08-14T23:09:08.6733889Z           v2.0.
2025-08-14T23:09:08.6733944Z 
2025-08-14T23:09:08.6734024Z         Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-08-14T23:09:08.6734217Z     force_reload (bool, optional): whether to force a fresh download of
2025-08-14T23:09:08.6734397Z         the github repo unconditionally. Does not have any effect if
2025-08-14T23:09:08.6734552Z         ``source = 'local'``. Default is ``False``.
2025-08-14T23:09:08.6734711Z     verbose (bool, optional): If ``False``, mute messages about hitting
2025-08-14T23:09:08.6734902Z         local caches. Note that the message about first download cannot be
2025-08-14T23:09:08.6735087Z         muted. Does not have any effect if ``source = 'local'``.
2025-08-14T23:09:08.6735238Z         Default is ``True``.
2025-08-14T23:09:08.6735474Z     skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-08-14T23:09:08.6735717Z         specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-08-14T23:09:08.6735953Z         requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-08-14T23:09:08.6736152Z         ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-08-14T23:09:08.6736335Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-08-14T23:09:08.6736448Z 
2025-08-14T23:09:08.6736484Z Returns:
2025-08-14T23:09:08.6736600Z     The output of the ``model`` callable when called with the given
2025-08-14T23:09:08.6736744Z     ``*args`` and ``**kwargs``.
2025-08-14T23:09:08.6736813Z 
2025-08-14T23:09:08.6736849Z Example:
2025-08-14T23:09:08.6736948Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:08.6737078Z     >>> # from a github repo
2025-08-14T23:09:08.6737209Z     >>> repo = "pytorch/vision"
2025-08-14T23:09:08.6737324Z     >>> model = torch.hub.load(
2025-08-14T23:09:08.6737466Z     ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-08-14T23:09:08.6737639Z     ... )
2025-08-14T23:09:08.6737731Z     >>> # from a local directory
2025-08-14T23:09:08.6737855Z     >>> path = "/some/local/path/pytorch/vision"
2025-08-14T23:09:08.6737980Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:08.6738139Z     >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-08-14T23:09:08.6738265Z 
2025-08-14T23:09:08.6738365Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.6738489Z 
2025-08-14T23:09:08.6738653Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=657.
2025-08-14T23:09:08.6738932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.6739060Z 
2025-08-14T23:09:08.6739131Z Load a model from a local directory with a ``hubconf.py``.
2025-08-14T23:09:08.6739232Z 
2025-08-14T23:09:08.6739265Z Args:
2025-08-14T23:09:08.6739383Z     hubconf_dir (str): path to a local directory that contains a
2025-08-14T23:09:08.6739526Z         ``hubconf.py``.
2025-08-14T23:09:08.6739659Z     model (str): name of an entrypoint defined in the directory's
2025-08-14T23:09:08.6739796Z         ``hubconf.py``.
2025-08-14T23:09:08.6739929Z     *args (optional): the corresponding args for callable ``model``.
2025-08-14T23:09:08.6740116Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-08-14T23:09:08.6740230Z 
2025-08-14T23:09:08.6740265Z Returns:
2025-08-14T23:09:08.6740375Z     a single model with corresponding pretrained weights.
2025-08-14T23:09:08.6740471Z 
2025-08-14T23:09:08.6740508Z Example:
2025-08-14T23:09:08.6740602Z     >>> # xdoctest: +SKIP("stub local path")
2025-08-14T23:09:08.6740730Z     >>> path = "/some/local/path/pytorch/vision"
2025-08-14T23:09:08.6740857Z     >>> model = _load_local(
2025-08-14T23:09:08.6740956Z     ...     path,
2025-08-14T23:09:08.6741046Z     ...     "resnet50",
2025-08-14T23:09:08.6741158Z     ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-08-14T23:09:08.6741281Z     ... )
2025-08-14T23:09:08.6741329Z 
2025-08-14T23:09:08.6741419Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.6741544Z 
2025-08-14T23:09:08.6741704Z 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.
2025-08-14T23:09:08.6741986Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.6742166Z Download object at the given URL to a local path.
2025-08-14T23:09:08.6742255Z 
2025-08-14T23:09:08.6742290Z     Args:
2025-08-14T23:09:08.6742382Z         url (str): URL of the object to download
2025-08-14T23:09:08.6742549Z         dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-08-14T23:09:08.6742816Z         hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-08-14T23:09:08.6743070Z             Default: None
2025-08-14T23:09:08.6743223Z         progress (bool, optional): whether or not to display a progress bar to stderr
2025-08-14T23:09:08.6743385Z             Default: True
2025-08-14T23:09:08.6743448Z 
2025-08-14T23:09:08.6750318Z     Example:
2025-08-14T23:09:08.6750596Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:08.6750779Z         >>> # xdoctest: +REQUIRES(POSIX)
2025-08-14T23:09:08.6750956Z         >>> torch.hub.download_url_to_file(
2025-08-14T23:09:08.6751168Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-08-14T23:09:08.6751358Z         ...     "/tmp/temporary_file",
2025-08-14T23:09:08.6751523Z         ... )
2025-08-14T23:09:08.6751589Z 
2025-08-14T23:09:08.6751648Z     
2025-08-14T23:09:08.6751874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.6752018Z 
2025-08-14T23:09:08.6752215Z 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.
2025-08-14T23:09:08.6752576Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.6753138Z Loads the Torch serialized object at the given URL.
2025-08-14T23:09:08.6753255Z 
2025-08-14T23:09:08.6753338Z     If downloaded file is a zip file, it will be automatically
2025-08-14T23:09:08.6753507Z     decompressed.
2025-08-14T23:09:08.6753612Z 
2025-08-14T23:09:08.6753699Z     If the object is already present in `model_dir`, it's deserialized and
2025-08-14T23:09:08.6753881Z     returned.
2025-08-14T23:09:08.6754049Z     The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-08-14T23:09:08.6754276Z     ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-08-14T23:09:08.6754405Z 
2025-08-14T23:09:08.6754452Z     Args:
2025-08-14T23:09:08.6754595Z         url (str): URL of the object to download
2025-08-14T23:09:08.6754782Z         model_dir (str, optional): directory in which to save the object
2025-08-14T23:09:08.6755041Z         map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-08-14T23:09:08.6755344Z         progress (bool, optional): whether or not to display a progress bar to stderr.
2025-08-14T23:09:08.6755543Z             Default: True
2025-08-14T23:09:08.6755761Z         check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-08-14T23:09:08.6756022Z             ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-08-14T23:09:08.6756257Z             digits of the SHA256 hash of the contents of the file. The hash is used to
2025-08-14T23:09:08.6756485Z             ensure unique names and to verify the contents of the file.
2025-08-14T23:09:08.6756662Z             Default: False
2025-08-14T23:09:08.6756864Z         file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-08-14T23:09:08.6757171Z         weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-08-14T23:09:08.6757442Z             Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-08-14T23:09:08.6757596Z 
2025-08-14T23:09:08.6758086Z     Example:
2025-08-14T23:09:08.6758239Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:08.6758422Z         >>> state_dict = torch.hub.load_state_dict_from_url(
2025-08-14T23:09:08.6758632Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-08-14T23:09:08.6758813Z         ... )
2025-08-14T23:09:08.6758893Z 
2025-08-14T23:09:08.6758935Z     
2025-08-14T23:09:08.6759112Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.6759245Z 
2025-08-14T23:09:08.7200051Z 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.
2025-08-14T23:09:08.7200427Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7200707Z 
2025-08-14T23:09:08.7200797Z dim_order(ambiguity_check=False) -> tuple
2025-08-14T23:09:08.7200900Z 
2025-08-14T23:09:08.7201013Z Returns the uniquely determined tuple of int describing the dim order or
2025-08-14T23:09:08.7201240Z physical layout of :attr:`self`.
2025-08-14T23:09:08.7201334Z 
2025-08-14T23:09:08.7201443Z The dim order represents how dimensions are laid out in memory of dense tensors,
2025-08-14T23:09:08.7201683Z starting from the outermost to the innermost dimension.
2025-08-14T23:09:08.7201804Z 
2025-08-14T23:09:08.7201903Z Note that the dim order may not always be uniquely determined.
2025-08-14T23:09:08.7202224Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-08-14T23:09:08.7202575Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-08-14T23:09:08.7202988Z into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-08-14T23:09:08.7203268Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-08-14T23:09:08.7206210Z Otherwise, it will raise TypeError.
2025-08-14T23:09:08.7206308Z 
2025-08-14T23:09:08.7206344Z Args:
2025-08-14T23:09:08.7206505Z     ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-08-14T23:09:08.7206653Z 
2025-08-14T23:09:08.7206697Z Examples::
2025-08-14T23:09:08.7206746Z 
2025-08-14T23:09:08.7206794Z     >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-08-14T23:09:08.7206906Z     (0, 1, 2, 3)
2025-08-14T23:09:08.7207019Z     >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-08-14T23:09:08.7207148Z     (0, 2, 1, 3)
2025-08-14T23:09:08.7207275Z     >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-08-14T23:09:08.7207415Z     (0, 2, 3, 1)
2025-08-14T23:09:08.7207512Z     >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-08-14T23:09:08.7208834Z     (0, 1, 2, 3)
2025-08-14T23:09:08.7208924Z     >>> try:
2025-08-14T23:09:08.7209036Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-08-14T23:09:08.7209176Z     ... except RuntimeError as e:
2025-08-14T23:09:08.7209283Z     ...     print(e)
2025-08-14T23:09:08.7209445Z     The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-08-14T23:09:08.7209630Z     >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-08-14T23:09:08.7209781Z     ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-08-14T23:09:08.7209940Z     ... )  # It can be mapped to contiguous format
2025-08-14T23:09:08.7210055Z     (0, 1, 2, 3)
2025-08-14T23:09:08.7210140Z     >>> try:
2025-08-14T23:09:08.7211170Z     ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-08-14T23:09:08.7211320Z     ... except TypeError as e:
2025-08-14T23:09:08.7211424Z     ...     print(e)
2025-08-14T23:09:08.7211561Z     The ambiguity_check argument must be a bool or a list of memory formats.
2025-08-14T23:09:08.7211677Z 
2025-08-14T23:09:08.7211713Z .. warning::
2025-08-14T23:09:08.7211831Z     The dim_order tensor API is experimental and subject to change.
2025-08-14T23:09:08.7211936Z 
2025-08-14T23:09:08.7212024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7212146Z 
2025-08-14T23:09:08.7385042Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=446.
2025-08-14T23:09:08.7385360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7387381Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-08-14T23:09:08.7387627Z 
2025-08-14T23:09:08.7387724Z         This is helpful when you want to visualize data over some
2025-08-14T23:09:08.7387883Z         range of inputs. See below for a plotting example.
2025-08-14T23:09:08.7387977Z 
2025-08-14T23:09:08.7388040Z         Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-08-14T23:09:08.7388204Z         inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-08-14T23:09:08.7388375Z         this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-08-14T23:09:08.7388544Z         G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-08-14T23:09:08.7388708Z         the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-08-14T23:09:08.7388855Z         to the result shape.
2025-08-14T23:09:08.7388928Z 
2025-08-14T23:09:08.7388968Z         .. note::
2025-08-14T23:09:08.7389080Z             0D inputs are treated equivalently to 1D inputs of a
2025-08-14T23:09:08.7389238Z             single element.
2025-08-14T23:09:08.7389307Z 
2025-08-14T23:09:08.7397395Z         .. warning::
2025-08-14T23:09:08.7397540Z             `torch.meshgrid(*tensors)` currently has the same behavior
2025-08-14T23:09:08.7397758Z             as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-08-14T23:09:08.7397852Z 
2025-08-14T23:09:08.7397914Z             In the future `torch.meshgrid` will transition to
2025-08-14T23:09:08.7398048Z             `indexing='xy'` as the default.
2025-08-14T23:09:08.7398126Z 
2025-08-14T23:09:08.7398200Z             https://github.com/pytorch/pytorch/issues/50276 tracks
2025-08-14T23:09:08.7398365Z             this issue with the goal of migrating to NumPy's behavior.
2025-08-14T23:09:08.7398464Z 
2025-08-14T23:09:08.7401254Z         .. seealso::
2025-08-14T23:09:08.7401313Z 
2025-08-14T23:09:08.7401380Z             :func:`torch.cartesian_prod` has the same effect but it
2025-08-14T23:09:08.7403502Z             collects the data in a tensor of vectors.
2025-08-14T23:09:08.7403591Z 
2025-08-14T23:09:08.7403634Z         Args:
2025-08-14T23:09:08.7403785Z             tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-08-14T23:09:08.7403983Z                 treated as tensors of size :math:`(1,)` automatically
2025-08-14T23:09:08.7404081Z 
2025-08-14T23:09:08.7404146Z             indexing: (str, optional): the indexing mode, either "xy"
2025-08-14T23:09:08.7404304Z                 or "ij", defaults to "ij". See warning for future changes.
2025-08-14T23:09:08.7404400Z 
2025-08-14T23:09:08.7404457Z                 If "xy" is selected, the first dimension corresponds
2025-08-14T23:09:08.7404607Z                 to the cardinality of the second input and the second
2025-08-14T23:09:08.7404760Z                 dimension corresponds to the cardinality of the first
2025-08-14T23:09:08.7404894Z                 input.
2025-08-14T23:09:08.7404955Z 
2025-08-14T23:09:08.7405010Z                 If "ij" is selected, the dimensions are in the same
2025-08-14T23:09:08.7405153Z                 order as the cardinality of the inputs.
2025-08-14T23:09:08.7405240Z 
2025-08-14T23:09:08.7405275Z         Returns:
2025-08-14T23:09:08.7405389Z             seq (sequence of Tensors): If the input has :math:`N`
2025-08-14T23:09:08.7405544Z             tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-08-14T23:09:08.7405703Z             output will also have :math:`N` tensors, where each tensor
2025-08-14T23:09:08.7405926Z             is of shape :math:`(S_0, ..., S_{N-1})`.
2025-08-14T23:09:08.7406009Z 
2025-08-14T23:09:08.7406050Z         Example::
2025-08-14T23:09:08.7406103Z 
2025-08-14T23:09:08.7406148Z             >>> x = torch.tensor([1, 2, 3])
2025-08-14T23:09:08.7406268Z             >>> y = torch.tensor([4, 5, 6])
2025-08-14T23:09:08.7406343Z 
2025-08-14T23:09:08.7406413Z             Observe the element-wise pairings across the grid, (1, 4),
2025-08-14T23:09:08.7406568Z             (1, 5), ..., (3, 6). This is the same thing as the
2025-08-14T23:09:08.7406696Z             cartesian product.
2025-08-14T23:09:08.7406899Z             >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-08-14T23:09:08.7407026Z             >>> grid_x
2025-08-14T23:09:08.7408271Z             tensor([[1, 1, 1],
2025-08-14T23:09:08.7408377Z                     [2, 2, 2],
2025-08-14T23:09:08.7408475Z                     [3, 3, 3]])
2025-08-14T23:09:08.7408572Z             >>> grid_y
2025-08-14T23:09:08.7408663Z             tensor([[4, 5, 6],
2025-08-14T23:09:08.7408759Z                     [4, 5, 6],
2025-08-14T23:09:08.7408854Z                     [4, 5, 6]])
2025-08-14T23:09:08.7408920Z 
2025-08-14T23:09:08.7409004Z             This correspondence can be seen when these grids are
2025-08-14T23:09:08.7409140Z             stacked properly.
2025-08-14T23:09:08.7409278Z             >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-08-14T23:09:08.7409435Z             ...             torch.cartesian_prod(x, y))
2025-08-14T23:09:08.7410632Z             True
2025-08-14T23:09:08.7410718Z 
2025-08-14T23:09:08.7410789Z             `torch.meshgrid` is commonly used to produce a grid for
2025-08-14T23:09:08.7410922Z             plotting.
2025-08-14T23:09:08.7411048Z             >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-08-14T23:09:08.7411179Z             >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-08-14T23:09:08.7411309Z             >>> import matplotlib.pyplot as plt
2025-08-14T23:09:08.7411439Z             >>> xs = torch.linspace(-5, 5, steps=100)
2025-08-14T23:09:08.7411566Z             >>> ys = torch.linspace(-5, 5, steps=100)
2025-08-14T23:09:08.7411698Z             >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-08-14T23:09:08.7411833Z             >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-08-14T23:09:08.7411959Z             >>> ax = plt.axes(projection='3d')
2025-08-14T23:09:08.7413246Z             >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-08-14T23:09:08.7413376Z             >>> plt.show()
2025-08-14T23:09:08.7413438Z 
2025-08-14T23:09:08.7413494Z         .. image:: ../_static/img/meshgrid.png
2025-08-14T23:09:08.7413609Z             :width: 512
2025-08-14T23:09:08.7413666Z 
2025-08-14T23:09:08.7413699Z         
2025-08-14T23:09:08.7413833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7413957Z 
2025-08-14T23:09:08.7414124Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=842.
2025-08-14T23:09:08.7414409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7414664Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-08-14T23:09:08.7414825Z 
2025-08-14T23:09:08.7414884Z     Returns the unique elements of the input tensor.
2025-08-14T23:09:08.7414975Z 
2025-08-14T23:09:08.7415079Z     .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-08-14T23:09:08.7416294Z         this function also eliminates non-consecutive duplicate values.
2025-08-14T23:09:08.7416404Z 
2025-08-14T23:09:08.7416485Z     .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-08-14T23:09:08.7416701Z         `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-08-14T23:09:08.7416931Z         Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-08-14T23:09:08.7417126Z         :func:`torch.unique_consecutive` which avoids the sorting.
2025-08-14T23:09:08.7417225Z 
2025-08-14T23:09:08.7417258Z     Args:
2025-08-14T23:09:08.7417346Z         input (Tensor): the input tensor
2025-08-14T23:09:08.7417498Z         sorted (bool): Whether to sort the unique elements in ascending order
2025-08-14T23:09:08.7417649Z             before returning as output.
2025-08-14T23:09:08.7417799Z         return_inverse (bool): Whether to also return the indices for where
2025-08-14T23:09:08.7417986Z             elements in the original input ended up in the returned unique list.
2025-08-14T23:09:08.7419232Z         return_counts (bool): Whether to also return the counts for each unique
2025-08-14T23:09:08.7419382Z             element.
2025-08-14T23:09:08.7419513Z         dim (int, optional): the dimension to operate upon. If ``None``, the
2025-08-14T23:09:08.7419693Z             unique of the flattened input is returned. Otherwise, each of the
2025-08-14T23:09:08.7419873Z             tensors indexed by the given dimension is treated as one of the
2025-08-14T23:09:08.7420052Z             elements to apply the unique operation upon. See examples for more
2025-08-14T23:09:08.7420203Z             details. Default: ``None``
2025-08-14T23:09:08.7420278Z 
2025-08-14T23:09:08.7420312Z     Returns:
2025-08-14T23:09:08.7420462Z         (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-08-14T23:09:08.7420599Z 
2025-08-14T23:09:08.7420687Z             - **output** (*Tensor*): the output list of unique scalar elements.
2025-08-14T23:09:08.7421796Z             - **inverse_indices** (*Tensor*): (optional) if
2025-08-14T23:09:08.7421954Z               :attr:`return_inverse` is True, there will be an additional
2025-08-14T23:09:08.7422150Z               returned tensor (same shape as input) representing the indices
2025-08-14T23:09:08.7422327Z               for where elements in the original input map to in the output;
2025-08-14T23:09:08.7422498Z               otherwise, this function will only return a single tensor.
2025-08-14T23:09:08.7422649Z             - **counts** (*Tensor*): (optional) if
2025-08-14T23:09:08.7422793Z               :attr:`return_counts` is True, there will be an additional
2025-08-14T23:09:08.7423063Z               returned tensor (same shape as output or output.size(dim),
2025-08-14T23:09:08.7423232Z               if dim was specified) representing the number of occurrences
2025-08-14T23:09:08.7423378Z               for each unique value or tensor.
2025-08-14T23:09:08.7423461Z 
2025-08-14T23:09:08.7423506Z     Example::
2025-08-14T23:09:08.7423558Z 
2025-08-14T23:09:08.7424590Z         >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-08-14T23:09:08.7424743Z         >>> output
2025-08-14T23:09:08.7424834Z         tensor([1, 2, 3])
2025-08-14T23:09:08.7424898Z 
2025-08-14T23:09:08.7424950Z         >>> output, inverse_indices = torch.unique(
2025-08-14T23:09:08.7425119Z         ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-08-14T23:09:08.7425272Z         >>> output
2025-08-14T23:09:08.7425360Z         tensor([1, 2, 3])
2025-08-14T23:09:08.7425459Z         >>> inverse_indices
2025-08-14T23:09:08.7425561Z         tensor([0, 2, 1, 2])
2025-08-14T23:09:08.7425628Z 
2025-08-14T23:09:08.7425676Z         >>> output, inverse_indices = torch.unique(
2025-08-14T23:09:08.7425843Z         ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-08-14T23:09:08.7426936Z         >>> output
2025-08-14T23:09:08.7427027Z         tensor([1, 2, 3])
2025-08-14T23:09:08.7427126Z         >>> inverse_indices
2025-08-14T23:09:08.7427224Z         tensor([[0, 2],
2025-08-14T23:09:08.7427318Z                 [1, 2]])
2025-08-14T23:09:08.7427377Z 
2025-08-14T23:09:08.7427417Z         >>> a = torch.tensor([
2025-08-14T23:09:08.7427515Z         ...     [
2025-08-14T23:09:08.7427603Z         ...         [1, 1, 0, 0],
2025-08-14T23:09:08.7427707Z         ...         [1, 1, 0, 0],
2025-08-14T23:09:08.7427809Z         ...         [0, 0, 1, 1],
2025-08-14T23:09:08.7427907Z         ...     ],
2025-08-14T23:09:08.7428917Z         ...     [
2025-08-14T23:09:08.7429005Z         ...         [0, 0, 1, 1],
2025-08-14T23:09:08.7429105Z         ...         [0, 0, 1, 1],
2025-08-14T23:09:08.7429203Z         ...         [1, 1, 1, 1],
2025-08-14T23:09:08.7429299Z         ...     ],
2025-08-14T23:09:08.7429382Z         ...     [
2025-08-14T23:09:08.7429466Z         ...         [1, 1, 0, 0],
2025-08-14T23:09:08.7429613Z         ...         [1, 1, 0, 0],
2025-08-14T23:09:08.7429741Z         ...         [0, 0, 1, 1],
2025-08-14T23:09:08.7429836Z         ...     ],
2025-08-14T23:09:08.7429920Z         ... ])
2025-08-14T23:09:08.7429971Z 
2025-08-14T23:09:08.7430987Z         >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-08-14T23:09:08.7431179Z         >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-08-14T23:09:08.7431342Z         >>> # each other, so one of them will be removed.
2025-08-14T23:09:08.7431469Z         >>> (a[0, :, :] == a[2, :, :]).all()
2025-08-14T23:09:08.7431578Z         tensor(True)
2025-08-14T23:09:08.7431679Z         >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-08-14T23:09:08.7431797Z         >>> a_unique_dim0
2025-08-14T23:09:08.7431895Z         tensor([[[0, 0, 1, 1],
2025-08-14T23:09:08.7431995Z                  [0, 0, 1, 1],
2025-08-14T23:09:08.7432094Z                  [1, 1, 1, 1]],
2025-08-14T23:09:08.7432214Z                 [[1, 1, 0, 0],
2025-08-14T23:09:08.7433342Z                  [1, 1, 0, 0],
2025-08-14T23:09:08.7433445Z                  [0, 0, 1, 1]]])
2025-08-14T23:09:08.7433512Z 
2025-08-14T23:09:08.7433617Z         >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-08-14T23:09:08.7433765Z         >>> # `a_unique_dim0`:
2025-08-14T23:09:08.7433878Z         >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-08-14T23:09:08.7433992Z         tensor(True)
2025-08-14T23:09:08.7434095Z         >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-08-14T23:09:08.7434207Z         tensor(True)
2025-08-14T23:09:08.7434263Z 
2025-08-14T23:09:08.7434338Z         >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-08-14T23:09:08.7434513Z         >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-08-14T23:09:08.7434656Z         >>> # them will be removed.
2025-08-14T23:09:08.7435725Z         >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-08-14T23:09:08.7435839Z         tensor(True)
2025-08-14T23:09:08.7435937Z         >>> torch.unique(a, dim=1)
2025-08-14T23:09:08.7436046Z         tensor([[[0, 0, 1, 1],
2025-08-14T23:09:08.7436144Z                  [1, 1, 0, 0]],
2025-08-14T23:09:08.7436245Z                 [[1, 1, 1, 1],
2025-08-14T23:09:08.7436341Z                  [0, 0, 1, 1]],
2025-08-14T23:09:08.7436438Z                 [[0, 0, 1, 1],
2025-08-14T23:09:08.7436536Z                  [1, 1, 0, 0]]])
2025-08-14T23:09:08.7436602Z 
2025-08-14T23:09:08.7436679Z         >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-08-14T23:09:08.7436848Z         >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-08-14T23:09:08.7437963Z         >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-08-14T23:09:08.7438106Z         >>> # sub-tensors will be removed.
2025-08-14T23:09:08.7438224Z         >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-08-14T23:09:08.7438329Z         tensor(True)
2025-08-14T23:09:08.7438426Z         >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-08-14T23:09:08.7438533Z         tensor(True)
2025-08-14T23:09:08.7438628Z         >>> torch.unique(a, dim=2)
2025-08-14T23:09:08.7438737Z         tensor([[[0, 1],
2025-08-14T23:09:08.7438831Z                  [0, 1],
2025-08-14T23:09:08.7438923Z                  [1, 0]],
2025-08-14T23:09:08.7439015Z                 [[1, 0],
2025-08-14T23:09:08.7440056Z                  [1, 0],
2025-08-14T23:09:08.7440147Z                  [1, 1]],
2025-08-14T23:09:08.7440239Z                 [[0, 1],
2025-08-14T23:09:08.7440328Z                  [0, 1],
2025-08-14T23:09:08.7440419Z                  [1, 0]]])
2025-08-14T23:09:08.7440508Z     
2025-08-14T23:09:08.7440640Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7440766Z 
2025-08-14T23:09:08.7510643Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1285.
2025-08-14T23:09:08.7512185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7519150Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-08-14T23:09:08.7519455Z 
2025-08-14T23:09:08.7519581Z     Loads an object saved with :func:`torch.save` from a file.
2025-08-14T23:09:08.7519763Z 
2025-08-14T23:09:08.7519909Z     :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-08-14T23:09:08.7520249Z     which underlie tensors, specially. They are first deserialized on the
2025-08-14T23:09:08.7520574Z     CPU and are then moved to the device they were saved from. If this fails
2025-08-14T23:09:08.7520904Z     (e.g. because the run time system doesn't have certain devices), an exception
2025-08-14T23:09:08.7521243Z     is raised. However, storages can be dynamically remapped to an alternative
2025-08-14T23:09:08.7521558Z     set of devices using the :attr:`map_location` argument.
2025-08-14T23:09:08.7521842Z 
2025-08-14T23:09:08.7521998Z     If :attr:`map_location` is a callable, it will be called once for each serialized
2025-08-14T23:09:08.7522340Z     storage with two arguments: storage and location. The storage argument
2025-08-14T23:09:08.7522735Z     will be the initial deserialization of the storage, residing on the CPU.
2025-08-14T23:09:08.7523169Z     Each serialized storage has a location tag associated with it which
2025-08-14T23:09:08.7523482Z     identifies the device it was saved from, and this tag is the second
2025-08-14T23:09:08.7523820Z     argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-08-14T23:09:08.7524164Z     for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-08-14T23:09:08.7524479Z     :attr:`map_location` should return either ``None`` or a storage. If
2025-08-14T23:09:08.7524807Z     :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-08-14T23:09:08.7525164Z     object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-08-14T23:09:08.7525526Z     fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-08-14T23:09:08.7525841Z 
2025-08-14T23:09:08.7525982Z     If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-08-14T23:09:08.7526321Z     a device tag, it indicates the location where all tensors should be loaded.
2025-08-14T23:09:08.7526518Z 
2025-08-14T23:09:08.7526670Z     Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-08-14T23:09:08.7527012Z     appearing in the file (keys), to ones that specify where to put the
2025-08-14T23:09:08.7528978Z     storages (values).
2025-08-14T23:09:08.7529065Z 
2025-08-14T23:09:08.7529165Z     User extensions can register their own location tags and tagging and
2025-08-14T23:09:08.7529425Z     deserialization methods using :func:`torch.serialization.register_package`.
2025-08-14T23:09:08.7529589Z 
2025-08-14T23:09:08.7529709Z     See :ref:`layout-control` for more advanced tools to manipulate a checkpoint.
2025-08-14T23:09:08.7529862Z 
2025-08-14T23:09:08.7529905Z     Args:
2025-08-14T23:09:08.7530102Z         f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-08-14T23:09:08.7530366Z             or a string or os.PathLike object containing a file name
2025-08-14T23:09:08.7530632Z         map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-08-14T23:09:08.7530863Z             locations
2025-08-14T23:09:08.7531037Z         pickle_module: module used for unpickling metadata and objects (has to
2025-08-14T23:09:08.7531263Z             match the :attr:`pickle_module` used to serialize file)
2025-08-14T23:09:08.7532800Z         weights_only: Indicates whether unpickler should be restricted to
2025-08-14T23:09:08.7533104Z             loading only tensors, primitive types, dictionaries
2025-08-14T23:09:08.7533323Z             and any types added via :func:`torch.serialization.add_safe_globals`.
2025-08-14T23:09:08.7533626Z             See :ref:`weights-only` for more details.
2025-08-14T23:09:08.7533878Z         mmap: Indicates whether the file should be mapped rather than loading all the storages into memory.
2025-08-14T23:09:08.7534209Z             Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-08-14T23:09:08.7534538Z             are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-08-14T23:09:08.7534871Z             second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-08-14T23:09:08.7535195Z             tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages
2025-08-14T23:09:08.7535449Z             will be lazily loaded when their data is accessed.
2025-08-14T23:09:08.7535718Z         pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-08-14T23:09:08.7537236Z             :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-08-14T23:09:08.7537463Z             :attr:`errors=...`.
2025-08-14T23:09:08.7537551Z 
2025-08-14T23:09:08.7537624Z     .. warning::
2025-08-14T23:09:08.7537786Z         :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-08-14T23:09:08.7538012Z         uses ``pickle`` module implicitly, which is known to be insecure.
2025-08-14T23:09:08.7538262Z         It is possible to construct malicious pickle data which will execute arbitrary code
2025-08-14T23:09:08.7538528Z         during unpickling. Never load data that could have come from an untrusted
2025-08-14T23:09:08.7538798Z         source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-08-14T23:09:08.7538966Z 
2025-08-14T23:09:08.7539008Z     .. note::
2025-08-14T23:09:08.7539188Z         When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-08-14T23:09:08.7539416Z         will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-08-14T23:09:08.7540609Z         and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-08-14T23:09:08.7540739Z 
2025-08-14T23:09:08.7540774Z     .. note::
2025-08-14T23:09:08.7540913Z         By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-08-14T23:09:08.7541113Z         case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-08-14T23:09:08.7541297Z         when loading files saved by Python 2 in Python 3.  If this default
2025-08-14T23:09:08.7541495Z         is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-08-14T23:09:08.7541706Z         these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-08-14T23:09:08.7541912Z         to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-08-14T23:09:08.7542115Z         as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-08-14T23:09:08.7542233Z 
2025-08-14T23:09:08.7542269Z     Example:
2025-08-14T23:09:08.7542373Z         >>> # xdoctest: +SKIP("undefined filepaths")
2025-08-14T23:09:08.7543540Z         >>> torch.load("tensors.pt", weights_only=True)
2025-08-14T23:09:08.7543673Z         # Load all tensors onto the CPU
2025-08-14T23:09:08.7543788Z         >>> torch.load(
2025-08-14T23:09:08.7543888Z         ...     "tensors.pt",
2025-08-14T23:09:08.7544005Z         ...     map_location=torch.device("cpu"),
2025-08-14T23:09:08.7544130Z         ...     weights_only=True,
2025-08-14T23:09:08.7544233Z         ... )
2025-08-14T23:09:08.7544339Z         # Load all tensors onto the CPU, using a function
2025-08-14T23:09:08.7544464Z         >>> torch.load(
2025-08-14T23:09:08.7544560Z         ...     "tensors.pt",
2025-08-14T23:09:08.7545627Z         ...     map_location=lambda storage, loc: storage,
2025-08-14T23:09:08.7545793Z         ...     weights_only=True,
2025-08-14T23:09:08.7545928Z         ... )
2025-08-14T23:09:08.7546021Z         # Load all tensors onto GPU 1
2025-08-14T23:09:08.7546133Z         >>> torch.load(
2025-08-14T23:09:08.7546228Z         ...     "tensors.pt",
2025-08-14T23:09:08.7546352Z         ...     map_location=lambda storage, loc: storage.cuda(1),
2025-08-14T23:09:08.7546486Z         ...     weights_only=True,
2025-08-14T23:09:08.7546600Z         ... )  # type: ignore[attr-defined]
2025-08-14T23:09:08.7546721Z         # Map tensors from GPU 1 to GPU 0
2025-08-14T23:09:08.7546835Z         >>> torch.load(
2025-08-14T23:09:08.7547962Z         ...     "tensors.pt",
2025-08-14T23:09:08.7548078Z         ...     map_location={"cuda:1": "cuda:0"},
2025-08-14T23:09:08.7548195Z         ...     weights_only=True,
2025-08-14T23:09:08.7548310Z         ... )
2025-08-14T23:09:08.7548407Z         # Load tensor from io.BytesIO object
2025-08-14T23:09:08.7548615Z         # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-08-14T23:09:08.7548789Z         >>> with open("tensor.pt", "rb") as f:
2025-08-14T23:09:08.7548917Z         ...     buffer = io.BytesIO(f.read())
2025-08-14T23:09:08.7549064Z         >>> torch.load(buffer, weights_only=False)
2025-08-14T23:09:08.7549204Z         # Load a module with 'ascii' encoding for unpickling
2025-08-14T23:09:08.7549380Z         # Loading from a module setting weights_only=False, warning this can be unsafe
2025-08-14T23:09:08.7550805Z         >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-08-14T23:09:08.7550954Z     
2025-08-14T23:09:08.7551088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7551213Z 
2025-08-14T23:09:08.7551393Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=375.
2025-08-14T23:09:08.7551679Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.7551886Z Registers the function implementation as the fallback for the given key.
2025-08-14T23:09:08.7552003Z 
2025-08-14T23:09:08.7552083Z         This function only works for a library with global namespace ("_").
2025-08-14T23:09:08.7552196Z 
2025-08-14T23:09:08.7552229Z         Args:
2025-08-14T23:09:08.7552374Z             fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-08-14T23:09:08.7552551Z                 to register a fallthrough.
2025-08-14T23:09:08.7552736Z             dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-08-14T23:09:08.7554067Z                           the dispatch key that the library was created with.
2025-08-14T23:09:08.7554289Z             with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-08-14T23:09:08.7554562Z                          to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-08-14T23:09:08.7554711Z 
2025-08-14T23:09:08.7554750Z         Example::
2025-08-14T23:09:08.7554805Z 
2025-08-14T23:09:08.7554851Z             >>> my_lib = Library("_", "IMPL")
2025-08-14T23:09:08.7554983Z             >>> def fallback_kernel(op, *args, **kwargs):
2025-08-14T23:09:08.7555116Z             >>>     # Handle all autocast ops generically
2025-08-14T23:09:08.7555235Z             >>>     # ...
2025-08-14T23:09:08.7555349Z             >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-08-14T23:09:08.7555468Z         
2025-08-14T23:09:08.7555724Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-08-14T23:09:08.7556948Z 
2025-08-14T23:09:08.7557001Z my_lib.fallback(fallback_kernel, "Autocast")
2025-08-14T23:09:08.7557114Z ^
2025-08-14T23:09:08.7583436Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=948.
2025-08-14T23:09:08.7583821Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.7584030Z Register a FakeTensor implementation ("fake impl") for this operator.
2025-08-14T23:09:08.7584145Z 
2025-08-14T23:09:08.7584224Z     Also sometimes known as a "meta kernel", "abstract impl".
2025-08-14T23:09:08.7584330Z 
2025-08-14T23:09:08.7584413Z     An "FakeTensor implementation" specifies the behavior of this operator on
2025-08-14T23:09:08.7584610Z     Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-08-14T23:09:08.7584806Z     certain properties (sizes/strides/storage_offset/device), it specifies
2025-08-14T23:09:08.7586562Z     what the properties of the output Tensors are.
2025-08-14T23:09:08.7586651Z 
2025-08-14T23:09:08.7586743Z     The FakeTensor implementation has the same signature as the operator.
2025-08-14T23:09:08.7586967Z     It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-08-14T23:09:08.7587162Z     implementation, assume that all Tensor inputs to the operator are
2025-08-14T23:09:08.7587354Z     regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-08-14T23:09:08.7587565Z     you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-08-14T23:09:08.7587751Z     The FakeTensor implementation must consist of only PyTorch operations
2025-08-14T23:09:08.7587953Z     (and may not directly access the storage or data of any input or
2025-08-14T23:09:08.7588104Z     intermediate Tensors).
2025-08-14T23:09:08.7588171Z 
2025-08-14T23:09:08.7588231Z     This API may be used as a decorator (see examples).
2025-08-14T23:09:08.7588324Z 
2025-08-14T23:09:08.7588379Z     For a detailed guide on custom ops, please see
2025-08-14T23:09:08.7588563Z     https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-08-14T23:09:08.7588680Z 
2025-08-14T23:09:08.7588713Z     Args:
2025-08-14T23:09:08.7588839Z         op_name: Operator name (along with the overload) or OpOverload object.
2025-08-14T23:09:08.7589001Z         func: Fake tensor implementation.
2025-08-14T23:09:08.7589152Z         lib (Optional[Library]): Library to register the fake tensor to.
2025-08-14T23:09:08.7589326Z         allow_override: Flag controlling if we want to override an
2025-08-14T23:09:08.7589491Z                         existing registered fake impl. This is by default off,
2025-08-14T23:09:08.7589654Z                         and will error you're trying to register a fake impl to
2025-08-14T23:09:08.7589814Z                         an operator that already has a fake impl. This also only
2025-08-14T23:09:08.7591187Z                         applies if the custom operator was not created via
2025-08-14T23:09:08.7591350Z                         torch.library.custom_op, as overriding and existing fake
2025-08-14T23:09:08.7591496Z                         impl is already allowed.
2025-08-14T23:09:08.7591574Z 
2025-08-14T23:09:08.7591610Z     Examples:
2025-08-14T23:09:08.7591698Z         >>> import torch
2025-08-14T23:09:08.7591804Z         >>> import numpy as np
2025-08-14T23:09:08.7591920Z         >>> from torch import Tensor
2025-08-14T23:09:08.7592028Z         >>>
2025-08-14T23:09:08.7592144Z         >>> # Example 1: an operator without data-dependent output shape
2025-08-14T23:09:08.7592324Z         >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-08-14T23:09:08.7592515Z         >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-08-14T23:09:08.7595056Z         >>>     raise NotImplementedError("Implementation goes here")
2025-08-14T23:09:08.7595194Z         >>>
2025-08-14T23:09:08.7595309Z         >>> @torch.library.register_fake("mylib::custom_linear")
2025-08-14T23:09:08.7595449Z         >>> def _(x, weight, bias):
2025-08-14T23:09:08.7595561Z         >>>     assert x.dim() == 2
2025-08-14T23:09:08.7595675Z         >>>     assert weight.dim() == 2
2025-08-14T23:09:08.7595794Z         >>>     assert bias.dim() == 1
2025-08-14T23:09:08.7595980Z         >>>     assert x.shape[1] == weight.shape[1]
2025-08-14T23:09:08.7596114Z         >>>     assert weight.shape[0] == bias.shape[0]
2025-08-14T23:09:08.7596246Z         >>>     assert x.device == weight.device
2025-08-14T23:09:08.7596358Z         >>>
2025-08-14T23:09:08.7597465Z         >>>     return (x @ weight.t()) + bias
2025-08-14T23:09:08.7597575Z         >>>
2025-08-14T23:09:08.7597683Z         >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-08-14T23:09:08.7597822Z         >>>     x = torch.randn(2, 3)
2025-08-14T23:09:08.7597932Z         >>>     w = torch.randn(3, 3)
2025-08-14T23:09:08.7598043Z         >>>     b = torch.randn(3)
2025-08-14T23:09:08.7598165Z         >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-08-14T23:09:08.7598283Z         >>>
2025-08-14T23:09:08.7598366Z         >>> assert y.shape == (2, 3)
2025-08-14T23:09:08.7598467Z         >>>
2025-08-14T23:09:08.7599576Z         >>> # Example 2: an operator with data-dependent output shape
2025-08-14T23:09:08.7599763Z         >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-08-14T23:09:08.7599930Z         >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-08-14T23:09:08.7600076Z         >>>     x_np = x.numpy(force=True)
2025-08-14T23:09:08.7600203Z         >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-08-14T23:09:08.7600339Z         >>>     return torch.tensor(res, device=x.device)
2025-08-14T23:09:08.7600454Z         >>>
2025-08-14T23:09:08.7600564Z         >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-08-14T23:09:08.7600696Z         >>> def _(x):
2025-08-14T23:09:08.7600812Z         >>> # Number of nonzero-elements is data-dependent.
2025-08-14T23:09:08.7600961Z         >>> # Since we cannot peek at the data in an fake impl,
2025-08-14T23:09:08.7602039Z         >>> # we use the ctx object to construct a new symint that
2025-08-14T23:09:08.7602183Z         >>> # represents the data-dependent size.
2025-08-14T23:09:08.7602317Z         >>>     ctx = torch.library.get_ctx()
2025-08-14T23:09:08.7602439Z         >>>     nnz = ctx.new_dynamic_size()
2025-08-14T23:09:08.7602555Z         >>>     shape = [nnz, x.dim()]
2025-08-14T23:09:08.7602689Z         >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-08-14T23:09:08.7602820Z         >>>     return result
2025-08-14T23:09:08.7602991Z         >>>
2025-08-14T23:09:08.7603106Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-08-14T23:09:08.7603236Z         >>>
2025-08-14T23:09:08.7603325Z         >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-08-14T23:09:08.7604414Z         >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-08-14T23:09:08.7604584Z         >>> trace.print_readable()
2025-08-14T23:09:08.7604685Z         >>>
2025-08-14T23:09:08.7604808Z         >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-08-14T23:09:08.7604924Z 
2025-08-14T23:09:08.7604957Z     
2025-08-14T23:09:08.7605180Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-08-14T23:09:08.7605388Z 
2025-08-14T23:09:08.7605422Z _._ = None
2025-08-14T23:09:08.7605499Z ^
2025-08-14T23:09:08.7605765Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1083.
2025-08-14T23:09:08.7606055Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7606235Z Register a backward formula for this custom op.
2025-08-14T23:09:08.7606321Z 
2025-08-14T23:09:08.7607314Z     In order for an operator to work with autograd, you need to register
2025-08-14T23:09:08.7607463Z     a backward formula:
2025-08-14T23:09:08.7607595Z     1. You must tell us how to compute gradients during the backward pass
2025-08-14T23:09:08.7607749Z     by providing us a "backward" function.
2025-08-14T23:09:08.7607903Z     2. If you need any values from the forward to compute gradients, you can
2025-08-14T23:09:08.7608124Z     use `setup_context` to save values for backward.
2025-08-14T23:09:08.7608215Z 
2025-08-14T23:09:08.7608298Z     ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-08-14T23:09:08.7608484Z     - ``grads`` is one or more gradients. The number of gradients matches
2025-08-14T23:09:08.7608633Z     the number of outputs of the operator.
2025-08-14T23:09:08.7608786Z     The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-08-14T23:09:08.7608980Z     :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-08-14T23:09:08.7610086Z     same as :meth:`torch.autograd.Function.backward`.
2025-08-14T23:09:08.7610180Z 
2025-08-14T23:09:08.7610257Z     ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-08-14T23:09:08.7610446Z     Please save quantities needed for backward onto the ``ctx`` object via
2025-08-14T23:09:08.7610678Z     either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-08-14T23:09:08.7610872Z     or assigning them as attributes of ``ctx``. If your custom op has
2025-08-14T23:09:08.7611081Z     kwarg-only arguments, we expect the signature of ``setup_context``
2025-08-14T23:09:08.7611260Z     to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-08-14T23:09:08.7611364Z 
2025-08-14T23:09:08.7611443Z     Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-08-14T23:09:08.7611653Z     they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-08-14T23:09:08.7611849Z     not depend on or mutate global state. If you need a non-traceable backward,
2025-08-14T23:09:08.7618500Z     you can make it a separate custom_op that you call inside ``backward_fn``.
2025-08-14T23:09:08.7618720Z 
2025-08-14T23:09:08.7618805Z     If you need different autograd behavior on different devices, then we
2025-08-14T23:09:08.7619002Z     recommend creating two different custom operators, one for each device
2025-08-14T23:09:08.7619206Z     that needs different behavior, and switching between them at runtime.
2025-08-14T23:09:08.7619321Z 
2025-08-14T23:09:08.7619364Z     Examples:
2025-08-14T23:09:08.7619457Z         >>> import torch
2025-08-14T23:09:08.7619571Z         >>> import numpy as np
2025-08-14T23:09:08.7619688Z         >>> from torch import Tensor
2025-08-14T23:09:08.7619799Z         >>>
2025-08-14T23:09:08.7619921Z         >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-08-14T23:09:08.7621854Z         >>> def numpy_sin(x: Tensor) -> Tensor:
2025-08-14T23:09:08.7622001Z         >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:08.7622118Z         >>>     y_np = np.sin(x_np)
2025-08-14T23:09:08.7622254Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-08-14T23:09:08.7622377Z         >>>
2025-08-14T23:09:08.7622484Z         >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-08-14T23:09:08.7622613Z         >>>     x, = inputs
2025-08-14T23:09:08.7622726Z         >>>     ctx.save_for_backward(x)
2025-08-14T23:09:08.7622834Z         >>>
2025-08-14T23:09:08.7623028Z         >>> def backward(ctx, grad):
2025-08-14T23:09:08.7623141Z         >>>     x, = ctx.saved_tensors
2025-08-14T23:09:08.7623255Z         >>>     return grad * x.cos()
2025-08-14T23:09:08.7623359Z         >>>
2025-08-14T23:09:08.7623452Z         >>> torch.library.register_autograd(
2025-08-14T23:09:08.7623596Z         ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-08-14T23:09:08.7623726Z         ... )
2025-08-14T23:09:08.7623803Z         >>>
2025-08-14T23:09:08.7623896Z         >>> x = torch.randn(3, requires_grad=True)
2025-08-14T23:09:08.7624016Z         >>> y = numpy_sin(x)
2025-08-14T23:09:08.7624145Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-08-14T23:09:08.7624288Z         >>> assert torch.allclose(grad_x, x.cos())
2025-08-14T23:09:08.7624403Z         >>>
2025-08-14T23:09:08.7625599Z         >>> # Example with a keyword-only arg
2025-08-14T23:09:08.7625827Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-08-14T23:09:08.7625990Z         >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-08-14T23:09:08.7626124Z         >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:08.7626235Z         >>>     y_np = x_np * val
2025-08-14T23:09:08.7626363Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-08-14T23:09:08.7626484Z         >>>
2025-08-14T23:09:08.7626608Z         >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-08-14T23:09:08.7626773Z         >>>     ctx.val = keyword_only_inputs["val"]
2025-08-14T23:09:08.7626886Z         >>>
2025-08-14T23:09:08.7626970Z         >>> def backward(ctx, grad):
2025-08-14T23:09:08.7628028Z         >>>     return grad * ctx.val
2025-08-14T23:09:08.7628133Z         >>>
2025-08-14T23:09:08.7628224Z         >>> torch.library.register_autograd(
2025-08-14T23:09:08.7628411Z         ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-08-14T23:09:08.7628546Z         ... )
2025-08-14T23:09:08.7628624Z         >>>
2025-08-14T23:09:08.7628714Z         >>> x = torch.randn(3, requires_grad=True)
2025-08-14T23:09:08.7628851Z         >>> y = numpy_mul(x, val=3.14)
2025-08-14T23:09:08.7628985Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-08-14T23:09:08.7629145Z         >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-08-14T23:09:08.7629248Z 
2025-08-14T23:09:08.7629279Z     
2025-08-14T23:09:08.7630329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7630459Z 
2025-08-14T23:09:08.7630641Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1482.
2025-08-14T23:09:08.7630916Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.7631114Z Returns the computed kernel for a given operator and dispatch key.
2025-08-14T23:09:08.7631224Z 
2025-08-14T23:09:08.7631304Z     This function retrieves the kernel that would be executed for a given
2025-08-14T23:09:08.7631501Z     operator and dispatch key combination. The returned SafeKernelFunction
2025-08-14T23:09:08.7631690Z     can be used to call the kernel in a boxed fashion. The intended use
2025-08-14T23:09:08.7631867Z     case for this function is to retrieve the original kernel for a given
2025-08-14T23:09:08.7632050Z     dispatch key and then register another kernel to the same dispatch key
2025-08-14T23:09:08.7632225Z     that calls into the original kernel for certain cases.
2025-08-14T23:09:08.7632320Z 
2025-08-14T23:09:08.7632353Z     Args:
2025-08-14T23:09:08.7633492Z         op: Operator name (along with the overload) or OpOverload object
2025-08-14T23:09:08.7633680Z             Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef.
2025-08-14T23:09:08.7633890Z         dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for.
2025-08-14T23:09:08.7634087Z             Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value.
2025-08-14T23:09:08.7634192Z 
2025-08-14T23:09:08.7634227Z     Returns:
2025-08-14T23:09:08.7634360Z         torch._C._SafeKernelFunction: A safe kernel function that can be used to
2025-08-14T23:09:08.7634517Z             call the kernel.
2025-08-14T23:09:08.7634585Z 
2025-08-14T23:09:08.7634617Z     Raises:
2025-08-14T23:09:08.7634717Z         RuntimeError: If the operator does not exist.
2025-08-14T23:09:08.7634806Z 
2025-08-14T23:09:08.7634840Z     Example:
2025-08-14T23:09:08.7634935Z         >>> # Get the CPU kernel for torch.add
2025-08-14T23:09:08.7636015Z         >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU")
2025-08-14T23:09:08.7636150Z         >>>
2025-08-14T23:09:08.7636240Z         >>> # You can also use DispatchKey enum
2025-08-14T23:09:08.7636414Z         >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU)
2025-08-14T23:09:08.7636598Z         >>>
2025-08-14T23:09:08.7636702Z         >>> # Or use an OpOverload directly
2025-08-14T23:09:08.7636859Z         >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU")
2025-08-14T23:09:08.7637008Z         >>>
2025-08-14T23:09:08.7637127Z         >>> # Example: Using get_kernel in a custom op with conditional dispatch
2025-08-14T23:09:08.7637286Z         >>> # Get the original kernel for torch.sin
2025-08-14T23:09:08.7637445Z         >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU")
2025-08-14T23:09:08.7638521Z         >>>
2025-08-14T23:09:08.7638643Z         >>> # If input has negative values, use original sin, otherwise return zeros
2025-08-14T23:09:08.7638811Z         >>> def conditional_sin_impl(dispatch_keys, x):
2025-08-14T23:09:08.7638937Z         >>>     if (x < 0).any():
2025-08-14T23:09:08.7639073Z         >>>         return original_sin_kernel.call_boxed(dispatch_keys, x)
2025-08-14T23:09:08.7639233Z         >>>     else:
2025-08-14T23:09:08.7639335Z         >>>         return torch.zeros_like(x)
2025-08-14T23:09:08.7639444Z         >>>
2025-08-14T23:09:08.7639543Z         >>> lib = torch.library.Library("aten", "IMPL")
2025-08-14T23:09:08.7639736Z         >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet
2025-08-14T23:09:08.7639928Z         >>> which needs to be the first argument to ``kernel.call_boxed``
2025-08-14T23:09:08.7641035Z         >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True)
2025-08-14T23:09:08.7641171Z         >>>
2025-08-14T23:09:08.7641256Z         >>> # Test the conditional behavior
2025-08-14T23:09:08.7641378Z         >>> x_positive = torch.tensor([1.0, 2.0])
2025-08-14T23:09:08.7641504Z         >>> x_mixed = torch.tensor([-1.0, 2.0])
2025-08-14T23:09:08.7641626Z         >>> torch.sin(x_positive)
2025-08-14T23:09:08.7641735Z         tensor([0., 0.])
2025-08-14T23:09:08.7641836Z         >>> torch.sin(x_mixed)
2025-08-14T23:09:08.7641943Z         tensor([-0.8415, 0.9093])
2025-08-14T23:09:08.7642044Z     
2025-08-14T23:09:08.7642250Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12))
2025-08-14T23:09:08.7643452Z 
2025-08-14T23:09:08.7643520Z which needs to be the first argument to ``kernel.call_boxed``
2025-08-14T23:09:08.7643651Z       ^
2025-08-14T23:09:08.7643853Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1571.
2025-08-14T23:09:08.7644132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7644334Z Given an operator and some sample arguments, tests if the operator is
2025-08-14T23:09:08.7644483Z     registered correctly.
2025-08-14T23:09:08.7644550Z 
2025-08-14T23:09:08.7644626Z     That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-08-14T23:09:08.7644824Z     custom op, you specified metadata (e.g. mutability info) about the custom op
2025-08-14T23:09:08.7645023Z     and these APIs require that the functions you pass them satisfy certain
2025-08-14T23:09:08.7645217Z     properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-08-14T23:09:08.7646406Z     ``opcheck`` tests these metadata and properties.
2025-08-14T23:09:08.7646501Z 
2025-08-14T23:09:08.7646547Z     Concretely, we test the following:
2025-08-14T23:09:08.7646623Z 
2025-08-14T23:09:08.7646692Z     - test_schema: If the schema matches the implementation of
2025-08-14T23:09:08.7646870Z       the operator. For example: if the schema specifies a Tensor is mutated,
2025-08-14T23:09:08.7647059Z       then we check the implementation mutates the Tensor. If the schema
2025-08-14T23:09:08.7647237Z       specifies that we return a new Tensor, then we check that the
2025-08-14T23:09:08.7647417Z       implementation returns a new Tensor (instead of an existing one or
2025-08-14T23:09:08.7647570Z       a view of an existing one).
2025-08-14T23:09:08.7647759Z     - test_autograd_registration: If the operator supports training
2025-08-14T23:09:08.7647955Z       (autograd): we check that its autograd formula is registered via
2025-08-14T23:09:08.7648137Z       torch.library.register_autograd or a manual registration to one
2025-08-14T23:09:08.7649257Z       or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-08-14T23:09:08.7649421Z       registrations may lead to undefined behavior.
2025-08-14T23:09:08.7649576Z     - test_faketensor: If the operator has a FakeTensor kernel
2025-08-14T23:09:08.7649740Z       (and if it is correct). The FakeTensor kernel is necessary (
2025-08-14T23:09:08.7649914Z       but not sufficient) for the operator to work with PyTorch compilation
2025-08-14T23:09:08.7650106Z       APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-08-14T23:09:08.7650285Z       (also sometimes known as a meta kernel) was registered for the
2025-08-14T23:09:08.7650482Z       operator and that it is correct. This test takes the result of
2025-08-14T23:09:08.7650658Z       running the operator on real tensors and the result of running
2025-08-14T23:09:08.7650829Z       the operator on FakeTensors and checks that they have the same
2025-08-14T23:09:08.7651007Z       Tensor metadata (sizes/strides/dtype/device/etc).
2025-08-14T23:09:08.7652103Z     - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-08-14T23:09:08.7652276Z       with PyTorch compilation APIs (torch.compile/export/FX).
2025-08-14T23:09:08.7652449Z       This checks that the outputs (and gradients, if applicable) are the
2025-08-14T23:09:08.7652614Z       same under eager-mode PyTorch and torch.compile.
2025-08-14T23:09:08.7652771Z       This test is a superset of ``test_faketensor`` and is an e2e test;
2025-08-14T23:09:08.7652998Z       other things it tests are that the operator supports
2025-08-14T23:09:08.7653169Z       functionalization and that the backward pass (if it exists) also
2025-08-14T23:09:08.7653333Z       supports FakeTensor and functionalization.
2025-08-14T23:09:08.7653422Z 
2025-08-14T23:09:08.7653495Z     For best results, please call ``opcheck`` multiple times with a
2025-08-14T23:09:08.7653668Z     representative set of inputs. If your operator supports
2025-08-14T23:09:08.7653851Z     autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-08-14T23:09:08.7655006Z     if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-08-14T23:09:08.7655181Z     use ``opcheck`` with inputs on all supported devices.
2025-08-14T23:09:08.7655275Z 
2025-08-14T23:09:08.7655309Z     Args:
2025-08-14T23:09:08.7655422Z         op: The operator. Must either be a function decorated with
2025-08-14T23:09:08.7655599Z             :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-08-14T23:09:08.7655793Z             found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-08-14T23:09:08.7655948Z         args: The args to the operator
2025-08-14T23:09:08.7656068Z         kwargs: The kwargs to the operator
2025-08-14T23:09:08.7656214Z         test_utils: Tests that we should run. Default: all of them.
2025-08-14T23:09:08.7656367Z             Example: ("test_schema", "test_faketensor")
2025-08-14T23:09:08.7656523Z         raise_exception: If we should raise an exception on the first
2025-08-14T23:09:08.7657625Z             error. If False, we will return a dict with information
2025-08-14T23:09:08.7657763Z             on if each test passed or not.
2025-08-14T23:09:08.7657923Z         rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-08-14T23:09:08.7658096Z             If specified ``atol`` must also be specified.
2025-08-14T23:09:08.7658251Z             If omitted, default values based on the ``dtype`` are selected
2025-08-14T23:09:08.7658416Z             (see the table in :func:`torch.testing.assert_close`).
2025-08-14T23:09:08.7658593Z         atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-08-14T23:09:08.7658802Z             If specified ``rtol`` must also be specified.
2025-08-14T23:09:08.7658971Z             If omitted, default values based on the ``dtype`` are selected
2025-08-14T23:09:08.7659138Z             (see the table in :func:`torch.testing.assert_close`).
2025-08-14T23:09:08.7659233Z 
2025-08-14T23:09:08.7659271Z     .. warning::
2025-08-14T23:09:08.7660261Z 
2025-08-14T23:09:08.7660343Z         opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-08-14T23:09:08.7660532Z         opcheck tests if your usage of torch.library APIs is correct while
2025-08-14T23:09:08.7660714Z         :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-08-14T23:09:08.7660901Z         mathematically correct. Use both to test custom ops that support
2025-08-14T23:09:08.7661055Z         gradient computation.
2025-08-14T23:09:08.7661124Z 
2025-08-14T23:09:08.7661158Z     Example:
2025-08-14T23:09:08.7661207Z 
2025-08-14T23:09:08.7661302Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:08.7661468Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-08-14T23:09:08.7661631Z         >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-08-14T23:09:08.7661782Z         >>>     x_np = x.numpy(force=True)
2025-08-14T23:09:08.7661896Z         >>>     z_np = x_np * y
2025-08-14T23:09:08.7671114Z         >>>     return torch.from_numpy(z_np).to(x.device)
2025-08-14T23:09:08.7671247Z         >>>
2025-08-14T23:09:08.7671375Z         >>> @numpy_mul.register_fake
2025-08-14T23:09:08.7671495Z         >>> def _(x, y):
2025-08-14T23:09:08.7671606Z         >>>     return torch.empty_like(x)
2025-08-14T23:09:08.7671721Z         >>>
2025-08-14T23:09:08.7671821Z         >>> def setup_context(ctx, inputs, output):
2025-08-14T23:09:08.7671948Z         >>>     y, = inputs
2025-08-14T23:09:08.7672049Z         >>>     ctx.y = y
2025-08-14T23:09:08.7672147Z         >>>
2025-08-14T23:09:08.7672236Z         >>> def backward(ctx, grad):
2025-08-14T23:09:08.7674072Z         >>>     return grad * ctx.y, None
2025-08-14T23:09:08.7674189Z         >>>
2025-08-14T23:09:08.7674318Z         >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-08-14T23:09:08.7674471Z         >>>
2025-08-14T23:09:08.7674559Z         >>> sample_inputs = [
2025-08-14T23:09:08.7674672Z         >>>     (torch.randn(3), 3.14),
2025-08-14T23:09:08.7674803Z         >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-08-14T23:09:08.7674947Z         >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-08-14T23:09:08.7675111Z         >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-08-14T23:09:08.7675251Z         >>> ]
2025-08-14T23:09:08.7676563Z         >>>
2025-08-14T23:09:08.7676655Z         >>> for args in sample_inputs:
2025-08-14T23:09:08.7676792Z         >>>     torch.library.opcheck(numpy_mul, args)
2025-08-14T23:09:08.7676884Z 
2025-08-14T23:09:08.7676917Z     
2025-08-14T23:09:08.7677056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7677186Z 
2025-08-14T23:09:08.7948474Z 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=1851.
2025-08-14T23:09:08.7949466Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7950052Z Computes the minimum storage size to hold the given tensor geometry.
2025-08-14T23:09:08.7950367Z 
2025-08-14T23:09:08.7950471Z     Example
2025-08-14T23:09:08.7950704Z     =======
2025-08-14T23:09:08.7950834Z 
2025-08-14T23:09:08.7955511Z     This is the size of a newly allocated tensor's storage, in units of elements
2025-08-14T23:09:08.7955870Z 
2025-08-14T23:09:08.7955993Z     >>> t = torch.empty((10, 20))
2025-08-14T23:09:08.7956430Z     >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset())
2025-08-14T23:09:08.7956868Z     200
2025-08-14T23:09:08.7956994Z 
2025-08-14T23:09:08.7957334Z     >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:08.7957745Z     >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11))
2025-08-14T23:09:08.7958117Z     >>> size = compute_required_storage_length(
2025-08-14T23:09:08.7958493Z     ...     t2.shape, t2.stride(), t2.storage_offset()
2025-08-14T23:09:08.7958816Z     ... )
2025-08-14T23:09:08.7959048Z     >>> size == t.storage().size()
2025-08-14T23:09:08.7959164Z     True
2025-08-14T23:09:08.7959211Z 
2025-08-14T23:09:08.7959286Z     A valid tensor may have a larger storage size, but never smaller
2025-08-14T23:09:08.7959392Z 
2025-08-14T23:09:08.7959433Z     >>> slice = torch.empty(100)[20:40]
2025-08-14T23:09:08.7959549Z     >>> slice.storage().size()
2025-08-14T23:09:08.7960683Z     100
2025-08-14T23:09:08.7960726Z 
2025-08-14T23:09:08.7960770Z     >>> compute_required_storage_length(
2025-08-14T23:09:08.7960910Z     ...     slice.shape, slice.stride(), slice.storage_offset()
2025-08-14T23:09:08.7961037Z     ... )
2025-08-14T23:09:08.7961110Z     40
2025-08-14T23:09:08.7961188Z 
2025-08-14T23:09:08.7961219Z     
2025-08-14T23:09:08.7961350Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7961496Z 
2025-08-14T23:09:08.7965948Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202.
2025-08-14T23:09:08.7966241Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7966652Z Return the sum of each row of the given sparse tensor.
2025-08-14T23:09:08.7969155Z 
2025-08-14T23:09:08.7969730Z     Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-08-14T23:09:08.7969953Z     dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-08-14T23:09:08.7970151Z     reduce over all of them. When sum over all ``sparse_dim``, this method
2025-08-14T23:09:08.7970331Z     returns a dense tensor instead of a sparse tensor.
2025-08-14T23:09:08.7970450Z 
2025-08-14T23:09:08.7970563Z     All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-08-14T23:09:08.7970770Z     tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-08-14T23:09:08.7970887Z 
2025-08-14T23:09:08.7970971Z     During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-08-14T23:09:08.7971179Z     will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-08-14T23:09:08.7971304Z 
2025-08-14T23:09:08.7971342Z     Args:
2025-08-14T23:09:08.7971446Z         input (Tensor): the input sparse tensor
2025-08-14T23:09:08.7973040Z         dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-08-14T23:09:08.7973217Z             over all dims.
2025-08-14T23:09:08.7973375Z         dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-08-14T23:09:08.7973548Z             Default: dtype of :attr:`input`.
2025-08-14T23:09:08.7973629Z 
2025-08-14T23:09:08.7973671Z     Example::
2025-08-14T23:09:08.7973725Z 
2025-08-14T23:09:08.7973760Z         >>> nnz = 3
2025-08-14T23:09:08.7973854Z         >>> dims = [5, 5, 2, 3]
2025-08-14T23:09:08.7973984Z         >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-08-14T23:09:08.7974152Z                            torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-08-14T23:09:08.7974306Z         >>> V = torch.randn(nnz, dims[2], dims[3])
2025-08-14T23:09:08.7974429Z         >>> size = torch.Size(dims)
2025-08-14T23:09:08.7975657Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:08.7975795Z         >>> S = torch.sparse_coo_tensor(I, V, size)
2025-08-14T23:09:08.7975912Z         >>> S
2025-08-14T23:09:08.7976004Z         tensor(indices=tensor([[2, 0, 3],
2025-08-14T23:09:08.7976130Z                                [2, 4, 1]]),
2025-08-14T23:09:08.7976268Z                values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-08-14T23:09:08.7976408Z                                [ 0.3411,  0.0918, -0.2312]],
2025-08-14T23:09:08.7976564Z 
2025-08-14T23:09:08.7976644Z                               [[ 0.5348,  0.0634, -2.0494],
2025-08-14T23:09:08.7976769Z                                [-0.7125, -1.0646,  2.1844]],
2025-08-14T23:09:08.7976849Z 
2025-08-14T23:09:08.7976893Z                               [[ 0.1276,  0.1874, -0.6334],
2025-08-14T23:09:08.7977016Z                                [-1.9682, -0.5340,  0.7483]]]),
2025-08-14T23:09:08.7978184Z                size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-08-14T23:09:08.7978275Z 
2025-08-14T23:09:08.7978348Z         # when sum over only part of sparse_dims, return a sparse tensor
2025-08-14T23:09:08.7978496Z         >>> torch.sparse.sum(S, [1, 3])
2025-08-14T23:09:08.7978616Z         tensor(indices=tensor([[0, 2, 3]]),
2025-08-14T23:09:08.7978738Z                values=tensor([[-1.4512,  0.4073],
2025-08-14T23:09:08.7978856Z                               [-0.8901,  0.2017],
2025-08-14T23:09:08.7979009Z                               [-0.3183, -1.7539]]),
2025-08-14T23:09:08.7979141Z                size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-08-14T23:09:08.7979230Z 
2025-08-14T23:09:08.7979289Z         # when sum over all sparse dim, return a dense tensor
2025-08-14T23:09:08.7979448Z         # with summed dims squeezed
2025-08-14T23:09:08.7979565Z         >>> torch.sparse.sum(S, [0, 1, 3])
2025-08-14T23:09:08.7980668Z         tensor([-2.6596, -1.1450])
2025-08-14T23:09:08.7980768Z     
2025-08-14T23:09:08.7980901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7981026Z 
2025-08-14T23:09:08.7981214Z 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.
2025-08-14T23:09:08.7981517Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.7981713Z Decorate function, to extend gradcheck for sparse tensors.
2025-08-14T23:09:08.7981814Z 
2025-08-14T23:09:08.7981899Z     Decorator for torch.autograd.gradcheck or its functools.partial
2025-08-14T23:09:08.7982089Z     variants that extends the gradcheck function with support to input
2025-08-14T23:09:08.7982266Z     functions that operate on or/and return sparse tensors.
2025-08-14T23:09:08.7982365Z 
2025-08-14T23:09:08.7982441Z     The specified gradcheck function itself is guaranteed to operate
2025-08-14T23:09:08.7982590Z     on strided tensors only.
2025-08-14T23:09:08.7983744Z 
2025-08-14T23:09:08.7983781Z     For example:
2025-08-14T23:09:08.7983837Z 
2025-08-14T23:09:08.7983924Z     >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck)
2025-08-14T23:09:08.7984079Z     >>> x = (
2025-08-14T23:09:08.7984187Z     ...     torch.tensor([[0, 1], [2, 3]], dtype=torch.float64)
2025-08-14T23:09:08.7984320Z     ...     .to_sparse_coo()
2025-08-14T23:09:08.7984426Z     ...     .requires_grad_(True)
2025-08-14T23:09:08.7984529Z     ... )
2025-08-14T23:09:08.7984625Z     >>> gradcheck(lambda x: x.to_sparse_csr(), x)
2025-08-14T23:09:08.7984741Z     True
2025-08-14T23:09:08.7984817Z     
2025-08-14T23:09:08.7986629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.7986764Z 
2025-08-14T23:09:08.8086974Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=434.
2025-08-14T23:09:08.8087543Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.8087840Z Retrieves the CUDA runtime API module.
2025-08-14T23:09:08.8087980Z 
2025-08-14T23:09:08.8087987Z 
2025-08-14T23:09:08.8088138Z     This function initializes the CUDA runtime environment if it is not already
2025-08-14T23:09:08.8088478Z     initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-08-14T23:09:08.8088812Z     runtime API module provides access to various CUDA runtime functions.
2025-08-14T23:09:08.8089006Z 
2025-08-14T23:09:08.8089069Z     Args:
2025-08-14T23:09:08.8089704Z         ``None``
2025-08-14T23:09:08.8089795Z 
2025-08-14T23:09:08.8089927Z     Returns:
2025-08-14T23:09:08.8090108Z         module: The CUDA runtime API module (_cudart).
2025-08-14T23:09:08.8096101Z 
2025-08-14T23:09:08.8096168Z     Raises:
2025-08-14T23:09:08.8096403Z         RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-08-14T23:09:08.8096806Z         AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-08-14T23:09:08.8097068Z 
2025-08-14T23:09:08.8097156Z     Example of CUDA operations with profiling:
2025-08-14T23:09:08.8097336Z         >>> import torch
2025-08-14T23:09:08.8097506Z         >>> from torch.cuda import cudart, check_error
2025-08-14T23:09:08.8097667Z         >>> import os
2025-08-14T23:09:08.8097782Z         >>>
2025-08-14T23:09:08.8097901Z         >>> os.environ["CUDA_PROFILE"] = "1"
2025-08-14T23:09:08.8098046Z         >>>
2025-08-14T23:09:08.8098262Z         >>> def perform_cuda_operations_with_streams():
2025-08-14T23:09:08.8100041Z         >>>     stream = torch.cuda.Stream()
2025-08-14T23:09:08.8100210Z         >>>     with torch.cuda.stream(stream):
2025-08-14T23:09:08.8100437Z         >>>         x = torch.randn(100, 100, device='cuda')
2025-08-14T23:09:08.8100612Z         >>>         y = torch.randn(100, 100, device='cuda')
2025-08-14T23:09:08.8100771Z         >>>         z = torch.mul(x, y)
2025-08-14T23:09:08.8100917Z         >>>     return z
2025-08-14T23:09:08.8101036Z         >>>
2025-08-14T23:09:08.8101151Z         >>> torch.cuda.synchronize()
2025-08-14T23:09:08.8101318Z         >>> print("====== Start nsys profiling ======")
2025-08-14T23:09:08.8101501Z         >>> check_error(cudart().cudaProfilerStart())
2025-08-14T23:09:08.8103134Z         >>> with torch.autograd.profiler.emit_nvtx():
2025-08-14T23:09:08.8103337Z         >>>     result = perform_cuda_operations_with_streams()
2025-08-14T23:09:08.8103520Z         >>>     print("CUDA operations completed.")
2025-08-14T23:09:08.8103714Z         >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-08-14T23:09:08.8103903Z         >>> print("====== End nsys profiling ======")
2025-08-14T23:09:08.8104014Z 
2025-08-14T23:09:08.8104112Z     To run this example and save the profiling information, execute:
2025-08-14T23:09:08.8104429Z         >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-08-14T23:09:08.8104643Z 
2025-08-14T23:09:08.8104757Z     This command profiles the CUDA operations in the provided script and saves
2025-08-14T23:09:08.8105010Z     the profiling information to a file named `trace_name.prof`.
2025-08-14T23:09:08.8105254Z     The `--profile-from-start off` option ensures that profiling starts only
2025-08-14T23:09:08.8105479Z     after the `cudaProfilerStart` call in the script.
2025-08-14T23:09:08.8106974Z     The `--csv` and `--print-summary` options format the profiling output as a
2025-08-14T23:09:08.8107196Z     CSV file and print a summary, respectively.
2025-08-14T23:09:08.8107423Z     The `-o` option specifies the output file name, and the `-f` option forces the
2025-08-14T23:09:08.8107657Z     overwrite of the output file if it already exists.
2025-08-14T23:09:08.8107820Z     
2025-08-14T23:09:08.8108139Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-08-14T23:09:08.8108403Z 
2025-08-14T23:09:08.8108529Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-08-14T23:09:08.8108722Z ^
2025-08-14T23:09:08.8497179Z 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.
2025-08-14T23:09:08.8498059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.8498419Z 
2025-08-14T23:09:08.8504140Z Append the given callback function to this ``Future``, which will be run
2025-08-14T23:09:08.8504975Z when the ``Future`` is completed.  Multiple callbacks can be added to
2025-08-14T23:09:08.8505488Z the same ``Future``, but the order in which they will be executed cannot
2025-08-14T23:09:08.8505985Z be guaranteed (to enforce a certain order consider chaining:
2025-08-14T23:09:08.8506470Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-08-14T23:09:08.8506969Z is the reference to this ``Future``. The callback function can use the
2025-08-14T23:09:08.8507477Z :meth:`value` method to get the value. Note that if this ``Future`` is
2025-08-14T23:09:08.8507985Z already completed, the given callback will be run immediately inline.
2025-08-14T23:09:08.8508309Z 
2025-08-14T23:09:08.8508513Z If the ``Future``'s value contains tensors that reside on GPUs, the
2025-08-14T23:09:08.8509021Z callback might be invoked while the async kernels that are populating
2025-08-14T23:09:08.8509604Z those tensors haven't yet finished executing on the device. However, the
2025-08-14T23:09:08.8510131Z callback will be invoked with some dedicated streams set as current
2025-08-14T23:09:08.8510634Z (fetched from a global pool) which will be synchronized with those
2025-08-14T23:09:08.8510900Z kernels. Hence any operation performed by the callback on these tensors
2025-08-14T23:09:08.8511099Z will be scheduled on the device after the kernels complete. In other
2025-08-14T23:09:08.8511287Z words, as long as the callback doesn't switch streams, it can safely
2025-08-14T23:09:08.8513133Z manipulate the result without any additional synchronization. This is
2025-08-14T23:09:08.8513323Z similar to the non-blocking behavior of :meth:`wait`.
2025-08-14T23:09:08.8513419Z 
2025-08-14T23:09:08.8513497Z Similarly, if the callback returns a value that contains tensors that
2025-08-14T23:09:08.8513677Z reside on a GPU, it can do so even if the kernels that are producing
2025-08-14T23:09:08.8513858Z these tensors are still running on the device, as long as the callback
2025-08-14T23:09:08.8514050Z didn't change streams during its execution. If one wants to change
2025-08-14T23:09:08.8514232Z streams, one must be careful to re-synchronize them with the original
2025-08-14T23:09:08.8514422Z streams, that is, those that were current when the callback was invoked.
2025-08-14T23:09:08.8514533Z 
2025-08-14T23:09:08.8514567Z Args:
2025-08-14T23:09:08.8514688Z     callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-08-14T23:09:08.8514844Z                             the only argument.
2025-08-14T23:09:08.8515987Z 
2025-08-14T23:09:08.8516023Z Returns:
2025-08-14T23:09:08.8516134Z     A new ``Future`` object that holds the return value of the
2025-08-14T23:09:08.8516299Z     ``callback`` and will be marked as completed when the given
2025-08-14T23:09:08.8516441Z     ``callback`` finishes.
2025-08-14T23:09:08.8516506Z 
2025-08-14T23:09:08.8516605Z .. note:: Note that if the callback function throws, either
2025-08-14T23:09:08.8516782Z     through the original future being completed with an exception and
2025-08-14T23:09:08.8516965Z     calling ``fut.wait()``, or through other code in the callback, the
2025-08-14T23:09:08.8517143Z     future returned by ``then`` will be marked appropriately with the
2025-08-14T23:09:08.8517323Z     encountered error. However, if this callback later completes
2025-08-14T23:09:08.8517505Z     additional futures, those futures are not marked as completed with
2025-08-14T23:09:08.8517686Z     an error and the user is responsible for handling completion/waiting
2025-08-14T23:09:08.8518785Z     on those futures independently.
2025-08-14T23:09:08.8518860Z 
2025-08-14T23:09:08.8518896Z Example::
2025-08-14T23:09:08.8518943Z 
2025-08-14T23:09:08.8519003Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-08-14T23:09:08.8519128Z     >>> def callback(fut):
2025-08-14T23:09:08.8519245Z     ...     print(f"RPC return value is {fut.wait()}.")
2025-08-14T23:09:08.8519377Z     >>> fut = torch.futures.Future()
2025-08-14T23:09:08.8519516Z     >>> # The inserted callback will print the return value when
2025-08-14T23:09:08.8519710Z     >>> # receiving the response from "worker1"
2025-08-14T23:09:08.8519833Z     >>> cb_fut = fut.then(callback)
2025-08-14T23:09:08.8519944Z     >>> chain_cb_fut = cb_fut.then(
2025-08-14T23:09:08.8520072Z     ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-08-14T23:09:08.8521124Z     ... )
2025-08-14T23:09:08.8521209Z     >>> fut.set_result(5)
2025-08-14T23:09:08.8521311Z     RPC return value is 5.
2025-08-14T23:09:08.8521410Z     Chained cb done. None
2025-08-14T23:09:08.8521471Z 
2025-08-14T23:09:08.8521562Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.8521687Z 
2025-08-14T23:09:08.8521881Z 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.
2025-08-14T23:09:08.8522184Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.8522339Z 
2025-08-14T23:09:08.8522417Z Set the result for this ``Future``, which will mark this ``Future`` as
2025-08-14T23:09:08.8522600Z completed and trigger all attached callbacks. Note that a ``Future``
2025-08-14T23:09:08.8522771Z cannot be marked completed twice.
2025-08-14T23:09:08.8522844Z 
2025-08-14T23:09:08.8523959Z If the result contains tensors that reside on GPUs, this method can be
2025-08-14T23:09:08.8524148Z called even if the asynchronous kernels that are populating those
2025-08-14T23:09:08.8524330Z tensors haven't yet completed running on the device, provided that the
2025-08-14T23:09:08.8524519Z streams on which those kernels were enqueued are set as the current ones
2025-08-14T23:09:08.8524705Z when this method is called. Put simply, it's safe to call this method
2025-08-14T23:09:08.8524888Z immediately after launching those kernels, without any additional
2025-08-14T23:09:08.8525076Z synchronization, as long as one doesn't change streams in between. This
2025-08-14T23:09:08.8525267Z method will record events on all the relevant current streams and will
2025-08-14T23:09:08.8525448Z use them to ensure proper scheduling for all the consumers of this
2025-08-14T23:09:08.8525589Z ``Future``.
2025-08-14T23:09:08.8525637Z 
2025-08-14T23:09:08.8525670Z Args:
2025-08-14T23:09:08.8526796Z     result (object): the result object of this ``Future``.
2025-08-14T23:09:08.8526894Z 
2025-08-14T23:09:08.8526930Z Example::
2025-08-14T23:09:08.8526975Z 
2025-08-14T23:09:08.8527033Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-08-14T23:09:08.8527159Z     >>> import threading
2025-08-14T23:09:08.8527254Z     >>> import time
2025-08-14T23:09:08.8527350Z     >>> def slow_set_future(fut, value):
2025-08-14T23:09:08.8527463Z     ...     time.sleep(0.5)
2025-08-14T23:09:08.8527568Z     ...     fut.set_result(value)
2025-08-14T23:09:08.8527681Z     >>> fut = torch.futures.Future()
2025-08-14T23:09:08.8527793Z     >>> t = threading.Thread(
2025-08-14T23:09:08.8527900Z     ...     target=slow_set_future,
2025-08-14T23:09:08.8528932Z     ...     args=(fut, torch.ones(2) * 3)
2025-08-14T23:09:08.8529046Z     ... )
2025-08-14T23:09:08.8529122Z     >>> t.start()
2025-08-14T23:09:08.8529213Z     >>> print(fut.wait())
2025-08-14T23:09:08.8529307Z     tensor([3., 3.])
2025-08-14T23:09:08.8529397Z     >>> t.join()
2025-08-14T23:09:08.8529448Z 
2025-08-14T23:09:08.8529540Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.8529665Z 
2025-08-14T23:09:08.8557237Z 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.
2025-08-14T23:09:08.8557549Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.8557759Z Check if the current accelerator is available at runtime: it was build, all the
2025-08-14T23:09:08.8557955Z     required drivers are available and at least one device is visible.
2025-08-14T23:09:08.8559465Z     See :ref:`accelerator<accelerators>` for details.
2025-08-14T23:09:08.8559620Z 
2025-08-14T23:09:08.8559675Z     Returns:
2025-08-14T23:09:08.8559820Z         bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-08-14T23:09:08.8559953Z 
2025-08-14T23:09:08.8560042Z     .. note:: This API delegates to the device-specific version of `is_available`.
2025-08-14T23:09:08.8560253Z         On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set,
2025-08-14T23:09:08.8560466Z         this function will NOT poison fork. Otherwise, it will. For more details, see
2025-08-14T23:09:08.8560649Z         :ref:`multiprocessing-poison-fork-note`.
2025-08-14T23:09:08.8560741Z 
2025-08-14T23:09:08.8560776Z     Example::
2025-08-14T23:09:08.8560827Z 
2025-08-14T23:09:08.8560922Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:08.8561079Z     
2025-08-14T23:09:08.8561327Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-08-14T23:09:08.8562606Z 
2025-08-14T23:09:08.8562698Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:08.8562882Z                                         ^
2025-08-14T23:09:08.8564874Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=212.
2025-08-14T23:09:08.8565178Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:08.8565507Z Wait for all kernels in all streams on the given device to complete.
2025-08-14T23:09:08.8565615Z 
2025-08-14T23:09:08.8565648Z     Args:
2025-08-14T23:09:08.8565871Z         device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-08-14T23:09:08.8566112Z             the current :ref:`accelerator<accelerators>` device type. If not given,
2025-08-14T23:09:08.8566305Z             use :func:`torch.accelerator.current_device_index` by default.
2025-08-14T23:09:08.8566411Z 
2025-08-14T23:09:08.8566519Z     .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-08-14T23:09:08.8567988Z 
2025-08-14T23:09:08.8568027Z     Example::
2025-08-14T23:09:08.8568081Z 
2025-08-14T23:09:08.8568144Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:08.8568324Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:08.8568510Z         >>> start_event = torch.Event(enable_timing=True)
2025-08-14T23:09:08.8568647Z         >>> end_event = torch.Event(enable_timing=True)
2025-08-14T23:09:08.8568778Z         >>> start_event.record()
2025-08-14T23:09:08.8568932Z         >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-08-14T23:09:08.8569094Z         >>> sum = torch.sum(tensor)
2025-08-14T23:09:08.8569207Z         >>> end_event.record()
2025-08-14T23:09:08.8569324Z         >>> torch.accelerator.synchronize()
2025-08-14T23:09:08.8569471Z         >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-08-14T23:09:08.8569600Z     
2025-08-14T23:09:08.8569827Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-08-14T23:09:08.8570046Z 
2025-08-14T23:09:08.8570135Z assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:08.8570297Z                                         ^
2025-08-14T23:09:08.8576285Z 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.
2025-08-14T23:09:08.8576591Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.8576796Z Compiles compute shader from source and allows one to invoke kernels
2025-08-14T23:09:08.8576965Z     defined there from the comfort of Python runtime
2025-08-14T23:09:08.8577168Z     Example::
2025-08-14T23:09:08.8577219Z 
2025-08-14T23:09:08.8577273Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-08-14T23:09:08.8577407Z         >>> lib = torch.mps.compile_shader(
2025-08-14T23:09:08.8577616Z         ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-08-14T23:09:08.8577810Z         ...  )
2025-08-14T23:09:08.8577904Z         >>> x = torch.zeros(16, device="mps")
2025-08-14T23:09:08.8579327Z         >>> lib.full(x, 3.14)
2025-08-14T23:09:08.8579433Z     
2025-08-14T23:09:08.8579576Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.8579700Z 
2025-08-14T23:09:08.8781321Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=39.
2025-08-14T23:09:08.8783406Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.8783953Z 
2025-08-14T23:09:08.8784208Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-08-14T23:09:08.8784837Z maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-08-14T23:09:08.8785352Z pushes the map into PyTorch operations called by ``func``, effectively
2025-08-14T23:09:08.8785789Z vectorizing those operations.
2025-08-14T23:09:08.8785994Z 
2025-08-14T23:09:08.8790138Z vmap is useful for handling batch dimensions: one can write a function
2025-08-14T23:09:08.8790510Z ``func`` that runs on examples and then lift it to a function that can
2025-08-14T23:09:08.8790773Z take batches of examples with ``vmap(func)``. vmap can also be used to
2025-08-14T23:09:08.8791036Z compute batched gradients when composed with autograd.
2025-08-14T23:09:08.8791179Z 
2025-08-14T23:09:08.8791268Z .. note::
2025-08-14T23:09:08.8791447Z     :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-08-14T23:09:08.8791681Z     convenience. Use whichever one you'd like.
2025-08-14T23:09:08.8791803Z 
2025-08-14T23:09:08.8791852Z Args:
2025-08-14T23:09:08.8792023Z     func (function): A Python function that takes one or more arguments.
2025-08-14T23:09:08.8792253Z         Must return one or more Tensors.
2025-08-14T23:09:08.8792471Z     in_dims (int or nested structure): Specifies which dimension of the
2025-08-14T23:09:08.8792726Z         inputs should be mapped over. ``in_dims`` should have a
2025-08-14T23:09:08.8793068Z         structure like the inputs. If the ``in_dim`` for a particular
2025-08-14T23:09:08.8793317Z         input is None, then that indicates there is no map dimension.
2025-08-14T23:09:08.8793513Z         Default: 0.
2025-08-14T23:09:08.8793697Z     out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-08-14T23:09:08.8793948Z         should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-08-14T23:09:08.8794184Z         it should have one element per output. Default: 0.
2025-08-14T23:09:08.8794425Z     randomness (str): Specifies whether the randomness in this
2025-08-14T23:09:08.8794689Z         vmap should be the same or different across batches. If 'different',
2025-08-14T23:09:08.8794953Z         the randomness for each batch will be different. If 'same', the
2025-08-14T23:09:08.8795210Z         randomness will be the same across batches. If 'error', any calls to
2025-08-14T23:09:08.8795484Z         random functions will error. Default: 'error'. WARNING: this flag
2025-08-14T23:09:08.8795751Z         only applies to random PyTorch operations and does not apply to
2025-08-14T23:09:08.8795982Z         Python's random module or numpy randomness.
2025-08-14T23:09:08.8796226Z     chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-08-14T23:09:08.8796510Z         If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-08-14T23:09:08.8796812Z         Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-08-14T23:09:08.8801983Z         If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-08-14T23:09:08.8802137Z 
2025-08-14T23:09:08.8802177Z Returns:
2025-08-14T23:09:08.8802313Z     Returns a new "batched" function. It takes the same inputs as
2025-08-14T23:09:08.8802514Z     ``func``, except each input has an extra dimension at the index
2025-08-14T23:09:08.8802710Z     specified by ``in_dims``. It takes returns the same outputs as
2025-08-14T23:09:08.8802990Z     ``func``, except each output has an extra dimension at the index
2025-08-14T23:09:08.8803153Z     specified by ``out_dims``.
2025-08-14T23:09:08.8803235Z 
2025-08-14T23:09:08.8803274Z .. warning:
2025-08-14T23:09:08.8803411Z     :func:`vmap` works best with functional-style code. Please do not
2025-08-14T23:09:08.8803613Z     perform any side-effects in ``func``, with the exception of
2025-08-14T23:09:08.8806197Z     in-place PyTorch operations. Examples of side-effects include mutating
2025-08-14T23:09:08.8806485Z     Python data structures and assigning values to variables not captured
2025-08-14T23:09:08.8806657Z     in ``func``.
2025-08-14T23:09:08.8806718Z 
2025-08-14T23:09:08.8806817Z One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-08-14T23:09:08.8807068Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-08-14T23:09:08.8807270Z rummaging through docs, use :func:`vmap` to construct a new function.
2025-08-14T23:09:08.8807382Z 
2025-08-14T23:09:08.8807447Z     >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:08.8807526Z     >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-08-14T23:09:08.8807576Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-08-14T23:09:08.8807616Z     >>> batched_dot(x, y)
2025-08-14T23:09:08.8807619Z 
2025-08-14T23:09:08.8807700Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-08-14T23:09:08.8807742Z model authoring experience.
2025-08-14T23:09:08.8807745Z 
2025-08-14T23:09:08.8807790Z     >>> batch_size, feature_size = 3, 5
2025-08-14T23:09:08.8807858Z     >>> weights = torch.randn(feature_size, requires_grad=True)
2025-08-14T23:09:08.8807891Z     >>>
2025-08-14T23:09:08.8807933Z     >>> def model(feature_vec):
2025-08-14T23:09:08.8809073Z     >>> # Very simple linear model with activation
2025-08-14T23:09:08.8809124Z     >>>     return feature_vec.dot(weights).relu()
2025-08-14T23:09:08.8809155Z     >>>
2025-08-14T23:09:08.8809215Z     >>> examples = torch.randn(batch_size, feature_size)
2025-08-14T23:09:08.8809262Z     >>> result = torch.vmap(model)(examples)
2025-08-14T23:09:08.8809264Z 
2025-08-14T23:09:08.8809351Z :func:`vmap` can also help vectorize computations that were previously difficult
2025-08-14T23:09:08.8809433Z or impossible to batch. One example is higher-order gradient computation.
2025-08-14T23:09:08.8809515Z The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-08-14T23:09:08.8809598Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-08-14T23:09:08.8809689Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-08-14T23:09:08.8809770Z we can vectorize the whole computation, computing the Jacobian in a single
2025-08-14T23:09:08.8809814Z call to ``autograd.grad``.
2025-08-14T23:09:08.8809817Z 
2025-08-14T23:09:08.8809850Z     >>> # Setup
2025-08-14T23:09:08.8809884Z     >>> N = 5
2025-08-14T23:09:08.8809921Z     >>> f = lambda x: x**2
2025-08-14T23:09:08.8809969Z     >>> x = torch.randn(N, requires_grad=True)
2025-08-14T23:09:08.8810002Z     >>> y = f(x)
2025-08-14T23:09:08.8810039Z     >>> I_N = torch.eye(N)
2025-08-14T23:09:08.8810071Z     >>>
2025-08-14T23:09:08.8810111Z     >>> # Sequential approach
2025-08-14T23:09:08.8810188Z     >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-08-14T23:09:08.8810235Z     >>>                  for v in I_N.unbind()]
2025-08-14T23:09:08.8811196Z     >>> jacobian = torch.stack(jacobian_rows)
2025-08-14T23:09:08.8811260Z     >>>
2025-08-14T23:09:08.8811328Z     >>> # vectorized gradient computation
2025-08-14T23:09:08.8811366Z     >>> def get_vjp(v):
2025-08-14T23:09:08.8811415Z     >>>     return torch.autograd.grad(y, x, v)
2025-08-14T23:09:08.8811461Z     >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-08-14T23:09:08.8811464Z 
2025-08-14T23:09:08.8811555Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-08-14T23:09:08.8811558Z 
2025-08-14T23:09:08.8811599Z     >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:08.8811642Z     >>> batched_dot = torch.vmap(
2025-08-14T23:09:08.8811682Z     ...     torch.vmap(torch.dot)
2025-08-14T23:09:08.8811728Z     ... )  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-08-14T23:09:08.8811782Z     >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-08-14T23:09:08.8811830Z     >>> batched_dot(x, y)  # tensor of size [2, 3]
2025-08-14T23:09:08.8811832Z 
2025-08-14T23:09:08.8811955Z If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-08-14T23:09:08.8812015Z the dimension that each inputs are batched along as
2025-08-14T23:09:08.8812017Z 
2025-08-14T23:09:08.8812056Z     >>> torch.dot  # [N], [N] -> []
2025-08-14T23:09:08.8812153Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-08-14T23:09:08.8812199Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-08-14T23:09:08.8812235Z     >>> batched_dot(
2025-08-14T23:09:08.8812267Z     ...     x, y
2025-08-14T23:09:08.8812337Z     ... )  # output is [5] instead of [2] if batched along the 0th dimension
2025-08-14T23:09:08.8812339Z 
2025-08-14T23:09:08.8813361Z If there are multiple inputs each of which is batched along different dimensions,
2025-08-14T23:09:08.8813437Z ``in_dims`` must be a tuple with the batch dimension for each input as
2025-08-14T23:09:08.8813458Z 
2025-08-14T23:09:08.8813499Z     >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:08.8813584Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-08-14T23:09:08.8813634Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-08-14T23:09:08.8813668Z     >>> batched_dot(
2025-08-14T23:09:08.8813700Z     ...     x, y
2025-08-14T23:09:08.8813771Z     ... )  # second arg doesn't have a batch dim because in_dim[1] was None
2025-08-14T23:09:08.8813773Z 
2025-08-14T23:09:08.8813855Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-08-14T23:09:08.8813899Z matching the shape of the input:
2025-08-14T23:09:08.8813901Z 
2025-08-14T23:09:08.8813955Z     >>> f = lambda dict: torch.dot(dict["x"], dict["y"])
2025-08-14T23:09:08.8813999Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-08-14T23:09:08.8814039Z     >>> input = {"x": x, "y": y}
2025-08-14T23:09:08.8814105Z     >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},))
2025-08-14T23:09:08.8814144Z     >>> batched_dot(input)
2025-08-14T23:09:08.8814146Z 
2025-08-14T23:09:08.8814244Z By default, the output is batched along the first dimension. However, it can be batched
2025-08-14T23:09:08.8814292Z along any dimension by using ``out_dims``
2025-08-14T23:09:08.8814294Z 
2025-08-14T23:09:08.8814331Z     >>> f = lambda x: x**2
2025-08-14T23:09:08.8814372Z     >>> x = torch.randn(2, 5)
2025-08-14T23:09:08.8814420Z     >>> batched_pow = torch.vmap(f, out_dims=1)
2025-08-14T23:09:08.8814460Z     >>> batched_pow(x)  # [5, 2]
2025-08-14T23:09:08.8814462Z 
2025-08-14T23:09:08.8815447Z For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-08-14T23:09:08.8815483Z accept kwargs
2025-08-14T23:09:08.8815486Z 
2025-08-14T23:09:08.8815524Z     >>> x = torch.randn([2, 5])
2025-08-14T23:09:08.8815562Z     >>> def fn(x, scale=4.):
2025-08-14T23:09:08.8815599Z     >>>   return x * scale
2025-08-14T23:09:08.8815630Z     >>>
2025-08-14T23:09:08.8815673Z     >>> batched_pow = torch.vmap(fn)
2025-08-14T23:09:08.8815727Z     >>> assert torch.allclose(batched_pow(x), x * 4)
2025-08-14T23:09:08.8815808Z     >>> batched_pow(x, scale=x)  # scale is not batched, output has shape [2, 2, 5]
2025-08-14T23:09:08.8815845Z 
2025-08-14T23:09:08.8815897Z .. note::
2025-08-14T23:09:08.8815978Z     vmap does not provide general autobatching or handle variable-length
2025-08-14T23:09:08.8816019Z     sequences out of the box.
2025-08-14T23:09:08.8816021Z 
2025-08-14T23:09:08.8816108Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.8816110Z 
2025-08-14T23:09:08.8816275Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=306.
2025-08-14T23:09:08.8816366Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.8816448Z ``grad`` operator helps computing gradients of ``func`` with respect to the
2025-08-14T23:09:08.8816520Z     input(s) specified by ``argnums``. This operator can be nested to
2025-08-14T23:09:08.8816562Z     compute higher-order gradients.
2025-08-14T23:09:08.8816565Z 
2025-08-14T23:09:08.8816616Z     Args:
2025-08-14T23:09:08.8816690Z         func (Callable): A Python function that takes one or more arguments.
2025-08-14T23:09:08.8816784Z             Must return a single-element Tensor. If specified ``has_aux`` equals ``True``,
2025-08-14T23:09:08.8816896Z             function can return a tuple of single-element Tensor and other auxiliary objects:
2025-08-14T23:09:08.8817791Z             ``(output, aux)``.
2025-08-14T23:09:08.8817887Z         argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to.
2025-08-14T23:09:08.8817959Z             ``argnums`` can be single integer or tuple of integers. Default: 0.
2025-08-14T23:09:08.8818035Z         has_aux (bool): Flag indicating that ``func`` returns a tensor and other
2025-08-14T23:09:08.8818097Z             auxiliary objects: ``(output, aux)``. Default: False.
2025-08-14T23:09:08.8818100Z 
2025-08-14T23:09:08.8818132Z     Returns:
2025-08-14T23:09:08.8818233Z         Function to compute gradients with respect to its inputs. By default, the output of
2025-08-14T23:09:08.8818316Z         the function is the gradient tensor(s) with respect to the first argument.
2025-08-14T23:09:08.8818412Z         If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects
2025-08-14T23:09:08.8818498Z         is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with
2025-08-14T23:09:08.8818553Z         respect to each ``argnums`` value is returned.
2025-08-14T23:09:08.8818555Z 
2025-08-14T23:09:08.8818594Z     Example of using ``grad``:
2025-08-14T23:09:08.8818596Z 
2025-08-14T23:09:08.8818635Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:08.8818677Z         >>> from torch.func import grad
2025-08-14T23:09:08.8818717Z         >>> x = torch.randn([])
2025-08-14T23:09:08.8818764Z         >>> cos_x = grad(lambda x: torch.sin(x))(x)
2025-08-14T23:09:08.8818811Z         >>> assert torch.allclose(cos_x, x.cos())
2025-08-14T23:09:08.8818843Z         >>>
2025-08-14T23:09:08.8818887Z         >>> # Second-order gradients
2025-08-14T23:09:08.8818946Z         >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
2025-08-14T23:09:08.8818998Z         >>> assert torch.allclose(neg_sin_x, -x.sin())
2025-08-14T23:09:08.8819002Z 
2025-08-14T23:09:08.8819929Z     When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:
2025-08-14T23:09:08.8819931Z 
2025-08-14T23:09:08.8819971Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:08.8820017Z         >>> from torch.func import grad, vmap
2025-08-14T23:09:08.8820062Z         >>> batch_size, feature_size = 3, 5
2025-08-14T23:09:08.8820093Z         >>>
2025-08-14T23:09:08.8820138Z         >>> def model(weights, feature_vec):
2025-08-14T23:09:08.8820187Z         >>> # Very simple linear model with activation
2025-08-14T23:09:08.8820233Z         >>>     assert feature_vec.dim() == 1
2025-08-14T23:09:08.8820280Z         >>>     return feature_vec.dot(weights).relu()
2025-08-14T23:09:08.8820311Z         >>>
2025-08-14T23:09:08.8820389Z         >>> def compute_loss(weights, example, target):
2025-08-14T23:09:08.8820447Z         >>>     y = model(weights, example)
2025-08-14T23:09:08.8820500Z         >>>     return ((y - target) ** 2).mean()  # MSELoss
2025-08-14T23:09:08.8820532Z         >>>
2025-08-14T23:09:08.8820601Z         >>> weights = torch.randn(feature_size, requires_grad=True)
2025-08-14T23:09:08.8820656Z         >>> examples = torch.randn(batch_size, feature_size)
2025-08-14T23:09:08.8820701Z         >>> targets = torch.randn(batch_size)
2025-08-14T23:09:08.8820752Z         >>> inputs = (weights, examples, targets)
2025-08-14T23:09:08.8820839Z         >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(
2025-08-14T23:09:08.8820875Z         ...     *inputs
2025-08-14T23:09:08.8820908Z         ... )
2025-08-14T23:09:08.8820911Z 
2025-08-14T23:09:08.8820977Z     Example of using ``grad`` with ``has_aux`` and ``argnums``:
2025-08-14T23:09:08.8821874Z 
2025-08-14T23:09:08.8821932Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:08.8821977Z         >>> from torch.func import grad
2025-08-14T23:09:08.8822022Z         >>> def my_loss_func(y, y_pred):
2025-08-14T23:09:08.8822074Z         >>>    loss_per_sample = (0.5 * y_pred - y) ** 2
2025-08-14T23:09:08.8822137Z         >>>    loss = loss_per_sample.mean()
2025-08-14T23:09:08.8822187Z         >>>    return loss, (y_pred, loss_per_sample)
2025-08-14T23:09:08.8822220Z         >>>
2025-08-14T23:09:08.8822277Z         >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True)
2025-08-14T23:09:08.8822319Z         >>> y_true = torch.rand(4)
2025-08-14T23:09:08.8822371Z         >>> y_preds = torch.rand(4, requires_grad=True)
2025-08-14T23:09:08.8822414Z         >>> out = fn(y_true, y_preds)
2025-08-14T23:09:08.8822502Z         >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample))
2025-08-14T23:09:08.8822505Z 
2025-08-14T23:09:08.8822539Z     .. note::
2025-08-14T23:09:08.8822603Z         Using PyTorch ``torch.no_grad`` together with ``grad``.
2025-08-14T23:09:08.8822608Z 
2025-08-14T23:09:08.8822666Z         Case 1: Using ``torch.no_grad`` inside a function:
2025-08-14T23:09:08.8822669Z 
2025-08-14T23:09:08.8822709Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:08.8822748Z             >>> def f(x):
2025-08-14T23:09:08.8822790Z             >>>     with torch.no_grad():
2025-08-14T23:09:08.8822828Z             >>>         c = x ** 2
2025-08-14T23:09:08.8822867Z             >>>     return x - c
2025-08-14T23:09:08.8822870Z 
2025-08-14T23:09:08.8823003Z         In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``.
2025-08-14T23:09:08.8823006Z 
2025-08-14T23:09:08.8823951Z         Case 2: Using ``grad`` inside ``torch.no_grad`` context manager:
2025-08-14T23:09:08.8823954Z 
2025-08-14T23:09:08.8823995Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:08.8824035Z             >>> with torch.no_grad():
2025-08-14T23:09:08.8824073Z             >>>     grad(f)(x)
2025-08-14T23:09:08.8824075Z 
2025-08-14T23:09:08.8824157Z         In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the
2025-08-14T23:09:08.8824237Z         outer one. This is because ``grad`` is a "function transform": its result
2025-08-14T23:09:08.8824314Z         should not depend on the result of a context manager outside of ``f``.
2025-08-14T23:09:08.8824316Z 
2025-08-14T23:09:08.8824348Z     
2025-08-14T23:09:08.8824435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.8824438Z 
2025-08-14T23:09:08.9817806Z 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.
2025-08-14T23:09:08.9818185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:08.9818198Z 
2025-08-14T23:09:08.9818458Z Config object that specifies the supported data types passed as arguments to
2025-08-14T23:09:08.9818738Z quantize ops in the reference model spec, for input and output activations,
2025-08-14T23:09:08.9819333Z weights, and biases.
2025-08-14T23:09:08.9819470Z 
2025-08-14T23:09:08.9819655Z For example, consider the following reference model:
2025-08-14T23:09:08.9819670Z 
2025-08-14T23:09:08.9819851Z   quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-08-14T23:09:08.9819859Z 
2025-08-14T23:09:08.9820085Z The pattern in the square brackets refers to the reference pattern of
2025-08-14T23:09:08.9820331Z statically quantized linear. Setting the input dtype as `torch.quint8`
2025-08-14T23:09:08.9820568Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-08-14T23:09:08.9820792Z to the first quantize op (quant1). Similarly, setting the output dtype as
2025-08-14T23:09:08.9830289Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-08-14T23:09:08.9830429Z the second quantize op (quant2).
2025-08-14T23:09:08.9830438Z 
2025-08-14T23:09:08.9830812Z Note that the dtype here does not refer to the interface dtypes of the
2025-08-14T23:09:08.9831035Z op. For example, the "input dtype" here is not the dtype of the input
2025-08-14T23:09:08.9831172Z tensor passed to the quantized linear op. Though it can still be the
2025-08-14T23:09:08.9831353Z same as the interface dtype, this is not always the case, e.g. the
2025-08-14T23:09:08.9831492Z interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-08-14T23:09:08.9831613Z specified in the DTypeConfig would still be quint8. The semantics of
2025-08-14T23:09:08.9831744Z dtypes here are the same as the semantics of the dtypes specified in
2025-08-14T23:09:08.9831808Z the observers.
2025-08-14T23:09:08.9831811Z 
2025-08-14T23:09:08.9831934Z These dtypes are matched against the ones specified in the user's
2025-08-14T23:09:08.9832067Z QConfig. If there is a match, and the QConfig satisfies the constraints
2025-08-14T23:09:08.9832196Z specified in the DTypeConfig (if any), then we will quantize the given
2025-08-14T23:09:08.9832328Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-08-14T23:09:08.9832416Z the pattern will not be quantized.
2025-08-14T23:09:08.9832421Z 
2025-08-14T23:09:08.9832514Z Example usage::
2025-08-14T23:09:08.9832522Z 
2025-08-14T23:09:08.9832588Z     >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:08.9832666Z     >>> dtype_config1 = DTypeConfig(
2025-08-14T23:09:08.9832739Z     ...     input_dtype=torch.quint8,
2025-08-14T23:09:08.9832814Z     ...     output_dtype=torch.quint8,
2025-08-14T23:09:08.9832887Z     ...     weight_dtype=torch.qint8,
2025-08-14T23:09:08.9835359Z     ...     bias_dtype=torch.float)
2025-08-14T23:09:08.9835366Z 
2025-08-14T23:09:08.9835448Z     >>> dtype_config2 = DTypeConfig(
2025-08-14T23:09:08.9835539Z     ...     input_dtype=DTypeWithConstraints(
2025-08-14T23:09:08.9835605Z     ...         dtype=torch.quint8,
2025-08-14T23:09:08.9835677Z     ...         quant_min_lower_bound=0,
2025-08-14T23:09:08.9835754Z     ...         quant_max_upper_bound=255,
2025-08-14T23:09:08.9835809Z     ...     ),
2025-08-14T23:09:08.9835903Z     ...     output_dtype=DTypeWithConstraints(
2025-08-14T23:09:08.9835968Z     ...         dtype=torch.quint8,
2025-08-14T23:09:08.9836038Z     ...         quant_min_lower_bound=0,
2025-08-14T23:09:08.9836117Z     ...         quant_max_upper_bound=255,
2025-08-14T23:09:08.9836170Z     ...     ),
2025-08-14T23:09:08.9836257Z     ...     weight_dtype=DTypeWithConstraints(
2025-08-14T23:09:08.9836321Z     ...         dtype=torch.qint8,
2025-08-14T23:09:08.9836397Z     ...         quant_min_lower_bound=-128,
2025-08-14T23:09:08.9836469Z     ...         quant_max_upper_bound=127,
2025-08-14T23:09:08.9836522Z     ...     ),
2025-08-14T23:09:08.9836589Z     ...     bias_dtype=torch.float)
2025-08-14T23:09:08.9836593Z 
2025-08-14T23:09:08.9836675Z     >>> dtype_config1.input_dtype
2025-08-14T23:09:08.9836718Z     torch.quint8
2025-08-14T23:09:08.9836722Z 
2025-08-14T23:09:08.9836773Z     >>> dtype_config2.input_dtype
2025-08-14T23:09:08.9836810Z     torch.quint8
2025-08-14T23:09:08.9836854Z 
2025-08-14T23:09:08.9838220Z     >>> dtype_config2.input_dtype_with_constraints
2025-08-14T23:09:08.9838593Z     DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-08-14T23:09:08.9838599Z 
2025-08-14T23:09:08.9838723Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:08.9838725Z 
2025-08-14T23:09:09.0137813Z 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.
2025-08-14T23:09:09.0138428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.0138459Z 
2025-08-14T23:09:09.0138837Z Takes in optional filter values and generates two tables with desired information.
2025-08-14T23:09:09.0138846Z 
2025-08-14T23:09:09.0139050Z The generated tables are presented in both a list-of-lists format
2025-08-14T23:09:09.0139061Z 
2025-08-14T23:09:09.0139175Z The reason for the two tables are that they handle different things:
2025-08-14T23:09:09.0139291Z 1.) the first table handles all tensor level information
2025-08-14T23:09:09.0139386Z 2.) the second table handles and displays all channel based information
2025-08-14T23:09:09.0139389Z 
2025-08-14T23:09:09.0139531Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-08-14T23:09:09.0139669Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-08-14T23:09:09.0139818Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-08-14T23:09:09.0139821Z 
2025-08-14T23:09:09.0139870Z Tensor table columns:
2025-08-14T23:09:09.0139982Z     idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:09.0140068Z     ----  ---------  ---------   ---------   ---------        ---------
2025-08-14T23:09:09.0140072Z 
2025-08-14T23:09:09.0140130Z Per-Channel table columns:
2025-08-14T23:09:09.0142849Z     idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:09.0143075Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-08-14T23:09:09.0143078Z 
2025-08-14T23:09:09.0143119Z Args:
2025-08-14T23:09:09.0143239Z     feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:09.0143301Z         contain this filter substring
2025-08-14T23:09:09.0143381Z         Default = "", results in all the features being printed
2025-08-14T23:09:09.0143494Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:09.0143607Z         Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:09.0143610Z 
2025-08-14T23:09:09.0143668Z Returns a dictionary with two keys:
2025-08-14T23:09:09.0143757Z     (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-08-14T23:09:09.0143823Z     "tensor_level_info", "channel_level_info"
2025-08-14T23:09:09.0143882Z         Each key maps to a tuple with:
2025-08-14T23:09:09.0143942Z             A list of the headers of each table
2025-08-14T23:09:09.0144023Z             A list of lists containing the table information row by row
2025-08-14T23:09:09.0144099Z             The 0th index row will contain the headers of the columns
2025-08-14T23:09:09.0144158Z             The rest of the rows will contain data
2025-08-14T23:09:09.0144160Z 
2025-08-14T23:09:09.0144203Z Example Use:
2025-08-14T23:09:09.0144264Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:09.0144335Z     >>> mod_report_visualizer.generate_filtered_tables(
2025-08-14T23:09:09.0144424Z     ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:09.0144544Z     ... )  # generates table with per_channel_min info for all modules in block 1 of the model
2025-08-14T23:09:09.0144593Z 
2025-08-14T23:09:09.0144723Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.0144726Z 
2025-08-14T23:09:09.0145088Z 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.
2025-08-14T23:09:09.0145205Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.0145207Z 
2025-08-14T23:09:09.0145324Z Takes in optional filter values and prints out formatted tables of the information.
2025-08-14T23:09:09.0145326Z 
2025-08-14T23:09:09.0145472Z The reason for the two tables printed out instead of one large one are that they handle different things:
2025-08-14T23:09:09.0145548Z 1.) the first table handles all tensor level information
2025-08-14T23:09:09.0145664Z 2.) the second table handles and displays all channel based information
2025-08-14T23:09:09.0145669Z 
2025-08-14T23:09:09.0145803Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-08-14T23:09:09.0145964Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-08-14T23:09:09.0146111Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-08-14T23:09:09.0146117Z 
2025-08-14T23:09:09.0146164Z Tensor table columns:
2025-08-14T23:09:09.0146252Z  idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:09.0146328Z ----  ---------  ---------   ---------   ---------        ---------
2025-08-14T23:09:09.0146331Z 
2025-08-14T23:09:09.0147868Z Per-Channel table columns:
2025-08-14T23:09:09.0147872Z 
2025-08-14T23:09:09.0147972Z  idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:09.0148051Z ----  ---------  -------  ---------   ---------   ---------        ---------
2025-08-14T23:09:09.0148056Z 
2025-08-14T23:09:09.0148094Z Args:
2025-08-14T23:09:09.0148211Z     feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:09.0148265Z         contain this filter substring
2025-08-14T23:09:09.0148341Z         Default = "", results in all the features being printed
2025-08-14T23:09:09.0148450Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:09.0148554Z         Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:09.0148557Z 
2025-08-14T23:09:09.0148599Z Example Use:
2025-08-14T23:09:09.0148658Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:09.0148740Z     >>> mod_report_visualizer.generate_table_visualization(
2025-08-14T23:09:09.0148845Z     ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:09.0148883Z     ... )
2025-08-14T23:09:09.0148958Z     >>> # prints out neatly formatted table with per_channel_min info
2025-08-14T23:09:09.0149012Z     >>> # for all modules in block 1 of the model
2025-08-14T23:09:09.0149014Z 
2025-08-14T23:09:09.0149109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.0149112Z 
2025-08-14T23:09:09.0149426Z 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.
2025-08-14T23:09:09.0149523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.0149526Z 
2025-08-14T23:09:09.0149614Z Takes in a feature and optional module_filter and plots of the desired data.
2025-08-14T23:09:09.0149616Z 
2025-08-14T23:09:09.0149716Z For per channel features, it averages the value across the channels and plots a point
2025-08-14T23:09:09.0150861Z per module. The reason for this is that for models with hundreds of channels, it can
2025-08-14T23:09:09.0151006Z be hard to differentiate one channel line from another, and so the point of generating
2025-08-14T23:09:09.0151107Z a single average point per module is to give a sense of general trends that encourage
2025-08-14T23:09:09.0151150Z further deep dives.
2025-08-14T23:09:09.0151153Z 
2025-08-14T23:09:09.0151186Z Note:
2025-08-14T23:09:09.0151282Z     Only features in the report that have tensor value data are plottable by this class
2025-08-14T23:09:09.0151352Z     When the tensor information is plotted, it will plot:
2025-08-14T23:09:09.0151409Z         idx as the x val, feature value as the y_val
2025-08-14T23:09:09.0151474Z     When the channel information is plotted, it will plot:
2025-08-14T23:09:09.0151575Z         the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-08-14T23:09:09.0151660Z         The reason for this is that we want to be able to compare values across the
2025-08-14T23:09:09.0151761Z         channels for same layer, and it will be hard if values are staggered by idx
2025-08-14T23:09:09.0151830Z         This means each module is represented by only 1 x value
2025-08-14T23:09:09.0151877Z Args:
2025-08-14T23:09:09.0151963Z     feature_filter (str): Filters the features presented to only those that
2025-08-14T23:09:09.0152009Z         contain this filter substring
2025-08-14T23:09:09.0152102Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:09.0152194Z         Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:09.0152196Z 
2025-08-14T23:09:09.0152233Z Example Use:
2025-08-14T23:09:09.0152285Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:09.0152349Z     >>> mod_report_visualizer.generate_plot_visualization(
2025-08-14T23:09:09.0152425Z     ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:09.0153591Z     ... )
2025-08-14T23:09:09.0153667Z     >>> # outputs line plot of per_channel_min information for all
2025-08-14T23:09:09.0153742Z     >>> # modules in block1 of model each channel gets it's own line,
2025-08-14T23:09:09.0153822Z     >>> # and it's plotted across the in-order modules on the x-axis
2025-08-14T23:09:09.0153826Z 
2025-08-14T23:09:09.0153919Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.0153922Z 
2025-08-14T23:09:09.0154241Z 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.
2025-08-14T23:09:09.0154335Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.0154338Z 
2025-08-14T23:09:09.0154439Z Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-08-14T23:09:09.0154441Z 
2025-08-14T23:09:09.0154475Z Note:
2025-08-14T23:09:09.0154574Z     Only features in the report that have tensor value data can be viewed as a histogram
2025-08-14T23:09:09.0154672Z     If you want to plot a histogram from all the channel values of a specific feature for
2025-08-14T23:09:09.0154763Z         a specific model, make sure to specify both the model and the feature properly
2025-08-14T23:09:09.0154854Z         in the filters and you should be able to see a distribution of the channel data
2025-08-14T23:09:09.0154856Z 
2025-08-14T23:09:09.0154890Z Args:
2025-08-14T23:09:09.0154987Z     feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:09.0155032Z         contain this filter substring
2025-08-14T23:09:09.0155096Z         Default = "", results in all the features being printed
2025-08-14T23:09:09.0155191Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:09.0155280Z         Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:09.0155421Z     num_bins (int, optional): The number of bins to create the histogram with
2025-08-14T23:09:09.0155495Z         Default = 10, the values will be split into 10 equal sized bins
2025-08-14T23:09:09.0155498Z 
2025-08-14T23:09:09.0156528Z Example Use:
2025-08-14T23:09:09.0156573Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.0156683Z     >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-08-14T23:09:09.0156753Z     ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:09.0156785Z     ... )
2025-08-14T23:09:09.0156879Z     # outputs histogram of per_channel_min information for all modules in block1 of model
2025-08-14T23:09:09.0156968Z         information is gathered across all channels for all modules in block 1 for the
2025-08-14T23:09:09.0157043Z         per_channel_min and is displayed in a histogram of equally sized bins
2025-08-14T23:09:09.0157046Z 
2025-08-14T23:09:09.0157156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.0157161Z 
2025-08-14T23:09:09.0212259Z 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.
2025-08-14T23:09:09.0212432Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.0212435Z 
2025-08-14T23:09:09.0212553Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into
2025-08-14T23:09:09.0212663Z with their models. The class works with all traceable GraphModules to help diagnose issues,
2025-08-14T23:09:09.0212802Z though the requirements on the type of model more-so depends on the specific report the user
2025-08-14T23:09:09.0213018Z is trying to generate. With respect to the reports, the ModelReport class is initialized with
2025-08-14T23:09:09.0213116Z a set of Detector classes, each of which generate reports on quantization configuration
2025-08-14T23:09:09.0213163Z issues a use might have.
2025-08-14T23:09:09.0213166Z 
2025-08-14T23:09:09.0213217Z Currently supports generating reports on:
2025-08-14T23:09:09.0213304Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module)
2025-08-14T23:09:09.0215362Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules)
2025-08-14T23:09:09.0215482Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules)
2025-08-14T23:09:09.0215564Z - Suggestions for outlier detection for all layers (Graph Modules)
2025-08-14T23:09:09.0215567Z 
2025-08-14T23:09:09.0215705Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver)
2025-08-14T23:09:09.0215832Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport
2025-08-14T23:09:09.0215956Z class compiles the report generated by each Detector class into a single report to return to the user. It also
2025-08-14T23:09:09.0216036Z has the capability to remove all the observers it inserted as well.
2025-08-14T23:09:09.0216040Z 
2025-08-14T23:09:09.0216144Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule
2025-08-14T23:09:09.0216148Z 
2025-08-14T23:09:09.0216279Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class
2025-08-14T23:09:09.0216388Z     Make sure that these are all unique types of detectors [do not have more than 1 of the same class]
2025-08-14T23:09:09.0216391Z 
2025-08-14T23:09:09.0216492Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors.
2025-08-14T23:09:09.0216575Z     This set is generated by calling the get_detector_name() of each detector
2025-08-14T23:09:09.0216578Z 
2025-08-14T23:09:09.0216696Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest
2025-08-14T23:09:09.0216807Z     The purpose of this is to keep track of what observers were inserted for each detector, so that they
2025-08-14T23:09:09.0216908Z     can be removed at the end if desired
2025-08-14T23:09:09.0216911Z 
2025-08-14T23:09:09.0217019Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not
2025-08-14T23:09:09.0217102Z     This is to ensure we only insert observers once with the ModelReport instance
2025-08-14T23:09:09.0217105Z 
2025-08-14T23:09:09.0217193Z * :attr:`_removed_observers` A boolean to track if we have removed observers already
2025-08-14T23:09:09.0217294Z     The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport
2025-08-14T23:09:09.0217405Z     instance. This also allows the functionality where we can generate the report multiple times
2025-08-14T23:09:09.0217463Z     as long as we haven't removed the observers yet.
2025-08-14T23:09:09.0217465Z 
2025-08-14T23:09:09.0218657Z Note:
2025-08-14T23:09:09.0218793Z     This class was initially designed to work with the Fx Graph Mode workflow in mind. However,
2025-08-14T23:09:09.0218905Z     full functionality is available as long as there is a traceable GraphModule that is being used.
2025-08-14T23:09:09.0219024Z     One method to get a traceable GraphModule without going through the Fx workflow is to use
2025-08-14T23:09:09.0219070Z     the QuantizationTracer class.
2025-08-14T23:09:09.0219073Z 
2025-08-14T23:09:09.0219116Z General Flow for Fx workflow:
2025-08-14T23:09:09.0219247Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model
2025-08-14T23:09:09.0219295Z 2.) Prepare your model with prepare_fx
2025-08-14T23:09:09.0219380Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers
2025-08-14T23:09:09.0219424Z 4.) Calibrate your model with data
2025-08-14T23:09:09.0219543Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
2025-08-14T23:09:09.0219579Z Optional
2025-08-14T23:09:09.0219678Z     6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance
2025-08-14T23:09:09.0219766Z     7.) To help in parsing report information and debugging, view report info as a:
2025-08-14T23:09:09.0219805Z         - Table
2025-08-14T23:09:09.0219841Z         - Histogram
2025-08-14T23:09:09.0219876Z         - Line plot
2025-08-14T23:09:09.0219984Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions
2025-08-14T23:09:09.0219987Z 
2025-08-14T23:09:09.0220032Z Example (with QuantizationTracer):
2025-08-14T23:09:09.0220072Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.0220114Z     >>> # get the necessary qconfig
2025-08-14T23:09:09.0221142Z     >>> config = PrepareCustomConfig()
2025-08-14T23:09:09.0221209Z     >>> skipped_module_names, skipped_module_classes = (
2025-08-14T23:09:09.0221272Z     ...     get_skipped_module_name_and_classes(config, False)
2025-08-14T23:09:09.0221305Z     ... )
2025-08-14T23:09:09.0221311Z 
2025-08-14T23:09:09.0221369Z     >>> # initialize our model and get GraphModule
2025-08-14T23:09:09.0221410Z     >>> model = SomeModel()
2025-08-14T23:09:09.0221501Z     >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)
2025-08-14T23:09:09.0221564Z     >>> graph_module = GraphModule(model, tracer.trace(model))
2025-08-14T23:09:09.0221566Z 
2025-08-14T23:09:09.0221626Z     >>> # get our set of detectors and ModelReport instance
2025-08-14T23:09:09.0221665Z     >>> detector_set = set(
2025-08-14T23:09:09.0221698Z     ...     [
2025-08-14T23:09:09.0221752Z     ...         DynamicStaticDetector(tolerance=0.5),
2025-08-14T23:09:09.0221827Z     ...         InputWeightEqualizationDetector(ratio_threshold=0.7),
2025-08-14T23:09:09.0221859Z     ...     ]
2025-08-14T23:09:09.0221890Z     ... )
2025-08-14T23:09:09.0221966Z     >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set)
2025-08-14T23:09:09.0221968Z 
2025-08-14T23:09:09.0222027Z     >>> # now we insert the observers and calibrate the model
2025-08-14T23:09:09.0222156Z     >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration()
2025-08-14T23:09:09.0222207Z     >>> for i in range(num_callibration_batches):
2025-08-14T23:09:09.0222261Z     >>>     example_input = get_callibration_input()
2025-08-14T23:09:09.0222314Z     >>>     tracer_model_with_observers(example_input)
2025-08-14T23:09:09.0222316Z 
2025-08-14T23:09:09.0222408Z     >>> # finally we generate the reports and optionally remove the observers we inserted
2025-08-14T23:09:09.0223525Z     >>> reports = tracer_reporter.generate_model_report(
2025-08-14T23:09:09.0223575Z     ...     remove_inserted_observers=True
2025-08-14T23:09:09.0223608Z     ... )
2025-08-14T23:09:09.0223610Z 
2025-08-14T23:09:09.0223691Z     >>> # Optional: we can generate the qconfig mapping based on the suggestions
2025-08-14T23:09:09.0223751Z     >>> qconfigs = model_report.generate_qconfig_mapping()
2025-08-14T23:09:09.0223755Z 
2025-08-14T23:09:09.0223868Z     >>> # Optional: we can generate the equalization mapping based on the suggestions
2025-08-14T23:09:09.0223937Z     >>> qconfigs = model_report.generate_equalization_mapping()
2025-08-14T23:09:09.0223961Z 
2025-08-14T23:09:09.0224057Z     >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired
2025-08-14T23:09:09.0224132Z     >>> model_report_visualizer = tracer_reporter.generate_visualizer()
2025-08-14T23:09:09.0224136Z 
2025-08-14T23:09:09.0224137Z 
2025-08-14T23:09:09.0224228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.0224230Z 
2025-08-14T23:09:09.2500031Z 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.
2025-08-14T23:09:09.2501202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.2501569Z 
2025-08-14T23:09:09.2501833Z The Activation sparsifier class aims to sparsify/prune activations in a neural
2025-08-14T23:09:09.2508056Z network. The idea is to attach the sparsifier to a layer (or layers) and it
2025-08-14T23:09:09.2508388Z zeroes out the activations based on the mask_fn (or sparsification function)
2025-08-14T23:09:09.2508641Z input by the user.
2025-08-14T23:09:09.2508853Z The mask_fn is applied once all the inputs are aggregated and reduced i.e.
2025-08-14T23:09:09.2509124Z mask = mask_fn(reduce_fn(aggregate_fn(activations)))
2025-08-14T23:09:09.2509272Z 
2025-08-14T23:09:09.2509332Z Note::
2025-08-14T23:09:09.2509579Z     The sparsification mask is computed on the input **before it goes through the attached layer**.
2025-08-14T23:09:09.2509811Z 
2025-08-14T23:09:09.2509866Z Args:
2025-08-14T23:09:09.2510001Z     model (nn.Module):
2025-08-14T23:09:09.2510217Z         The model whose layers will be sparsified. The layers that needs to be
2025-08-14T23:09:09.2510531Z         sparsified should be added separately using the register_layer() function
2025-08-14T23:09:09.2510787Z     aggregate_fn (Optional, Callable):
2025-08-14T23:09:09.2511048Z         default aggregate_fn that is used if not specified while registering the layer.
2025-08-14T23:09:09.2511343Z         specifies how inputs should be aggregated over time.
2025-08-14T23:09:09.2511650Z         The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor.
2025-08-14T23:09:09.2511909Z         Example
2025-08-14T23:09:09.2512097Z             def add_agg_fn(tensor1, tensor2):  return tensor1 + tensor2
2025-08-14T23:09:09.2512326Z             reduce_fn (Optional, Callable):
2025-08-14T23:09:09.2512585Z                 default reduce_fn that is used if not specified while registering the layer.
2025-08-14T23:09:09.2513059Z                 reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after
2025-08-14T23:09:09.2513332Z                 calling agg_fn() on all inputs.
2025-08-14T23:09:09.2513516Z                 Example
2025-08-14T23:09:09.2513876Z             def mean_reduce_fn(agg_tensor):    return agg_tensor.mean(dim=0)
2025-08-14T23:09:09.2514110Z             mask_fn (Optional, Callable):
2025-08-14T23:09:09.2514401Z                 default mask_fn that is used to create the sparsification mask using the tensor obtained after
2025-08-14T23:09:09.2514765Z                 calling the reduce_fn(). This is used by default if a custom one is passed in the
2025-08-14T23:09:09.2515028Z                 register_layer().
2025-08-14T23:09:09.2515328Z                 Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config
2025-08-14T23:09:09.2515632Z                 arguments.
2025-08-14T23:09:09.2518059Z             features (Optional, list):
2025-08-14T23:09:09.2518241Z                 default selected features to sparsify.
2025-08-14T23:09:09.2518462Z                 If this is non-empty, then the mask_fn will be applied for each feature of the input.
2025-08-14T23:09:09.2518704Z                 For example,
2025-08-14T23:09:09.2518895Z             mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features]
2025-08-14T23:09:09.2519097Z             feature_dim (Optional, int):
2025-08-14T23:09:09.2519342Z                 default dimension of input features. Again, features along this dim will be chosen
2025-08-14T23:09:09.2519548Z                 for sparsification.
2025-08-14T23:09:09.2519685Z             sparse_config (Dict):
2025-08-14T23:09:09.2519863Z                 Default configuration for the mask_fn. This config will be passed
2025-08-14T23:09:09.2520045Z                 with the mask_fn()
2025-08-14T23:09:09.2520129Z 
2025-08-14T23:09:09.2521427Z Example:
2025-08-14T23:09:09.2521531Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.2521654Z     >>> model = SomeModel()
2025-08-14T23:09:09.2521832Z     >>> act_sparsifier = ActivationSparsifier(...)  # init activation sparsifier
2025-08-14T23:09:09.2522020Z     >>> # Initialize aggregate_fn
2025-08-14T23:09:09.2522153Z     >>> def agg_fn(x, y):
2025-08-14T23:09:09.2522271Z     >>>     return x + y
2025-08-14T23:09:09.2522383Z     >>>
2025-08-14T23:09:09.2522484Z     >>> # Initialize reduce_fn
2025-08-14T23:09:09.2522610Z     >>> def reduce_fn(x):
2025-08-14T23:09:09.2522730Z     >>>     return torch.mean(x, dim=0)
2025-08-14T23:09:09.2524083Z     >>>
2025-08-14T23:09:09.2524182Z     >>> # Initialize mask_fn
2025-08-14T23:09:09.2524304Z     >>> def mask_fn(data):
2025-08-14T23:09:09.2524444Z     >>>     return torch.eye(data.shape).to(data.device)
2025-08-14T23:09:09.2524586Z     >>>
2025-08-14T23:09:09.2524672Z     >>>
2025-08-14T23:09:09.2524774Z     >>> act_sparsifier.register_layer(
2025-08-14T23:09:09.2524913Z     ...     model.some_layer,
2025-08-14T23:09:09.2525037Z     ...     aggregate_fn=agg_fn,
2025-08-14T23:09:09.2525164Z     ...     reduce_fn=reduce_fn,
2025-08-14T23:09:09.2525287Z     ...     mask_fn=mask_fn,
2025-08-14T23:09:09.2526605Z     ... )
2025-08-14T23:09:09.2526696Z     >>>
2025-08-14T23:09:09.2526796Z     >>> # start training process
2025-08-14T23:09:09.2526921Z     >>> for _ in [...]:
2025-08-14T23:09:09.2527032Z     >>> # epoch starts
2025-08-14T23:09:09.2527176Z     >>> # model.forward(), compute_loss() and model.backwards()
2025-08-14T23:09:09.2527338Z     >>> # epoch ends
2025-08-14T23:09:09.2527456Z     >>>     act_sparsifier.step()
2025-08-14T23:09:09.2527589Z     >>> # end training process
2025-08-14T23:09:09.2527719Z     >>> sparsifier.squash_mask()
2025-08-14T23:09:09.2527804Z 
2025-08-14T23:09:09.2527911Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.2529023Z 
2025-08-14T23:09:09.2529317Z 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.
2025-08-14T23:09:09.2529713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.2529841Z 
2025-08-14T23:09:09.2529947Z Abstract method that needs to be implemented by the child class.
2025-08-14T23:09:09.2530160Z The expected return type should is a dictionary of name to schedule_param value
2025-08-14T23:09:09.2530381Z The returned values will be updated in sparsifier when the scheduler step() function
2025-08-14T23:09:09.2530544Z is called.
2025-08-14T23:09:09.2530592Z 
2025-08-14T23:09:09.2530627Z Example:
2025-08-14T23:09:09.2530714Z     >>> def get_schedule_param(self):
2025-08-14T23:09:09.2530828Z     ...     new_param = {}
2025-08-14T23:09:09.2530951Z     ...     for name in self.sparsifier.data_groups.keys():
2025-08-14T23:09:09.2531085Z     ...         new_param[name] = (
2025-08-14T23:09:09.2532194Z     ...             self.sparsifier.data_groups[name][self.schedule_param] * 0.5
2025-08-14T23:09:09.2532337Z     ...         )
2025-08-14T23:09:09.2532427Z     ...     return new_param
2025-08-14T23:09:09.2532494Z 
2025-08-14T23:09:09.2532634Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param]
2025-08-14T23:09:09.2532823Z would be halved
2025-08-14T23:09:09.2532877Z 
2025-08-14T23:09:09.2533043Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.2533189Z 
2025-08-14T23:09:09.2598056Z 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.
2025-08-14T23:09:09.2598419Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.2598954Z Squashes the sparse masks into the appropriate tensors.
2025-08-14T23:09:09.2599172Z 
2025-08-14T23:09:09.2599324Z         If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-08-14T23:09:09.2604090Z         the module will have a `sparse_params` dict attached to it.
2025-08-14T23:09:09.2604295Z 
2025-08-14T23:09:09.2604360Z         Args:
2025-08-14T23:09:09.2604599Z             params_to_keep: List of keys to save in the module or a dict
2025-08-14T23:09:09.2604912Z                             representing the modules and keys that will have
2025-08-14T23:09:09.2605178Z                             sparsity parameters saved
2025-08-14T23:09:09.2605478Z             params_to_keep_per_layer: Dict to specify the params that should be
2025-08-14T23:09:09.2605876Z                             saved for specific layers. The keys in the dict
2025-08-14T23:09:09.2606150Z                             should be the module fqn, while the values should
2025-08-14T23:09:09.2606427Z                             be a list of strings with the names of the variables
2025-08-14T23:09:09.2606683Z                             to save in the `sparse_params`
2025-08-14T23:09:09.2606841Z 
2025-08-14T23:09:09.2606907Z         Examples:
2025-08-14T23:09:09.2609021Z             >>> # xdoctest: +SKIP("locals are undefined")
2025-08-14T23:09:09.2609215Z             >>> # Don't save any sparse params
2025-08-14T23:09:09.2609395Z             >>> sparsifier.squash_mask()
2025-08-14T23:09:09.2609595Z             >>> hasattr(model.submodule1, "sparse_params")
2025-08-14T23:09:09.2609769Z             False
2025-08-14T23:09:09.2609849Z 
2025-08-14T23:09:09.2609917Z             >>> # Keep sparse params per layer
2025-08-14T23:09:09.2610092Z             >>> sparsifier.squash_mask(
2025-08-14T23:09:09.2610270Z             ...     params_to_keep_per_layer={
2025-08-14T23:09:09.2610459Z             ...         "submodule1.linear1": ("foo", "bar"),
2025-08-14T23:09:09.2610658Z             ...         "submodule2.linear42": ("baz",),
2025-08-14T23:09:09.2610827Z             ...     }
2025-08-14T23:09:09.2612538Z             ... )
2025-08-14T23:09:09.2612701Z             >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:09.2613015Z             {'foo': 42, 'bar': 24}
2025-08-14T23:09:09.2613219Z             >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:09.2613412Z             {'baz': 0.1}
2025-08-14T23:09:09.2613707Z 
2025-08-14T23:09:09.2613778Z             >>> # Keep sparse params for all layers
2025-08-14T23:09:09.2614054Z             >>> sparsifier.squash_mask(params_to_keep=("foo", "bar"))
2025-08-14T23:09:09.2614283Z             >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:09.2614470Z             {'foo': 42, 'bar': 24}
2025-08-14T23:09:09.2614656Z             >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:09.2614845Z             {'foo': 42, 'bar': 24}
2025-08-14T23:09:09.2614945Z 
2025-08-14T23:09:09.2615052Z             >>> # Keep some sparse params for all layers, and specific ones for
2025-08-14T23:09:09.2616763Z             >>> # some other layers
2025-08-14T23:09:09.2616932Z             >>> sparsifier.squash_mask(
2025-08-14T23:09:09.2617109Z             ...     params_to_keep=("foo", "bar"),
2025-08-14T23:09:09.2617284Z             ...     params_to_keep_per_layer={"submodule2.linear42": ("baz",)},
2025-08-14T23:09:09.2617434Z             ... )
2025-08-14T23:09:09.2617625Z             >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:09.2617771Z             {'foo': 42, 'bar': 24}
2025-08-14T23:09:09.2617913Z             >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:09.2618108Z             {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-08-14T23:09:09.2618228Z         
2025-08-14T23:09:09.2618381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.2619590Z 
2025-08-14T23:09:09.3133276Z 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.
2025-08-14T23:09:09.3134185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3134695Z A quantized long short-term memory (LSTM).
2025-08-14T23:09:09.3134928Z 
2025-08-14T23:09:09.3135212Z     For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-08-14T23:09:09.3135589Z 
2025-08-14T23:09:09.3135708Z     Attributes:
2025-08-14T23:09:09.3135991Z         layers : instances of the `_LSTMLayer`
2025-08-14T23:09:09.3136221Z 
2025-08-14T23:09:09.3136350Z     .. note::
2025-08-14T23:09:09.3136702Z         To access the weights and biases, you need to access them per layer.
2025-08-14T23:09:09.3137206Z         See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-08-14T23:09:09.3137482Z 
2025-08-14T23:09:09.3137582Z     Examples::
2025-08-14T23:09:09.3141017Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3141178Z         >>> custom_module_config = {
2025-08-14T23:09:09.3141342Z         ...     'float_to_observed_custom_module_class': {
2025-08-14T23:09:09.3141512Z         ...         nn.LSTM: nn.quantizable.LSTM,
2025-08-14T23:09:09.3141657Z         ...     },
2025-08-14T23:09:09.3141790Z         ...     'observed_to_quantized_custom_module_class': {
2025-08-14T23:09:09.3141972Z         ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-08-14T23:09:09.3142122Z         ...     }
2025-08-14T23:09:09.3142229Z         ... }
2025-08-14T23:09:09.3142391Z         >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-08-14T23:09:09.3142624Z         >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-08-14T23:09:09.3144339Z     
2025-08-14T23:09:09.3144510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3144666Z 
2025-08-14T23:09:09.3181203Z 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.
2025-08-14T23:09:09.3181644Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3181850Z Applies a 1D convolution over a quantized input signal composed of
2025-08-14T23:09:09.3182006Z     several quantized input planes.
2025-08-14T23:09:09.3182085Z 
2025-08-14T23:09:09.3182165Z     For details on input arguments, parameters, and implementation see
2025-08-14T23:09:09.3182394Z     :class:`~torch.nn.Conv1d`.
2025-08-14T23:09:09.3182465Z 
2025-08-14T23:09:09.3182522Z     .. note::
2025-08-14T23:09:09.3182650Z         Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-08-14T23:09:09.3182760Z 
2025-08-14T23:09:09.3182793Z     .. note::
2025-08-14T23:09:09.3184820Z         Only `torch.quint8` is supported for the input data type.
2025-08-14T23:09:09.3184934Z 
2025-08-14T23:09:09.3184936Z 
2025-08-14T23:09:09.3184974Z     Attributes:
2025-08-14T23:09:09.3185107Z         weight (Tensor):     packed tensor derived from the learnable weight
2025-08-14T23:09:09.3185258Z                              parameter.
2025-08-14T23:09:09.3185388Z         scale (Tensor):      scalar for the output scale
2025-08-14T23:09:09.3185538Z         zero_point (Tensor): scalar for the output zero point
2025-08-14T23:09:09.3185633Z 
2025-08-14T23:09:09.3185693Z     See :class:`~torch.nn.Conv1d` for other attributes.
2025-08-14T23:09:09.3185784Z 
2025-08-14T23:09:09.3185855Z     Examples::
2025-08-14T23:09:09.3185912Z 
2025-08-14T23:09:09.3185972Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-08-14T23:09:09.3186113Z         >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-08-14T23:09:09.3187431Z         >>> input = torch.randn(20, 16, 100)
2025-08-14T23:09:09.3187559Z         >>> # quantize input to quint8
2025-08-14T23:09:09.3187674Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3187822Z         >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-08-14T23:09:09.3187984Z         ...                                     dtype=torch.quint8)
2025-08-14T23:09:09.3188110Z         >>> output = m(q_input)
2025-08-14T23:09:09.3188179Z 
2025-08-14T23:09:09.3188211Z     
2025-08-14T23:09:09.3188343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3188467Z 
2025-08-14T23:09:09.3378344Z 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.
2025-08-14T23:09:09.3379275Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3386035Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-08-14T23:09:09.3386543Z 
2025-08-14T23:09:09.3386804Z     Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-08-14T23:09:09.3387392Z     Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-08-14T23:09:09.3387937Z     Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-08-14T23:09:09.3388348Z     (UAI 2018).
2025-08-14T23:09:09.3388505Z 
2025-08-14T23:09:09.3388728Z     Exponential Moving Average is a variation of `Polyak averaging`_,
2025-08-14T23:09:09.3389275Z     but using exponential weights instead of equal weights across iterations.
2025-08-14T23:09:09.3389471Z 
2025-08-14T23:09:09.3389591Z     AveragedModel class creates a copy of the provided module :attr:`model`
2025-08-14T23:09:09.3389838Z     on the device :attr:`device` and allows to compute running averages of the
2025-08-14T23:09:09.3390000Z     parameters of the :attr:`model`.
2025-08-14T23:09:09.3390082Z 
2025-08-14T23:09:09.3390121Z     Args:
2025-08-14T23:09:09.3390229Z         model (torch.nn.Module): model to use with SWA/EMA
2025-08-14T23:09:09.3390413Z         device (torch.device, optional): if provided, the averaged model will be
2025-08-14T23:09:09.3390579Z             stored on the :attr:`device`
2025-08-14T23:09:09.3390735Z         avg_fn (function, optional): the averaging function used to update
2025-08-14T23:09:09.3390918Z             parameters; the function must take in the current value of the
2025-08-14T23:09:09.3391107Z             :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-08-14T23:09:09.3391296Z             parameter, and the number of models already averaged; if None,
2025-08-14T23:09:09.3391468Z             an equally weighted average is used (default: None)
2025-08-14T23:09:09.3391734Z         multi_avg_fn (function, optional): the averaging function used to update
2025-08-14T23:09:09.3393362Z             parameters inplace; the function must take in the current values of the
2025-08-14T23:09:09.3393577Z             :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-08-14T23:09:09.3393793Z             parameters as a list, and the number of models already averaged; if None,
2025-08-14T23:09:09.3393966Z             an equally weighted average is used (default: None)
2025-08-14T23:09:09.3394131Z         use_buffers (bool): if ``True``, it will compute running averages for
2025-08-14T23:09:09.3394318Z             both the parameters and the buffers of the model. (default: ``False``)
2025-08-14T23:09:09.3394433Z 
2025-08-14T23:09:09.3394468Z     Example:
2025-08-14T23:09:09.3394571Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:09.3394705Z         >>> loader, optimizer, model, loss_fn = ...
2025-08-14T23:09:09.3394883Z         >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-08-14T23:09:09.3395063Z         >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-08-14T23:09:09.3396279Z         >>>                                     T_max=300)
2025-08-14T23:09:09.3396398Z         >>> swa_start = 160
2025-08-14T23:09:09.3396519Z         >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-08-14T23:09:09.3396647Z         >>> for i in range(300):
2025-08-14T23:09:09.3396762Z         >>>      for input, target in loader:
2025-08-14T23:09:09.3396882Z         >>>          optimizer.zero_grad()
2025-08-14T23:09:09.3397010Z         >>>          loss_fn(model(input), target).backward()
2025-08-14T23:09:09.3397138Z         >>>          optimizer.step()
2025-08-14T23:09:09.3397251Z         >>>      if i > swa_start:
2025-08-14T23:09:09.3397372Z         >>>          swa_model.update_parameters(model)
2025-08-14T23:09:09.3397496Z         >>>          swa_scheduler.step()
2025-08-14T23:09:09.3398565Z         >>>      else:
2025-08-14T23:09:09.3398663Z         >>>          scheduler.step()
2025-08-14T23:09:09.3398767Z         >>>
2025-08-14T23:09:09.3398871Z         >>> # Update bn statistics for the swa_model at the end
2025-08-14T23:09:09.3399024Z         >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-08-14T23:09:09.3399137Z 
2025-08-14T23:09:09.3399243Z     You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-08-14T23:09:09.3399454Z     If no averaging function is provided, the default is to compute
2025-08-14T23:09:09.3399614Z     equally-weighted average of the weights (SWA).
2025-08-14T23:09:09.3399703Z 
2025-08-14T23:09:09.3399737Z     Example:
2025-08-14T23:09:09.3399838Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:09.3400953Z         >>> # Compute exponential moving averages of the weights and buffers
2025-08-14T23:09:09.3401132Z         >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-08-14T23:09:09.3401312Z         >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-08-14T23:09:09.3401425Z 
2025-08-14T23:09:09.3401463Z     .. note::
2025-08-14T23:09:09.3401591Z         When using SWA/EMA with models containing Batch Normalization you may
2025-08-14T23:09:09.3401775Z         need to update the activation statistics for Batch Normalization.
2025-08-14T23:09:09.3401962Z         This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-08-14T23:09:09.3402160Z         or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-08-14T23:09:09.3402360Z         statistics in a post-training step by passing data through the model. The
2025-08-14T23:09:09.3402559Z         second does it during the parameter update phase by averaging all buffers.
2025-08-14T23:09:09.3402761Z         Empirical evidence has shown that updating the statistics in normalization
2025-08-14T23:09:09.3403982Z         layers increases accuracy, but you may wish to empirically test which
2025-08-14T23:09:09.3404224Z         approach yields the best results in your problem.
2025-08-14T23:09:09.3404317Z 
2025-08-14T23:09:09.3404351Z     .. note::
2025-08-14T23:09:09.3404490Z         :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-08-14T23:09:09.3404615Z 
2025-08-14T23:09:09.3404648Z     .. note::
2025-08-14T23:09:09.3404767Z         When :meth:`update_parameters` is called for the first time (i.e.
2025-08-14T23:09:09.3404942Z         :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-08-14T23:09:09.3405114Z         to the parameters of :class:`AveragedModel`. For every subsequent
2025-08-14T23:09:09.3405285Z         call of :meth:`update_parameters` the function `avg_fn` is used
2025-08-14T23:09:09.3405428Z         to update the parameters.
2025-08-14T23:09:09.3405500Z 
2025-08-14T23:09:09.3405581Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-08-14T23:09:09.3406822Z         https://arxiv.org/abs/1803.05407
2025-08-14T23:09:09.3406997Z     .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-08-14T23:09:09.3407148Z         Average:
2025-08-14T23:09:09.3407263Z         https://arxiv.org/abs/1806.05594
2025-08-14T23:09:09.3407414Z     .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-08-14T23:09:09.3407563Z         https://arxiv.org/abs/1904.11943
2025-08-14T23:09:09.3407717Z     .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-08-14T23:09:09.3407869Z         Generalizes Well:
2025-08-14T23:09:09.3407974Z         https://arxiv.org/abs/2001.02312
2025-08-14T23:09:09.3408088Z     .. _Polyak averaging:
2025-08-14T23:09:09.3408219Z         https://paperswithcode.com/method/polyak-averaging
2025-08-14T23:09:09.3409284Z     
2025-08-14T23:09:09.3409418Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3409540Z 
2025-08-14T23:09:09.3409710Z 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.
2025-08-14T23:09:09.3409992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3410197Z Anneals the learning rate in each parameter group to a fixed value.
2025-08-14T23:09:09.3410308Z 
2025-08-14T23:09:09.3410390Z     This learning rate scheduler is meant to be used with Stochastic Weight
2025-08-14T23:09:09.3410584Z     Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-08-14T23:09:09.3410694Z 
2025-08-14T23:09:09.3410726Z     Args:
2025-08-14T23:09:09.3410838Z         optimizer (torch.optim.Optimizer): wrapped optimizer
2025-08-14T23:09:09.3411009Z         swa_lrs (float or list): the learning rate value for all param groups
2025-08-14T23:09:09.3411166Z             together or separately for each group.
2025-08-14T23:09:09.3412257Z         annealing_epochs (int): number of epochs in the annealing phase
2025-08-14T23:09:09.3412404Z             (default: 10)
2025-08-14T23:09:09.3412543Z         annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-08-14T23:09:09.3412726Z             strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-08-14T23:09:09.3412875Z             (default: "cos")
2025-08-14T23:09:09.3413086Z         last_epoch (int): the index of the last epoch (default: -1)
2025-08-14T23:09:09.3413188Z 
2025-08-14T23:09:09.3413255Z     The :class:`SWALR` scheduler can be used together with other
2025-08-14T23:09:09.3413431Z     schedulers to switch to a constant learning rate late in the training
2025-08-14T23:09:09.3413581Z     as in the example below.
2025-08-14T23:09:09.3413647Z 
2025-08-14T23:09:09.3413681Z     Example:
2025-08-14T23:09:09.3413781Z         >>> # xdoctest: +SKIP("Undefined variables")
2025-08-14T23:09:09.3414854Z         >>> loader, optimizer, model = ...
2025-08-14T23:09:09.3414974Z         >>> lr_lambda = lambda epoch: 0.9
2025-08-14T23:09:09.3415130Z         >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-08-14T23:09:09.3415329Z         >>>        lr_lambda=lr_lambda)
2025-08-14T23:09:09.3415467Z         >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-08-14T23:09:09.3415629Z         >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-08-14T23:09:09.3415767Z         >>> swa_start = 160
2025-08-14T23:09:09.3415870Z         >>> for i in range(300):
2025-08-14T23:09:09.3415982Z         >>>      for input, target in loader:
2025-08-14T23:09:09.3416100Z         >>>          optimizer.zero_grad()
2025-08-14T23:09:09.3416227Z         >>>          loss_fn(model(input), target).backward()
2025-08-14T23:09:09.3417300Z         >>>          optimizer.step()
2025-08-14T23:09:09.3417414Z         >>>      if i > swa_start:
2025-08-14T23:09:09.3417524Z         >>>          swa_scheduler.step()
2025-08-14T23:09:09.3417634Z         >>>      else:
2025-08-14T23:09:09.3417730Z         >>>          scheduler.step()
2025-08-14T23:09:09.3417828Z 
2025-08-14T23:09:09.3417910Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-08-14T23:09:09.3418067Z         https://arxiv.org/abs/1803.05407
2025-08-14T23:09:09.3418191Z     
2025-08-14T23:09:09.3418320Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3418444Z 
2025-08-14T23:09:09.3479212Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/optimizer.py line=867.
2025-08-14T23:09:09.3481709Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3481892Z Load the optimizer state.
2025-08-14T23:09:09.3481961Z 
2025-08-14T23:09:09.3481997Z         Args:
2025-08-14T23:09:09.3482122Z             state_dict (dict): optimizer state. Should be an object returned
2025-08-14T23:09:09.3482277Z                 from a call to :meth:`state_dict`.
2025-08-14T23:09:09.3482361Z 
2025-08-14T23:09:09.3482407Z         .. warning::
2025-08-14T23:09:09.3482581Z             Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`,
2025-08-14T23:09:09.3482810Z             as calling it beforehand will overwrite the loaded learning rates.
2025-08-14T23:09:09.3482995Z 
2025-08-14T23:09:09.3483031Z         .. note::
2025-08-14T23:09:09.3483187Z             The names of the parameters (if they exist under the "param_names" key of each param group
2025-08-14T23:09:09.3483398Z             in :meth:`state_dict`) will not affect the loading process.
2025-08-14T23:09:09.3483608Z             To use the parameters' names for custom cases (such as when the parameters in the loaded state dict
2025-08-14T23:09:09.3483813Z             differ from those initialized in the optimizer),
2025-08-14T23:09:09.3484011Z             a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict
2025-08-14T23:09:09.3484187Z             accordingly.
2025-08-14T23:09:09.3484351Z             If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override
2025-08-14T23:09:09.3485839Z             the current names, if present, in the optimizer state. If they do not exist in loaded state dict,
2025-08-14T23:09:09.3486046Z             the optimizer ``param_names`` will remain unchanged.
2025-08-14T23:09:09.3486141Z 
2025-08-14T23:09:09.3486177Z         Example:
2025-08-14T23:09:09.3486271Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3486390Z             >>> model = torch.nn.Linear(10, 10)
2025-08-14T23:09:09.3486536Z             >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4)
2025-08-14T23:09:09.3486693Z             >>> scheduler1 = torch.optim.lr_scheduler.LinearLR(
2025-08-14T23:09:09.3486826Z             ...     optim,
2025-08-14T23:09:09.3486928Z             ...     start_factor=0.1,
2025-08-14T23:09:09.3487038Z             ...     end_factor=1,
2025-08-14T23:09:09.3487145Z             ...     total_iters=20,
2025-08-14T23:09:09.3488305Z             ... )
2025-08-14T23:09:09.3488461Z             >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR(
2025-08-14T23:09:09.3488602Z             ...     optim,
2025-08-14T23:09:09.3488702Z             ...     T_max=80,
2025-08-14T23:09:09.3488803Z             ...     eta_min=3e-5,
2025-08-14T23:09:09.3488902Z             ... )
2025-08-14T23:09:09.3489008Z             >>> lr = torch.optim.lr_scheduler.SequentialLR(
2025-08-14T23:09:09.3489132Z             ...     optim,
2025-08-14T23:09:09.3489243Z             ...     schedulers=[scheduler1, scheduler2],
2025-08-14T23:09:09.3489367Z             ...     milestones=[20],
2025-08-14T23:09:09.3489470Z             ... )
2025-08-14T23:09:09.3491421Z             >>> lr.load_state_dict(torch.load("./save_seq.pt"))
2025-08-14T23:09:09.3491593Z             >>> # now load the optimizer checkpoint after loading the LRScheduler
2025-08-14T23:09:09.3491765Z             >>> optim.load_state_dict(torch.load("./save_optim.pt"))
2025-08-14T23:09:09.3491896Z 
2025-08-14T23:09:09.3491930Z         
2025-08-14T23:09:09.3492066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3492190Z 
2025-08-14T23:09:09.3580998Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=808.
2025-08-14T23:09:09.3581440Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3581686Z Contains a list of schedulers expected to be called sequentially during the optimization process.
2025-08-14T23:09:09.3581834Z 
2025-08-14T23:09:09.3581961Z     Specifically, the schedulers will be called according to the milestone points, which should provide exact
2025-08-14T23:09:09.3582197Z     intervals by which each scheduler should be called at a given epoch.
2025-08-14T23:09:09.3584307Z 
2025-08-14T23:09:09.3584351Z     Args:
2025-08-14T23:09:09.3584458Z         optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:09.3584613Z         schedulers (list): List of chained schedulers.
2025-08-14T23:09:09.3584780Z         milestones (list): List of integers that reflects milestone points.
2025-08-14T23:09:09.3584958Z         last_epoch (int): The index of last epoch. Default: -1.
2025-08-14T23:09:09.3585058Z 
2025-08-14T23:09:09.3585099Z     Example:
2025-08-14T23:09:09.3585193Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3585318Z         >>> # Assuming optimizer uses lr = 0.05 for all groups
2025-08-14T23:09:09.3585451Z         >>> # lr = 0.005     if epoch == 0
2025-08-14T23:09:09.3585571Z         >>> # lr = 0.005     if epoch == 1
2025-08-14T23:09:09.3585682Z         >>> # lr = 0.005     if epoch == 2
2025-08-14T23:09:09.3587006Z         >>> # ...
2025-08-14T23:09:09.3587099Z         >>> # lr = 0.05      if epoch == 20
2025-08-14T23:09:09.3587212Z         >>> # lr = 0.045     if epoch == 21
2025-08-14T23:09:09.3587321Z         >>> # lr = 0.0405    if epoch == 22
2025-08-14T23:09:09.3587463Z         >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20)
2025-08-14T23:09:09.3587632Z         >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
2025-08-14T23:09:09.3587765Z         >>> scheduler = SequentialLR(
2025-08-14T23:09:09.3587879Z         ...     optimizer,
2025-08-14T23:09:09.3587994Z         ...     schedulers=[scheduler1, scheduler2],
2025-08-14T23:09:09.3588119Z         ...     milestones=[20],
2025-08-14T23:09:09.3588219Z         ... )
2025-08-14T23:09:09.3589242Z         >>> for epoch in range(100):
2025-08-14T23:09:09.3589353Z         >>>     train(...)
2025-08-14T23:09:09.3589452Z         >>>     validate(...)
2025-08-14T23:09:09.3589557Z         >>>     scheduler.step()
2025-08-14T23:09:09.3589626Z 
2025-08-14T23:09:09.3589697Z     .. image:: ../scripts/lr_scheduler_images/SequentialLR.png
2025-08-14T23:09:09.3589826Z     
2025-08-14T23:09:09.3589958Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3590083Z 
2025-08-14T23:09:09.3595936Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1233.
2025-08-14T23:09:09.3596343Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:09.3596542Z Reduce learning rate when a metric has stopped improving.
2025-08-14T23:09:09.3598201Z 
2025-08-14T23:09:09.3598283Z     Models often benefit from reducing the learning rate by a factor
2025-08-14T23:09:09.3598468Z     of 2-10 once learning stagnates. This scheduler reads a metrics
2025-08-14T23:09:09.3598641Z     quantity and if no improvement is seen for a 'patience' number
2025-08-14T23:09:09.3598805Z     of epochs, the learning rate is reduced.
2025-08-14T23:09:09.3598929Z 
2025-08-14T23:09:09.3598982Z     Args:
2025-08-14T23:09:09.3599126Z         optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:09.3599343Z         mode (str): One of `min`, `max`. In `min` mode, lr will
2025-08-14T23:09:09.3599571Z             be reduced when the quantity monitored has stopped
2025-08-14T23:09:09.3599799Z             decreasing; in `max` mode it will be reduced when the
2025-08-14T23:09:09.3600067Z             quantity monitored has stopped increasing. Default: 'min'.
2025-08-14T23:09:09.3601828Z         factor (float): Factor by which the learning rate will be
2025-08-14T23:09:09.3602076Z             reduced. new_lr = lr * factor. Default: 0.1.
2025-08-14T23:09:09.3602330Z         patience (int): The number of allowed epochs with no improvement after
2025-08-14T23:09:09.3602583Z             which the learning rate will be reduced.
2025-08-14T23:09:09.3602833Z             For example, consider the case of having no patience (`patience = 0`).
2025-08-14T23:09:09.3603274Z             In the first epoch, a baseline is established and is always considered good as there's no previous baseline.
2025-08-14T23:09:09.3603626Z             In the second epoch, if the performance is worse than the baseline,
2025-08-14T23:09:09.3603886Z             we have what is considered an intolerable epoch.
2025-08-14T23:09:09.3604126Z             Since the count of intolerable epochs (1) is greater than the patience level (0),
2025-08-14T23:09:09.3604320Z             the learning rate is reduced at the end of this epoch.
2025-08-14T23:09:09.3605770Z             From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch
2025-08-14T23:09:09.3606031Z             if the performance is worse than the baseline. If the performance improves or remains the same,
2025-08-14T23:09:09.3606228Z             the learning rate is not adjusted.
2025-08-14T23:09:09.3606344Z             Default: 10.
2025-08-14T23:09:09.3606476Z         threshold (float): Threshold for measuring the new optimum,
2025-08-14T23:09:09.3606641Z             to only focus on significant changes. Default: 1e-4.
2025-08-14T23:09:09.3606798Z         threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
2025-08-14T23:09:09.3606955Z             dynamic_threshold = best * ( 1 + threshold ) in 'max'
2025-08-14T23:09:09.3607136Z             mode or best * ( 1 - threshold ) in `min` mode.
2025-08-14T23:09:09.3607283Z             In `abs` mode, dynamic_threshold = best + threshold in
2025-08-14T23:09:09.3607443Z             `max` mode or best - threshold in `min` mode. Default: 'rel'.
2025-08-14T23:09:09.3607607Z         cooldown (int): Number of epochs to wait before resuming
2025-08-14T23:09:09.3607768Z             normal operation after lr has been reduced. Default: 0.
2025-08-14T23:09:09.3607926Z         min_lr (float or list): A scalar or a list of scalars. A
2025-08-14T23:09:09.3608078Z             lower bound on the learning rate of all param groups
2025-08-14T23:09:09.3609184Z             or each group respectively. Default: 0.
2025-08-14T23:09:09.3609338Z         eps (float): Minimal decay applied to lr. If the difference
2025-08-14T23:09:09.3609500Z             between new and old lr is smaller than eps, the update is
2025-08-14T23:09:09.3609643Z             ignored. Default: 1e-8.
2025-08-14T23:09:09.3609755Z 
2025-08-14T23:09:09.3609791Z     Example:
2025-08-14T23:09:09.3609900Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3610049Z         >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
2025-08-14T23:09:09.3610225Z         >>> scheduler = ReduceLROnPlateau(optimizer, "min")
2025-08-14T23:09:09.3610357Z         >>> for epoch in range(10):
2025-08-14T23:09:09.3610463Z         >>>     train(...)
2025-08-14T23:09:09.3610567Z         >>>     val_loss = validate(...)
2025-08-14T23:09:09.3612215Z         >>> # Note that step should be called after validate()
2025-08-14T23:09:09.3612441Z         >>>     scheduler.step(val_loss)
2025-08-14T23:09:09.3612520Z 
2025-08-14T23:09:09.3612594Z     .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png
2025-08-14T23:09:09.3612729Z     
2025-08-14T23:09:09.3612991Z Original Error: IndentationError('unexpected indent', ('<source_block>', 8, 4, '    scheduler.step(val_loss)\n', 8, -1))
2025-08-14T23:09:09.3613206Z 
2025-08-14T23:09:09.3613252Z     scheduler.step(val_loss)
2025-08-14T23:09:09.3613357Z    ^
2025-08-14T23:09:09.3613569Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1430.
2025-08-14T23:09:09.3613894Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3614134Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).
2025-08-14T23:09:09.3614281Z 
2025-08-14T23:09:09.3615899Z     The policy cycles the learning rate between two boundaries with a constant frequency,
2025-08-14T23:09:09.3616135Z     as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_.
2025-08-14T23:09:09.3616342Z     The distance between the two boundaries can be scaled on a per-iteration
2025-08-14T23:09:09.3616494Z     or per-cycle basis.
2025-08-14T23:09:09.3616561Z 
2025-08-14T23:09:09.3616648Z     Cyclical learning rate policy changes the learning rate after every batch.
2025-08-14T23:09:09.3616844Z     `step` should be called after a batch has been used for training.
2025-08-14T23:09:09.3616953Z 
2025-08-14T23:09:09.3617035Z     This class has three built-in policies, as put forth in the paper:
2025-08-14T23:09:09.3617144Z 
2025-08-14T23:09:09.3617219Z     * "triangular": A basic triangular cycle without amplitude scaling.
2025-08-14T23:09:09.3617427Z     * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
2025-08-14T23:09:09.3617679Z     * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
2025-08-14T23:09:09.3617865Z       at each cycle iteration.
2025-08-14T23:09:09.3617935Z 
2025-08-14T23:09:09.3618020Z     This implementation was adapted from the github repo: `bckenstler/CLR`_
2025-08-14T23:09:09.3618138Z 
2025-08-14T23:09:09.3618171Z     Args:
2025-08-14T23:09:09.3618267Z         optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:09.3618420Z         base_lr (float or list): Initial learning rate which is the
2025-08-14T23:09:09.3619577Z             lower boundary in the cycle for each parameter group.
2025-08-14T23:09:09.3619746Z         max_lr (float or list): Upper learning rate boundaries in the cycle
2025-08-14T23:09:09.3619901Z             for each parameter group. Functionally,
2025-08-14T23:09:09.3620043Z             it defines the cycle amplitude (max_lr - base_lr).
2025-08-14T23:09:09.3620184Z             The lr at any cycle is the sum of base_lr
2025-08-14T23:09:09.3620320Z             and some scaling of the amplitude; therefore
2025-08-14T23:09:09.3620458Z             max_lr may not actually be reached depending on
2025-08-14T23:09:09.3620586Z             scaling function.
2025-08-14T23:09:09.3620716Z         step_size_up (int): Number of training iterations in the
2025-08-14T23:09:09.3620863Z             increasing half of a cycle. Default: 2000
2025-08-14T23:09:09.3621013Z         step_size_down (int): Number of training iterations in the
2025-08-14T23:09:09.3622133Z             decreasing half of a cycle. If step_size_down is None,
2025-08-14T23:09:09.3622279Z             it is set to step_size_up. Default: None
2025-08-14T23:09:09.3622424Z         mode (str): One of {triangular, triangular2, exp_range}.
2025-08-14T23:09:09.3622575Z             Values correspond to policies detailed above.
2025-08-14T23:09:09.3622718Z             If scale_fn is not None, this argument is ignored.
2025-08-14T23:09:09.3622848Z             Default: 'triangular'
2025-08-14T23:09:09.3623084Z         gamma (float): Constant in 'exp_range' scaling function:
2025-08-14T23:09:09.3623221Z             gamma**(cycle iterations)
2025-08-14T23:09:09.3623329Z             Default: 1.0
2025-08-14T23:09:09.3623459Z         scale_fn (function): Custom scaling policy defined by a single
2025-08-14T23:09:09.3623605Z             argument lambda function, where
2025-08-14T23:09:09.3624644Z             0 <= scale_fn(x) <= 1 for all x >= 0.
2025-08-14T23:09:09.3624802Z             If specified, then 'mode' is ignored.
2025-08-14T23:09:09.3624919Z             Default: None
2025-08-14T23:09:09.3625029Z         scale_mode (str): {'cycle', 'iterations'}.
2025-08-14T23:09:09.3625192Z             Defines whether scale_fn is evaluated on
2025-08-14T23:09:09.3625326Z             cycle number or cycle iterations (training
2025-08-14T23:09:09.3625455Z             iterations since start of cycle).
2025-08-14T23:09:09.3625495Z             Default: 'cycle'
2025-08-14T23:09:09.3625568Z         cycle_momentum (bool): If ``True``, momentum is cycled inversely
2025-08-14T23:09:09.3625635Z             to learning rate between 'base_momentum' and 'max_momentum'.
2025-08-14T23:09:09.3625673Z             Default: True
2025-08-14T23:09:09.3625750Z         base_momentum (float or list): Lower momentum boundaries in the cycle
2025-08-14T23:09:09.3625824Z             for each parameter group. Note that momentum is cycled inversely
2025-08-14T23:09:09.3625889Z             to learning rate; at the peak of a cycle, momentum is
2025-08-14T23:09:09.3626851Z             'base_momentum' and learning rate is 'max_lr'.
2025-08-14T23:09:09.3626891Z             Default: 0.8
2025-08-14T23:09:09.3626970Z         max_momentum (float or list): Upper momentum boundaries in the cycle
2025-08-14T23:09:09.3627020Z             for each parameter group. Functionally,
2025-08-14T23:09:09.3627090Z             it defines the cycle amplitude (max_momentum - base_momentum).
2025-08-14T23:09:09.3627155Z             The momentum at any cycle is the difference of max_momentum
2025-08-14T23:09:09.3627209Z             and some scaling of the amplitude; therefore
2025-08-14T23:09:09.3627271Z             base_momentum may not actually be reached depending on
2025-08-14T23:09:09.3627338Z             scaling function. Note that momentum is cycled inversely
2025-08-14T23:09:09.3627411Z             to learning rate; at the start of a cycle, momentum is 'max_momentum'
2025-08-14T23:09:09.3627458Z             and learning rate is 'base_lr'
2025-08-14T23:09:09.3627495Z             Default: 0.9
2025-08-14T23:09:09.3627576Z         last_epoch (int): The index of the last batch. This parameter is used when
2025-08-14T23:09:09.3627655Z             resuming a training job. Since `step()` should be invoked after each
2025-08-14T23:09:09.3627732Z             batch instead of after each epoch, this number represents the total
2025-08-14T23:09:09.3627810Z             number of *batches* computed, not the total number of epochs computed.
2025-08-14T23:09:09.3627879Z             When last_epoch=-1, the schedule is started from the beginning.
2025-08-14T23:09:09.3627917Z             Default: -1
2025-08-14T23:09:09.3627920Z 
2025-08-14T23:09:09.3627953Z     Example:
2025-08-14T23:09:09.3627995Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3628076Z         >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
2025-08-14T23:09:09.3628135Z         >>> scheduler = torch.optim.lr_scheduler.CyclicLR(
2025-08-14T23:09:09.3629066Z         ...     optimizer,
2025-08-14T23:09:09.3629132Z         ...     base_lr=0.01,
2025-08-14T23:09:09.3629188Z         ...     max_lr=0.1,
2025-08-14T23:09:09.3629231Z         ...     step_size_up=10,
2025-08-14T23:09:09.3629265Z         ... )
2025-08-14T23:09:09.3629324Z         >>> data_loader = torch.utils.data.DataLoader(...)
2025-08-14T23:09:09.3629367Z         >>> for epoch in range(10):
2025-08-14T23:09:09.3629411Z         >>>     for batch in data_loader:
2025-08-14T23:09:09.3629450Z         >>>         train_batch(...)
2025-08-14T23:09:09.3629491Z         >>>         scheduler.step()
2025-08-14T23:09:09.3629493Z 
2025-08-14T23:09:09.3629553Z     .. image:: ../scripts/lr_scheduler_images/CyclicLR.png
2025-08-14T23:09:09.3629556Z 
2025-08-14T23:09:09.3629665Z     .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
2025-08-14T23:09:09.3629727Z     .. _bckenstler/CLR: https://github.com/bckenstler/CLR
2025-08-14T23:09:09.3629759Z     
2025-08-14T23:09:09.3629869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3629875Z 
2025-08-14T23:09:09.3630085Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1722.
2025-08-14T23:09:09.3630191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3630278Z Set the learning rate of each parameter group using a cosine annealing schedule.
2025-08-14T23:09:09.3630281Z 
2025-08-14T23:09:09.3630348Z     The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
2025-08-14T23:09:09.3630432Z     is the number of epochs since the last restart and :math:`T_{i}` is the number
2025-08-14T23:09:09.3630482Z     of epochs between two warm restarts in SGDR:
2025-08-14T23:09:09.3630485Z 
2025-08-14T23:09:09.3631412Z     .. math::
2025-08-14T23:09:09.3631485Z         \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
2025-08-14T23:09:09.3631545Z         \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
2025-08-14T23:09:09.3631550Z 
2025-08-14T23:09:09.3631612Z     When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
2025-08-14T23:09:09.3631684Z     When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
2025-08-14T23:09:09.3631687Z 
2025-08-14T23:09:09.3631728Z     It has been proposed in
2025-08-14T23:09:09.3631792Z     `SGDR: Stochastic Gradient Descent with Warm Restarts`_.
2025-08-14T23:09:09.3631794Z 
2025-08-14T23:09:09.3631826Z     Args:
2025-08-14T23:09:09.3631878Z         optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:09.3631938Z         T_0 (int): Number of iterations until the first restart.
2025-08-14T23:09:09.3632040Z         T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1.
2025-08-14T23:09:09.3632110Z         eta_min (float, optional): Minimum learning rate. Default: 0.
2025-08-14T23:09:09.3632187Z         last_epoch (int, optional): The index of the last epoch. Default: -1.
2025-08-14T23:09:09.3632190Z 
2025-08-14T23:09:09.3632256Z     .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
2025-08-14T23:09:09.3632302Z         https://arxiv.org/abs/1608.03983
2025-08-14T23:09:09.3632306Z 
2025-08-14T23:09:09.3632339Z     Example:
2025-08-14T23:09:09.3632380Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3632449Z         >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
2025-08-14T23:09:09.3632532Z         >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
2025-08-14T23:09:09.3632573Z         ...     optimizer, T_0=20
2025-08-14T23:09:09.3632606Z         ... )
2025-08-14T23:09:09.3632647Z         >>> for epoch in range(100):
2025-08-14T23:09:09.3633647Z         >>>     train(...)
2025-08-14T23:09:09.3633687Z         >>>     validate(...)
2025-08-14T23:09:09.3633728Z         >>>     scheduler.step()
2025-08-14T23:09:09.3633730Z 
2025-08-14T23:09:09.3633815Z     .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png
2025-08-14T23:09:09.3633876Z     
2025-08-14T23:09:09.3633981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3633984Z 
2025-08-14T23:09:09.3634174Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1872.
2025-08-14T23:09:09.3634264Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.3634371Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy.
2025-08-14T23:09:09.3634373Z 
2025-08-14T23:09:09.3634477Z     The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
2025-08-14T23:09:09.3634579Z     learning rate and then from that maximum learning rate to some minimum learning rate much
2025-08-14T23:09:09.3634625Z     lower than the initial learning rate.
2025-08-14T23:09:09.3634723Z     This policy was initially described in the paper `Super-Convergence:
2025-08-14T23:09:09.3634802Z     Very Fast Training of Neural Networks Using Large Learning Rates`_.
2025-08-14T23:09:09.3634804Z 
2025-08-14T23:09:09.3634890Z     The 1cycle learning rate policy changes the learning rate after every batch.
2025-08-14T23:09:09.3634983Z     `step` should be called after a batch has been used for training.
2025-08-14T23:09:09.3634985Z 
2025-08-14T23:09:09.3635028Z     This scheduler is not chainable.
2025-08-14T23:09:09.3635030Z 
2025-08-14T23:09:09.3635113Z     Note also that the total number of steps in the cycle can be determined in one
2025-08-14T23:09:09.3635164Z     of two ways (listed in order of precedence):
2025-08-14T23:09:09.3635167Z 
2025-08-14T23:09:09.3635224Z     #. A value for total_steps is explicitly provided.
2025-08-14T23:09:09.3635292Z     #. A number of epochs (epochs) and a number of steps per epoch
2025-08-14T23:09:09.3636256Z        (steps_per_epoch) are provided.
2025-08-14T23:09:09.3636322Z        In this case, the number of total steps is inferred by
2025-08-14T23:09:09.3636374Z        total_steps = epochs * steps_per_epoch
2025-08-14T23:09:09.3636378Z 
2025-08-14T23:09:09.3636461Z     You must either provide a value for total_steps or provide a value for both
2025-08-14T23:09:09.3636505Z     epochs and steps_per_epoch.
2025-08-14T23:09:09.3636508Z 
2025-08-14T23:09:09.3636612Z     The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
2025-08-14T23:09:09.3636713Z     claims that "unpublished work has shown even better results by using only two phases". To
2025-08-14T23:09:09.3636796Z     mimic the behaviour of the original paper instead, set ``three_phase=True``.
2025-08-14T23:09:09.3636798Z 
2025-08-14T23:09:09.3636831Z     Args:
2025-08-14T23:09:09.3636882Z         optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:09.3636953Z         max_lr (float or list): Upper learning rate boundaries in the cycle
2025-08-14T23:09:09.3636998Z             for each parameter group.
2025-08-14T23:09:09.3637075Z         total_steps (int): The total number of steps in the cycle. Note that
2025-08-14T23:09:09.3637151Z             if a value is not provided here, then it must be inferred by providing
2025-08-14T23:09:09.3637203Z             a value for epochs and steps_per_epoch.
2025-08-14T23:09:09.3637241Z             Default: None
2025-08-14T23:09:09.3637313Z         epochs (int): The number of epochs to train for. This is used along
2025-08-14T23:09:09.3637400Z             with steps_per_epoch in order to infer the total number of steps in the cycle
2025-08-14T23:09:09.3637453Z             if a value for total_steps is not provided.
2025-08-14T23:09:09.3637491Z             Default: None
2025-08-14T23:09:09.3637570Z         steps_per_epoch (int): The number of steps per epoch to train for. This is
2025-08-14T23:09:09.3638537Z             used along with epochs in order to infer the total number of steps in the
2025-08-14T23:09:09.3638597Z             cycle if a value for total_steps is not provided.
2025-08-14T23:09:09.3638656Z             Default: None
2025-08-14T23:09:09.3638752Z         pct_start (float): The percentage of the cycle (in number of steps) spent
2025-08-14T23:09:09.3638797Z             increasing the learning rate.
2025-08-14T23:09:09.3638835Z             Default: 0.3
2025-08-14T23:09:09.3638883Z         anneal_strategy (str): {'cos', 'linear'}
2025-08-14T23:09:09.3638970Z             Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
2025-08-14T23:09:09.3639010Z             linear annealing.
2025-08-14T23:09:09.3639047Z             Default: 'cos'
2025-08-14T23:09:09.3639120Z         cycle_momentum (bool): If ``True``, momentum is cycled inversely
2025-08-14T23:09:09.3639186Z             to learning rate between 'base_momentum' and 'max_momentum'.
2025-08-14T23:09:09.3639223Z             Default: True
2025-08-14T23:09:09.3639299Z         base_momentum (float or list): Lower momentum boundaries in the cycle
2025-08-14T23:09:09.3639384Z             for each parameter group. Note that momentum is cycled inversely
2025-08-14T23:09:09.3639449Z             to learning rate; at the peak of a cycle, momentum is
2025-08-14T23:09:09.3639503Z             'base_momentum' and learning rate is 'max_lr'.
2025-08-14T23:09:09.3639555Z             Default: 0.85
2025-08-14T23:09:09.3639631Z         max_momentum (float or list): Upper momentum boundaries in the cycle
2025-08-14T23:09:09.3639681Z             for each parameter group. Functionally,
2025-08-14T23:09:09.3639750Z             it defines the cycle amplitude (max_momentum - base_momentum).
2025-08-14T23:09:09.3639798Z             Note that momentum is cycled inversely
2025-08-14T23:09:09.3640761Z             to learning rate; at the start of a cycle, momentum is 'max_momentum'
2025-08-14T23:09:09.3640807Z             and learning rate is 'base_lr'
2025-08-14T23:09:09.3640844Z             Default: 0.95
2025-08-14T23:09:09.3640912Z         div_factor (float): Determines the initial learning rate via
2025-08-14T23:09:09.3640957Z             initial_lr = max_lr/div_factor
2025-08-14T23:09:09.3640995Z             Default: 25
2025-08-14T23:09:09.3641071Z         final_div_factor (float): Determines the minimum learning rate via
2025-08-14T23:09:09.3641120Z             min_lr = initial_lr/final_div_factor
2025-08-14T23:09:09.3641156Z             Default: 1e4
2025-08-14T23:09:09.3641245Z         three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
2025-08-14T23:09:09.3641333Z             learning rate according to 'final_div_factor' instead of modifying the second
2025-08-14T23:09:09.3641418Z             phase (the first two phases will be symmetrical about the step indicated by
2025-08-14T23:09:09.3641454Z             'pct_start').
2025-08-14T23:09:09.3641533Z         last_epoch (int): The index of the last batch. This parameter is used when
2025-08-14T23:09:09.3641610Z             resuming a training job. Since `step()` should be invoked after each
2025-08-14T23:09:09.3641686Z             batch instead of after each epoch, this number represents the total
2025-08-14T23:09:09.3641767Z             number of *batches* computed, not the total number of epochs computed.
2025-08-14T23:09:09.3641837Z             When last_epoch=-1, the schedule is started from the beginning.
2025-08-14T23:09:09.3641874Z             Default: -1
2025-08-14T23:09:09.3641877Z 
2025-08-14T23:09:09.3641910Z     Example:
2025-08-14T23:09:09.3641950Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.3642886Z         >>> data_loader = torch.utils.data.DataLoader(...)
2025-08-14T23:09:09.3643065Z         >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
2025-08-14T23:09:09.3643127Z         >>> scheduler = torch.optim.lr_scheduler.OneCycleLR(
2025-08-14T23:09:09.3643204Z         ...     optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10
2025-08-14T23:09:09.3643237Z         ... )
2025-08-14T23:09:09.3643279Z         >>> for epoch in range(10):
2025-08-14T23:09:09.3643322Z         >>>     for batch in data_loader:
2025-08-14T23:09:09.3643387Z         >>>         train_batch(...)
2025-08-14T23:09:09.3643447Z         >>>         optimizer.step()
2025-08-14T23:09:09.3643485Z         >>>         scheduler.step()
2025-08-14T23:09:09.3643489Z 
2025-08-14T23:09:09.3643554Z     .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png
2025-08-14T23:09:09.3643556Z 
2025-08-14T23:09:09.3643660Z     .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
2025-08-14T23:09:09.3643706Z         https://arxiv.org/abs/1708.07120
2025-08-14T23:09:09.3643737Z     
2025-08-14T23:09:09.3643827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.3643829Z 
2025-08-14T23:09:09.6225884Z 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.
2025-08-14T23:09:09.6226205Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.6226333Z 
2025-08-14T23:09:09.6226534Z Registers a fake kernel based on the given operator profiles. This fake
2025-08-14T23:09:09.6226817Z kernel registration will override any existing fake kernel registrations.
2025-08-14T23:09:09.6226875Z 
2025-08-14T23:09:09.6227100Z The input is a dictionary mapping operator names to a set of operator
2025-08-14T23:09:09.6232449Z profiles, which we will use to generate fake kernels. The operator profiles
2025-08-14T23:09:09.6232695Z are a record of the input and output tensor metadata. Based on this
2025-08-14T23:09:09.6233275Z information we will match a given input to the recorded profile, and return
2025-08-14T23:09:09.6233507Z an output with the same metadata as in the recorded profile. If a profile
2025-08-14T23:09:09.6233668Z doesn't exist then an exception will be thrown.
2025-08-14T23:09:09.6233676Z 
2025-08-14T23:09:09.6233905Z The fake kernel generation is considered unsafe because it relies on the
2025-08-14T23:09:09.6234150Z rigid, pre-defined operator profiles that do not account for potential
2025-08-14T23:09:09.6234402Z variations in output behavior. Specifically, the generated kernels assume a
2025-08-14T23:09:09.6234654Z fixed relationship between input and output ranks. However, in reality, it's
2025-08-14T23:09:09.6234902Z possible that data-dependent operations may produce outputs of different
2025-08-14T23:09:09.6235118Z ranks even when given inputs of the same rank. The generated fake kernels
2025-08-14T23:09:09.6235332Z are inflexible and unable to accommodate these nuances, making them
2025-08-14T23:09:09.6235446Z potentially unsafe.
2025-08-14T23:09:09.6235455Z 
2025-08-14T23:09:09.6235541Z Args:
2025-08-14T23:09:09.6235776Z     op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator
2025-08-14T23:09:09.6235974Z         name to a set of operator profiles from which we will generate fake
2025-08-14T23:09:09.6236069Z         kernels.
2025-08-14T23:09:09.6236077Z 
2025-08-14T23:09:09.6236168Z Examples:
2025-08-14T23:09:09.6236177Z 
2025-08-14T23:09:09.6236371Z     >>> # Example: Registering an op-profile from draft-export
2025-08-14T23:09:09.6236475Z     >>> import torch
2025-08-14T23:09:09.6236653Z     >>> from torch.export._draft_export import draft_export
2025-08-14T23:09:09.6236751Z     >>>
2025-08-14T23:09:09.6238994Z     >>> @torch.library.custom_op("mylib::foo", mutates_args=())
2025-08-14T23:09:09.6239064Z     >>> def foo(x: Tensor, y: Tensor) -> Tensor:
2025-08-14T23:09:09.6239114Z     >>>     return x + y
2025-08-14T23:09:09.6239153Z     >>>
2025-08-14T23:09:09.6239211Z     >>> class M(torch.nn.Module):
2025-08-14T23:09:09.6239264Z     >>>     def forward(self, a, b):
2025-08-14T23:09:09.6239340Z     >>>         res = torch.ops.mylib.foo(a, b)  # no fake impl
2025-08-14T23:09:09.6239388Z     >>>         return res
2025-08-14T23:09:09.6239429Z     >>>
2025-08-14T23:09:09.6239512Z     >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4))
2025-08-14T23:09:09.6239552Z     >>>
2025-08-14T23:09:09.6239689Z     >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles):
2025-08-14T23:09:09.6239830Z     >>>     decomp = ep.run_decompositions()
2025-08-14T23:09:09.6239834Z 
2025-08-14T23:09:09.6239839Z 
2025-08-14T23:09:09.6239951Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.6239954Z 
2025-08-14T23:09:09.6275617Z 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.
2025-08-14T23:09:09.6275798Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:09.6276337Z Register a FakeTensor implementation for this custom op.
2025-08-14T23:09:09.6276355Z 
2025-08-14T23:09:09.6276595Z         This is necessary to get the operator to work efficiently with torch.compile.
2025-08-14T23:09:09.6276601Z 
2025-08-14T23:09:09.6276788Z         The Fake impl (sometimes also known as a meta kernel or abstract impl)
2025-08-14T23:09:09.6281620Z         specifies the behavior of this operator on Tensors that carry no data.
2025-08-14T23:09:09.6281792Z         Given some input Tensors with certain properties
2025-08-14T23:09:09.6282072Z         (sizes/strides/storage_offset/device), it specifies what the properties of
2025-08-14T23:09:09.6282171Z         the output Tensors are.
2025-08-14T23:09:09.6282178Z 
2025-08-14T23:09:09.6282358Z         Please see :func:`torch.library.register_fake` for more details.
2025-08-14T23:09:09.6282364Z 
2025-08-14T23:09:09.6282440Z         Args:
2025-08-14T23:09:09.6282595Z             fn (Callable): The function to register as the FakeTensor
2025-08-14T23:09:09.6282706Z                 implementation.
2025-08-14T23:09:09.6282711Z 
2025-08-14T23:09:09.6282792Z         Examples:
2025-08-14T23:09:09.6282880Z             >>> import torch
2025-08-14T23:09:09.6283152Z             >>> import numpy as np
2025-08-14T23:09:09.6283252Z             >>> from torch import Tensor
2025-08-14T23:09:09.6283334Z             >>>
2025-08-14T23:09:09.6283503Z             >>> # Example 1: an operator without data-dependent output shape
2025-08-14T23:09:09.6283675Z             >>> @torch.library.custom_op("mylib::linear", mutates_args=())
2025-08-14T23:09:09.6283840Z             >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-08-14T23:09:09.6283953Z             >>>     return (x @ weight.t()) + bias
2025-08-14T23:09:09.6284025Z             >>>
2025-08-14T23:09:09.6284126Z             >>> @linear.register_fake
2025-08-14T23:09:09.6284216Z             >>> def _(x, weight, bias):
2025-08-14T23:09:09.6284304Z             >>>     assert x.dim() == 2
2025-08-14T23:09:09.6284408Z             >>>     assert weight.dim() == 2
2025-08-14T23:09:09.6287730Z             >>>     assert bias.dim() == 1
2025-08-14T23:09:09.6287839Z             >>>     assert x.shape[1] == weight.shape[1]
2025-08-14T23:09:09.6287931Z             >>>     assert weight.shape[0] == bias.shape[0]
2025-08-14T23:09:09.6288019Z             >>>     assert x.device == weight.device
2025-08-14T23:09:09.6288123Z             >>>     return x.new_empty(x.size(0), weight.size(0))
2025-08-14T23:09:09.6288179Z             >>>
2025-08-14T23:09:09.6288249Z             >>> x = torch.randn(2, 2)
2025-08-14T23:09:09.6288324Z             >>> weight = torch.randn(2, 2)
2025-08-14T23:09:09.6288389Z             >>> bias = torch.randn(2)
2025-08-14T23:09:09.6288483Z             >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:09.6288601Z             >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias)
2025-08-14T23:09:09.6288689Z             >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:09.6288840Z             >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias))
2025-08-14T23:09:09.6288896Z             >>>
2025-08-14T23:09:09.6289007Z             >>> # Example 2: an operator with data-dependent output shape
2025-08-14T23:09:09.6289133Z             >>> @torch.library.custom_op("mylib::nonzero", mutates_args=())
2025-08-14T23:09:09.6289285Z             >>> def nonzero(x: Tensor) -> Tensor:
2025-08-14T23:09:09.6289402Z             >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:09.6289492Z             >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-08-14T23:09:09.6289582Z             >>>     return torch.tensor(res, device=x.device)
2025-08-14T23:09:09.6289634Z             >>>
2025-08-14T23:09:09.6291469Z             >>> @nonzero.register_fake
2025-08-14T23:09:09.6291538Z             >>> def _(x):
2025-08-14T23:09:09.6291641Z             >>> # Number of nonzero-elements is data-dependent.
2025-08-14T23:09:09.6291747Z             >>> # Since we cannot peek at the data in an abstract impl,
2025-08-14T23:09:09.6291847Z             >>> # we use the ctx object to construct a new symint that
2025-08-14T23:09:09.6291933Z             >>> # represents the data-dependent size.
2025-08-14T23:09:09.6292014Z             >>>     ctx = torch.library.get_ctx()
2025-08-14T23:09:09.6292125Z             >>>     nnz = ctx.new_dynamic_size()
2025-08-14T23:09:09.6292202Z             >>>     shape = [nnz, x.dim()]
2025-08-14T23:09:09.6292304Z             >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-08-14T23:09:09.6292398Z             >>>     return result
2025-08-14T23:09:09.6292454Z             >>>
2025-08-14T23:09:09.6292531Z             >>> x = torch.tensor([0, 1, 2, 0, 0, 1])
2025-08-14T23:09:09.6292619Z             >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:09.6292714Z             >>> out = torch.compile(nonzero, fullgraph=True)(x)
2025-08-14T23:09:09.6292798Z             >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:09.6293017Z             >>> assert torch.allclose(out, x.nonzero())
2025-08-14T23:09:09.6293022Z 
2025-08-14T23:09:09.6293079Z         
2025-08-14T23:09:09.6293378Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('<source_block>', 37, 1, '_._ = None\n', 37, 2))
2025-08-14T23:09:09.6293382Z 
2025-08-14T23:09:09.6293442Z _._ = None
2025-08-14T23:09:09.6293495Z ^
2025-08-14T23:09:09.6312448Z 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.
2025-08-14T23:09:09.6312654Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.6313542Z Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-08-14T23:09:09.6313615Z 
2025-08-14T23:09:09.6313717Z     This is a more structured way of using triton kernels with PyTorch.
2025-08-14T23:09:09.6313834Z     Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-08-14T23:09:09.6313952Z     (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-08-14T23:09:09.6314019Z     that is simpler;
2025-08-14T23:09:09.6314135Z     only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-08-14T23:09:09.6314246Z     want to create an operator that behaves like PyTorch built-in operators.
2025-08-14T23:09:09.6314350Z     For example, you may use a ``torch.library`` wrapper API to define the
2025-08-14T23:09:09.6314447Z     behavior of the triton kernel when passed a tensor subclass or under
2025-08-14T23:09:09.6314500Z     a TorchDispatchMode.
2025-08-14T23:09:09.6314503Z 
2025-08-14T23:09:09.6314612Z     Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-08-14T23:09:09.6314665Z     when the implementation
2025-08-14T23:09:09.6314761Z     consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-08-14T23:09:09.6314842Z     custom operators as opaque (:func:`torch.compile` and
2025-08-14T23:09:09.6314958Z     :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-08-14T23:09:09.6315054Z     makes the implementation visible to these subsystems, allowing them
2025-08-14T23:09:09.6315145Z     to optimize the triton kernel(s).
2025-08-14T23:09:09.6315148Z 
2025-08-14T23:09:09.6317458Z     Note that ``fn`` must only consist of calls to PyTorch-understood
2025-08-14T23:09:09.6317616Z     operators and triton kernels. Any triton kernels called inside ``fn``
2025-08-14T23:09:09.6317724Z     must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-08-14T23:09:09.6317727Z 
2025-08-14T23:09:09.6317770Z     Args:
2025-08-14T23:09:09.6317870Z         name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-08-14T23:09:09.6317960Z             e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-08-14T23:09:09.6318037Z             in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-08-14T23:09:09.6318133Z             To avoid name collisions, please use your project name as the namespace;
2025-08-14T23:09:09.6318222Z             e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-08-14T23:09:09.6318338Z         mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-08-14T23:09:09.6318465Z             This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-08-14T23:09:09.6318571Z             it pessimistically assumes that all inputs to the operator are being mutated.
2025-08-14T23:09:09.6318673Z         schema (None | str): A schema string for the operator. If None
2025-08-14T23:09:09.6318756Z             (recommended) we'll infer a schema for the operator from its type
2025-08-14T23:09:09.6318840Z             annotations. We recommend letting us infer a schema unless you
2025-08-14T23:09:09.6318892Z             have a specific reason not to.
2025-08-14T23:09:09.6318963Z             Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-08-14T23:09:09.6318966Z 
2025-08-14T23:09:09.6319009Z     Example::
2025-08-14T23:09:09.6319012Z 
2025-08-14T23:09:09.6319074Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:09.6319122Z         >>> import torch
2025-08-14T23:09:09.6319191Z         >>> from torch.library import triton_op, wrap_triton
2025-08-14T23:09:09.6319232Z         >>>
2025-08-14T23:09:09.6319282Z         >>> import triton
2025-08-14T23:09:09.6319338Z         >>> from triton import language as tl
2025-08-14T23:09:09.6319382Z         >>>
2025-08-14T23:09:09.6319425Z         >>> @triton.jit
2025-08-14T23:09:09.6319473Z         >>> def add_kernel(
2025-08-14T23:09:09.6319516Z         >>>     in_ptr0,
2025-08-14T23:09:09.6320885Z         >>>     in_ptr1,
2025-08-14T23:09:09.6320932Z         >>>     out_ptr,
2025-08-14T23:09:09.6320977Z         >>>     n_elements,
2025-08-14T23:09:09.6321030Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-08-14T23:09:09.6321068Z         >>> ):
2025-08-14T23:09:09.6321117Z         >>>     pid = tl.program_id(axis=0)
2025-08-14T23:09:09.6321169Z         >>>     block_start = pid * BLOCK_SIZE
2025-08-14T23:09:09.6321235Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-08-14T23:09:09.6321284Z         >>>     mask = offsets < n_elements
2025-08-14T23:09:09.6321343Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-08-14T23:09:09.6321402Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-08-14T23:09:09.6321448Z         >>>     output = x + y
2025-08-14T23:09:09.6321518Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-08-14T23:09:09.6321555Z         >>>
2025-08-14T23:09:09.6321616Z         >>> @triton_op("mylib::add", mutates_args={})
2025-08-14T23:09:09.6321696Z         >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-08-14T23:09:09.6321749Z         >>>     output = torch.empty_like(x)
2025-08-14T23:09:09.6321799Z         >>>     n_elements = output.numel()
2025-08-14T23:09:09.6321835Z         >>>
2025-08-14T23:09:09.6321881Z         >>>     def grid(meta):
2025-08-14T23:09:09.6321954Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-08-14T23:09:09.6321991Z         >>>
2025-08-14T23:09:09.6323268Z         >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-08-14T23:09:09.6323390Z         >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-08-14T23:09:09.6323455Z         >>>     return output
2025-08-14T23:09:09.6323493Z         >>>
2025-08-14T23:09:09.6323538Z         >>> @torch.compile
2025-08-14T23:09:09.6323580Z         >>> def f(x, y):
2025-08-14T23:09:09.6323626Z         >>>     return add(x, y)
2025-08-14T23:09:09.6323662Z         >>>
2025-08-14T23:09:09.6323716Z         >>> x = torch.randn(3, device="cuda")
2025-08-14T23:09:09.6323769Z         >>> y = torch.randn(3, device="cuda")
2025-08-14T23:09:09.6323805Z         >>>
2025-08-14T23:09:09.6323845Z         >>> z = f(x, y)
2025-08-14T23:09:09.6323894Z         >>> assert torch.allclose(z, x + y)
2025-08-14T23:09:09.6323897Z 
2025-08-14T23:09:09.6323933Z     
2025-08-14T23:09:09.6324036Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.6324039Z 
2025-08-14T23:09:09.6324263Z 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.
2025-08-14T23:09:09.6324375Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.6324497Z Allows capture of a triton kernel into a graph via make_fx or
2025-08-14T23:09:09.6324546Z     non-strict ``torch.export``.
2025-08-14T23:09:09.6324549Z 
2025-08-14T23:09:09.6324630Z     These technologies perform Dispatcher-based tracing (via
2025-08-14T23:09:09.6324714Z     ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-08-14T23:09:09.6325984Z     The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-08-14T23:09:09.6326041Z     can actually be traced into a graph.
2025-08-14T23:09:09.6326044Z 
2025-08-14T23:09:09.6326134Z     Please use this API together with :func:`torch.library.triton_op`.
2025-08-14T23:09:09.6326136Z 
2025-08-14T23:09:09.6326175Z     Examples:
2025-08-14T23:09:09.6326178Z 
2025-08-14T23:09:09.6326224Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.6326270Z         >>> import torch
2025-08-14T23:09:09.6326317Z         >>> import triton
2025-08-14T23:09:09.6326369Z         >>> from triton import language as tl
2025-08-14T23:09:09.6326453Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-08-14T23:09:09.6326508Z         >>> from torch.library import wrap_triton
2025-08-14T23:09:09.6326560Z         >>>
2025-08-14T23:09:09.6326601Z         >>> @triton.jit
2025-08-14T23:09:09.6326644Z         >>> def add_kernel(
2025-08-14T23:09:09.6326683Z         >>>     in_ptr0,
2025-08-14T23:09:09.6326723Z         >>>     in_ptr1,
2025-08-14T23:09:09.6326762Z         >>>     out_ptr,
2025-08-14T23:09:09.6326803Z         >>>     n_elements,
2025-08-14T23:09:09.6326854Z         >>>     BLOCK_SIZE: "tl.constexpr",
2025-08-14T23:09:09.6326891Z         >>> ):
2025-08-14T23:09:09.6326942Z         >>>     pid = tl.program_id(axis=0)
2025-08-14T23:09:09.6326992Z         >>>     block_start = pid * BLOCK_SIZE
2025-08-14T23:09:09.6327059Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-08-14T23:09:09.6328168Z         >>>     mask = offsets < n_elements
2025-08-14T23:09:09.6328230Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-08-14T23:09:09.6328283Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-08-14T23:09:09.6328323Z         >>>     output = x + y
2025-08-14T23:09:09.6328379Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-08-14T23:09:09.6328411Z         >>>
2025-08-14T23:09:09.6328448Z         >>> def add(x, y):
2025-08-14T23:09:09.6328493Z         >>>     output = torch.empty_like(x)
2025-08-14T23:09:09.6328535Z         >>>     n_elements = output.numel()
2025-08-14T23:09:09.6328567Z         >>>
2025-08-14T23:09:09.6328607Z         >>>     def grid_fn(meta):
2025-08-14T23:09:09.6328671Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-08-14T23:09:09.6328701Z         >>>
2025-08-14T23:09:09.6328773Z         >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-08-14T23:09:09.6328835Z         >>>     return output
2025-08-14T23:09:09.6328888Z         >>>
2025-08-14T23:09:09.6328934Z         >>> x = torch.randn(3, device="cuda")
2025-08-14T23:09:09.6328978Z         >>> y = torch.randn(3, device="cuda")
2025-08-14T23:09:09.6329020Z         >>> gm = make_fx(add)(x, y)
2025-08-14T23:09:09.6329058Z         >>> print(gm.code)
2025-08-14T23:09:09.6329101Z         >>> # def forward(self, x_1, y_1):
2025-08-14T23:09:09.6329187Z         >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-08-14T23:09:09.6330194Z         >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-08-14T23:09:09.6330247Z         >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-08-14T23:09:09.6330294Z         >>> #         grid = [(1, 1, 1)], kwargs = {
2025-08-14T23:09:09.6330353Z         >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-08-14T23:09:09.6330422Z         >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-08-14T23:09:09.6330463Z         >>> #         })
2025-08-14T23:09:09.6330504Z         >>> #     return empty_like
2025-08-14T23:09:09.6330520Z 
2025-08-14T23:09:09.6330551Z     
2025-08-14T23:09:09.6330641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.6330643Z 
2025-08-14T23:09:09.7743814Z 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.
2025-08-14T23:09:09.7744352Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.7744678Z Return a new dict with new, potentially nested, key value pair
2025-08-14T23:09:09.7744840Z 
2025-08-14T23:09:09.7748514Z     >>> purchase = {
2025-08-14T23:09:09.7748695Z     ...     "name": "Alice",
2025-08-14T23:09:09.7748893Z     ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:09.7749118Z     ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:09.7749285Z     ... }
2025-08-14T23:09:09.7749469Z     >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-08-14T23:09:09.7749699Z     {'credit card': '5555-1234-1234-1234',
2025-08-14T23:09:09.7749863Z      'name': 'Alice',
2025-08-14T23:09:09.7750043Z      'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-08-14T23:09:09.7750230Z     
2025-08-14T23:09:09.7750427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.7750607Z 
2025-08-14T23:09:09.7750928Z 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.
2025-08-14T23:09:09.7751417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.7751679Z Update value in a (potentially) nested dictionary
2025-08-14T23:09:09.7751839Z 
2025-08-14T23:09:09.7751898Z     inputs:
2025-08-14T23:09:09.7752041Z     d - dictionary on which to operate
2025-08-14T23:09:09.7752270Z     keys - list or tuple giving the location of the value to be changed in d
2025-08-14T23:09:09.7752507Z     func - function to operate on that value
2025-08-14T23:09:09.7752626Z 
2025-08-14T23:09:09.7752730Z     If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-08-14T23:09:09.7753169Z     original dictionary with v replaced by func(v), but does not mutate the
2025-08-14T23:09:09.7753400Z     original dictionary.
2025-08-14T23:09:09.7753497Z 
2025-08-14T23:09:09.7753608Z     If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-08-14T23:09:09.7753882Z     specified by the keys, with the innermost value set to func(default).
2025-08-14T23:09:09.7754048Z 
2025-08-14T23:09:09.7754111Z     >>> inc = lambda x: x + 1
2025-08-14T23:09:09.7754277Z     >>> update_in({"a": 0}, ["a"], inc)
2025-08-14T23:09:09.7754438Z     {'a': 1}
2025-08-14T23:09:09.7756741Z 
2025-08-14T23:09:09.7756810Z     >>> transaction = {
2025-08-14T23:09:09.7756997Z     ...     "name": "Alice",
2025-08-14T23:09:09.7757193Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:09.7757414Z     ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:09.7757579Z     ... }
2025-08-14T23:09:09.7757760Z     >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-08-14T23:09:09.7757982Z     {'credit card': '5555-1234-1234-1234',
2025-08-14T23:09:09.7758127Z      'name': 'Alice',
2025-08-14T23:09:09.7758264Z      'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-08-14T23:09:09.7758376Z 
2025-08-14T23:09:09.7758432Z     >>> # updating a value when k0 is not in d
2025-08-14T23:09:09.7758579Z     >>> update_in({}, [1, 2, 3], str, default="bar")
2025-08-14T23:09:09.7759902Z     {1: {2: {3: 'bar'}}}
2025-08-14T23:09:09.7760020Z     >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-08-14T23:09:09.7760184Z     {1: 'foo', 2: {3: {4: 1}}}
2025-08-14T23:09:09.7760292Z     
2025-08-14T23:09:09.7760440Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.7760610Z 
2025-08-14T23:09:09.7760853Z 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.
2025-08-14T23:09:09.7761231Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.7761444Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-08-14T23:09:09.7761554Z 
2025-08-14T23:09:09.7761631Z     If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-08-14T23:09:09.7761825Z     ``no_default`` is specified, then it raises KeyError or IndexError.
2025-08-14T23:09:09.7761948Z 
2025-08-14T23:09:09.7762033Z     ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-08-14T23:09:09.7763427Z     structures such as dictionaries and lists.
2025-08-14T23:09:09.7763524Z 
2025-08-14T23:09:09.7763568Z     >>> transaction = {
2025-08-14T23:09:09.7763680Z     ...     "name": "Alice",
2025-08-14T23:09:09.7763832Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:09.7764002Z     ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:09.7764127Z     ... }
2025-08-14T23:09:09.7764236Z     >>> get_in(["purchase", "items", 0], transaction)
2025-08-14T23:09:09.7764371Z     'Apple'
2025-08-14T23:09:09.7764470Z     >>> get_in(["name"], transaction)
2025-08-14T23:09:09.7764589Z     'Alice'
2025-08-14T23:09:09.7764694Z     >>> get_in(["purchase", "total"], transaction)
2025-08-14T23:09:09.7766014Z     >>> get_in(["purchase", "items", "apple"], transaction)
2025-08-14T23:09:09.7766174Z     >>> get_in(["purchase", "items", 10], transaction)
2025-08-14T23:09:09.7766328Z     >>> get_in(["purchase", "total"], transaction, 0)
2025-08-14T23:09:09.7766458Z     0
2025-08-14T23:09:09.7766554Z     >>> get_in(["y"], {}, no_default=True)
2025-08-14T23:09:09.7766691Z     Traceback (most recent call last):
2025-08-14T23:09:09.7766811Z         ...
2025-08-14T23:09:09.7766899Z     KeyError: 'y'
2025-08-14T23:09:09.7766963Z 
2025-08-14T23:09:09.7767001Z     See Also:
2025-08-14T23:09:09.7767096Z         itertoolz.get
2025-08-14T23:09:09.7768242Z         operator.getitem
2025-08-14T23:09:09.7768339Z     
2025-08-14T23:09:09.7768469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.7768593Z 
2025-08-14T23:09:09.7768808Z 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.
2025-08-14T23:09:09.7769145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.7769312Z Group a collection by a key function
2025-08-14T23:09:09.7769389Z 
2025-08-14T23:09:09.7769454Z     >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-08-14T23:09:09.7769654Z     >>> groupby(len, names)  # doctest: +SKIP
2025-08-14T23:09:09.7769795Z     {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-08-14T23:09:09.7769893Z 
2025-08-14T23:09:09.7769935Z     >>> iseven = lambda x: x % 2 == 0
2025-08-14T23:09:09.7770074Z     >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-08-14T23:09:09.7771155Z     {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-08-14T23:09:09.7771236Z 
2025-08-14T23:09:09.7771291Z     Non-callable keys imply grouping on a member.
2025-08-14T23:09:09.7771378Z 
2025-08-14T23:09:09.7771413Z     >>> groupby(
2025-08-14T23:09:09.7771498Z     ...     "gender",
2025-08-14T23:09:09.7771586Z     ...     [
2025-08-14T23:09:09.7771677Z     ...         {"name": "Alice", "gender": "F"},
2025-08-14T23:09:09.7771800Z     ...         {"name": "Bob", "gender": "M"},
2025-08-14T23:09:09.7771922Z     ...         {"name": "Charlie", "gender": "M"},
2025-08-14T23:09:09.7772032Z     ...     ],
2025-08-14T23:09:09.7772143Z     ... )  # doctest:+SKIP
2025-08-14T23:09:09.7772252Z     {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-08-14T23:09:09.7773389Z      'M': [{'gender': 'M', 'name': 'Bob'},
2025-08-14T23:09:09.7773535Z            {'gender': 'M', 'name': 'Charlie'}]}
2025-08-14T23:09:09.7773615Z 
2025-08-14T23:09:09.7773668Z     Not to be confused with ``itertools.groupby``
2025-08-14T23:09:09.7773754Z 
2025-08-14T23:09:09.7773787Z     See Also:
2025-08-14T23:09:09.7773866Z         countby
2025-08-14T23:09:09.7773946Z     
2025-08-14T23:09:09.7774076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.7774200Z 
2025-08-14T23:09:09.8272814Z 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.
2025-08-14T23:09:09.8273858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.8274585Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
2025-08-14T23:09:09.8280061Z     Label will only appear if CPU activity tracing is enabled.
2025-08-14T23:09:09.8280362Z 
2025-08-14T23:09:09.8280452Z     It is useful when tracing the code profile.
2025-08-14T23:09:09.8280568Z 
2025-08-14T23:09:09.8280616Z     Args:
2025-08-14T23:09:09.8280750Z         name (str): Label assigned to the block of code.
2025-08-14T23:09:09.8280914Z         node_id (int): ID of node, for distributed profiling. Unset in
2025-08-14T23:09:09.8281062Z         non-distributed cases.
2025-08-14T23:09:09.8281133Z 
2025-08-14T23:09:09.8281169Z     Example:
2025-08-14T23:09:09.8281292Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
2025-08-14T23:09:09.8281454Z         >>> x = torch.randn((1, 1), requires_grad=True)
2025-08-14T23:09:09.8281609Z         >>> with torch.autograd.profiler.profile() as prof:
2025-08-14T23:09:09.8281746Z         ...     y = x**2
2025-08-14T23:09:09.8281875Z         ...     with torch.autograd.profiler.record_function(
2025-08-14T23:09:09.8282016Z         ...         "label-z"
2025-08-14T23:09:09.8282125Z         ...     ):  # label the block
2025-08-14T23:09:09.8282239Z         ...         z = y**3
2025-08-14T23:09:09.8282343Z         ...     y.backward()
2025-08-14T23:09:09.8283744Z         >>> # xdoctest: +IGNORE_WANT
2025-08-14T23:09:09.8283875Z         >>> # NOTE: some columns were removed for brevity
2025-08-14T23:09:09.8284039Z         >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
2025-08-14T23:09:09.8284230Z         -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:09.8284415Z         Name                                 Self CPU total %  CPU time avg     Number of Calls
2025-08-14T23:09:09.8284593Z         -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:09.8284758Z         pow                                  60.77%           47.470us         3
2025-08-14T23:09:09.8284977Z         mul                                  21.73%           25.465us         2
2025-08-14T23:09:09.8285130Z         PowBackward0                         12.03%           121.891us        1
2025-08-14T23:09:09.8285315Z         torch::autograd::AccumulateGrad      2.70%            6.324us          1
2025-08-14T23:09:09.8285484Z         label-z                              2.13%            12.421us         1
2025-08-14T23:09:09.8286751Z         torch::autograd::GraphRoot           0.64%            1.503us          1
2025-08-14T23:09:09.8286933Z         -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:09.8287086Z         Self CPU time total: 234.344us
2025-08-14T23:09:09.8287205Z         CUDA time total: 0.000us
2025-08-14T23:09:09.8287277Z 
2025-08-14T23:09:09.8287310Z     
2025-08-14T23:09:09.8287447Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.8287575Z 
2025-08-14T23:09:09.9133807Z 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.
2025-08-14T23:09:09.9134804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9135326Z Register a container-like type as pytree node.
2025-08-14T23:09:09.9135570Z 
2025-08-14T23:09:09.9135669Z     Args:
2025-08-14T23:09:09.9139691Z         cls (type): A Python type to treat as an internal pytree node.
2025-08-14T23:09:09.9139976Z         flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-08-14T23:09:09.9140265Z             ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-08-14T23:09:09.9140555Z             recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-08-14T23:09:09.9140790Z             passed to the ``unflatten_fn``.
2025-08-14T23:09:09.9141019Z         unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-08-14T23:09:09.9141315Z             returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-08-14T23:09:09.9141560Z             The function should return an instance of ``cls``.
2025-08-14T23:09:09.9141801Z         serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-08-14T23:09:09.9142046Z             qualified name used when serializing the tree spec.
2025-08-14T23:09:09.9142306Z         to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-08-14T23:09:09.9144378Z             to convert the context of the pytree to a custom json dumpable representation. This is
2025-08-14T23:09:09.9144673Z             used for json serialization, which is being used in :mod:`torch.export` right now.
2025-08-14T23:09:09.9144976Z         from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-08-14T23:09:09.9145279Z             how to convert the custom json dumpable representation of the context back to the
2025-08-14T23:09:09.9145561Z             original context. This is used for json deserialization, which is being used in
2025-08-14T23:09:09.9145785Z             :mod:`torch.export` right now.
2025-08-14T23:09:09.9145889Z 
2025-08-14T23:09:09.9145941Z     Example::
2025-08-14T23:09:09.9146008Z 
2025-08-14T23:09:09.9146064Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9146225Z         >>> # Registry a Python type with lambda functions
2025-08-14T23:09:09.9146392Z         >>> register_pytree_node(
2025-08-14T23:09:09.9146527Z         ...     set,
2025-08-14T23:09:09.9147985Z         ...     lambda s: (sorted(s), None, None),
2025-08-14T23:09:09.9148153Z         ...     lambda children, _: set(children),
2025-08-14T23:09:09.9148297Z         ... )
2025-08-14T23:09:09.9148401Z     
2025-08-14T23:09:09.9148572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9148803Z 
2025-08-14T23:09:09.9203348Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1218.
2025-08-14T23:09:09.9203686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9203915Z 
2025-08-14T23:09:09.9204018Z Context passed to policy function during selective checkpointing.
2025-08-14T23:09:09.9204132Z 
2025-08-14T23:09:09.9204233Z This class is used to pass relevant metadata to the policy function during
2025-08-14T23:09:09.9204440Z selective checkpointing. The metadata includes whether the current invocation
2025-08-14T23:09:09.9206317Z of the policy function is during recomputation or not.
2025-08-14T23:09:09.9206423Z 
2025-08-14T23:09:09.9206476Z Example:
2025-08-14T23:09:09.9206570Z     >>> # xdoctest: +SKIP(stub)
2025-08-14T23:09:09.9206676Z     >>>
2025-08-14T23:09:09.9206783Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-08-14T23:09:09.9206962Z     >>>    print(ctx.is_recompute)
2025-08-14T23:09:09.9207093Z     >>>
2025-08-14T23:09:09.9207236Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-08-14T23:09:09.9207423Z     >>>
2025-08-14T23:09:09.9207522Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-08-14T23:09:09.9208911Z     >>>     fn, x, y,
2025-08-14T23:09:09.9209016Z     >>>     use_reentrant=False,
2025-08-14T23:09:09.9209130Z     >>>     context_fn=context_fn,
2025-08-14T23:09:09.9209233Z     >>> )
2025-08-14T23:09:09.9209278Z 
2025-08-14T23:09:09.9209369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9209496Z 
2025-08-14T23:09:09.9209710Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1358.
2025-08-14T23:09:09.9210038Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9210170Z 
2025-08-14T23:09:09.9210258Z Helper to avoid recomputing certain ops during activation checkpointing.
2025-08-14T23:09:09.9210376Z 
2025-08-14T23:09:09.9210457Z Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-08-14T23:09:09.9210631Z operations are recomputed during the backward pass.
2025-08-14T23:09:09.9210726Z 
2025-08-14T23:09:09.9210757Z Args:
2025-08-14T23:09:09.9210846Z     policy_fn_or_list (Callable or List):
2025-08-14T23:09:09.9210990Z       - If a policy function is provided, it should accept a
2025-08-14T23:09:09.9211170Z         :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-08-14T23:09:09.9211365Z         kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-08-14T23:09:09.9211556Z         indicating whether the execution of the op should be recomputed or not.
2025-08-14T23:09:09.9212708Z       - If a list of operations is provided, it is equivalent to a policy
2025-08-14T23:09:09.9213021Z         returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-08-14T23:09:09.9213205Z         operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-08-14T23:09:09.9213351Z         operations.
2025-08-14T23:09:09.9213481Z     allow_cache_entry_mutation (bool, optional): By default, an error is
2025-08-14T23:09:09.9213666Z         raised if any tensors cached by selective activation checkpoint are
2025-08-14T23:09:09.9213850Z         mutated in order to ensure correctness. If set to `True`, this check
2025-08-14T23:09:09.9213994Z         is disabled.
2025-08-14T23:09:09.9214083Z Returns:
2025-08-14T23:09:09.9214170Z     A tuple of two context managers.
2025-08-14T23:09:09.9214245Z 
2025-08-14T23:09:09.9214278Z Example:
2025-08-14T23:09:09.9215301Z     >>> # xdoctest: +REQUIRES(LINUX)
2025-08-14T23:09:09.9215415Z     >>> import functools
2025-08-14T23:09:09.9215508Z     >>>
2025-08-14T23:09:09.9215599Z     >>> x = torch.rand(10, 10, requires_grad=True)
2025-08-14T23:09:09.9215728Z     >>> y = torch.rand(10, 10, requires_grad=True)
2025-08-14T23:09:09.9215871Z     >>>
2025-08-14T23:09:09.9215966Z     >>> ops_to_save = [
2025-08-14T23:09:09.9216071Z     >>>    torch.ops.aten.mm.default,
2025-08-14T23:09:09.9216181Z     >>> ]
2025-08-14T23:09:09.9216255Z     >>>
2025-08-14T23:09:09.9216343Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2025-08-14T23:09:09.9218221Z     >>>    if op in ops_to_save:
2025-08-14T23:09:09.9218346Z     >>>        return CheckpointPolicy.MUST_SAVE
2025-08-14T23:09:09.9218465Z     >>>    else:
2025-08-14T23:09:09.9218571Z     >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-08-14T23:09:09.9218693Z     >>>
2025-08-14T23:09:09.9218830Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-08-14T23:09:09.9218993Z     >>>
2025-08-14T23:09:09.9219073Z     >>> # or equivalently
2025-08-14T23:09:09.9219231Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-08-14T23:09:09.9219390Z     >>>
2025-08-14T23:09:09.9219498Z     >>> def fn(x, y):
2025-08-14T23:09:09.9220575Z     >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-08-14T23:09:09.9220716Z     >>>
2025-08-14T23:09:09.9220836Z     >>> out = torch.utils.checkpoint.checkpoint(
2025-08-14T23:09:09.9220958Z     >>>     fn, x, y,
2025-08-14T23:09:09.9221054Z     >>>     use_reentrant=False,
2025-08-14T23:09:09.9221163Z     >>>     context_fn=context_fn,
2025-08-14T23:09:09.9221266Z     >>> )
2025-08-14T23:09:09.9221311Z 
2025-08-14T23:09:09.9221402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9221526Z 
2025-08-14T23:09:09.9406604Z 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.
2025-08-14T23:09:09.9407342Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9407614Z 
2025-08-14T23:09:09.9411678Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-08-14T23:09:09.9411998Z 
2025-08-14T23:09:09.9412221Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-08-14T23:09:09.9412683Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-08-14T23:09:09.9413317Z server like load. It can emulate multiple calling threads to a single module
2025-08-14T23:09:09.9413725Z provided. In the future we plan to enhance this component to support inter and
2025-08-14T23:09:09.9414141Z intra-op parallelism as well as multiple models running in a single process.
2025-08-14T23:09:09.9414386Z 
2025-08-14T23:09:09.9414571Z Please note that even though nn.Module is supported, it might incur an overhead
2025-08-14T23:09:09.9414981Z from the need to hold GIL every time we execute Python code or pass around
2025-08-14T23:09:09.9415383Z inputs as Python objects. As soon as you have a ScriptModule version of your
2025-08-14T23:09:09.9415791Z model for inference deployment it is better to switch to using it in this
2025-08-14T23:09:09.9416102Z benchmark.
2025-08-14T23:09:09.9416201Z 
2025-08-14T23:09:09.9417979Z Example::
2025-08-14T23:09:09.9418064Z 
2025-08-14T23:09:09.9418136Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:09.9418347Z     >>> from torch.utils import ThroughputBenchmark
2025-08-14T23:09:09.9418567Z     >>> bench = ThroughputBenchmark(my_module)
2025-08-14T23:09:09.9418789Z     >>> # Pre-populate benchmark's data set with the inputs
2025-08-14T23:09:09.9418999Z     >>> for input in inputs:
2025-08-14T23:09:09.9419225Z     ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-08-14T23:09:09.9419478Z     ...     bench.add_input(input[0], x2=input[1])
2025-08-14T23:09:09.9419717Z     >>> # Inputs supplied above are randomly used during the execution
2025-08-14T23:09:09.9419949Z     >>> stats = bench.benchmark(
2025-08-14T23:09:09.9420122Z     ...     num_calling_threads=4,
2025-08-14T23:09:09.9421761Z     ...     num_warmup_iters = 100,
2025-08-14T23:09:09.9422078Z     ...     num_iters = 1000,
2025-08-14T23:09:09.9422277Z     ... )
2025-08-14T23:09:09.9422450Z     >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-08-14T23:09:09.9422709Z     >>> print("Number of iterations: {}".format(stats.num_iters))
2025-08-14T23:09:09.9422860Z 
2025-08-14T23:09:09.9423132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9423320Z 
2025-08-14T23:09:09.9485614Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1147.
2025-08-14T23:09:09.9486969Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9487338Z 
2025-08-14T23:09:09.9487463Z Create a :class:`setuptools.Extension` for C++.
2025-08-14T23:09:09.9487649Z 
2025-08-14T23:09:09.9487822Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:09.9495398Z bare minimum (but often sufficient) arguments to build a C++ extension.
2025-08-14T23:09:09.9495648Z 
2025-08-14T23:09:09.9495809Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:09.9496221Z constructor. Full list arguments can be found at
2025-08-14T23:09:09.9496658Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-08-14T23:09:09.9496947Z 
2025-08-14T23:09:09.9497047Z .. warning::
2025-08-14T23:09:09.9497265Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:09.9497569Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:09.9497855Z     the user's responsibility in their library to not use APIs from
2025-08-14T23:09:09.9500982Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:09.9501313Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:09.9501614Z     example, to give access to custom ops from python, the library should
2025-08-14T23:09:09.9501865Z     register the ops through the dispatcher.
2025-08-14T23:09:09.9502004Z 
2025-08-14T23:09:09.9502126Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:09.9502413Z     as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:09.9502704Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:09.9503152Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:09.9503440Z     safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:09.9503726Z     different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:09.9503966Z     CPython version of choice.
2025-08-14T23:09:09.9504074Z 
2025-08-14T23:09:09.9504128Z Example:
2025-08-14T23:09:09.9504268Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9504461Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:09.9504672Z     >>> from setuptools import setup
2025-08-14T23:09:09.9504916Z     >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-08-14T23:09:09.9505139Z     >>> setup(
2025-08-14T23:09:09.9505282Z     ...     name='extension',
2025-08-14T23:09:09.9505439Z     ...     ext_modules=[
2025-08-14T23:09:09.9505593Z     ...         CppExtension(
2025-08-14T23:09:09.9505757Z     ...             name='extension',
2025-08-14T23:09:09.9505946Z     ...             sources=['extension.cpp'],
2025-08-14T23:09:09.9506141Z     ...             extra_compile_args=['-g'],
2025-08-14T23:09:09.9506355Z     ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-08-14T23:09:09.9506550Z     ...     ],
2025-08-14T23:09:09.9506678Z     ...     cmdclass={
2025-08-14T23:09:09.9506835Z     ...         'build_ext': BuildExtension
2025-08-14T23:09:09.9507006Z     ...     })
2025-08-14T23:09:09.9507081Z 
2025-08-14T23:09:09.9507215Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9507489Z 
2025-08-14T23:09:09.9507826Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1217.
2025-08-14T23:09:09.9508187Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9508341Z 
2025-08-14T23:09:09.9508415Z Create a :class:`setuptools.Extension` for CUDA/C++.
2025-08-14T23:09:09.9508531Z 
2025-08-14T23:09:09.9510248Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:09.9510486Z bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-08-14T23:09:09.9510706Z extension. This includes the CUDA include path, library path and runtime
2025-08-14T23:09:09.9510880Z library.
2025-08-14T23:09:09.9510935Z 
2025-08-14T23:09:09.9511023Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:09.9511246Z constructor. Full list arguments can be found at
2025-08-14T23:09:09.9511486Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-08-14T23:09:09.9511662Z 
2025-08-14T23:09:09.9511724Z .. warning::
2025-08-14T23:09:09.9511874Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:09.9512095Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:09.9512306Z     the user's responsibility in their library to not use APIs from
2025-08-14T23:09:09.9513801Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:09.9514034Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:09.9514249Z     example, to give access to custom ops from python, the library should
2025-08-14T23:09:09.9514435Z     register the ops through the dispatcher.
2025-08-14T23:09:09.9514530Z 
2025-08-14T23:09:09.9514625Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:09.9514843Z     as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:09.9515053Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:09.9515269Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:09.9515480Z     safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:09.9515693Z     different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:09.9515868Z     CPython version of choice.
2025-08-14T23:09:09.9515949Z 
2025-08-14T23:09:09.9517098Z Example:
2025-08-14T23:09:09.9517189Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9517311Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:09.9517442Z     >>> from setuptools import setup
2025-08-14T23:09:09.9517594Z     >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-08-14T23:09:09.9517738Z     >>> setup(
2025-08-14T23:09:09.9517831Z     ...     name='cuda_extension',
2025-08-14T23:09:09.9517939Z     ...     ext_modules=[
2025-08-14T23:09:09.9518037Z     ...         CUDAExtension(
2025-08-14T23:09:09.9518147Z     ...                 name='cuda_extension',
2025-08-14T23:09:09.9518285Z     ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:09.9519344Z     ...                 extra_compile_args={'cxx': ['-g'],
2025-08-14T23:09:09.9519482Z     ...                                     'nvcc': ['-O2']},
2025-08-14T23:09:09.9519618Z     ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-08-14T23:09:09.9519742Z     ...     ],
2025-08-14T23:09:09.9519823Z     ...     cmdclass={
2025-08-14T23:09:09.9519922Z     ...         'build_ext': BuildExtension
2025-08-14T23:09:09.9520029Z     ...     })
2025-08-14T23:09:09.9520079Z 
2025-08-14T23:09:09.9520117Z Compute capabilities:
2025-08-14T23:09:09.9520177Z 
2025-08-14T23:09:09.9520283Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-08-14T23:09:09.9520574Z building process of the extension, plus PTX. If down the road a new card is installed the
2025-08-14T23:09:09.9520812Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-08-14T23:09:09.9521974Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-08-14T23:09:09.9522210Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-08-14T23:09:09.9522387Z support (see below for details on PTX).
2025-08-14T23:09:09.9522465Z 
2025-08-14T23:09:09.9522572Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-08-14T23:09:09.9522757Z CCs you want the extension to support:
2025-08-14T23:09:09.9522835Z 
2025-08-14T23:09:09.9522987Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-08-14T23:09:09.9523194Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-08-14T23:09:09.9523316Z 
2025-08-14T23:09:09.9523429Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-08-14T23:09:09.9523715Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-08-14T23:09:09.9523958Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-08-14T23:09:09.9524194Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-08-14T23:09:09.9525391Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-08-14T23:09:09.9525632Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-08-14T23:09:09.9525952Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-08-14T23:09:09.9526202Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-08-14T23:09:09.9526384Z "8.0 8.6" would be better.
2025-08-14T23:09:09.9526449Z 
2025-08-14T23:09:09.9526552Z Note that while it's possible to include all supported archs, the more archs get included the
2025-08-14T23:09:09.9526789Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-08-14T23:09:09.9526924Z 
2025-08-14T23:09:09.9527037Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-08-14T23:09:09.9527263Z To workaround the issue, move python binding logic to pure C++ file.
2025-08-14T23:09:09.9527375Z 
2025-08-14T23:09:09.9527409Z Example use:
2025-08-14T23:09:09.9527497Z     #include <ATen/ATen.h>
2025-08-14T23:09:09.9528555Z     at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-08-14T23:09:09.9528646Z 
2025-08-14T23:09:09.9528680Z Instead of:
2025-08-14T23:09:09.9528769Z     #include <torch/extension.h>
2025-08-14T23:09:09.9528897Z     torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-08-14T23:09:09.9528989Z 
2025-08-14T23:09:09.9529087Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-08-14T23:09:09.9529389Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-08-14T23:09:09.9529596Z 
2025-08-14T23:09:09.9529638Z Relocatable device code linking:
2025-08-14T23:09:09.9529709Z 
2025-08-14T23:09:09.9529807Z If you want to reference device symbols across compilation units (across object files),
2025-08-14T23:09:09.9530028Z the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-08-14T23:09:09.9530275Z An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-08-14T23:09:09.9531466Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-08-14T23:09:09.9531728Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-08-14T23:09:09.9531985Z helps reduce the protentional perf degradation of `-rdc`.
2025-08-14T23:09:09.9532144Z Note that it needs to be used at both steps to be useful.
2025-08-14T23:09:09.9532241Z 
2025-08-14T23:09:09.9532368Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-08-14T23:09:09.9532591Z There is also a case where `-dlink` is used without `-rdc`:
2025-08-14T23:09:09.9532778Z when an extension is linked against a static lib containing rdc-compiled objects
2025-08-14T23:09:09.9533059Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-08-14T23:09:09.9533172Z 
2025-08-14T23:09:09.9533244Z Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-08-14T23:09:09.9533351Z 
2025-08-14T23:09:09.9533385Z Example:
2025-08-14T23:09:09.9533470Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9534535Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:09.9534691Z     >>> CUDAExtension(
2025-08-14T23:09:09.9534793Z     ...        name='cuda_extension',
2025-08-14T23:09:09.9534925Z     ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:09.9535075Z     ...        dlink=True,
2025-08-14T23:09:09.9535181Z     ...        dlink_libraries=["dlink_lib"],
2025-08-14T23:09:09.9535306Z     ...        extra_compile_args={'cxx': ['-g'],
2025-08-14T23:09:09.9535435Z     ...                            'nvcc': ['-O2', '-rdc=true']})
2025-08-14T23:09:09.9535518Z 
2025-08-14T23:09:09.9535608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9535732Z 
2025-08-14T23:09:09.9535918Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1408.
2025-08-14T23:09:09.9536220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9536347Z 
2025-08-14T23:09:09.9537341Z Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-08-14T23:09:09.9537438Z 
2025-08-14T23:09:09.9537526Z Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:09.9537715Z bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-08-14T23:09:09.9537853Z extension.
2025-08-14T23:09:09.9537901Z 
2025-08-14T23:09:09.9537975Z All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:09.9538115Z constructor.
2025-08-14T23:09:09.9538164Z 
2025-08-14T23:09:09.9538199Z .. warning::
2025-08-14T23:09:09.9538326Z     The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:09.9538517Z     with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:09.9538700Z     the user's responsibility in their library to not use APIs from
2025-08-14T23:09:09.9538885Z     libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:09.9539975Z     APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:09.9540164Z     example, to give access to custom ops from python, the library should
2025-08-14T23:09:09.9540321Z     register the ops through the dispatcher.
2025-08-14T23:09:09.9540404Z 
2025-08-14T23:09:09.9540483Z     Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:09.9540668Z     as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:09.9540847Z     the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:09.9541032Z     -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:09.9541214Z     safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:09.9541397Z     different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:09.9541547Z     CPython version of choice.
2025-08-14T23:09:09.9541618Z 
2025-08-14T23:09:09.9541651Z Example:
2025-08-14T23:09:09.9542660Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9542826Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:09.9543071Z     >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-08-14T23:09:09.9543218Z     >>> setup(
2025-08-14T23:09:09.9543308Z     ...     name='xpu_extension',
2025-08-14T23:09:09.9543415Z     ...     ext_modules=[
2025-08-14T23:09:09.9543513Z     ...     SyclExtension(
2025-08-14T23:09:09.9543619Z     ...                 name='xpu_extension',
2025-08-14T23:09:09.9543756Z     ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-08-14T23:09:09.9543917Z     ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-08-14T23:09:09.9544048Z     ...     ],
2025-08-14T23:09:09.9545074Z     ...     cmdclass={
2025-08-14T23:09:09.9545175Z     ...         'build_ext': BuildExtension
2025-08-14T23:09:09.9545281Z     ...     })
2025-08-14T23:09:09.9545329Z 
2025-08-14T23:09:09.9545456Z By default the extension will be compiled to run on all archs of the cards visible during the
2025-08-14T23:09:09.9545684Z building process of the extension. If down the road a new card is installed the
2025-08-14T23:09:09.9545915Z extension may need to be recompiled. You can override the default behavior using
2025-08-14T23:09:09.9546141Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-08-14T23:09:09.9546312Z to support:
2025-08-14T23:09:09.9546359Z 
2025-08-14T23:09:09.9546435Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-08-14T23:09:09.9546542Z 
2025-08-14T23:09:09.9546645Z Note that while it's possible to include all supported archs, the more archs get included the
2025-08-14T23:09:09.9546880Z slower the building process will be, as it will build a separate kernel image for each arch.
2025-08-14T23:09:09.9547943Z 
2025-08-14T23:09:09.9548000Z Note: Ninja is required to build SyclExtension.
2025-08-14T23:09:09.9548086Z 
2025-08-14T23:09:09.9548176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9548301Z 
2025-08-14T23:09:09.9548473Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1585.
2025-08-14T23:09:09.9548765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9548891Z 
2025-08-14T23:09:09.9548947Z Load a PyTorch C++ extension just-in-time (JIT).
2025-08-14T23:09:09.9549033Z 
2025-08-14T23:09:09.9549109Z To load an extension, a Ninja build file is emitted, which is used to
2025-08-14T23:09:09.9549288Z compile the given sources into a dynamic library. This library is
2025-08-14T23:09:09.9549473Z subsequently loaded into the current Python process as a module and
2025-08-14T23:09:09.9549631Z returned from this function, ready for use.
2025-08-14T23:09:09.9549710Z 
2025-08-14T23:09:09.9549785Z By default, the directory to which the build file is emitted and the
2025-08-14T23:09:09.9550900Z resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-08-14T23:09:09.9551095Z ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-08-14T23:09:09.9551278Z the name of the extension. This location can be overridden in two ways.
2025-08-14T23:09:09.9551458Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-08-14T23:09:09.9551641Z replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-08-14T23:09:09.9551828Z into subfolders of this directory. Second, if the ``build_directory``
2025-08-14T23:09:09.9552015Z argument to this function is supplied, it overrides the entire path, i.e.
2025-08-14T23:09:09.9552192Z the library will be compiled into that folder directly.
2025-08-14T23:09:09.9552286Z 
2025-08-14T23:09:09.9552359Z To compile the sources, the default system compiler (``c++``) is used,
2025-08-14T23:09:09.9552548Z which can be overridden by setting the ``CXX`` environment variable. To pass
2025-08-14T23:09:09.9552775Z additional arguments to the compilation process, ``extra_cflags`` or
2025-08-14T23:09:09.9554007Z ``extra_ldflags`` can be provided. For example, to compile your extension
2025-08-14T23:09:09.9554201Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-08-14T23:09:09.9554367Z ``extra_cflags`` to pass further include directories.
2025-08-14T23:09:09.9554458Z 
2025-08-14T23:09:09.9554540Z CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-08-14T23:09:09.9554721Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-08-14T23:09:09.9554903Z detected and compiled with nvcc rather than the C++ compiler. This includes
2025-08-14T23:09:09.9555097Z passing the CUDA lib64 directory as a library directory, and linking
2025-08-14T23:09:09.9555260Z ``cudart``. You can pass additional flags to nvcc via
2025-08-14T23:09:09.9555421Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-08-14T23:09:09.9555624Z heuristics for finding the CUDA install directory are used, which usually
2025-08-14T23:09:09.9555814Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-08-14T23:09:09.9556908Z safest option.
2025-08-14T23:09:09.9556964Z 
2025-08-14T23:09:09.9557046Z SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-08-14T23:09:09.9557232Z files (``.sycl``) along with other sources. Such files will be detected
2025-08-14T23:09:09.9557413Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-08-14T23:09:09.9557595Z than the C++ compiler. You can pass additional flags to SYCL compiler
2025-08-14T23:09:09.9557771Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-08-14T23:09:09.9557949Z SYCL compiler is expected to be found via system PATH environment
2025-08-14T23:09:09.9558088Z variable.
2025-08-14T23:09:09.9558133Z 
2025-08-14T23:09:09.9558166Z Args:
2025-08-14T23:09:09.9558288Z     name: The name of the extension to build. This MUST be the same as the
2025-08-14T23:09:09.9558439Z         name of the pybind11 module!
2025-08-14T23:09:09.9559514Z     sources: A list of relative or absolute paths to C++ source files.
2025-08-14T23:09:09.9559702Z     extra_cflags: optional list of compiler flags to forward to the build.
2025-08-14T23:09:09.9559893Z     extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-08-14T23:09:09.9559936Z         when building CUDA sources.
2025-08-14T23:09:09.9560012Z     extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-08-14T23:09:09.9560059Z         compiler when building SYCL sources.
2025-08-14T23:09:09.9560135Z     extra_ldflags: optional list of linker flags to forward to the build.
2025-08-14T23:09:09.9560211Z     extra_include_paths: optional list of include directories to forward
2025-08-14T23:09:09.9560247Z         to the build.
2025-08-14T23:09:09.9560314Z     build_directory: optional path to use as build workspace.
2025-08-14T23:09:09.9560383Z     verbose: If ``True``, turns on verbose logging of load steps.
2025-08-14T23:09:09.9560463Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-08-14T23:09:09.9560525Z         the build. If set to ``None`` (default), this value is
2025-08-14T23:09:09.9560600Z         automatically determined based on the existence of ``.cu`` or
2025-08-14T23:09:09.9560662Z         ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-08-14T23:09:09.9560705Z         and libraries to be included.
2025-08-14T23:09:09.9560780Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-08-14T23:09:09.9560840Z         the build. If set to ``None`` (default), this value is
2025-08-14T23:09:09.9560914Z         automatically determined based on the existence of ``.sycl`` in
2025-08-14T23:09:09.9561900Z         ``sources``. Set it to `True`` to force SYCL headers and
2025-08-14T23:09:09.9561944Z         libraries to be included.
2025-08-14T23:09:09.9562020Z     is_python_module: If ``True`` (default), imports the produced shared
2025-08-14T23:09:09.9562130Z         library as a Python module. If ``False``, behavior depends on
2025-08-14T23:09:09.9562171Z         ``is_standalone``.
2025-08-14T23:09:09.9562243Z     is_standalone: If ``False`` (default) loads the constructed extension
2025-08-14T23:09:09.9562312Z         into the process as a plain dynamic library. If ``True``, build a
2025-08-14T23:09:09.9562354Z         standalone executable.
2025-08-14T23:09:09.9562356Z 
2025-08-14T23:09:09.9562389Z Returns:
2025-08-14T23:09:09.9562432Z     If ``is_python_module`` is ``True``:
2025-08-14T23:09:09.9562498Z         Returns the loaded PyTorch extension as a Python module.
2025-08-14T23:09:09.9562500Z 
2025-08-14T23:09:09.9562572Z     If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-08-14T23:09:09.9562647Z         Returns nothing. (The shared library is loaded into the process as
2025-08-14T23:09:09.9562684Z         a side effect.)
2025-08-14T23:09:09.9562686Z 
2025-08-14T23:09:09.9562746Z     If ``is_standalone`` is ``True``.
2025-08-14T23:09:09.9562818Z         Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-08-14T23:09:09.9562963Z         added to the PATH environment variable as a side effect.)
2025-08-14T23:09:09.9562965Z 
2025-08-14T23:09:09.9562998Z Example:
2025-08-14T23:09:09.9563038Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9563090Z     >>> from torch.utils.cpp_extension import load
2025-08-14T23:09:09.9563126Z     >>> module = load(
2025-08-14T23:09:09.9563165Z     ...     name='extension',
2025-08-14T23:09:09.9564184Z     ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:09.9564227Z     ...     extra_cflags=['-O2'],
2025-08-14T23:09:09.9564265Z     ...     verbose=True)
2025-08-14T23:09:09.9564267Z 
2025-08-14T23:09:09.9564355Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9564358Z 
2025-08-14T23:09:09.9564542Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1890.
2025-08-14T23:09:09.9564633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9564638Z 
2025-08-14T23:09:09.9564715Z Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-08-14T23:09:09.9564717Z 
2025-08-14T23:09:09.9564796Z This function behaves exactly like :func:`load`, but takes its sources as
2025-08-14T23:09:09.9564878Z strings rather than filenames. These strings are stored to files in the
2025-08-14T23:09:09.9564953Z build directory, after which the behavior of :func:`load_inline` is
2025-08-14T23:09:09.9564994Z identical to :func:`load`.
2025-08-14T23:09:09.9564997Z 
2025-08-14T23:09:09.9565028Z See `the
2025-08-14T23:09:09.9565144Z tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-08-14T23:09:09.9565191Z for good examples of using this function.
2025-08-14T23:09:09.9565193Z 
2025-08-14T23:09:09.9565279Z Sources may omit two required parts of a typical non-inline C++ extension:
2025-08-14T23:09:09.9565363Z the necessary header includes, as well as the (pybind11) binding code. More
2025-08-14T23:09:09.9565449Z precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-08-14T23:09:09.9565519Z single ``.cpp`` file. This file is then prepended with ``#include
2025-08-14T23:09:09.9565557Z <torch/extension.h>``
2025-08-14T23:09:09.9565559Z 
2025-08-14T23:09:09.9565638Z Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-08-14T23:09:09.9565765Z automatically generated for each function specified. ``functions`` can
2025-08-14T23:09:09.9566783Z either be a list of function names, or a dictionary mapping from function
2025-08-14T23:09:09.9566863Z names to docstrings. If a list is given, the name of each function is used
2025-08-14T23:09:09.9566899Z as its docstring.
2025-08-14T23:09:09.9566902Z 
2025-08-14T23:09:09.9566979Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-08-14T23:09:09.9567098Z file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-08-14T23:09:09.9567174Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-08-14T23:09:09.9567253Z separately, but ultimately linked into a single library. Note that no
2025-08-14T23:09:09.9567333Z bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-08-14T23:09:09.9567408Z to a CUDA kernel, you must create a C++ function that calls it, and either
2025-08-14T23:09:09.9567484Z declare or define this C++ function in one of the ``cpp_sources`` (and
2025-08-14T23:09:09.9567528Z include its name in ``functions``).
2025-08-14T23:09:09.9567530Z 
2025-08-14T23:09:09.9567607Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-08-14T23:09:09.9567683Z file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-08-14T23:09:09.9567774Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-08-14T23:09:09.9567852Z linked into a single library. Note that no bindings are generated for
2025-08-14T23:09:09.9567927Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-08-14T23:09:09.9568020Z create a C++ function that calls it, and either declare or define this
2025-08-14T23:09:09.9568090Z C++ function in one of the ``cpp_sources`` (and include its name
2025-08-14T23:09:09.9568126Z in ``functions``).
2025-08-14T23:09:09.9568128Z 
2025-08-14T23:09:09.9568129Z 
2025-08-14T23:09:09.9568131Z 
2025-08-14T23:09:09.9568199Z See :func:`load` for a description of arguments omitted below.
2025-08-14T23:09:09.9568201Z 
2025-08-14T23:09:09.9568233Z Args:
2025-08-14T23:09:09.9569245Z     cpp_sources: A string, or list of strings, containing C++ source code.
2025-08-14T23:09:09.9569327Z     cuda_sources: A string, or list of strings, containing CUDA source code.
2025-08-14T23:09:09.9569405Z     sycl_sources: A string, or list of strings, containing SYCL source code.
2025-08-14T23:09:09.9569484Z     functions: A list of function names for which to generate function
2025-08-14T23:09:09.9569559Z         bindings. If a dictionary is given, it should map function names to
2025-08-14T23:09:09.9569630Z         docstrings (which are otherwise just the function names).
2025-08-14T23:09:09.9569707Z     with_cuda: Determines whether CUDA headers and libraries are added to
2025-08-14T23:09:09.9569766Z         the build. If set to ``None`` (default), this value is
2025-08-14T23:09:09.9569839Z         automatically determined based on whether ``cuda_sources`` is
2025-08-14T23:09:09.9569896Z         provided. Set it to ``True`` to force CUDA headers
2025-08-14T23:09:09.9569940Z         and libraries to be included.
2025-08-14T23:09:09.9570015Z     with_sycl: Determines whether SYCL headers and libraries are added to
2025-08-14T23:09:09.9570072Z         the build. If set to ``None`` (default), this value is
2025-08-14T23:09:09.9570146Z         automatically determined based on whether ``sycl_sources`` is
2025-08-14T23:09:09.9570204Z         provided. Set it to ``True`` to force SYCL headers
2025-08-14T23:09:09.9570247Z         and libraries to be included.
2025-08-14T23:09:09.9570321Z     with_pytorch_error_handling: Determines whether pytorch error and
2025-08-14T23:09:09.9570393Z         warning macros are handled by pytorch instead of pybind. To do
2025-08-14T23:09:09.9570469Z         this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-08-14T23:09:09.9570540Z         function. This redirection might cause issues in obscure cases
2025-08-14T23:09:09.9570606Z         of cpp. This flag should be set to ``False`` when this redirect
2025-08-14T23:09:09.9570642Z         causes issues.
2025-08-14T23:09:09.9571657Z     no_implicit_headers: If ``True``, skips automatically adding headers, most notably
2025-08-14T23:09:09.9571742Z         ``#include <torch/extension.h>`` and ``#include <torch/types.h>`` lines.
2025-08-14T23:09:09.9571804Z         Use this option to improve cold start times when you
2025-08-14T23:09:09.9571946Z         already include the necessary headers in your source code. Default: ``False``.
2025-08-14T23:09:09.9571949Z 
2025-08-14T23:09:09.9571984Z Example:
2025-08-14T23:09:09.9572043Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:09.9572101Z     >>> from torch.utils.cpp_extension import load_inline
2025-08-14T23:09:09.9572136Z     >>> source = """
2025-08-14T23:09:09.9572193Z     at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-08-14T23:09:09.9572235Z       return x.sin() + y.sin();
2025-08-14T23:09:09.9572267Z     }
2025-08-14T23:09:09.9572300Z     """
2025-08-14T23:09:09.9572354Z     >>> module = load_inline(name='inline_extension',
2025-08-14T23:09:09.9572401Z     ...                      cpp_sources=[source],
2025-08-14T23:09:09.9572446Z     ...                      functions=['sin_add'])
2025-08-14T23:09:09.9572449Z 
2025-08-14T23:09:09.9572482Z .. note::
2025-08-14T23:09:09.9572582Z     Since load_inline will just-in-time compile the source code, please ensure
2025-08-14T23:09:09.9572665Z     that you have the right toolchains installed in the runtime. For example,
2025-08-14T23:09:09.9572742Z     when loading C++, make sure a C++ compiler is available. If you're loading
2025-08-14T23:09:09.9572842Z     a CUDA extension, you will need to additionally install the corresponding CUDA
2025-08-14T23:09:09.9572994Z     toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-08-14T23:09:09.9574020Z     are not included when you install torch and must be additionally installed.
2025-08-14T23:09:09.9574022Z 
2025-08-14T23:09:09.9574116Z     During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-08-14T23:09:09.9574193Z     the extension. This may use up too many resources on some systems. One
2025-08-14T23:09:09.9574270Z     can control the number of workers by setting the `MAX_JOBS` environment
2025-08-14T23:09:09.9574316Z     variable to a non-negative number.
2025-08-14T23:09:09.9574323Z 
2025-08-14T23:09:09.9574413Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9574415Z 
2025-08-14T23:09:09.9884685Z 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.
2025-08-14T23:09:09.9885202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9885982Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights).
2025-08-14T23:09:09.9886035Z 
2025-08-14T23:09:09.9886154Z     Args:
2025-08-14T23:09:09.9886436Z         weights (sequence)   : a sequence of weights, not necessary summing up to one
2025-08-14T23:09:09.9886610Z         num_samples (int): number of samples to draw
2025-08-14T23:09:09.9886846Z         replacement (bool): if ``True``, samples are drawn with replacement.
2025-08-14T23:09:09.9887078Z             If not, they are drawn without replacement, which means that when a
2025-08-14T23:09:09.9887236Z             sample index is drawn for a row, it cannot be drawn again for that row.
2025-08-14T23:09:09.9887350Z         generator (Generator): Generator used in sampling.
2025-08-14T23:09:09.9887359Z 
2025-08-14T23:09:09.9887420Z     Example:
2025-08-14T23:09:09.9887518Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:09.9887566Z         >>> list(
2025-08-14T23:09:09.9890556Z         ...     WeightedRandomSampler(
2025-08-14T23:09:09.9890651Z         ...         [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True
2025-08-14T23:09:09.9890705Z         ...     )
2025-08-14T23:09:09.9890755Z         ... )
2025-08-14T23:09:09.9890804Z         [4, 4, 1, 4, 5]
2025-08-14T23:09:09.9890855Z         >>> list(
2025-08-14T23:09:09.9890918Z         ...     WeightedRandomSampler(
2025-08-14T23:09:09.9891004Z         ...         [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False
2025-08-14T23:09:09.9891049Z         ...     )
2025-08-14T23:09:09.9891100Z         ... )
2025-08-14T23:09:09.9891286Z         [0, 1, 4, 3, 2]
2025-08-14T23:09:09.9891378Z     
2025-08-14T23:09:09.9891501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9891507Z 
2025-08-14T23:09:09.9891762Z 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.
2025-08-14T23:09:09.9891887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9891979Z Wraps another sampler to yield a mini-batch of indices.
2025-08-14T23:09:09.9891982Z 
2025-08-14T23:09:09.9892027Z     Args:
2025-08-14T23:09:09.9892138Z         sampler (Sampler or Iterable): Base sampler. Can be any iterable object
2025-08-14T23:09:09.9892205Z         batch_size (int): Size of mini-batch.
2025-08-14T23:09:09.9892305Z         drop_last (bool): If ``True``, the sampler will drop the last batch if
2025-08-14T23:09:09.9892418Z             its size would be less than ``batch_size``
2025-08-14T23:09:09.9892423Z 
2025-08-14T23:09:09.9892473Z     Example:
2025-08-14T23:09:09.9892518Z         >>> list(
2025-08-14T23:09:09.9892580Z         ...     BatchSampler(
2025-08-14T23:09:09.9892721Z         ...         SequentialSampler(range(10)), batch_size=3, drop_last=False
2025-08-14T23:09:09.9892769Z         ...     )
2025-08-14T23:09:09.9892813Z         ... )
2025-08-14T23:09:09.9892876Z         [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
2025-08-14T23:09:09.9894713Z         >>> list(
2025-08-14T23:09:09.9894846Z         ...     BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True)
2025-08-14T23:09:09.9894890Z         ... )
2025-08-14T23:09:09.9894942Z         [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
2025-08-14T23:09:09.9894985Z     
2025-08-14T23:09:09.9895104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9895108Z 
2025-08-14T23:09:09.9925865Z 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.
2025-08-14T23:09:09.9926053Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:09.9926150Z Sampler that restricts data loading to a subset of the dataset.
2025-08-14T23:09:09.9926153Z 
2025-08-14T23:09:09.9926208Z     It is especially useful in conjunction with
2025-08-14T23:09:09.9926302Z     :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-08-14T23:09:09.9926401Z     process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-08-14T23:09:09.9926485Z     :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-08-14T23:09:09.9926537Z     original dataset that is exclusive to it.
2025-08-14T23:09:09.9926539Z 
2025-08-14T23:09:09.9926577Z     .. note::
2025-08-14T23:09:09.9926665Z         Dataset is assumed to be of constant size and that any instance of it always
2025-08-14T23:09:09.9926722Z         returns the same elements in the same order.
2025-08-14T23:09:09.9926725Z 
2025-08-14T23:09:09.9926761Z     Args:
2025-08-14T23:09:09.9926809Z         dataset: Dataset used for sampling.
2025-08-14T23:09:09.9926894Z         num_replicas (int, optional): Number of processes participating in
2025-08-14T23:09:09.9928418Z             distributed training. By default, :attr:`world_size` is retrieved from the
2025-08-14T23:09:09.9928470Z             current distributed group.
2025-08-14T23:09:09.9928570Z         rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-08-14T23:09:09.9928645Z             By default, :attr:`rank` is retrieved from the current distributed
2025-08-14T23:09:09.9928680Z             group.
2025-08-14T23:09:09.9928770Z         shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-08-14T23:09:09.9928808Z             indices.
2025-08-14T23:09:09.9928886Z         seed (int, optional): random seed used to shuffle the sampler if
2025-08-14T23:09:09.9929021Z             :attr:`shuffle=True`. This number should be identical across all
2025-08-14T23:09:09.9929105Z             processes in the distributed group. Default: ``0``.
2025-08-14T23:09:09.9929184Z         drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-08-14T23:09:09.9929256Z             tail of the data to make it evenly divisible across the number of
2025-08-14T23:09:09.9929328Z             replicas. If ``False``, the sampler will add extra indices to make
2025-08-14T23:09:09.9929403Z             the data evenly divisible across the replicas. Default: ``False``.
2025-08-14T23:09:09.9929406Z 
2025-08-14T23:09:09.9929442Z     .. warning::
2025-08-14T23:09:09.9929514Z         In distributed mode, calling the :meth:`set_epoch` method at
2025-08-14T23:09:09.9929608Z         the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-08-14T23:09:09.9929706Z         is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-08-14T23:09:09.9929776Z         the same ordering will be always used.
2025-08-14T23:09:09.9929780Z 
2025-08-14T23:09:09.9929816Z     Example::
2025-08-14T23:09:09.9929819Z 
2025-08-14T23:09:09.9929880Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:09.9931029Z         >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-08-14T23:09:09.9931101Z         >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-08-14T23:09:09.9931150Z         ...                     sampler=sampler)
2025-08-14T23:09:09.9931205Z         >>> for epoch in range(start_epoch, n_epochs):
2025-08-14T23:09:09.9931249Z         ...     if is_distributed:
2025-08-14T23:09:09.9931296Z         ...         sampler.set_epoch(epoch)
2025-08-14T23:09:09.9931335Z         ...     train(loader)
2025-08-14T23:09:09.9931368Z     
2025-08-14T23:09:09.9931461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:09.9931463Z 
2025-08-14T23:09:10.0019713Z 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.
2025-08-14T23:09:10.0019901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0019907Z 
2025-08-14T23:09:10.0019952Z Iterable-style DataPipe.
2025-08-14T23:09:10.0019955Z 
2025-08-14T23:09:10.0020048Z All DataPipes that represent an iterable of data samples should subclass this.
2025-08-14T23:09:10.0020140Z This style of DataPipes is particularly useful when data come from a stream, or
2025-08-14T23:09:10.0020265Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its
2025-08-14T23:09:10.0020371Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``.
2025-08-14T23:09:10.0020373Z 
2025-08-14T23:09:10.0020458Z All subclasses should overwrite :meth:`__iter__`, which would return an
2025-08-14T23:09:10.0020579Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its
2025-08-14T23:09:10.0020707Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should
2025-08-14T23:09:10.0020805Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers,
2025-08-14T23:09:10.0022649Z and various state variables within the custom ``IterDataPipe``.
2025-08-14T23:09:10.0022653Z 
2025-08-14T23:09:10.0022690Z Note:
2025-08-14T23:09:10.0022781Z     Only `one` iterator can be valid for each ``IterDataPipe`` at a time,
2025-08-14T23:09:10.0023005Z     and the creation a second iterator will invalidate the first one. This constraint is necessary because
2025-08-14T23:09:10.0023135Z     some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators.
2025-08-14T23:09:10.0023231Z     The code example below presents details on how this constraint looks in practice.
2025-08-14T23:09:10.0023412Z     If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_.
2025-08-14T23:09:10.0023435Z 
2025-08-14T23:09:10.0023537Z These DataPipes can be invoked in two ways, using the class constructor or applying their
2025-08-14T23:09:10.0023669Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes).
2025-08-14T23:09:10.0023781Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple
2025-08-14T23:09:10.0023825Z operations in succession.
2025-08-14T23:09:10.0023828Z 
2025-08-14T23:09:10.0023882Z .. _GitHub IterDataPipe Single Iterator Issue:
2025-08-14T23:09:10.0023941Z     https://github.com/pytorch/data/issues/45
2025-08-14T23:09:10.0023943Z 
2025-08-14T23:09:10.0023975Z Note:
2025-08-14T23:09:10.0024061Z     When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
2025-08-14T23:09:10.0024176Z     item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader`
2025-08-14T23:09:10.0024260Z     iterator. When :attr:`num_workers > 0`, each worker process will have a
2025-08-14T23:09:10.0024342Z     different copy of the DataPipe object, so it is often desired to configure
2025-08-14T23:09:10.0024446Z     each copy independently to avoid having duplicate data returned from the
2025-08-14T23:09:10.0024532Z     workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
2025-08-14T23:09:10.0024621Z     process, returns information about the worker. It can be used in either the
2025-08-14T23:09:10.0024708Z     dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
2025-08-14T23:09:10.0025947Z     :attr:`worker_init_fn` option to modify each copy's behavior.
2025-08-14T23:09:10.0025950Z 
2025-08-14T23:09:10.0025986Z Examples:
2025-08-14T23:09:10.0026024Z     General Usage:
2025-08-14T23:09:10.0026066Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:10.0026147Z         >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
2025-08-14T23:09:10.0026196Z         >>> dp = IterableWrapper(range(10))
2025-08-14T23:09:10.0026268Z         >>> map_dp_1 = Mapper(dp, lambda x: x + 1)  # Using class constructor
2025-08-14T23:09:10.0026310Z         >>> map_dp_2 = dp.map(
2025-08-14T23:09:10.0026350Z         ...     lambda x: x + 1
2025-08-14T23:09:10.0026404Z         ... )  # Using functional form (recommended)
2025-08-14T23:09:10.0026445Z         >>> list(map_dp_1)
2025-08-14T23:09:10.0026484Z         [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
2025-08-14T23:09:10.0026521Z         >>> list(map_dp_2)
2025-08-14T23:09:10.0026558Z         [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
2025-08-14T23:09:10.0026617Z         >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0)
2025-08-14T23:09:10.0026656Z         >>> list(filter_dp)
2025-08-14T23:09:10.0026690Z         [2, 4, 6, 8, 10]
2025-08-14T23:09:10.0026735Z     Single Iterator Constraint Example:
2025-08-14T23:09:10.0026811Z         >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
2025-08-14T23:09:10.0026864Z         >>> source_dp = IterableWrapper(range(10))
2025-08-14T23:09:10.0026906Z         >>> it1 = iter(source_dp)
2025-08-14T23:09:10.0027922Z         >>> list(it1)
2025-08-14T23:09:10.0027968Z         [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2025-08-14T23:09:10.0028009Z         >>> it1 = iter(source_dp)
2025-08-14T23:09:10.0028044Z         >>> it2 = iter(
2025-08-14T23:09:10.0028082Z         ...     source_dp
2025-08-14T23:09:10.0028143Z         ... )  # The creation of a new iterator invalidates `it1`
2025-08-14T23:09:10.0028178Z         >>> next(it2)
2025-08-14T23:09:10.0028210Z         0
2025-08-14T23:09:10.0028279Z         >>> next(it1)  # Further usage of `it1` will raise a `RunTimeError`
2025-08-14T23:09:10.0028282Z 
2025-08-14T23:09:10.0028369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0028372Z 
2025-08-14T23:09:10.0148332Z 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.
2025-08-14T23:09:10.0148584Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0149077Z 
2025-08-14T23:09:10.0149364Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``.
2025-08-14T23:09:10.0149371Z 
2025-08-14T23:09:10.0149436Z (functional name: ``groupby``).
2025-08-14T23:09:10.0149439Z 
2025-08-14T23:09:10.0149578Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group
2025-08-14T23:09:10.0149695Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full,
2025-08-14T23:09:10.0149802Z the DataPipe will yield the largest batch with the same key, provided that its size is larger
2025-08-14T23:09:10.0149913Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``.
2025-08-14T23:09:10.0149915Z 
2025-08-14T23:09:10.0150115Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity
2025-08-14T23:09:10.0152280Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``.
2025-08-14T23:09:10.0152321Z 
2025-08-14T23:09:10.0152359Z Args:
2025-08-14T23:09:10.0152418Z     datapipe: Iterable datapipe to be grouped
2025-08-14T23:09:10.0152518Z     group_key_fn: Function used to generate group key from the data of the source datapipe
2025-08-14T23:09:10.0152611Z     keep_key: Option to yield the matching key along with the items in a tuple,
2025-08-14T23:09:10.0152679Z         resulting in `(key, [items])` otherwise returning [items]
2025-08-14T23:09:10.0152739Z     buffer_size: The size of buffer for ungrouped data
2025-08-14T23:09:10.0152838Z     group_size: The max size of each group, a batch is yielded as soon as it reaches this size
2025-08-14T23:09:10.0153165Z     guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full
2025-08-14T23:09:10.0153291Z     drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer
2025-08-14T23:09:10.0153342Z         when the buffer is full
2025-08-14T23:09:10.0153344Z 
2025-08-14T23:09:10.0153377Z Example:
2025-08-14T23:09:10.0153419Z     >>> import os
2025-08-14T23:09:10.0153461Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:10.0153534Z     >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:10.0153573Z     >>> def group_fn(file):
2025-08-14T23:09:10.0153632Z     ...     return os.path.basename(file).split(".")[0]
2025-08-14T23:09:10.0153680Z     >>> source_dp = IterableWrapper(
2025-08-14T23:09:10.0153745Z     ...     ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"]
2025-08-14T23:09:10.0153778Z     ... )
2025-08-14T23:09:10.0153836Z     >>> dp0 = source_dp.groupby(group_key_fn=group_fn)
2025-08-14T23:09:10.0153869Z     >>> list(dp0)
2025-08-14T23:09:10.0155718Z     [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']]
2025-08-14T23:09:10.0155846Z     >>> # A group is yielded as soon as its size equals to `group_size`
2025-08-14T23:09:10.0155953Z     >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2)
2025-08-14T23:09:10.0156006Z     >>> list(dp1)
2025-08-14T23:09:10.0156096Z     [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
2025-08-14T23:09:10.0156239Z     >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size`
2025-08-14T23:09:10.0156284Z     >>> dp2 = source_dp.groupby(
2025-08-14T23:09:10.0156327Z     ...     group_key_fn=group_fn,
2025-08-14T23:09:10.0156365Z     ...     buffer_size=3,
2025-08-14T23:09:10.0156403Z     ...     group_size=3,
2025-08-14T23:09:10.0156444Z     ...     guaranteed_group_size=2,
2025-08-14T23:09:10.0156478Z     ... )
2025-08-14T23:09:10.0156511Z     >>> list(dp2)
2025-08-14T23:09:10.0156573Z     [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
2025-08-14T23:09:10.0156625Z 
2025-08-14T23:09:10.0156738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0156741Z 
2025-08-14T23:09:10.0157003Z 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.
2025-08-14T23:09:10.0157098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0157100Z 
2025-08-14T23:09:10.0157230Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``).
2025-08-14T23:09:10.0157232Z 
2025-08-14T23:09:10.0157263Z Args:
2025-08-14T23:09:10.0157327Z     datapipe: Iterable datapipe that provides pathnames
2025-08-14T23:09:10.0159091Z     mode: An optional string that specifies the mode in which
2025-08-14T23:09:10.0159178Z         the file is opened by ``open()``. It defaults to ``r``, other options are
2025-08-14T23:09:10.0159281Z         ``b`` for reading in binary mode and ``t`` for text mode.
2025-08-14T23:09:10.0159356Z     encoding: An optional string that specifies the encoding of the
2025-08-14T23:09:10.0159475Z         underlying file. It defaults to ``None`` to match the default encoding of ``open``.
2025-08-14T23:09:10.0159523Z     length: Nominal length of the datapipe
2025-08-14T23:09:10.0159525Z 
2025-08-14T23:09:10.0159557Z Note:
2025-08-14T23:09:10.0159650Z     The opened file handles will be closed by Python's GC periodically. Users can choose
2025-08-14T23:09:10.0159693Z     to close them explicitly.
2025-08-14T23:09:10.0159696Z 
2025-08-14T23:09:10.0159729Z Example:
2025-08-14T23:09:10.0159767Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:10.0159820Z     >>> from torchdata.datapipes.iter import (
2025-08-14T23:09:10.0159856Z     ...     FileLister,
2025-08-14T23:09:10.0159892Z     ...     FileOpener,
2025-08-14T23:09:10.0159929Z     ...     StreamReader,
2025-08-14T23:09:10.0159961Z     ... )
2025-08-14T23:09:10.0160045Z     >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt"))
2025-08-14T23:09:10.0160085Z     >>> dp = FileOpener(dp)
2025-08-14T23:09:10.0160124Z     >>> dp = StreamReader(dp)
2025-08-14T23:09:10.0160159Z     >>> list(dp)
2025-08-14T23:09:10.0160197Z     [('./abc.txt', 'abc')]
2025-08-14T23:09:10.0160199Z 
2025-08-14T23:09:10.0160287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0160290Z 
2025-08-14T23:09:10.0189155Z 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.
2025-08-14T23:09:10.0189467Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0189495Z 
2025-08-14T23:09:10.0189696Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``).
2025-08-14T23:09:10.0189707Z 
2025-08-14T23:09:10.0189823Z A list of the child DataPipes is returned from this operation.
2025-08-14T23:09:10.0189841Z 
2025-08-14T23:09:10.0189880Z Args:
2025-08-14T23:09:10.0189937Z     datapipe: Iterable DataPipe being filtered
2025-08-14T23:09:10.0190020Z     num_instances: number of instances of the DataPipe to create
2025-08-14T23:09:10.0190157Z     classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None``
2025-08-14T23:09:10.0190302Z     drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None``
2025-08-14T23:09:10.0190414Z     buffer_size: this defines the maximum number of inputs that the buffer can hold across all child
2025-08-14T23:09:10.0190492Z         DataPipes while waiting for their values to be yielded.
2025-08-14T23:09:10.0190560Z         Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
2025-08-14T23:09:10.0190562Z 
2025-08-14T23:09:10.0190602Z Examples:
2025-08-14T23:09:10.0190656Z     >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:10.0190868Z     >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:10.0190911Z     >>> def odd_or_even(n):
2025-08-14T23:09:10.0190955Z     ...     return n % 2
2025-08-14T23:09:10.0191007Z     >>> source_dp = IterableWrapper(range(5))
2025-08-14T23:09:10.0191091Z     >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even)
2025-08-14T23:09:10.0193601Z     >>> list(dp1)
2025-08-14T23:09:10.0193650Z     [0, 2, 4]
2025-08-14T23:09:10.0193687Z     >>> list(dp2)
2025-08-14T23:09:10.0193726Z     [1, 3]
2025-08-14T23:09:10.0193818Z     >>> # It can also filter out any element that gets `None` from the `classifier_fn`
2025-08-14T23:09:10.0193866Z     >>> def odd_or_even_no_zero(n):
2025-08-14T23:09:10.0193913Z     ...     return n % 2 if n != 0 else None
2025-08-14T23:09:10.0193955Z     >>> dp1, dp2 = source_dp.demux(
2025-08-14T23:09:10.0194047Z     ...     num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True
2025-08-14T23:09:10.0194130Z     ... )
2025-08-14T23:09:10.0194164Z     >>> list(dp1)
2025-08-14T23:09:10.0194202Z     [2, 4]
2025-08-14T23:09:10.0194235Z     >>> list(dp2)
2025-08-14T23:09:10.0194301Z     [1, 3]
2025-08-14T23:09:10.0194303Z 
2025-08-14T23:09:10.0194396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0194398Z 
2025-08-14T23:09:10.0194639Z 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.
2025-08-14T23:09:10.0194732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0194734Z 
2025-08-14T23:09:10.0194844Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``).
2025-08-14T23:09:10.0194847Z 
2025-08-14T23:09:10.0194967Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration,
2025-08-14T23:09:10.0195042Z and so on. It ends when the shortest input DataPipe is exhausted.
2025-08-14T23:09:10.0195046Z 
2025-08-14T23:09:10.0195077Z Args:
2025-08-14T23:09:10.0196438Z     datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted
2025-08-14T23:09:10.0196444Z 
2025-08-14T23:09:10.0196480Z Example:
2025-08-14T23:09:10.0196532Z     >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:10.0196600Z     >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:10.0196640Z     >>> dp1, dp2, dp3 = (
2025-08-14T23:09:10.0196684Z     ...     IterableWrapper(range(3)),
2025-08-14T23:09:10.0196730Z     ...     IterableWrapper(range(10, 15)),
2025-08-14T23:09:10.0196773Z     ...     IterableWrapper(range(20, 25)),
2025-08-14T23:09:10.0196805Z     ... )
2025-08-14T23:09:10.0196846Z     >>> list(dp1.mux(dp2, dp3))
2025-08-14T23:09:10.0196885Z     [0, 10, 20, 1, 11, 21, 2, 12, 22]
2025-08-14T23:09:10.0196888Z 
2025-08-14T23:09:10.0196981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0196984Z 
2025-08-14T23:09:10.0197207Z 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.
2025-08-14T23:09:10.0197300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.0197302Z 
2025-08-14T23:09:10.0197409Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
2025-08-14T23:09:10.0197411Z 
2025-08-14T23:09:10.0197492Z The output is stopped as soon as the shortest input DataPipe is exhausted.
2025-08-14T23:09:10.0197494Z 
2025-08-14T23:09:10.0197526Z Args:
2025-08-14T23:09:10.0197581Z     *datapipes: Iterable DataPipes being aggregated
2025-08-14T23:09:10.0197584Z 
2025-08-14T23:09:10.0197618Z Example:
2025-08-14T23:09:10.0197666Z     >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:10.0197733Z     >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:10.0197816Z     >>> dp1, dp2, dp3 = (
2025-08-14T23:09:10.0198855Z     ...     IterableWrapper(range(5)),
2025-08-14T23:09:10.0198905Z     ...     IterableWrapper(range(10, 15)),
2025-08-14T23:09:10.0198949Z     ...     IterableWrapper(range(20, 25)),
2025-08-14T23:09:10.0198981Z     ... )
2025-08-14T23:09:10.0199021Z     >>> list(dp1.zip(dp2, dp3))
2025-08-14T23:09:10.0199075Z     [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)]
2025-08-14T23:09:10.0199077Z 
2025-08-14T23:09:10.0199164Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.0199167Z 
2025-08-14T23:09:10.5381605Z 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.
2025-08-14T23:09:10.5382579Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:10.5383497Z Asserts that ``actual`` and ``expected`` are close.
2025-08-14T23:09:10.5389450Z 
2025-08-14T23:09:10.5389754Z     If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-08-14T23:09:10.5390164Z 
2025-08-14T23:09:10.5390245Z     .. math::
2025-08-14T23:09:10.5390358Z 
2025-08-14T23:09:10.5390626Z         \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-08-14T23:09:10.5390951Z 
2025-08-14T23:09:10.5391201Z     Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-08-14T23:09:10.5391662Z     only considered equal to each other if ``equal_nan`` is ``True``.
2025-08-14T23:09:10.5391883Z 
2025-08-14T23:09:10.5392031Z     In addition, they are only considered close if they have the same
2025-08-14T23:09:10.5392253Z 
2025-08-14T23:09:10.5392405Z     - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-08-14T23:09:10.5392727Z     - ``dtype`` (if ``check_dtype`` is ``True``),
2025-08-14T23:09:10.5393205Z     - ``layout`` (if ``check_layout`` is ``True``), and
2025-08-14T23:09:10.5393492Z     - stride (if ``check_stride`` is ``True``).
2025-08-14T23:09:10.5393666Z 
2025-08-14T23:09:10.5393877Z     If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-08-14T23:09:10.5394166Z 
2025-08-14T23:09:10.5394416Z     If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-08-14T23:09:10.5394994Z     checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-08-14T23:09:10.5395495Z     or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-08-14T23:09:10.5396001Z     are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-08-14T23:09:10.5396333Z 
2025-08-14T23:09:10.5396540Z     If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-08-14T23:09:10.5397063Z     :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-08-14T23:09:10.5397388Z     definition above.
2025-08-14T23:09:10.5397479Z 
2025-08-14T23:09:10.5397636Z     ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-08-14T23:09:10.5398029Z     :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-08-14T23:09:10.5398443Z     have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-08-14T23:09:10.5398857Z     or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-08-14T23:09:10.5399204Z     their elements are considered close according to the above definition.
2025-08-14T23:09:10.5399410Z 
2025-08-14T23:09:10.5399463Z     .. note::
2025-08-14T23:09:10.5399537Z 
2025-08-14T23:09:10.5399730Z         Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-08-14T23:09:10.5400103Z         :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-08-14T23:09:10.5400452Z         Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-08-14T23:09:10.5400641Z 
2025-08-14T23:09:10.5400692Z     Args:
2025-08-14T23:09:10.5400821Z         actual (Any): Actual input.
2025-08-14T23:09:10.5400996Z         expected (Any): Expected input.
2025-08-14T23:09:10.5401285Z         allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-08-14T23:09:10.5401597Z             are allowed. Otherwise type equality is required.
2025-08-14T23:09:10.5401938Z         rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-08-14T23:09:10.5402306Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-08-14T23:09:10.5402687Z         atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-08-14T23:09:10.5403170Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-08-14T23:09:10.5403481Z         equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-08-14T23:09:10.5403803Z         check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-08-14T23:09:10.5404126Z             :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-08-14T23:09:10.5404423Z             :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-08-14T23:09:10.5404765Z         check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-08-14T23:09:10.5405159Z             check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-08-14T23:09:10.5405472Z             :func:`torch.promote_types`) before being compared.
2025-08-14T23:09:10.5405962Z         check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-08-14T23:09:10.5406349Z             check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-08-14T23:09:10.5406619Z             compared.
2025-08-14T23:09:10.5406884Z         check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-08-14T23:09:10.5407239Z         msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-08-14T23:09:10.5410174Z             the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-08-14T23:09:10.5410420Z             should return the new message.
2025-08-14T23:09:10.5410509Z 
2025-08-14T23:09:10.5410550Z     Raises:
2025-08-14T23:09:10.5410697Z         ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-08-14T23:09:10.5410899Z         ValueError: If only ``rtol`` or ``atol`` is specified.
2025-08-14T23:09:10.5411132Z         AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-08-14T23:09:10.5411431Z         AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-08-14T23:09:10.5411653Z             different types.
2025-08-14T23:09:10.5411865Z         AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-08-14T23:09:10.5412178Z         AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-08-14T23:09:10.5412545Z         AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-08-14T23:09:10.5414209Z         AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-08-14T23:09:10.5414424Z             :attr:`~torch.Tensor.layout`.
2025-08-14T23:09:10.5414601Z         AssertionError: If only one of corresponding tensors is quantized.
2025-08-14T23:09:10.5414870Z         AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-08-14T23:09:10.5415162Z         AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-08-14T23:09:10.5415362Z             :attr:`~torch.Tensor.device`.
2025-08-14T23:09:10.5415570Z         AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-08-14T23:09:10.5415938Z         AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-08-14T23:09:10.5416252Z         AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-08-14T23:09:10.5416447Z 
2025-08-14T23:09:10.5416587Z     The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-08-14T23:09:10.5416824Z     ``dtype``'s, the maximum of both tolerances is used.
2025-08-14T23:09:10.5416926Z 
2025-08-14T23:09:10.5418021Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5418163Z     | ``dtype``                 | ``rtol``   | ``atol`` |
2025-08-14T23:09:10.5418286Z     +===========================+============+==========+
2025-08-14T23:09:10.5418417Z     | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-08-14T23:09:10.5418549Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5418681Z     | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-08-14T23:09:10.5418815Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5418947Z     | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5419076Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5419205Z     | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-08-14T23:09:10.5419331Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5420352Z     | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-08-14T23:09:10.5420484Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5420613Z     | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5420741Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5420871Z     | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-08-14T23:09:10.5420999Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5421128Z     | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5421258Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5421385Z     | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5421515Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5421642Z     | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5422636Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5422766Z     | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5422963Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5423092Z     | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:10.5423217Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5423341Z     | other                     | ``0.0``    | ``0.0``  |
2025-08-14T23:09:10.5423465Z     +---------------------------+------------+----------+
2025-08-14T23:09:10.5423546Z 
2025-08-14T23:09:10.5423586Z     .. note::
2025-08-14T23:09:10.5423666Z 
2025-08-14T23:09:10.5423818Z         :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-08-14T23:09:10.5424103Z         to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-08-14T23:09:10.5424349Z         define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-08-14T23:09:10.5425367Z 
2025-08-14T23:09:10.5425411Z         >>> import functools
2025-08-14T23:09:10.5425572Z         >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-08-14T23:09:10.5425743Z         >>> assert_equal(1e-9, 1e-10)
2025-08-14T23:09:10.5425862Z         Traceback (most recent call last):
2025-08-14T23:09:10.5425971Z         ...
2025-08-14T23:09:10.5426065Z         AssertionError: Scalars are not equal!
2025-08-14T23:09:10.5426181Z         <BLANKLINE>
2025-08-14T23:09:10.5426300Z         Expected 1e-10 but got 1e-09.
2025-08-14T23:09:10.5426424Z         Absolute difference: 9.000000000000001e-10
2025-08-14T23:09:10.5426547Z         Relative difference: 9.0
2025-08-14T23:09:10.5426618Z 
2025-08-14T23:09:10.5426671Z     Examples:
2025-08-14T23:09:10.5427632Z         >>> # tensor to tensor comparison
2025-08-14T23:09:10.5427763Z         >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-08-14T23:09:10.5427901Z         >>> actual = torch.acos(torch.cos(expected))
2025-08-14T23:09:10.5428040Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5428128Z 
2025-08-14T23:09:10.5428172Z         >>> # scalar to scalar comparison
2025-08-14T23:09:10.5428283Z         >>> import math
2025-08-14T23:09:10.5428383Z         >>> expected = math.sqrt(2.0)
2025-08-14T23:09:10.5428496Z         >>> actual = 2.0 / math.sqrt(2.0)
2025-08-14T23:09:10.5428622Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5428710Z 
2025-08-14T23:09:10.5428760Z         >>> # numpy array to numpy array comparison
2025-08-14T23:09:10.5428884Z         >>> import numpy as np
2025-08-14T23:09:10.5429867Z         >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-08-14T23:09:10.5429995Z         >>> actual = np.arccos(np.cos(expected))
2025-08-14T23:09:10.5430130Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5430217Z 
2025-08-14T23:09:10.5430263Z         >>> # sequence to sequence comparison
2025-08-14T23:09:10.5430379Z         >>> import numpy as np
2025-08-14T23:09:10.5430535Z         >>> # The types of the sequences do not have to match. They only have to have the same
2025-08-14T23:09:10.5430709Z         >>> # length and their elements have to match.
2025-08-14T23:09:10.5430850Z         >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-08-14T23:09:10.5430985Z         >>> actual = tuple(expected)
2025-08-14T23:09:10.5431123Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5431209Z 
2025-08-14T23:09:10.5432179Z         >>> # mapping to mapping comparison
2025-08-14T23:09:10.5432310Z         >>> from collections import OrderedDict
2025-08-14T23:09:10.5432428Z         >>> import numpy as np
2025-08-14T23:09:10.5432534Z         >>> foo = torch.tensor(1.0)
2025-08-14T23:09:10.5432642Z         >>> bar = 2.0
2025-08-14T23:09:10.5432736Z         >>> baz = np.array(3.0)
2025-08-14T23:09:10.5432968Z         >>> # The types and a possible ordering of mappings do not have to match. They only
2025-08-14T23:09:10.5433166Z         >>> # have to have the same set of keys and their elements have to match.
2025-08-14T23:09:10.5433347Z         >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-08-14T23:09:10.5433508Z         >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-08-14T23:09:10.5433645Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5433731Z 
2025-08-14T23:09:10.5434669Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-08-14T23:09:10.5434795Z         >>> actual = expected.clone()
2025-08-14T23:09:10.5434986Z         >>> # By default, directly related instances can be compared
2025-08-14T23:09:10.5435160Z         >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-08-14T23:09:10.5435343Z         >>> # This check can be made more strict with allow_subclasses=False
2025-08-14T23:09:10.5435491Z         >>> torch.testing.assert_close(
2025-08-14T23:09:10.5435640Z         ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-08-14T23:09:10.5435778Z         ... )
2025-08-14T23:09:10.5435870Z         Traceback (most recent call last):
2025-08-14T23:09:10.5435978Z         ...
2025-08-14T23:09:10.5436096Z         TypeError: No comparison pair was able to handle inputs of type
2025-08-14T23:09:10.5437158Z         <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-08-14T23:09:10.5437354Z         >>> # If the inputs are not directly related, they are never considered close
2025-08-14T23:09:10.5437557Z         >>> torch.testing.assert_close(actual.numpy(), expected)
2025-08-14T23:09:10.5437702Z         Traceback (most recent call last):
2025-08-14T23:09:10.5437809Z         ...
2025-08-14T23:09:10.5437954Z         TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-08-14T23:09:10.5438151Z         and <class 'torch.Tensor'>.
2025-08-14T23:09:10.5438316Z         >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-08-14T23:09:10.5438489Z         >>> # their type if check_dtype=False.
2025-08-14T23:09:10.5438631Z         >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-08-14T23:09:10.5438728Z 
2025-08-14T23:09:10.5438769Z         >>> # NaN != NaN by default.
2025-08-14T23:09:10.5439762Z         >>> expected = torch.tensor(float("Nan"))
2025-08-14T23:09:10.5439886Z         >>> actual = expected.clone()
2025-08-14T23:09:10.5440015Z         >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:10.5440147Z         Traceback (most recent call last):
2025-08-14T23:09:10.5440255Z         ...
2025-08-14T23:09:10.5440349Z         AssertionError: Scalars are not close!
2025-08-14T23:09:10.5440461Z         <BLANKLINE>
2025-08-14T23:09:10.5440558Z         Expected nan but got nan.
2025-08-14T23:09:10.5440684Z         Absolute difference: nan (up to 1e-05 allowed)
2025-08-14T23:09:10.5440825Z         Relative difference: nan (up to 1.3e-06 allowed)
2025-08-14T23:09:10.5440983Z         >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-08-14T23:09:10.5441951Z 
2025-08-14T23:09:10.5442001Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-08-14T23:09:10.5442128Z         >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-08-14T23:09:10.5442263Z         >>> # The default error message can be overwritten.
2025-08-14T23:09:10.5442394Z         >>> torch.testing.assert_close(
2025-08-14T23:09:10.5442537Z         ...     actual, expected, msg="Argh, the tensors are not close!"
2025-08-14T23:09:10.5442669Z         ... )
2025-08-14T23:09:10.5442764Z         Traceback (most recent call last):
2025-08-14T23:09:10.5442869Z         ...
2025-08-14T23:09:10.5443064Z         AssertionError: Argh, the tensors are not close!
2025-08-14T23:09:10.5443230Z         >>> # If msg is a callable, it can be used to augment the generated message with
2025-08-14T23:09:10.5443384Z         >>> # extra information
2025-08-14T23:09:10.5444369Z         >>> torch.testing.assert_close(
2025-08-14T23:09:10.5444516Z         ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-08-14T23:09:10.5444656Z         ... )
2025-08-14T23:09:10.5444746Z         Traceback (most recent call last):
2025-08-14T23:09:10.5444852Z         ...
2025-08-14T23:09:10.5444936Z         AssertionError: Header
2025-08-14T23:09:10.5445037Z         <BLANKLINE>
2025-08-14T23:09:10.5445130Z         Tensor-likes are not close!
2025-08-14T23:09:10.5445235Z         <BLANKLINE>
2025-08-14T23:09:10.5445331Z         Mismatched elements: 2 / 3 (66.7%)
2025-08-14T23:09:10.5445488Z         Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-08-14T23:09:10.5446672Z         Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-08-14T23:09:10.5446824Z         <BLANKLINE>
2025-08-14T23:09:10.5446907Z         Footer
2025-08-14T23:09:10.5446986Z     
2025-08-14T23:09:10.5447119Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:10.5447242Z 
2025-08-14T23:09:11.0872728Z 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.
2025-08-14T23:09:11.0873840Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.0874179Z 
2025-08-14T23:09:11.0874385Z Creates a RelaxedOneHotCategorical distribution parametrized by
2025-08-14T23:09:11.0881124Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-08-14T23:09:11.0881890Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-08-14T23:09:11.0882341Z its samples are on simplex, and are reparametrizable.
2025-08-14T23:09:11.0882570Z 
2025-08-14T23:09:11.0882674Z Example::
2025-08-14T23:09:11.0882862Z 
2025-08-14T23:09:11.0883220Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:11.0883563Z     >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-08-14T23:09:11.0883894Z     ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-08-14T23:09:11.0884178Z     >>> m.sample()
2025-08-14T23:09:11.0884401Z     tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-08-14T23:09:11.0884585Z 
2025-08-14T23:09:11.0884662Z Args:
2025-08-14T23:09:11.0884887Z     temperature (Tensor): relaxation temperature
2025-08-14T23:09:11.0885196Z     probs (Tensor): event probabilities
2025-08-14T23:09:11.0885540Z     logits (Tensor): unnormalized log probability for each event
2025-08-14T23:09:11.0885905Z 
2025-08-14T23:09:11.0886113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.0886402Z 
2025-08-14T23:09:11.0886933Z 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.
2025-08-14T23:09:11.0887559Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.0887766Z 
2025-08-14T23:09:11.0887893Z The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-08-14T23:09:11.0888207Z distribution where all component are from different parameterizations of
2025-08-14T23:09:11.0888519Z the same distribution type. It is parameterized by a `Categorical`
2025-08-14T23:09:11.0888812Z "selecting distribution" (over `k` component) and a component
2025-08-14T23:09:11.0889099Z distribution, i.e., a `Distribution` with a rightmost batch shape
2025-08-14T23:09:11.0889368Z (equal to `[k]`) which indexes each (batch of) component.
2025-08-14T23:09:11.0889518Z 
2025-08-14T23:09:11.0889581Z Examples::
2025-08-14T23:09:11.0889660Z 
2025-08-14T23:09:11.0889741Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.0892497Z     >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-08-14T23:09:11.0892742Z     >>> # weighted normal distributions
2025-08-14T23:09:11.0893049Z     >>> mix = D.Categorical(torch.ones(5,))
2025-08-14T23:09:11.0893262Z     >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-08-14T23:09:11.0893472Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:11.0893598Z 
2025-08-14T23:09:11.0893711Z     >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-08-14T23:09:11.0893954Z     >>> # weighted bivariate normal distributions
2025-08-14T23:09:11.0894151Z     >>> mix = D.Categorical(torch.ones(5,))
2025-08-14T23:09:11.0894337Z     >>> comp = D.Independent(D.Normal(
2025-08-14T23:09:11.0894536Z     ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-08-14T23:09:11.0894739Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:11.0896676Z 
2025-08-14T23:09:11.0896842Z     >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-08-14T23:09:11.0897110Z     >>> # consisting of 5 random weighted bivariate normal distributions
2025-08-14T23:09:11.0897349Z     >>> mix = D.Categorical(torch.rand(3,5))
2025-08-14T23:09:11.0897516Z     >>> comp = D.Independent(D.Normal(
2025-08-14T23:09:11.0897668Z     ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-08-14T23:09:11.0897823Z     >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:11.0897916Z 
2025-08-14T23:09:11.0897956Z Args:
2025-08-14T23:09:11.0898099Z     mixture_distribution: `torch.distributions.Categorical`-like
2025-08-14T23:09:11.0898312Z         instance. Manages the probability of selecting component.
2025-08-14T23:09:11.0898509Z         The number of categories must match the rightmost batch
2025-08-14T23:09:11.0898706Z         dimension of the `component_distribution`. Must have either
2025-08-14T23:09:11.0900091Z         scalar `batch_shape` or `batch_shape` matching
2025-08-14T23:09:11.0900262Z         `component_distribution.batch_shape[:-1]`
2025-08-14T23:09:11.0900458Z     component_distribution: `torch.distributions.Distribution`-like
2025-08-14T23:09:11.0900696Z         instance. Right-most batch dimension indexes component.
2025-08-14T23:09:11.0900818Z 
2025-08-14T23:09:11.0900923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.0901072Z 
2025-08-14T23:09:11.3151030Z 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.
2025-08-14T23:09:11.3151414Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3151556Z 
2025-08-14T23:09:11.3151633Z Creates a RelaxedBernoulli distribution, parametrized by
2025-08-14T23:09:11.3154855Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-08-14T23:09:11.3155084Z (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-08-14T23:09:11.3155285Z so the values are in (0, 1), and has reparametrizable samples.
2025-08-14T23:09:11.3155393Z 
2025-08-14T23:09:11.3155453Z Example::
2025-08-14T23:09:11.3155503Z 
2025-08-14T23:09:11.3155567Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:11.3155713Z     >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-08-14T23:09:11.3155849Z     ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-08-14T23:09:11.3155975Z     >>> m.sample()
2025-08-14T23:09:11.3156080Z     tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-08-14T23:09:11.3156159Z 
2025-08-14T23:09:11.3156191Z Args:
2025-08-14T23:09:11.3156288Z     temperature (Tensor): relaxation temperature
2025-08-14T23:09:11.3156443Z     probs (Number, Tensor): the probability of sampling `1`
2025-08-14T23:09:11.3156613Z     logits (Number, Tensor): the log-odds of sampling `1`
2025-08-14T23:09:11.3156712Z 
2025-08-14T23:09:11.3156804Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3156937Z 
2025-08-14T23:09:11.3266742Z 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.
2025-08-14T23:09:11.3267120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3267319Z 
2025-08-14T23:09:11.3267451Z Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-08-14T23:09:11.3267691Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-08-14T23:09:11.3267795Z 
2025-08-14T23:09:11.3267866Z     covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-08-14T23:09:11.3267969Z 
2025-08-14T23:09:11.3268006Z Example:
2025-08-14T23:09:11.3269980Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-08-14T23:09:11.3270144Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:11.3270421Z     >>> m = LowRankMultivariateNormal(
2025-08-14T23:09:11.3270608Z     ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-08-14T23:09:11.3270744Z     ... )
2025-08-14T23:09:11.3270893Z     >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-08-14T23:09:11.3271074Z     tensor([-0.2102, -0.5429])
2025-08-14T23:09:11.3271144Z 
2025-08-14T23:09:11.3271178Z Args:
2025-08-14T23:09:11.3271315Z     loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-08-14T23:09:11.3271527Z     cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-08-14T23:09:11.3271701Z         `batch_shape + event_shape + (rank,)`
2025-08-14T23:09:11.3273118Z     cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-08-14T23:09:11.3273295Z         `batch_shape + event_shape`
2025-08-14T23:09:11.3273371Z 
2025-08-14T23:09:11.3273404Z Note:
2025-08-14T23:09:11.3273591Z     The computation for determinant and inverse of covariance matrix is avoided when
2025-08-14T23:09:11.3273809Z     `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-08-14T23:09:11.3274057Z     <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-08-14T23:09:11.3274271Z     `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-08-14T23:09:11.3274494Z     Thanks to these formulas, we just need to compute the determinant and inverse of
2025-08-14T23:09:11.3274667Z     the small size "capacitance" matrix::
2025-08-14T23:09:11.3274748Z 
2025-08-14T23:09:11.3274817Z         capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-08-14T23:09:11.3274919Z 
2025-08-14T23:09:11.3275009Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3276140Z 
2025-08-14T23:09:11.3912559Z 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.
2025-08-14T23:09:11.3913047Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3913208Z 
2025-08-14T23:09:11.3913302Z Test if two objects are equal, and print an error message if test fails.
2025-08-14T23:09:11.3913432Z 
2025-08-14T23:09:11.3913494Z The test is performed with ``actual == desired``.
2025-08-14T23:09:11.3913600Z 
2025-08-14T23:09:11.3913641Z Parameters
2025-08-14T23:09:11.3913737Z ----------
2025-08-14T23:09:11.3913829Z test_string : str
2025-08-14T23:09:11.3913943Z     The message supplied to AssertionError.
2025-08-14T23:09:11.3914075Z actual : object
2025-08-14T23:09:11.3916940Z     The object to test for equality against `desired`.
2025-08-14T23:09:11.3917105Z desired : object
2025-08-14T23:09:11.3917210Z     The expected result.
2025-08-14T23:09:11.3917280Z 
2025-08-14T23:09:11.3917319Z Examples
2025-08-14T23:09:11.3917402Z --------
2025-08-14T23:09:11.3917503Z >>> np.testing.print_assert_equal(
2025-08-14T23:09:11.3917651Z ...     "Test XYZ of func xyz", [0, 1], [0, 1]
2025-08-14T23:09:11.3917784Z ... )  # doctest: +SKIP
2025-08-14T23:09:11.3917902Z >>> np.testing.print_assert_equal(
2025-08-14T23:09:11.3918034Z ...     "Test XYZ of func xyz", [0, 1], [0, 2]
2025-08-14T23:09:11.3918156Z ... )  # doctest: +SKIP
2025-08-14T23:09:11.3918274Z Traceback (most recent call last):
2025-08-14T23:09:11.3918393Z ...
2025-08-14T23:09:11.3918494Z AssertionError: Test XYZ of func xyz failed
2025-08-14T23:09:11.3918620Z ACTUAL:
2025-08-14T23:09:11.3918706Z [0, 1]
2025-08-14T23:09:11.3918795Z DESIRED:
2025-08-14T23:09:11.3918883Z [0, 2]
2025-08-14T23:09:11.3918930Z 
2025-08-14T23:09:11.3918932Z 
2025-08-14T23:09:11.3921100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3921265Z 
2025-08-14T23:09:11.3921481Z 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.
2025-08-14T23:09:11.3921936Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3922081Z 
2025-08-14T23:09:11.3922161Z Raises an AssertionError if two items are not equal up to desired
2025-08-14T23:09:11.3922316Z precision.
2025-08-14T23:09:11.3922368Z 
2025-08-14T23:09:11.3924247Z .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:11.3924428Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:11.3924611Z           instead of this function for more consistent floating point
2025-08-14T23:09:11.3924760Z           comparisons.
2025-08-14T23:09:11.3924825Z 
2025-08-14T23:09:11.3924910Z The test verifies that the elements of `actual` and `desired` satisfy.
2025-08-14T23:09:11.3925029Z 
2025-08-14T23:09:11.3926644Z     ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-08-14T23:09:11.3926750Z 
2025-08-14T23:09:11.3926834Z That is a looser test than originally documented, but agrees with what the
2025-08-14T23:09:11.3927075Z actual implementation in `assert_array_almost_equal` did up to rounding
2025-08-14T23:09:11.3927276Z vagaries. An exception is raised at conflicting values. For ndarrays this
2025-08-14T23:09:11.3927468Z delegates to assert_array_almost_equal
2025-08-14T23:09:11.3927546Z 
2025-08-14T23:09:11.3927581Z Parameters
2025-08-14T23:09:11.3927659Z ----------
2025-08-14T23:09:11.3927739Z actual : array_like
2025-08-14T23:09:11.3927832Z     The object to check.
2025-08-14T23:09:11.3927929Z desired : array_like
2025-08-14T23:09:11.3928023Z     The expected object.
2025-08-14T23:09:11.3928121Z decimal : int, optional
2025-08-14T23:09:11.3928226Z     Desired precision, default is 7.
2025-08-14T23:09:11.3928337Z err_msg : str, optional
2025-08-14T23:09:11.3928450Z     The error message to be printed in case of failure.
2025-08-14T23:09:11.3928579Z verbose : bool, optional
2025-08-14T23:09:11.3929750Z     If True, the conflicting values are appended to the error message.
2025-08-14T23:09:11.3929866Z 
2025-08-14T23:09:11.3929900Z Raises
2025-08-14T23:09:11.3929973Z ------
2025-08-14T23:09:11.3930052Z AssertionError
2025-08-14T23:09:11.3930174Z   If actual and desired are not equal up to specified precision.
2025-08-14T23:09:11.3930281Z 
2025-08-14T23:09:11.3930314Z See Also
2025-08-14T23:09:11.3930388Z --------
2025-08-14T23:09:11.3930515Z assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:11.3930684Z                  relative and/or absolute precision.
2025-08-14T23:09:11.3930841Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:11.3930952Z 
2025-08-14T23:09:11.3930985Z Examples
2025-08-14T23:09:11.3932022Z --------
2025-08-14T23:09:11.3932131Z >>> from torch._numpy.testing import assert_almost_equal
2025-08-14T23:09:11.3932275Z >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-08-14T23:09:11.3932421Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-08-14T23:09:11.3932564Z Traceback (most recent call last):
2025-08-14T23:09:11.3932670Z     ...
2025-08-14T23:09:11.3932749Z AssertionError:
2025-08-14T23:09:11.3932849Z Arrays are not almost equal to 10 decimals
2025-08-14T23:09:11.3933061Z  ACTUAL: 2.3333333333333
2025-08-14T23:09:11.3933154Z  DESIRED: 2.33333334
2025-08-14T23:09:11.3933211Z 
2025-08-14T23:09:11.3933249Z >>> assert_almost_equal(
2025-08-14T23:09:11.3934363Z ...     np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9
2025-08-14T23:09:11.3934503Z ... )
2025-08-14T23:09:11.3934590Z Traceback (most recent call last):
2025-08-14T23:09:11.3934694Z     ...
2025-08-14T23:09:11.3934770Z AssertionError:
2025-08-14T23:09:11.3934868Z Arrays are not almost equal to 9 decimals
2025-08-14T23:09:11.3934981Z <BLANKLINE>
2025-08-14T23:09:11.3935068Z Mismatched elements: 1 / 2 (50%)
2025-08-14T23:09:11.3935184Z Max absolute difference: 6.666699636781459e-09
2025-08-14T23:09:11.3935314Z Max relative difference: 2.8571569790287484e-09
2025-08-14T23:09:11.3935448Z  x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-08-14T23:09:11.3936590Z  y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-08-14T23:09:11.3936677Z 
2025-08-14T23:09:11.3936679Z 
2025-08-14T23:09:11.3936772Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3936896Z 
2025-08-14T23:09:11.3937088Z 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.
2025-08-14T23:09:11.3937398Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3937525Z 
2025-08-14T23:09:11.3937607Z Raises an AssertionError if two items are not equal up to significant
2025-08-14T23:09:11.3937750Z digits.
2025-08-14T23:09:11.3937793Z 
2025-08-14T23:09:11.3937858Z .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:11.3938020Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:11.3938204Z           instead of this function for more consistent floating point
2025-08-14T23:09:11.3938344Z           comparisons.
2025-08-14T23:09:11.3938404Z 
2025-08-14T23:09:11.3938472Z Given two numbers, check that they are approximately equal.
2025-08-14T23:09:11.3939624Z Approximately equal is defined as the number of significant digits
2025-08-14T23:09:11.3939768Z that agree.
2025-08-14T23:09:11.3939816Z 
2025-08-14T23:09:11.3939849Z Parameters
2025-08-14T23:09:11.3939927Z ----------
2025-08-14T23:09:11.3940006Z actual : scalar
2025-08-14T23:09:11.3940094Z     The object to check.
2025-08-14T23:09:11.3940188Z desired : scalar
2025-08-14T23:09:11.3940276Z     The expected object.
2025-08-14T23:09:11.3940375Z significant : int, optional
2025-08-14T23:09:11.3940485Z     Desired precision, default is 7.
2025-08-14T23:09:11.3940597Z err_msg : str, optional
2025-08-14T23:09:11.3941799Z     The error message to be printed in case of failure.
2025-08-14T23:09:11.3941940Z verbose : bool, optional
2025-08-14T23:09:11.3942081Z     If True, the conflicting values are appended to the error message.
2025-08-14T23:09:11.3942191Z 
2025-08-14T23:09:11.3942226Z Raises
2025-08-14T23:09:11.3942299Z ------
2025-08-14T23:09:11.3942376Z AssertionError
2025-08-14T23:09:11.3942498Z   If actual and desired are not equal up to specified precision.
2025-08-14T23:09:11.3942602Z 
2025-08-14T23:09:11.3942635Z See Also
2025-08-14T23:09:11.3942710Z --------
2025-08-14T23:09:11.3942837Z assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:11.3944215Z                  relative and/or absolute precision.
2025-08-14T23:09:11.3944379Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:11.3944489Z 
2025-08-14T23:09:11.3944524Z Examples
2025-08-14T23:09:11.3944600Z --------
2025-08-14T23:09:11.3944688Z >>> np.testing.assert_approx_equal(
2025-08-14T23:09:11.3944806Z ...     0.12345677777777e-20, 0.1234567e-20
2025-08-14T23:09:11.3944918Z ... )  # doctest: +SKIP
2025-08-14T23:09:11.3945022Z >>> np.testing.assert_approx_equal(
2025-08-14T23:09:11.3945132Z ...     0.12345670e-20,
2025-08-14T23:09:11.3945231Z ...     0.12345671e-20,  # doctest: +SKIP
2025-08-14T23:09:11.3945342Z ...     significant=8,
2025-08-14T23:09:11.3946427Z ... )
2025-08-14T23:09:11.3946541Z >>> np.testing.assert_approx_equal(
2025-08-14T23:09:11.3946650Z ...     0.12345670e-20,
2025-08-14T23:09:11.3946746Z ...     0.12345672e-20,  # doctest: +SKIP
2025-08-14T23:09:11.3946856Z ...     significant=8,
2025-08-14T23:09:11.3946943Z ... )
2025-08-14T23:09:11.3947027Z Traceback (most recent call last):
2025-08-14T23:09:11.3947131Z     ...
2025-08-14T23:09:11.3947207Z AssertionError:
2025-08-14T23:09:11.3947308Z Items are not equal to 8 significant digits:
2025-08-14T23:09:11.3947425Z  ACTUAL: 1.234567e-21
2025-08-14T23:09:11.3948532Z  DESIRED: 1.2345672e-21
2025-08-14T23:09:11.3948591Z 
2025-08-14T23:09:11.3948651Z the evaluated condition that raises the exception is
2025-08-14T23:09:11.3948745Z 
2025-08-14T23:09:11.3948822Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1)
2025-08-14T23:09:11.3949000Z True
2025-08-14T23:09:11.3949065Z 
2025-08-14T23:09:11.3949067Z 
2025-08-14T23:09:11.3949156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3949281Z 
2025-08-14T23:09:11.3949473Z 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.
2025-08-14T23:09:11.3949784Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3949913Z 
2025-08-14T23:09:11.3949988Z Raises an AssertionError if two array_like objects are not equal.
2025-08-14T23:09:11.3950097Z 
2025-08-14T23:09:11.3950172Z Given two array_like objects, check that the shape is equal and all
2025-08-14T23:09:11.3950357Z elements of these objects are equal (but see the Notes for the special
2025-08-14T23:09:11.3950542Z handling of a scalar). An exception is raised at shape mismatch or
2025-08-14T23:09:11.3951784Z conflicting values. In contrast to the standard usage in numpy, NaNs
2025-08-14T23:09:11.3951974Z are compared like numbers, no assertion is raised if both objects have
2025-08-14T23:09:11.3952142Z NaNs in the same positions.
2025-08-14T23:09:11.3952209Z 
2025-08-14T23:09:11.3952290Z The usual caution for verifying equality with floating point numbers is
2025-08-14T23:09:11.3952436Z advised.
2025-08-14T23:09:11.3952480Z 
2025-08-14T23:09:11.3952515Z Parameters
2025-08-14T23:09:11.3952591Z ----------
2025-08-14T23:09:11.3952669Z x : array_like
2025-08-14T23:09:11.3952759Z     The actual object to check.
2025-08-14T23:09:11.3952859Z y : array_like
2025-08-14T23:09:11.3953056Z     The desired, expected object.
2025-08-14T23:09:11.3954182Z err_msg : str, optional
2025-08-14T23:09:11.3954298Z     The error message to be printed in case of failure.
2025-08-14T23:09:11.3954428Z verbose : bool, optional
2025-08-14T23:09:11.3954563Z     If True, the conflicting values are appended to the error message.
2025-08-14T23:09:11.3954707Z strict : bool, optional
2025-08-14T23:09:11.3954835Z     If True, raise an AssertionError when either the shape or the data
2025-08-14T23:09:11.3955007Z     type of the array_like objects does not match. The special
2025-08-14T23:09:11.3955178Z     handling for scalars mentioned in the Notes section is disabled.
2025-08-14T23:09:11.3955285Z 
2025-08-14T23:09:11.3955316Z Raises
2025-08-14T23:09:11.3955390Z ------
2025-08-14T23:09:11.3955467Z AssertionError
2025-08-14T23:09:11.3956560Z     If actual and desired objects are not equal.
2025-08-14T23:09:11.3956645Z 
2025-08-14T23:09:11.3956677Z See Also
2025-08-14T23:09:11.3956752Z --------
2025-08-14T23:09:11.3956877Z assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:11.3957045Z                  relative and/or absolute precision.
2025-08-14T23:09:11.3957201Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:11.3957310Z 
2025-08-14T23:09:11.3957343Z Notes
2025-08-14T23:09:11.3957418Z -----
2025-08-14T23:09:11.3957530Z When one of `x` and `y` is a scalar and the other is array_like, the
2025-08-14T23:09:11.3957709Z function checks that each element of the array_like object is equal to
2025-08-14T23:09:11.3958892Z the scalar. This behaviour can be disabled with the `strict` parameter.
2025-08-14T23:09:11.3959010Z 
2025-08-14T23:09:11.3959045Z Examples
2025-08-14T23:09:11.3959119Z --------
2025-08-14T23:09:11.3959213Z The first assert does not raise an exception:
2025-08-14T23:09:11.3959298Z 
2025-08-14T23:09:11.3959342Z >>> np.testing.assert_array_equal(
2025-08-14T23:09:11.3959471Z ...     [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan]
2025-08-14T23:09:11.3959589Z ... )
2025-08-14T23:09:11.3959632Z 
2025-08-14T23:09:11.3959713Z Use `assert_allclose` or one of the nulp (number of floating point values)
2025-08-14T23:09:11.3959868Z functions for these cases instead:
2025-08-14T23:09:11.3959940Z 
2025-08-14T23:09:11.3959984Z >>> np.testing.assert_allclose(
2025-08-14T23:09:11.3960174Z ...     [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0
2025-08-14T23:09:11.3960312Z ... )
2025-08-14T23:09:11.3961369Z 
2025-08-14T23:09:11.3961449Z As mentioned in the Notes section, `assert_array_equal` has special
2025-08-14T23:09:11.3961635Z handling for scalars. Here the test checks that each value in `x` is 3:
2025-08-14T23:09:11.3961749Z 
2025-08-14T23:09:11.3961790Z >>> x = np.full((2, 5), fill_value=3)
2025-08-14T23:09:11.3961906Z >>> np.testing.assert_array_equal(x, 3)
2025-08-14T23:09:11.3961983Z 
2025-08-14T23:09:11.3962057Z Use `strict` to raise an AssertionError when comparing a scalar with an
2025-08-14T23:09:11.3962196Z array:
2025-08-14T23:09:11.3962239Z 
2025-08-14T23:09:11.3962298Z >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-08-14T23:09:11.3962428Z Traceback (most recent call last):
2025-08-14T23:09:11.3962531Z     ...
2025-08-14T23:09:11.3962608Z AssertionError:
2025-08-14T23:09:11.3962696Z Arrays are not equal
2025-08-14T23:09:11.3962812Z <BLANKLINE>
2025-08-14T23:09:11.3963983Z (shapes (2, 5), () mismatch)
2025-08-14T23:09:11.3964089Z  x: torch.ndarray([[3, 3, 3, 3, 3],
2025-08-14T23:09:11.3964222Z         [3, 3, 3, 3, 3]])
2025-08-14T23:09:11.3964318Z  y: torch.ndarray(3)
2025-08-14T23:09:11.3964377Z 
2025-08-14T23:09:11.3964454Z The `strict` parameter also ensures that the array data types match:
2025-08-14T23:09:11.3964564Z 
2025-08-14T23:09:11.3964603Z >>> x = np.array([2, 2, 2])
2025-08-14T23:09:11.3964717Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32)
2025-08-14T23:09:11.3964859Z >>> np.testing.assert_array_equal(x, y, strict=True)
2025-08-14T23:09:11.3964990Z Traceback (most recent call last):
2025-08-14T23:09:11.3965094Z     ...
2025-08-14T23:09:11.3965170Z AssertionError:
2025-08-14T23:09:11.3966295Z Arrays are not equal
2025-08-14T23:09:11.3966385Z <BLANKLINE>
2025-08-14T23:09:11.3966484Z (dtypes dtype("int64"), dtype("float32") mismatch)
2025-08-14T23:09:11.3966611Z  x: torch.ndarray([2, 2, 2])
2025-08-14T23:09:11.3966719Z  y: torch.ndarray([2., 2., 2.])
2025-08-14T23:09:11.3966787Z 
2025-08-14T23:09:11.3966877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3967005Z 
2025-08-14T23:09:11.3967203Z 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.
2025-08-14T23:09:11.3967520Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3967648Z 
2025-08-14T23:09:11.3967723Z Raises an AssertionError if two objects are not equal up to desired
2025-08-14T23:09:11.3967866Z precision.
2025-08-14T23:09:11.3967914Z 
2025-08-14T23:09:11.3967979Z .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:11.3969088Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:11.3969256Z           instead of this function for more consistent floating point
2025-08-14T23:09:11.3969395Z           comparisons.
2025-08-14T23:09:11.3969455Z 
2025-08-14T23:09:11.3969541Z The test verifies identical shapes and that the elements of ``actual`` and
2025-08-14T23:09:11.3969697Z ``desired`` satisfy.
2025-08-14T23:09:11.3969754Z 
2025-08-14T23:09:11.3969806Z     ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-08-14T23:09:11.3969891Z 
2025-08-14T23:09:11.3969970Z That is a looser test than originally documented, but agrees with what the
2025-08-14T23:09:11.3970169Z actual implementation did up to rounding vagaries. An exception is raised
2025-08-14T23:09:11.3970367Z at shape mismatch or conflicting values. In contrast to the standard usage
2025-08-14T23:09:11.3970559Z in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-08-14T23:09:11.3971664Z objects have NaNs in the same positions.
2025-08-14T23:09:11.3971745Z 
2025-08-14T23:09:11.3971780Z Parameters
2025-08-14T23:09:11.3971857Z ----------
2025-08-14T23:09:11.3971934Z x : array_like
2025-08-14T23:09:11.3972025Z     The actual object to check.
2025-08-14T23:09:11.3972151Z y : array_like
2025-08-14T23:09:11.3972260Z     The desired, expected object.
2025-08-14T23:09:11.3972370Z decimal : int, optional
2025-08-14T23:09:11.3972474Z     Desired precision, default is 6.
2025-08-14T23:09:11.3972585Z err_msg : str, optional
2025-08-14T23:09:11.3972702Z   The error message to be printed in case of failure.
2025-08-14T23:09:11.3973939Z verbose : bool, optional
2025-08-14T23:09:11.3974078Z     If True, the conflicting values are appended to the error message.
2025-08-14T23:09:11.3974185Z 
2025-08-14T23:09:11.3974217Z Raises
2025-08-14T23:09:11.3974290Z ------
2025-08-14T23:09:11.3974367Z AssertionError
2025-08-14T23:09:11.3974488Z     If actual and desired are not equal up to specified precision.
2025-08-14T23:09:11.3974593Z 
2025-08-14T23:09:11.3974625Z See Also
2025-08-14T23:09:11.3974699Z --------
2025-08-14T23:09:11.3974824Z assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:11.3975020Z                  relative and/or absolute precision.
2025-08-14T23:09:11.3975182Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:11.3975291Z 
2025-08-14T23:09:11.3976298Z Examples
2025-08-14T23:09:11.3976375Z --------
2025-08-14T23:09:11.3976467Z the first assert does not raise an exception
2025-08-14T23:09:11.3976550Z 
2025-08-14T23:09:11.3976640Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan])
2025-08-14T23:09:11.3976766Z 
2025-08-14T23:09:11.3976812Z >>> np.testing.assert_array_almost_equal(
2025-08-14T23:09:11.3976950Z ...     [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5
2025-08-14T23:09:11.3977072Z ... )
2025-08-14T23:09:11.3977155Z Traceback (most recent call last):
2025-08-14T23:09:11.3977258Z     ...
2025-08-14T23:09:11.3977334Z AssertionError:
2025-08-14T23:09:11.3977432Z Arrays are not almost equal to 5 decimals
2025-08-14T23:09:11.3978481Z <BLANKLINE>
2025-08-14T23:09:11.3978569Z Mismatched elements: 1 / 3 (33.3%)
2025-08-14T23:09:11.3978691Z Max absolute difference: 5.999999999994898e-05
2025-08-14T23:09:11.3978822Z Max relative difference: 2.5713661239633743e-05
2025-08-14T23:09:11.3978964Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-08-14T23:09:11.3979119Z  y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-08-14T23:09:11.3979212Z 
2025-08-14T23:09:11.3979259Z >>> np.testing.assert_array_almost_equal(
2025-08-14T23:09:11.3979389Z ...     [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5
2025-08-14T23:09:11.3979504Z ... )
2025-08-14T23:09:11.3979587Z Traceback (most recent call last):
2025-08-14T23:09:11.3979690Z     ...
2025-08-14T23:09:11.3980712Z AssertionError:
2025-08-14T23:09:11.3980810Z Arrays are not almost equal to 5 decimals
2025-08-14T23:09:11.3980919Z <BLANKLINE>
2025-08-14T23:09:11.3981005Z x and y nan location mismatch:
2025-08-14T23:09:11.3981133Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-08-14T23:09:11.3981283Z  y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-08-14T23:09:11.3981375Z 
2025-08-14T23:09:11.3981376Z 
2025-08-14T23:09:11.3981466Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3981592Z 
2025-08-14T23:09:11.3981788Z 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.
2025-08-14T23:09:11.3982101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.3982302Z Context manager that resets warning registry for catching warnings
2025-08-14T23:09:11.3982412Z 
2025-08-14T23:09:11.3982498Z     Warnings can be slippery, because, whenever a warning is triggered, Python
2025-08-14T23:09:11.3983745Z     adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-08-14T23:09:11.3983943Z     it impossible to retrigger the warning in this module, whatever you put in
2025-08-14T23:09:11.3984147Z     the warnings filters.  This context manager accepts a sequence of `modules`
2025-08-14T23:09:11.3984358Z     as a keyword argument to its constructor and:
2025-08-14T23:09:11.3984444Z 
2025-08-14T23:09:11.3984525Z     * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-08-14T23:09:11.3984670Z       on entry;
2025-08-14T23:09:11.3984789Z     * resets ``__warningregistry__`` to its previous state on exit.
2025-08-14T23:09:11.3984893Z 
2025-08-14T23:09:11.3984971Z     This makes it possible to trigger any warning afresh inside the context
2025-08-14T23:09:11.3985151Z     manager without disturbing the state of warnings outside.
2025-08-14T23:09:11.3985252Z 
2025-08-14T23:09:11.3985335Z     For compatibility with Python 3.0, please consider all arguments to be
2025-08-14T23:09:11.3985485Z     keyword-only.
2025-08-14T23:09:11.3986506Z 
2025-08-14T23:09:11.3986542Z     Parameters
2025-08-14T23:09:11.3986626Z     ----------
2025-08-14T23:09:11.3986715Z     record : bool, optional
2025-08-14T23:09:11.3986874Z         Specifies whether warnings should be captured by a custom
2025-08-14T23:09:11.3987060Z         implementation of ``warnings.showwarning()`` and be appended to a list
2025-08-14T23:09:11.3987269Z         returned by the context manager. Otherwise None is returned by the
2025-08-14T23:09:11.3987455Z         context manager. The objects appended to the list are arguments whose
2025-08-14T23:09:11.3987632Z         attributes mirror the arguments to ``showwarning()``.
2025-08-14T23:09:11.3987771Z     modules : sequence, optional
2025-08-14T23:09:11.3987919Z         Sequence of modules for which to reset warnings registry on entry and
2025-08-14T23:09:11.3988099Z         restore on exit. To work correctly, all 'ignore' filters should
2025-08-14T23:09:11.3989207Z         filter by one of these modules.
2025-08-14T23:09:11.3989282Z 
2025-08-14T23:09:11.3989315Z     Examples
2025-08-14T23:09:11.3989396Z     --------
2025-08-14T23:09:11.3989479Z     >>> import warnings
2025-08-14T23:09:11.3989611Z     >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-08-14T23:09:11.3989760Z     ...     modules=[np.core.fromnumeric]
2025-08-14T23:09:11.3989868Z     ... ):
2025-08-14T23:09:11.3989962Z     ...     warnings.simplefilter("always")
2025-08-14T23:09:11.3990120Z     ...     warnings.filterwarnings("ignore", module="np.core.fromnumeric")
2025-08-14T23:09:11.3990297Z     ...     # do something that raises a warning but ignore those in
2025-08-14T23:09:11.3990436Z     ...     # np.core.fromnumeric
2025-08-14T23:09:11.3991498Z     
2025-08-14T23:09:11.3991628Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.3991750Z 
2025-08-14T23:09:11.5036437Z 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=722.
2025-08-14T23:09:11.5037480Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:11.5037845Z 
2025-08-14T23:09:11.5038175Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-08-14T23:09:11.5038813Z The submesh created consists of the dimensions and the communicators indicated by
2025-08-14T23:09:11.5039275Z ``mesh_dim_names``
2025-08-14T23:09:11.5039435Z 
2025-08-14T23:09:11.5039532Z Args:
2025-08-14T23:09:11.5039905Z     mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-08-14T23:09:11.5040443Z     mesh dimension of the DeviceMesh to create the submesh for.
2025-08-14T23:09:11.5048206Z Returns:
2025-08-14T23:09:11.5048380Z     A :class:`DeviceMesh` object
2025-08-14T23:09:11.5048507Z 
2025-08-14T23:09:11.5048694Z The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-08-14T23:09:11.5048998Z In the first example:
2025-08-14T23:09:11.5049261Z     Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-08-14T23:09:11.5049625Z     Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-08-14T23:09:11.5050284Z     Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-08-14T23:09:11.5050604Z     Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-08-14T23:09:11.5050929Z     Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-08-14T23:09:11.5051249Z     Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-08-14T23:09:11.5051442Z 
2025-08-14T23:09:11.5051511Z In the second example:
2025-08-14T23:09:11.5051778Z     Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-08-14T23:09:11.5054698Z     Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-08-14T23:09:11.5055081Z     Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-08-14T23:09:11.5055539Z     Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-08-14T23:09:11.5055762Z 
2025-08-14T23:09:11.5055861Z Example::
2025-08-14T23:09:11.5056026Z 
2025-08-14T23:09:11.5056114Z     >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:11.5056295Z     >>> from torch.distributed.device_mesh import DeviceMesh
2025-08-14T23:09:11.5056460Z     >>>
2025-08-14T23:09:11.5056612Z     >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-08-14T23:09:11.5056820Z     >>> # of cross-host(dim 0), and within-host (dim 1).
2025-08-14T23:09:11.5057053Z     >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-08-14T23:09:11.5057264Z     >>> tp_mesh = mesh_2d["tp"]
2025-08-14T23:09:11.5059977Z     >>> dp_mesh = mesh_2d["dp"]
2025-08-14T23:09:11.5060117Z     >>>
2025-08-14T23:09:11.5060223Z     >>> # Initialize a 3D mesh.
2025-08-14T23:09:11.5060439Z     >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-08-14T23:09:11.5060749Z     >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-08-14T23:09:11.5060984Z     >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-08-14T23:09:11.5061142Z     >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-08-14T23:09:11.5061241Z 
2025-08-14T23:09:11.5061526Z Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-08-14T23:09:11.5061861Z 
2025-08-14T23:09:11.5061972Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-08-14T23:09:11.5062187Z                                                                                  ^
2025-08-14T23:09:11.5199382Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2706.
2025-08-14T23:09:11.5201528Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5201694Z 
2025-08-14T23:09:11.5201805Z Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-08-14T23:09:11.5201943Z 
2025-08-14T23:09:11.5202040Z Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-08-14T23:09:11.5202248Z requests. NCCL, Gloo, and UCC backend are currently supported.
2025-08-14T23:09:11.5202364Z 
2025-08-14T23:09:11.5202399Z Args:
2025-08-14T23:09:11.5202533Z     p2p_op_list: A list of point-to-point operations(type of each operator is
2025-08-14T23:09:11.5202742Z         ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-08-14T23:09:11.5203173Z         matters and it needs to match with corresponding isend/irecv on the
2025-08-14T23:09:11.5203326Z         remote end.
2025-08-14T23:09:11.5203390Z 
2025-08-14T23:09:11.5204808Z Returns:
2025-08-14T23:09:11.5204964Z     A list of distributed request objects returned by calling the corresponding
2025-08-14T23:09:11.5205207Z     op in the op_list.
2025-08-14T23:09:11.5205297Z 
2025-08-14T23:09:11.5205339Z Examples:
2025-08-14T23:09:11.5205438Z     >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:11.5205597Z     >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-08-14T23:09:11.5205844Z     >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-08-14T23:09:11.5206012Z     >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-08-14T23:09:11.5206165Z     >>> recv_op = dist.P2POp(
2025-08-14T23:09:11.5206304Z     ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-08-14T23:09:11.5206440Z     ... )
2025-08-14T23:09:11.5207553Z     >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-08-14T23:09:11.5207683Z     >>> for req in reqs:
2025-08-14T23:09:11.5207779Z     >>>     req.wait()
2025-08-14T23:09:11.5207869Z     >>> recv_tensor
2025-08-14T23:09:11.5207960Z     tensor([2, 3])     # Rank 0
2025-08-14T23:09:11.5208100Z     tensor([0, 1])     # Rank 1
2025-08-14T23:09:11.5208166Z 
2025-08-14T23:09:11.5208258Z .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-08-14T23:09:11.5208500Z     the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-08-14T23:09:11.5208681Z     lead to unexpected hang issues.
2025-08-14T23:09:11.5208759Z 
2025-08-14T23:09:11.5208835Z     In addition, if this API is the first collective call in the ``group``
2025-08-14T23:09:11.5210265Z     passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-08-14T23:09:11.5210468Z     this API call; otherwise, the behavior is undefined. If this API call is
2025-08-14T23:09:11.5210660Z     not the first collective call in the ``group``, batched P2P operations
2025-08-14T23:09:11.5210843Z     involving only a subset of ranks of the ``group`` are allowed.
2025-08-14T23:09:11.5210950Z 
2025-08-14T23:09:11.5211041Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5211165Z 
2025-08-14T23:09:11.5211361Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2838.
2025-08-14T23:09:11.5211683Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5211815Z 
2025-08-14T23:09:11.5211909Z Reduces the tensor data across all machines in a way that all get the final result.
2025-08-14T23:09:11.5212039Z 
2025-08-14T23:09:11.5212117Z After the call ``tensor`` is going to be bitwise identical in all processes.
2025-08-14T23:09:11.5212232Z 
2025-08-14T23:09:11.5212276Z Complex tensors are supported.
2025-08-14T23:09:11.5212347Z 
2025-08-14T23:09:11.5212379Z Args:
2025-08-14T23:09:11.5213620Z     tensor (Tensor): Input and output of the collective. The function
2025-08-14T23:09:11.5213771Z         operates in-place.
2025-08-14T23:09:11.5213880Z     op (optional): One of the values from
2025-08-14T23:09:11.5214010Z         ``torch.distributed.ReduceOp``
2025-08-14T23:09:11.5214164Z         enum.  Specifies an operation used for element-wise reductions.
2025-08-14T23:09:11.5214351Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.5214517Z         the default process group will be used.
2025-08-14T23:09:11.5214671Z     async_op (bool, optional): Whether this op should be an async op
2025-08-14T23:09:11.5214776Z 
2025-08-14T23:09:11.5214808Z Returns:
2025-08-14T23:09:11.5214905Z     Async work handle, if async_op is set to True.
2025-08-14T23:09:11.5215046Z     None, if not async_op or if not part of the group
2025-08-14T23:09:11.5216103Z 
2025-08-14T23:09:11.5216138Z Examples:
2025-08-14T23:09:11.5216225Z     >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:11.5216351Z     >>> # All tensors below are of torch.int64 type.
2025-08-14T23:09:11.5216481Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.5216605Z     >>> device = torch.device(f"cuda:{rank}")
2025-08-14T23:09:11.5216818Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-08-14T23:09:11.5216965Z     >>> tensor
2025-08-14T23:09:11.5217058Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5217178Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5217301Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-08-14T23:09:11.5217415Z     >>> tensor
2025-08-14T23:09:11.5218457Z     tensor([4, 6], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5218574Z     tensor([4, 6], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5218650Z 
2025-08-14T23:09:11.5218702Z     >>> # All tensors below are of torch.cfloat type.
2025-08-14T23:09:11.5218830Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.5218945Z     >>> tensor = torch.tensor(
2025-08-14T23:09:11.5219071Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-08-14T23:09:11.5219198Z     ... ) + 2 * rank * (1 + 1j)
2025-08-14T23:09:11.5219295Z     >>> tensor
2025-08-14T23:09:11.5219424Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5219563Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5219695Z     >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-08-14T23:09:11.5220767Z     >>> tensor
2025-08-14T23:09:11.5220865Z     tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5220999Z     tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5221084Z 
2025-08-14T23:09:11.5221086Z 
2025-08-14T23:09:11.5221174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5221300Z 
2025-08-14T23:09:11.5221497Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3198.
2025-08-14T23:09:11.5221815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5221943Z 
2025-08-14T23:09:11.5222026Z Gathers picklable objects from the whole group in a single process.
2025-08-14T23:09:11.5222143Z 
2025-08-14T23:09:11.5222227Z Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-08-14T23:09:11.5222401Z object must be picklable in order to be gathered.
2025-08-14T23:09:11.5222489Z 
2025-08-14T23:09:11.5222521Z Args:
2025-08-14T23:09:11.5222614Z     obj (Any): Input object. Must be picklable.
2025-08-14T23:09:11.5223812Z     object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-08-14T23:09:11.5223991Z         should be correctly sized as the size of the group for this
2025-08-14T23:09:11.5224171Z         collective and will contain the output. Must be ``None`` on non-dst
2025-08-14T23:09:11.5224322Z         ranks. (default is ``None``)
2025-08-14T23:09:11.5224505Z     dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-08-14T23:09:11.5224718Z         (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-08-14T23:09:11.5224907Z     group: (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.5225092Z         the default process group will be used. Default is ``None``.
2025-08-14T23:09:11.5225314Z     group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-08-14T23:09:11.5225465Z 
2025-08-14T23:09:11.5225499Z Returns:
2025-08-14T23:09:11.5226851Z     None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-08-14T23:09:11.5227006Z     output of the collective.
2025-08-14T23:09:11.5227079Z 
2025-08-14T23:09:11.5227155Z .. note:: Note that this API differs slightly from the gather collective
2025-08-14T23:09:11.5227341Z     since it does not provide an async_op handle and thus will be a blocking
2025-08-14T23:09:11.5227487Z     call.
2025-08-14T23:09:11.5227533Z 
2025-08-14T23:09:11.5227615Z .. note:: For NCCL-based processed groups, internal tensor representations
2025-08-14T23:09:11.5227812Z     of objects must be moved to the GPU device before communication takes
2025-08-14T23:09:11.5228062Z     place. In this case, the device used is given by
2025-08-14T23:09:11.5228230Z     ``torch.cuda.current_device()`` and it is the user's responsibility to
2025-08-14T23:09:11.5228417Z     ensure that this is set so that each rank has an individual GPU, via
2025-08-14T23:09:11.5229884Z     ``torch.cuda.set_device()``.
2025-08-14T23:09:11.5229960Z 
2025-08-14T23:09:11.5230003Z .. warning::
2025-08-14T23:09:11.5230143Z     Object collectives have a number of serious performance and scalability
2025-08-14T23:09:11.5230325Z     limitations.  See :ref:`object_collectives` for details.
2025-08-14T23:09:11.5230424Z 
2025-08-14T23:09:11.5230458Z .. warning::
2025-08-14T23:09:11.5230577Z     :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-08-14T23:09:11.5230758Z     known to be insecure. It is possible to construct malicious pickle data
2025-08-14T23:09:11.5230987Z     which will execute arbitrary code during unpickling. Only call this
2025-08-14T23:09:11.5231145Z     function with data you trust.
2025-08-14T23:09:11.5231221Z 
2025-08-14T23:09:11.5231253Z .. warning::
2025-08-14T23:09:11.5231376Z     Calling :func:`gather_object` with GPU tensors is not well supported
2025-08-14T23:09:11.5231586Z     and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-08-14T23:09:11.5231762Z     pickled. Please consider using :func:`gather` instead.
2025-08-14T23:09:11.5231864Z 
2025-08-14T23:09:11.5231900Z Example::
2025-08-14T23:09:11.5232005Z     >>> # xdoctest: +SKIP("need process group init")
2025-08-14T23:09:11.5232166Z     >>> # Note: Process group initialization omitted on each rank.
2025-08-14T23:09:11.5232318Z     >>> import torch.distributed as dist
2025-08-14T23:09:11.5232443Z     >>> # Assumes world_size of 3.
2025-08-14T23:09:11.5232585Z     >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-08-14T23:09:11.5233817Z     >>> output = [None for _ in gather_objects]
2025-08-14T23:09:11.5233943Z     >>> dist.gather_object(
2025-08-14T23:09:11.5234054Z     ...     gather_objects[dist.get_rank()],
2025-08-14T23:09:11.5234183Z     ...     output if dist.get_rank() == 0 else None,
2025-08-14T23:09:11.5234304Z     ...     dst=0
2025-08-14T23:09:11.5234388Z     ... )
2025-08-14T23:09:11.5234464Z     >>> # On rank 0
2025-08-14T23:09:11.5234550Z     >>> output
2025-08-14T23:09:11.5234633Z     ['foo', 12, {1: 2}]
2025-08-14T23:09:11.5234693Z 
2025-08-14T23:09:11.5234781Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5234907Z 
2025-08-14T23:09:11.5235108Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3794.
2025-08-14T23:09:11.5236376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5236504Z 
2025-08-14T23:09:11.5236558Z Gathers tensors from the whole group in a list.
2025-08-14T23:09:11.5236644Z 
2025-08-14T23:09:11.5236699Z Complex and uneven sized tensors are supported.
2025-08-14T23:09:11.5236788Z 
2025-08-14T23:09:11.5236820Z Args:
2025-08-14T23:09:11.5236930Z     tensor_list (list[Tensor]): Output list. It should contain
2025-08-14T23:09:11.5237110Z         correctly-sized tensors to be used for output of the collective.
2025-08-14T23:09:11.5237267Z         Uneven sized tensors are supported.
2025-08-14T23:09:11.5237415Z     tensor (Tensor): Tensor to be broadcast from current process.
2025-08-14T23:09:11.5237600Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.5237763Z         the default process group will be used.
2025-08-14T23:09:11.5238836Z     async_op (bool, optional): Whether this op should be an async op
2025-08-14T23:09:11.5238944Z 
2025-08-14T23:09:11.5238978Z Returns:
2025-08-14T23:09:11.5239075Z     Async work handle, if async_op is set to True.
2025-08-14T23:09:11.5239216Z     None, if not async_op or if not part of the group
2025-08-14T23:09:11.5239337Z 
2025-08-14T23:09:11.5239372Z Examples:
2025-08-14T23:09:11.5239484Z     >>> # xdoctest: +SKIP("need process group init")
2025-08-14T23:09:11.5239620Z     >>> # All tensors below are of torch.int64 dtype.
2025-08-14T23:09:11.5239750Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.5239872Z     >>> device = torch.device(f"cuda:{rank}")
2025-08-14T23:09:11.5239987Z     >>> tensor_list = [
2025-08-14T23:09:11.5240120Z     ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-08-14T23:09:11.5241187Z     ... ]
2025-08-14T23:09:11.5241267Z     >>> tensor_list
2025-08-14T23:09:11.5241392Z     [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-08-14T23:09:11.5241565Z     [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-08-14T23:09:11.5241751Z     >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-08-14T23:09:11.5241898Z     >>> tensor
2025-08-14T23:09:11.5242015Z     tensor([1, 2], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5242137Z     tensor([3, 4], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5242257Z     >>> dist.all_gather(tensor_list, tensor)
2025-08-14T23:09:11.5242386Z     >>> tensor_list
2025-08-14T23:09:11.5242506Z     [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-08-14T23:09:11.5243685Z     [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-08-14T23:09:11.5243794Z 
2025-08-14T23:09:11.5243850Z     >>> # All tensors below are of torch.cfloat dtype.
2025-08-14T23:09:11.5243982Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.5244092Z     >>> tensor_list = [
2025-08-14T23:09:11.5244225Z     ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-08-14T23:09:11.5244366Z     ... ]
2025-08-14T23:09:11.5244443Z     >>> tensor_list
2025-08-14T23:09:11.5244583Z     [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-08-14T23:09:11.5244796Z     [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-08-14T23:09:11.5244956Z     >>> tensor = torch.tensor(
2025-08-14T23:09:11.5245085Z     ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-08-14T23:09:11.5246203Z     ... ) + 2 * rank * (1 + 1j)
2025-08-14T23:09:11.5246304Z     >>> tensor
2025-08-14T23:09:11.5246406Z     tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-08-14T23:09:11.5246543Z     tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-08-14T23:09:11.5246672Z     >>> dist.all_gather(tensor_list, tensor)
2025-08-14T23:09:11.5246782Z     >>> tensor_list
2025-08-14T23:09:11.5246918Z     [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-08-14T23:09:11.5247117Z     [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-08-14T23:09:11.5247237Z 
2025-08-14T23:09:11.5247239Z 
2025-08-14T23:09:11.5247328Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5247457Z 
2025-08-14T23:09:11.5251242Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4500.
2025-08-14T23:09:11.5253535Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5253687Z 
2025-08-14T23:09:11.5253775Z Split input tensor and then scatter the split list to all processes in a group.
2025-08-14T23:09:11.5253903Z 
2025-08-14T23:09:11.5253993Z Later the received tensors are concatenated from all the processes in the group
2025-08-14T23:09:11.5254163Z and returned as a single output tensor.
2025-08-14T23:09:11.5254244Z 
2025-08-14T23:09:11.5254287Z Complex tensors are supported.
2025-08-14T23:09:11.5254358Z 
2025-08-14T23:09:11.5254390Z Args:
2025-08-14T23:09:11.5254498Z     output (Tensor): Gathered concatenated output tensor.
2025-08-14T23:09:11.5254643Z     input (Tensor): Input tensor to scatter.
2025-08-14T23:09:11.5254881Z     output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-08-14T23:09:11.5255073Z         if specified None or empty, dim 0 of ``output`` tensor must divide
2025-08-14T23:09:11.5255229Z         equally by ``world_size``.
2025-08-14T23:09:11.5256668Z     input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-08-14T23:09:11.5256868Z         if specified None or empty, dim 0 of ``input`` tensor must divide
2025-08-14T23:09:11.5257022Z         equally by ``world_size``.
2025-08-14T23:09:11.5257177Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.5257341Z         the default process group will be used.
2025-08-14T23:09:11.5257495Z     async_op (bool, optional): Whether this op should be an async op.
2025-08-14T23:09:11.5257604Z 
2025-08-14T23:09:11.5257637Z Returns:
2025-08-14T23:09:11.5257733Z     Async work handle, if async_op is set to True.
2025-08-14T23:09:11.5257916Z     None, if not async_op or if not part of the group.
2025-08-14T23:09:11.5258008Z 
2025-08-14T23:09:11.5258044Z .. warning::
2025-08-14T23:09:11.5258159Z     `all_to_all_single` is experimental and subject to change.
2025-08-14T23:09:11.5258273Z 
2025-08-14T23:09:11.5258308Z Examples:
2025-08-14T23:09:11.5258397Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-08-14T23:09:11.5259686Z     >>> input = torch.arange(4) + rank * 4
2025-08-14T23:09:11.5259806Z     >>> input
2025-08-14T23:09:11.5259900Z     tensor([0, 1, 2, 3])     # Rank 0
2025-08-14T23:09:11.5260013Z     tensor([4, 5, 6, 7])     # Rank 1
2025-08-14T23:09:11.5260119Z     tensor([8, 9, 10, 11])   # Rank 2
2025-08-14T23:09:11.5260222Z     tensor([12, 13, 14, 15]) # Rank 3
2025-08-14T23:09:11.5260346Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-08-14T23:09:11.5260479Z     >>> dist.all_to_all_single(output, input)
2025-08-14T23:09:11.5260591Z     >>> output
2025-08-14T23:09:11.5260676Z     tensor([0, 4, 8, 12])    # Rank 0
2025-08-14T23:09:11.5260785Z     tensor([1, 5, 9, 13])    # Rank 1
2025-08-14T23:09:11.5261879Z     tensor([2, 6, 10, 14])   # Rank 2
2025-08-14T23:09:11.5261988Z     tensor([3, 7, 11, 15])   # Rank 3
2025-08-14T23:09:11.5262060Z 
2025-08-14T23:09:11.5262122Z     >>> # Essentially, it is similar to following operation:
2025-08-14T23:09:11.5262270Z     >>> scatter_list = list(input.chunk(world_size))
2025-08-14T23:09:11.5262406Z     >>> gather_list = list(output.chunk(world_size))
2025-08-14T23:09:11.5262531Z     >>> for i in range(world_size):
2025-08-14T23:09:11.5262685Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-08-14T23:09:11.5262804Z 
2025-08-14T23:09:11.5262851Z     >>> # Another example with uneven split
2025-08-14T23:09:11.5263056Z     >>> input
2025-08-14T23:09:11.5263162Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-08-14T23:09:11.5263315Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-08-14T23:09:11.5264435Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-08-14T23:09:11.5264587Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-08-14T23:09:11.5264717Z     >>> input_splits
2025-08-14T23:09:11.5264821Z     [2, 2, 1, 1]                                                     # Rank 0
2025-08-14T23:09:11.5264948Z     [3, 2, 2, 2]                                                     # Rank 1
2025-08-14T23:09:11.5265074Z     [2, 1, 1, 1]                                                     # Rank 2
2025-08-14T23:09:11.5265198Z     [2, 2, 2, 1]                                                     # Rank 3
2025-08-14T23:09:11.5265314Z     >>> output_splits
2025-08-14T23:09:11.5265416Z     [2, 3, 2, 2]                                                     # Rank 0
2025-08-14T23:09:11.5265541Z     [2, 2, 1, 2]                                                     # Rank 1
2025-08-14T23:09:11.5265666Z     [1, 2, 1, 2]                                                     # Rank 2
2025-08-14T23:09:11.5265841Z     [1, 2, 1, 1]                                                     # Rank 3
2025-08-14T23:09:11.5266889Z     >>> output = ...
2025-08-14T23:09:11.5267021Z     >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-08-14T23:09:11.5267162Z     >>> output
2025-08-14T23:09:11.5267267Z     tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-08-14T23:09:11.5267413Z     tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-08-14T23:09:11.5267559Z     tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-08-14T23:09:11.5267705Z     tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-08-14T23:09:11.5267798Z 
2025-08-14T23:09:11.5267800Z 
2025-08-14T23:09:11.5267863Z     >>> # Another example with tensors of torch.cfloat type.
2025-08-14T23:09:11.5267999Z     >>> input = torch.tensor(
2025-08-14T23:09:11.5268144Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-08-14T23:09:11.5268274Z     ... ) + 4 * rank * (1 + 1j)
2025-08-14T23:09:11.5269295Z     >>> input
2025-08-14T23:09:11.5269408Z     tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-08-14T23:09:11.5269592Z     tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-08-14T23:09:11.5269753Z     tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-08-14T23:09:11.5269918Z     tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-08-14T23:09:11.5270073Z     >>> output = torch.empty([4], dtype=torch.int64)
2025-08-14T23:09:11.5270207Z     >>> dist.all_to_all_single(output, input)
2025-08-14T23:09:11.5270319Z     >>> output
2025-08-14T23:09:11.5270428Z     tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-08-14T23:09:11.5270589Z     tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-08-14T23:09:11.5270750Z     tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-08-14T23:09:11.5271832Z     tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-08-14T23:09:11.5271932Z 
2025-08-14T23:09:11.5272021Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5272142Z 
2025-08-14T23:09:11.5272333Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4642.
2025-08-14T23:09:11.5272642Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5272769Z 
2025-08-14T23:09:11.5272963Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-08-14T23:09:11.5273124Z 
2025-08-14T23:09:11.5273169Z Complex tensors are supported.
2025-08-14T23:09:11.5273240Z 
2025-08-14T23:09:11.5273273Z Args:
2025-08-14T23:09:11.5273397Z     output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-08-14T23:09:11.5273547Z         per rank.
2025-08-14T23:09:11.5273679Z     input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-08-14T23:09:11.5274810Z     group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.5274978Z         the default process group will be used.
2025-08-14T23:09:11.5275134Z     async_op (bool, optional): Whether this op should be an async op.
2025-08-14T23:09:11.5275241Z 
2025-08-14T23:09:11.5275274Z Returns:
2025-08-14T23:09:11.5275370Z     Async work handle, if async_op is set to True.
2025-08-14T23:09:11.5275512Z     None, if not async_op or if not part of the group.
2025-08-14T23:09:11.5275602Z 
2025-08-14T23:09:11.5275637Z .. warning::
2025-08-14T23:09:11.5275742Z     `all_to_all` is experimental and subject to change.
2025-08-14T23:09:11.5275832Z 
2025-08-14T23:09:11.5275866Z Examples:
2025-08-14T23:09:11.5275956Z     >>> # xdoctest: +SKIP("Undefined rank")
2025-08-14T23:09:11.5276115Z     >>> input = torch.arange(4) + rank * 4
2025-08-14T23:09:11.5277254Z     >>> input = list(input.chunk(4))
2025-08-14T23:09:11.5277376Z     >>> input
2025-08-14T23:09:11.5277494Z     [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-08-14T23:09:11.5277649Z     [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-08-14T23:09:11.5277806Z     [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-08-14T23:09:11.5277970Z     [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-08-14T23:09:11.5278134Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-08-14T23:09:11.5278289Z     >>> dist.all_to_all(output, input)
2025-08-14T23:09:11.5278397Z     >>> output
2025-08-14T23:09:11.5278507Z     [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-08-14T23:09:11.5280130Z     [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-08-14T23:09:11.5280318Z     [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-08-14T23:09:11.5280473Z     [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-08-14T23:09:11.5280568Z 
2025-08-14T23:09:11.5280650Z     >>> # Essentially, it is similar to following operation:
2025-08-14T23:09:11.5280787Z     >>> scatter_list = input
2025-08-14T23:09:11.5280900Z     >>> gather_list = output
2025-08-14T23:09:11.5281010Z     >>> for i in range(world_size):
2025-08-14T23:09:11.5281168Z     >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-08-14T23:09:11.5281285Z 
2025-08-14T23:09:11.5281319Z     >>> input
2025-08-14T23:09:11.5281425Z     tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-08-14T23:09:11.5281575Z     tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-08-14T23:09:11.5281725Z     tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-08-14T23:09:11.5281875Z     tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-08-14T23:09:11.5282006Z     >>> input_splits
2025-08-14T23:09:11.5282111Z     [2, 2, 1, 1]                                                     # Rank 0
2025-08-14T23:09:11.5282242Z     [3, 2, 2, 2]                                                     # Rank 1
2025-08-14T23:09:11.5283449Z     [2, 1, 1, 1]                                                     # Rank 2
2025-08-14T23:09:11.5283576Z     [2, 2, 2, 1]                                                     # Rank 3
2025-08-14T23:09:11.5283692Z     >>> output_splits
2025-08-14T23:09:11.5283795Z     [2, 3, 2, 2]                                                     # Rank 0
2025-08-14T23:09:11.5283919Z     [2, 2, 1, 2]                                                     # Rank 1
2025-08-14T23:09:11.5284043Z     [1, 2, 1, 2]                                                     # Rank 2
2025-08-14T23:09:11.5284167Z     [1, 2, 1, 1]                                                     # Rank 3
2025-08-14T23:09:11.5284299Z     >>> input = list(input.split(input_splits))
2025-08-14T23:09:11.5284415Z     >>> input
2025-08-14T23:09:11.5284539Z     [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-08-14T23:09:11.5284720Z     [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-08-14T23:09:11.5286555Z     [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-08-14T23:09:11.5286736Z     [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-08-14T23:09:11.5286878Z     >>> output = ...
2025-08-14T23:09:11.5286980Z     >>> dist.all_to_all(output, input)
2025-08-14T23:09:11.5287092Z     >>> output
2025-08-14T23:09:11.5287213Z     [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-08-14T23:09:11.5287387Z     [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-08-14T23:09:11.5287564Z     [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-08-14T23:09:11.5287793Z     [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-08-14T23:09:11.5287901Z 
2025-08-14T23:09:11.5287966Z     >>> # Another example with tensors of torch.cfloat type.
2025-08-14T23:09:11.5288103Z     >>> input = torch.tensor(
2025-08-14T23:09:11.5289195Z     ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-08-14T23:09:11.5289325Z     ... ) + 4 * rank * (1 + 1j)
2025-08-14T23:09:11.5289434Z     >>> input = list(input.chunk(4))
2025-08-14T23:09:11.5289541Z     >>> input
2025-08-14T23:09:11.5289667Z     [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-08-14T23:09:11.5289850Z     [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-08-14T23:09:11.5290034Z     [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-08-14T23:09:11.5290244Z     [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-08-14T23:09:11.5290429Z     >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-08-14T23:09:11.5290593Z     >>> dist.all_to_all(output, input)
2025-08-14T23:09:11.5290701Z     >>> output
2025-08-14T23:09:11.5291737Z     [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-08-14T23:09:11.5291919Z     [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-08-14T23:09:11.5292098Z     [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-08-14T23:09:11.5292275Z     [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-08-14T23:09:11.5292384Z 
2025-08-14T23:09:11.5292386Z 
2025-08-14T23:09:11.5292478Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5292603Z 
2025-08-14T23:09:11.5292791Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2.
2025-08-14T23:09:11.5293190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5293319Z 
2025-08-14T23:09:11.5293366Z Module ``torch.distributed.launch``.
2025-08-14T23:09:11.5293444Z 
2025-08-14T23:09:11.5293533Z ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-08-14T23:09:11.5293714Z training processes on each of the training nodes.
2025-08-14T23:09:11.5293807Z 
2025-08-14T23:09:11.5294792Z .. warning::
2025-08-14T23:09:11.5294847Z 
2025-08-14T23:09:11.5294941Z     This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-08-14T23:09:11.5295070Z 
2025-08-14T23:09:11.5295154Z The utility can be used for single-node distributed training, in which one or
2025-08-14T23:09:11.5295356Z more processes per node will be spawned. The utility can be used for either
2025-08-14T23:09:11.5295553Z CPU training or GPU training. If the utility is used for GPU training,
2025-08-14T23:09:11.5295751Z each distributed process will be operating on a single GPU. This can achieve
2025-08-14T23:09:11.5295954Z well-improved single-node training performance. It can also be used in
2025-08-14T23:09:11.5296164Z multi-node distributed training, by spawning up multiple processes on each node
2025-08-14T23:09:11.5296373Z for well-improved multi-node distributed training performance as well.
2025-08-14T23:09:11.5296569Z This will especially be beneficial for systems with multiple Infiniband
2025-08-14T23:09:11.5296771Z interfaces that have direct-GPU support, since all of them can be utilized for
2025-08-14T23:09:11.5297840Z aggregated communication bandwidth.
2025-08-14T23:09:11.5297921Z 
2025-08-14T23:09:11.5298003Z In both cases of single-node distributed training or multi-node distributed
2025-08-14T23:09:11.5298203Z training, this utility will launch the given number of processes per node
2025-08-14T23:09:11.5298403Z (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-08-14T23:09:11.5298644Z or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-08-14T23:09:11.5298832Z and each process will be operating on a single GPU from *GPU 0 to
2025-08-14T23:09:11.5298987Z GPU (nproc_per_node - 1)*.
2025-08-14T23:09:11.5299055Z 
2025-08-14T23:09:11.5299096Z **How to use this module:**
2025-08-14T23:09:11.5299163Z 
2025-08-14T23:09:11.5299222Z 1. Single-Node multi-process distributed training
2025-08-14T23:09:11.5299313Z 
2025-08-14T23:09:11.5299347Z ::
2025-08-14T23:09:11.5299392Z 
2025-08-14T23:09:11.5299480Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:11.5300573Z                YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-08-14T23:09:11.5300728Z                arguments of your training script)
2025-08-14T23:09:11.5300811Z 
2025-08-14T23:09:11.5300889Z 2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-08-14T23:09:11.5301026Z 
2025-08-14T23:09:11.5301028Z 
2025-08-14T23:09:11.5301088Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-08-14T23:09:11.5301177Z 
2025-08-14T23:09:11.5301209Z ::
2025-08-14T23:09:11.5301271Z 
2025-08-14T23:09:11.5301354Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:11.5301536Z                --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-08-14T23:09:11.5301706Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-08-14T23:09:11.5301875Z                and all other arguments of your training script)
2025-08-14T23:09:11.5301969Z 
2025-08-14T23:09:11.5302003Z Node 2:
2025-08-14T23:09:11.5302047Z 
2025-08-14T23:09:11.5302080Z ::
2025-08-14T23:09:11.5303101Z 
2025-08-14T23:09:11.5303188Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:11.5303368Z                --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-08-14T23:09:11.5303537Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-08-14T23:09:11.5303706Z                and all other arguments of your training script)
2025-08-14T23:09:11.5303798Z 
2025-08-14T23:09:11.5303863Z 3. To look up what optional arguments this module offers:
2025-08-14T23:09:11.5303956Z 
2025-08-14T23:09:11.5303988Z ::
2025-08-14T23:09:11.5304030Z 
2025-08-14T23:09:11.5304085Z     python -m torch.distributed.launch --help
2025-08-14T23:09:11.5304172Z 
2025-08-14T23:09:11.5304174Z 
2025-08-14T23:09:11.5304213Z **Important Notices:**
2025-08-14T23:09:11.5304274Z 
2025-08-14T23:09:11.5304346Z 1. This utility and multi-process distributed (single-node or
2025-08-14T23:09:11.5304536Z multi-node) GPU training currently only achieves the best performance using
2025-08-14T23:09:11.5304744Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-08-14T23:09:11.5305820Z use for GPU training.
2025-08-14T23:09:11.5305883Z 
2025-08-14T23:09:11.5305963Z 2. In your training program, you must parse the command-line argument:
2025-08-14T23:09:11.5306159Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-08-14T23:09:11.5306354Z If your training program uses GPUs, you should ensure that your code only
2025-08-14T23:09:11.5306543Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-08-14T23:09:11.5306649Z 
2025-08-14T23:09:11.5306692Z Parsing the local_rank argument
2025-08-14T23:09:11.5306764Z 
2025-08-14T23:09:11.5306796Z ::
2025-08-14T23:09:11.5306838Z 
2025-08-14T23:09:11.5306879Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.5306980Z     >>> import argparse
2025-08-14T23:09:11.5307091Z     >>> parser = argparse.ArgumentParser()
2025-08-14T23:09:11.5308140Z     >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-08-14T23:09:11.5308291Z     >>> args = parser.parse_args()
2025-08-14T23:09:11.5308366Z 
2025-08-14T23:09:11.5308415Z Set your device to local rank using either
2025-08-14T23:09:11.5308495Z 
2025-08-14T23:09:11.5308565Z ::
2025-08-14T23:09:11.5308607Z 
2025-08-14T23:09:11.5308699Z     >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-08-14T23:09:11.5308808Z 
2025-08-14T23:09:11.5308841Z or
2025-08-14T23:09:11.5308882Z 
2025-08-14T23:09:11.5308913Z ::
2025-08-14T23:09:11.5308954Z 
2025-08-14T23:09:11.5309005Z     >>> with torch.cuda.device(args.local_rank):
2025-08-14T23:09:11.5309127Z     >>>    # your code to run
2025-08-14T23:09:11.5309227Z     >>>    ...
2025-08-14T23:09:11.5309278Z 
2025-08-14T23:09:11.5309319Z .. versionchanged:: 2.0.0
2025-08-14T23:09:11.5309383Z 
2025-08-14T23:09:11.5310368Z     The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-08-14T23:09:11.5310578Z     From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-08-14T23:09:11.5310751Z     previously used underscored ``--local_rank``.
2025-08-14T23:09:11.5310837Z 
2025-08-14T23:09:11.5310955Z     For backward compatibility, it may be necessary for users to handle both
2025-08-14T23:09:11.5311172Z     cases in their argument parsing code. This means including both ``"--local-rank"``
2025-08-14T23:09:11.5311379Z     and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-08-14T23:09:11.5311596Z     provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-08-14T23:09:11.5311799Z     --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-08-14T23:09:11.5311973Z     including ``"--local-rank"`` should be sufficient.
2025-08-14T23:09:11.5312064Z 
2025-08-14T23:09:11.5312146Z 3. In your training program, you are supposed to call the following function
2025-08-14T23:09:11.5312345Z at the beginning to start the distributed backend. It is strongly recommended
2025-08-14T23:09:11.5313540Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-08-14T23:09:11.5313728Z but ``env://`` is the one that is officially supported by this module.
2025-08-14T23:09:11.5313831Z 
2025-08-14T23:09:11.5313867Z ::
2025-08-14T23:09:11.5313911Z 
2025-08-14T23:09:11.5313989Z     >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-08-14T23:09:11.5314160Z     >>>                                      init_method='env://')
2025-08-14T23:09:11.5314251Z 
2025-08-14T23:09:11.5314335Z 4. In your training program, you can either use regular distributed functions
2025-08-14T23:09:11.5314542Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-08-14T23:09:11.5314738Z training program uses GPUs for training and you would like to use
2025-08-14T23:09:11.5314919Z :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-08-14T23:09:11.5315064Z here is how to configure it.
2025-08-14T23:09:11.5315135Z 
2025-08-14T23:09:11.5315166Z ::
2025-08-14T23:09:11.5316112Z 
2025-08-14T23:09:11.5316186Z     >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-08-14T23:09:11.5316350Z     >>>                                                   device_ids=[args.local_rank],
2025-08-14T23:09:11.5316495Z     >>>                                                   output_device=args.local_rank)
2025-08-14T23:09:11.5316586Z 
2025-08-14T23:09:11.5316673Z Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-08-14T23:09:11.5316876Z that your code will be operating on. This is generally the local rank of the
2025-08-14T23:09:11.5317075Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-08-14T23:09:11.5317270Z and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-08-14T23:09:11.5317414Z utility
2025-08-14T23:09:11.5317459Z 
2025-08-14T23:09:11.5317546Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-08-14T23:09:11.5317744Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-08-14T23:09:11.5317933Z ``--use-env=True``. You must adjust the subprocess example above to replace
2025-08-14T23:09:11.5319017Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-08-14T23:09:11.5319238Z will not pass ``--local-rank`` when you specify this flag.
2025-08-14T23:09:11.5319340Z 
2025-08-14T23:09:11.5319375Z .. warning::
2025-08-14T23:09:11.5319427Z 
2025-08-14T23:09:11.5319501Z     ``local_rank`` is NOT globally unique: it is only unique per process
2025-08-14T23:09:11.5319679Z     on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-08-14T23:09:11.5319825Z     write to a networked filesystem.  See
2025-08-14T23:09:11.5319989Z     https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-08-14T23:09:11.5320163Z     how things can go wrong if you don't do this correctly.
2025-08-14T23:09:11.5320259Z 
2025-08-14T23:09:11.5320261Z 
2025-08-14T23:09:11.5320263Z 
2025-08-14T23:09:11.5320265Z 
2025-08-14T23:09:11.5320355Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5320479Z 
2025-08-14T23:09:11.5726479Z 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.
2025-08-14T23:09:11.5732141Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5732645Z 
2025-08-14T23:09:11.5732884Z Custom reducer class that can be used to specify a custom operation that
2025-08-14T23:09:11.5733560Z reduces losses of multiple microbatches into one value.
2025-08-14T23:09:11.5733846Z 
2025-08-14T23:09:11.5733945Z Example:
2025-08-14T23:09:11.5734180Z >>> # xdoctest: +SKIP
2025-08-14T23:09:11.5734469Z >>> sum_reducer = _CustomReducer(
2025-08-14T23:09:11.5734786Z >>>     torch.tensor(0.0),
2025-08-14T23:09:11.5735075Z >>>     lambda a, b: a + b
2025-08-14T23:09:11.5735332Z >>> )
2025-08-14T23:09:11.5735451Z 
2025-08-14T23:09:11.5735713Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5735959Z 
2025-08-14T23:09:11.5749708Z 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.
2025-08-14T23:09:11.5752088Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5752226Z 
2025-08-14T23:09:11.5752306Z Set device mapping between each RPC caller and callee pair. This
2025-08-14T23:09:11.5752485Z function can be called multiple times to incrementally add
2025-08-14T23:09:11.5752634Z device placement configurations.
2025-08-14T23:09:11.5752709Z 
2025-08-14T23:09:11.5752742Z Args:
2025-08-14T23:09:11.5752828Z     to (str): Callee name.
2025-08-14T23:09:11.5753046Z     device_map (Dict of int, str, or torch.device): Device placement
2025-08-14T23:09:11.5753221Z         mappings from this worker to the callee. This map must be
2025-08-14T23:09:11.5753358Z         invertible.
2025-08-14T23:09:11.5753420Z 
2025-08-14T23:09:11.5753458Z Example:
2025-08-14T23:09:11.5754802Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.5754930Z     >>> # both workers
2025-08-14T23:09:11.5755025Z     >>> def add(x, y):
2025-08-14T23:09:11.5755138Z     >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-08-14T23:09:11.5755271Z     >>>     return x + y, (x + y).to(2)
2025-08-14T23:09:11.5755379Z     >>>
2025-08-14T23:09:11.5755457Z     >>> # on worker 0
2025-08-14T23:09:11.5755569Z     >>> options = TensorPipeRpcBackendOptions(
2025-08-14T23:09:11.5755696Z     >>>     num_worker_threads=8,
2025-08-14T23:09:11.5755811Z     >>>     device_maps={"worker1": {0: 1}}
2025-08-14T23:09:11.5755938Z     >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-08-14T23:09:11.5757142Z     >>> )
2025-08-14T23:09:11.5757246Z     >>> options.set_device_map("worker1", {1: 2})
2025-08-14T23:09:11.5757397Z     >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-08-14T23:09:11.5757516Z     >>>
2025-08-14T23:09:11.5757596Z     >>> rpc.init_rpc(
2025-08-14T23:09:11.5757693Z     >>>     "worker0",
2025-08-14T23:09:11.5757781Z     >>>     rank=0,
2025-08-14T23:09:11.5757929Z     >>>     world_size=2,
2025-08-14T23:09:11.5758062Z     >>>     backend=rpc.BackendType.TENSORPIPE,
2025-08-14T23:09:11.5758187Z     >>>     rpc_backend_options=options
2025-08-14T23:09:11.5759565Z     >>> )
2025-08-14T23:09:11.5759640Z     >>>
2025-08-14T23:09:11.5759729Z     >>> x = torch.ones(2)
2025-08-14T23:09:11.5759864Z     >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-08-14T23:09:11.5760033Z     >>> # The first argument will be moved to cuda:1 on worker1. When
2025-08-14T23:09:11.5760203Z     >>> # sending the return value back, it will follow the invert of
2025-08-14T23:09:11.5760373Z     >>> # the device map, and hence will be moved back to cuda:0 and
2025-08-14T23:09:11.5760510Z     >>> # cuda:1 on worker0
2025-08-14T23:09:11.5760631Z     >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-08-14T23:09:11.5760776Z     >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-08-14T23:09:11.5760868Z 
2025-08-14T23:09:11.5760984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5761130Z 
2025-08-14T23:09:11.5761326Z 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.
2025-08-14T23:09:11.5761658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5761787Z 
2025-08-14T23:09:11.5761872Z A decorator for a function indicating that the return value of the function
2025-08-14T23:09:11.5762069Z is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-08-14T23:09:11.5762261Z function can run asynchronously on the RPC callee. More specifically, the
2025-08-14T23:09:11.5762464Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-08-14T23:09:11.5762665Z function and installs subsequent processing steps as a callback to that
2025-08-14T23:09:11.5762870Z :class:`~torch.futures.Future`. The installed callback will read the value
2025-08-14T23:09:11.5763158Z from the :class:`~torch.futures.Future` when completed and send the
2025-08-14T23:09:11.5764375Z value back as the RPC response. That also means the returned
2025-08-14T23:09:11.5764563Z :class:`~torch.futures.Future` only exists on the callee side and is never
2025-08-14T23:09:11.5764759Z sent through RPC. This decorator is useful when the wrapped function's
2025-08-14T23:09:11.5764942Z (``fn``) execution needs to pause and resume due to, e.g., containing
2025-08-14T23:09:11.5765129Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-08-14T23:09:11.5765244Z 
2025-08-14T23:09:11.5765325Z .. note:: To enable asynchronous execution, applications must pass the
2025-08-14T23:09:11.5765516Z     function object returned by this decorator to RPC APIs. If RPC detected
2025-08-14T23:09:11.5765807Z     attributes installed by this decorator, it knows that this function
2025-08-14T23:09:11.5765986Z     returns a ``Future`` object and will handle that accordingly.
2025-08-14T23:09:11.5766166Z     However, this does not mean this decorator has to be outmost one when
2025-08-14T23:09:11.5766361Z     defining a function. For example, when combined with ``@staticmethod``
2025-08-14T23:09:11.5767504Z     or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-08-14T23:09:11.5767695Z     inner decorator to allow the target function be recognized as a static
2025-08-14T23:09:11.5767887Z     or class function. This target function can still execute asynchronously
2025-08-14T23:09:11.5768081Z     because, when accessed, the static or class method preserves attributes
2025-08-14T23:09:11.5768253Z     installed by ``@rpc.functions.async_execution``.
2025-08-14T23:09:11.5768346Z 
2025-08-14T23:09:11.5768349Z 
2025-08-14T23:09:11.5768385Z Example::
2025-08-14T23:09:11.5768504Z     The returned :class:`~torch.futures.Future` object can come from
2025-08-14T23:09:11.5768663Z     :meth:`~torch.distributed.rpc.rpc_async`,
2025-08-14T23:09:11.5768829Z     :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-08-14T23:09:11.5769053Z     constructor. The example below shows directly using the
2025-08-14T23:09:11.5769202Z     :class:`~torch.futures.Future` returned by
2025-08-14T23:09:11.5770264Z     :meth:`~torch.futures.Future.then`.
2025-08-14T23:09:11.5770344Z 
2025-08-14T23:09:11.5770391Z     >>> from torch.distributed import rpc
2025-08-14T23:09:11.5770501Z     >>>
2025-08-14T23:09:11.5770587Z     >>> # omitting setup and shutdown RPC
2025-08-14T23:09:11.5770695Z     >>>
2025-08-14T23:09:11.5770773Z     >>> # On all workers
2025-08-14T23:09:11.5770879Z     >>> @rpc.functions.async_execution
2025-08-14T23:09:11.5770999Z     >>> def async_add_chained(to, x, y, z):
2025-08-14T23:09:11.5771146Z     >>>     # This function runs on "worker1" and returns immediately when
2025-08-14T23:09:11.5771315Z     >>>     # the callback is installed through the `then(cb)` API. In the
2025-08-14T23:09:11.5771509Z     >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-08-14T23:09:11.5772599Z     >>>     # When the return value of that `rpc_async` arrives at
2025-08-14T23:09:11.5772760Z     >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-08-14T23:09:11.5773032Z     >>>     # and set the value for the previously returned `Future`, which
2025-08-14T23:09:11.5773200Z     >>>     # will then trigger RPC to send the result back to "worker0".
2025-08-14T23:09:11.5773364Z     >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:11.5773503Z     >>>         lambda fut: fut.wait() + z
2025-08-14T23:09:11.5773611Z     >>>     )
2025-08-14T23:09:11.5773690Z     >>>
2025-08-14T23:09:11.5773766Z     >>> # On worker0
2025-08-14T23:09:11.5773862Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.5773964Z     >>> ret = rpc.rpc_sync(
2025-08-14T23:09:11.5775000Z     >>>     "worker1",
2025-08-14T23:09:11.5775098Z     >>>     async_add_chained,
2025-08-14T23:09:11.5775210Z     >>>     args=("worker2", torch.ones(2), 1, 1)
2025-08-14T23:09:11.5775327Z     >>> )
2025-08-14T23:09:11.5775418Z     >>> print(ret)  # prints tensor([3., 3.])
2025-08-14T23:09:11.5775498Z 
2025-08-14T23:09:11.5775579Z     When combined with TorchScript decorators, this decorator must be the
2025-08-14T23:09:11.5775728Z     outmost one.
2025-08-14T23:09:11.5775781Z 
2025-08-14T23:09:11.5775825Z     >>> from torch import Tensor
2025-08-14T23:09:11.5775938Z     >>> from torch.futures import Future
2025-08-14T23:09:11.5776061Z     >>> from torch.distributed import rpc
2025-08-14T23:09:11.5776169Z     >>>
2025-08-14T23:09:11.5777174Z     >>> # omitting setup and shutdown RPC
2025-08-14T23:09:11.5777281Z     >>>
2025-08-14T23:09:11.5777357Z     >>> # On all workers
2025-08-14T23:09:11.5777457Z     >>> @torch.jit.script
2025-08-14T23:09:11.5777577Z     >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-08-14T23:09:11.5777701Z     >>>     return x + y
2025-08-14T23:09:11.5777791Z     >>>
2025-08-14T23:09:11.5777878Z     >>> @rpc.functions.async_execution
2025-08-14T23:09:11.5777994Z     >>> @torch.jit.script
2025-08-14T23:09:11.5778126Z     >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-08-14T23:09:11.5778280Z     >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-08-14T23:09:11.5779319Z     >>>
2025-08-14T23:09:11.5779395Z     >>> # On worker0
2025-08-14T23:09:11.5779489Z     >>> ret = rpc.rpc_sync(
2025-08-14T23:09:11.5779586Z     >>>     "worker1",
2025-08-14T23:09:11.5779677Z     >>>     async_add,
2025-08-14T23:09:11.5779777Z     >>>     args=("worker2", torch.ones(2), 1)
2025-08-14T23:09:11.5779886Z     >>> )
2025-08-14T23:09:11.5779975Z     >>> print(ret)  # prints tensor([2., 2.])
2025-08-14T23:09:11.5780055Z 
2025-08-14T23:09:11.5780132Z     When combined with static or class method, this decorator must be the
2025-08-14T23:09:11.5780277Z     inner one.
2025-08-14T23:09:11.5780327Z 
2025-08-14T23:09:11.5780372Z     >>> from torch.distributed import rpc
2025-08-14T23:09:11.5781397Z     >>>
2025-08-14T23:09:11.5781483Z     >>> # omitting setup and shutdown RPC
2025-08-14T23:09:11.5781620Z     >>>
2025-08-14T23:09:11.5781721Z     >>> # On all workers
2025-08-14T23:09:11.5781767Z     >>> class AsyncExecutionClass:
2025-08-14T23:09:11.5781800Z     >>>
2025-08-14T23:09:11.5781839Z     >>>     @staticmethod
2025-08-14T23:09:11.5781885Z     >>>     @rpc.functions.async_execution
2025-08-14T23:09:11.5781931Z     >>>     def static_async_add(to, x, y, z):
2025-08-14T23:09:11.5781996Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:11.5782041Z     >>>             lambda fut: fut.wait() + z
2025-08-14T23:09:11.5782074Z     >>>         )
2025-08-14T23:09:11.5782106Z     >>>
2025-08-14T23:09:11.5782142Z     >>>     @classmethod
2025-08-14T23:09:11.5782187Z     >>>     @rpc.functions.async_execution
2025-08-14T23:09:11.5782235Z     >>>     def class_async_add(cls, to, x, y, z):
2025-08-14T23:09:11.5782284Z     >>>         ret_fut = torch.futures.Future()
2025-08-14T23:09:11.5782358Z     >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:11.5783445Z     >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-08-14T23:09:11.5783481Z     >>>         )
2025-08-14T23:09:11.5783544Z     >>>         return ret_fut
2025-08-14T23:09:11.5783575Z     >>>
2025-08-14T23:09:11.5783621Z     >>>     @rpc.functions.async_execution
2025-08-14T23:09:11.5783669Z     >>>     def bound_async_add(self, to, x, y, z):
2025-08-14T23:09:11.5783732Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:11.5783776Z     >>>             lambda fut: fut.wait() + z
2025-08-14T23:09:11.5783808Z     >>>         )
2025-08-14T23:09:11.5783841Z     >>>
2025-08-14T23:09:11.5783874Z     >>> # On worker0
2025-08-14T23:09:11.5783913Z     >>> ret = rpc.rpc_sync(
2025-08-14T23:09:11.5783948Z     >>>     "worker1",
2025-08-14T23:09:11.5784000Z     >>>     AsyncExecutionClass.static_async_add,
2025-08-14T23:09:11.5784045Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:11.5784082Z     >>> )
2025-08-14T23:09:11.5784129Z     >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:11.5784161Z     >>>
2025-08-14T23:09:11.5784199Z     >>> ret = rpc.rpc_sync(
2025-08-14T23:09:11.5784237Z     >>>     "worker1",
2025-08-14T23:09:11.5784287Z     >>>     AsyncExecutionClass.class_async_add,
2025-08-14T23:09:11.5785257Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:11.5785291Z     >>> )
2025-08-14T23:09:11.5785337Z     >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:11.5785340Z 
2025-08-14T23:09:11.5785401Z     This decorator also works with RRef helpers, i.e., .
2025-08-14T23:09:11.5785455Z     :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-08-14T23:09:11.5785515Z     :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-08-14T23:09:11.5785570Z     :meth:`torch.distributed.rpc.RRef.remote`.
2025-08-14T23:09:11.5785573Z 
2025-08-14T23:09:11.5785618Z     >>> from torch.distributed import rpc
2025-08-14T23:09:11.5785650Z     >>>
2025-08-14T23:09:11.5785704Z     >>> # reuse the AsyncExecutionClass class above
2025-08-14T23:09:11.5785764Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:11.5785842Z     >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:11.5785889Z     >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:11.5785922Z     >>>
2025-08-14T23:09:11.5785978Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:11.5786063Z     >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-08-14T23:09:11.5786108Z     >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:11.5786140Z     >>>
2025-08-14T23:09:11.5786195Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:11.5786278Z     >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-08-14T23:09:11.5786323Z     >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:11.5786325Z 
2025-08-14T23:09:11.5786419Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5786464Z 
2025-08-14T23:09:11.5840655Z 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.
2025-08-14T23:09:11.5840773Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5840804Z 
2025-08-14T23:09:11.5840888Z It has the same API as ``torch.autograd.profiler.profile`` class,
2025-08-14T23:09:11.5840996Z except that it enables profiling on all threads running RPC server request callbacks.
2025-08-14T23:09:11.5840999Z 
2025-08-14T23:09:11.5841098Z Context manager that manages autograd profiler state and holds a summary of results.
2025-08-14T23:09:11.5841183Z Under the hood it just records events of functions being executed in C++ and
2025-08-14T23:09:11.5841273Z exposes those events to Python. You can wrap any code into it and it will
2025-08-14T23:09:11.5841381Z only report runtime of PyTorch functions.
2025-08-14T23:09:11.5841480Z Note: profiler is thread local and is automatically propagated into the async tasks
2025-08-14T23:09:11.5841505Z 
2025-08-14T23:09:11.5841543Z Args:
2025-08-14T23:09:11.5841642Z     enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-08-14T23:09:11.5841685Z         Default: ``True``.
2025-08-14T23:09:11.5841716Z 
2025-08-14T23:09:11.5841817Z     use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-08-14T23:09:11.5841893Z         Adds approximately 4us of overhead to each tensor operation.
2025-08-14T23:09:11.5841931Z         Default: ``False``
2025-08-14T23:09:11.5841934Z 
2025-08-14T23:09:11.5842016Z     record_shapes (bool, optional): If shapes recording is set, information
2025-08-14T23:09:11.5842096Z         about input dimensions will be collected. This allows one to see which
2025-08-14T23:09:11.5842175Z         dimensions have been used under the hood and further group by them
2025-08-14T23:09:11.5842257Z         using prof.key_averages(group_by_input_shape=True). Please note that
2025-08-14T23:09:11.5844460Z         shape recording might skew your profiling data. It is recommended to
2025-08-14T23:09:11.5844557Z         use separate runs with and without shape recording to validate the timing.
2025-08-14T23:09:11.5844645Z         Most likely the skew will be negligible for bottom most events (in a case
2025-08-14T23:09:11.5844721Z         of nested function calls). But for higher level functions the total
2025-08-14T23:09:11.5844795Z         self cpu time might be artificially increased because of the shape
2025-08-14T23:09:11.5844832Z         collection.
2025-08-14T23:09:11.5844835Z 
2025-08-14T23:09:11.5844931Z     profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-08-14T23:09:11.5844933Z 
2025-08-14T23:09:11.5844972Z .. warning::
2025-08-14T23:09:11.5845055Z     Enabling memory profiling incurs additional profiler overhead
2025-08-14T23:09:11.5845059Z 
2025-08-14T23:09:11.5845094Z .. warning::
2025-08-14T23:09:11.5845198Z     Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`),
2025-08-14T23:09:11.5845277Z     one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-08-14T23:09:11.5845365Z     DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-08-14T23:09:11.5845429Z     please use ``use_cuda = False`` or ``num_workers = 0``.
2025-08-14T23:09:11.5845431Z 
2025-08-14T23:09:11.5845467Z Example:
2025-08-14T23:09:11.5845508Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.5845544Z     >>> # On worker 0:
2025-08-14T23:09:11.5845580Z     >>> import torch
2025-08-14T23:09:11.5845632Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.5845689Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:11.5845809Z     >>> x, y = torch.tensor(1), torch.tensor(2)
2025-08-14T23:09:11.5845859Z     >>> outer_profile_rref = rpc.remote(
2025-08-14T23:09:11.5845980Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-08-14T23:09:11.5846013Z     ... )
2025-08-14T23:09:11.5846066Z     >>> outer_profile_rref.rpc_sync().__enter__()
2025-08-14T23:09:11.5846125Z     >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-08-14T23:09:11.5846169Z     >>> inner_profile_rref = rpc.remote(
2025-08-14T23:09:11.5846230Z     ...     dst_worker_name, rpc._server_process_global_profile
2025-08-14T23:09:11.5847387Z     ... )
2025-08-14T23:09:11.5847437Z     >>> inner_profile_rref.rpc_sync().__enter__()
2025-08-14T23:09:11.5847495Z     >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-08-14T23:09:11.5847561Z     >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-08-14T23:09:11.5847625Z     >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-08-14T23:09:11.5847687Z     >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-08-14T23:09:11.5847808Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5847923Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-08-14T23:09:11.5848028Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5848099Z     sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-08-14T23:09:11.5848171Z     empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-08-14T23:09:11.5848252Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5848298Z     Self CPU time total: 89.667us
2025-08-14T23:09:11.5848360Z     >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-08-14T23:09:11.5848442Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5848549Z     Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-08-14T23:09:11.5848631Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5848695Z     sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-08-14T23:09:11.5848761Z     empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-08-14T23:09:11.5848826Z     add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-08-14T23:09:11.5848906Z     ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:11.5849912Z     Self CPU time total: 213.926us
2025-08-14T23:09:11.5849956Z     >>> rpc.shutdown()
2025-08-14T23:09:11.5849961Z 
2025-08-14T23:09:11.5849998Z     >>> # On worker 1:
2025-08-14T23:09:11.5850052Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.5850108Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:11.5850173Z     >>> # wait for worker 0 to finish work, and then shutdown.
2025-08-14T23:09:11.5850210Z     >>> rpc.shutdown()
2025-08-14T23:09:11.5850213Z 
2025-08-14T23:09:11.5850306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5850308Z 
2025-08-14T23:09:11.5902285Z 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.
2025-08-14T23:09:11.5902841Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5902859Z 
2025-08-14T23:09:11.5903307Z Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-08-14T23:09:11.5903543Z 
2025-08-14T23:09:11.5903787Z The sharing is done as described by `ZeRO <https://arxiv.org/abs/1910.02054>`_.
2025-08-14T23:09:11.5903793Z 
2025-08-14T23:09:11.5903905Z The local optimizer instance in each rank is only
2025-08-14T23:09:11.5904064Z responsible for updating approximately ``1 / world_size`` parameters and
2025-08-14T23:09:11.5904201Z hence only needs to keep ``1 / world_size`` optimizer states. After
2025-08-14T23:09:11.5904354Z parameters are updated locally, each rank will broadcast its parameters to
2025-08-14T23:09:11.5904481Z all other peers to keep all model replicas in the same state.
2025-08-14T23:09:11.5904605Z ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-08-14T23:09:11.5904782Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-08-14T23:09:11.5916011Z memory consumption.
2025-08-14T23:09:11.5916018Z 
2025-08-14T23:09:11.5916166Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-08-14T23:09:11.5916361Z of parameters at each rank. Each parameter belongs to a single rank and is
2025-08-14T23:09:11.5916475Z not divided among ranks. The partition is arbitrary and might not match the
2025-08-14T23:09:11.5916582Z the parameter registration or usage order.
2025-08-14T23:09:11.5916585Z 
2025-08-14T23:09:11.5916634Z Arguments:
2025-08-14T23:09:11.5916733Z     params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-08-14T23:09:11.5916831Z         or :class:`dict` s giving all parameters, which will be sharded
2025-08-14T23:09:11.5916880Z         across ranks.
2025-08-14T23:09:11.5916882Z 
2025-08-14T23:09:11.5916930Z Keyword Args:
2025-08-14T23:09:11.5917035Z     optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-08-14T23:09:11.5917084Z         optimizer.
2025-08-14T23:09:11.5917184Z     process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-08-14T23:09:11.5917279Z         ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-08-14T23:09:11.5917352Z         :meth:`torch.distributed.init_process_group`).
2025-08-14T23:09:11.5917457Z     parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-08-14T23:09:11.5917559Z         packed into buckets to speed up communication, and ``param.data``
2025-08-14T23:09:11.5917656Z         fields point to bucket views at different offsets; if ``False``,
2025-08-14T23:09:11.5917751Z         each individual parameter is communicated separately, and each
2025-08-14T23:09:11.5917820Z         ``params.data`` stays intact (default: ``False``).
2025-08-14T23:09:11.5917917Z     overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-08-14T23:09:11.5919545Z         overlapped with :class:`DistributedDataParallel` 's gradient
2025-08-14T23:09:11.5919648Z         synchronization; this requires (1) either a functional optimizer
2025-08-14T23:09:11.5919737Z         for the ``optimizer_class`` argument or one with a functional
2025-08-14T23:09:11.5919816Z         equivalent and (2) registering a DDP communication hook
2025-08-14T23:09:11.5919913Z         constructed from one of the functions in ``ddp_zero_hook.py``;
2025-08-14T23:09:11.5920005Z         parameters are packed into buckets matching those in
2025-08-14T23:09:11.5920084Z         :class:`DistributedDataParallel`, meaning that the
2025-08-14T23:09:11.5920156Z         ``parameters_as_bucket_view`` argument is ignored.
2025-08-14T23:09:11.5920247Z         If ``False``, :meth:`step` runs disjointly after the backward pass
2025-08-14T23:09:11.5920292Z         (per normal).
2025-08-14T23:09:11.5920347Z         (default: ``False``)
2025-08-14T23:09:11.5920440Z     **defaults: any trailing arguments, which are forwarded to the local
2025-08-14T23:09:11.5920482Z         optimizer.
2025-08-14T23:09:11.5920486Z 
2025-08-14T23:09:11.5920538Z Example::
2025-08-14T23:09:11.5920543Z 
2025-08-14T23:09:11.5920594Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.5920651Z     >>> import torch.nn as nn
2025-08-14T23:09:11.5920746Z     >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-08-14T23:09:11.5920896Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-08-14T23:09:11.5921005Z     >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-08-14T23:09:11.5921072Z     >>> ddp = DDP(model, device_ids=[rank])
2025-08-14T23:09:11.5921131Z     >>> opt = ZeroRedundancyOptimizer(
2025-08-14T23:09:11.5921185Z     >>>     ddp.parameters(),
2025-08-14T23:09:11.5922543Z     >>>     optimizer_class=torch.optim.Adam,
2025-08-14T23:09:11.5922590Z     >>>     lr=0.01
2025-08-14T23:09:11.5922631Z     >>> )
2025-08-14T23:09:11.5922684Z     >>> ddp(inputs).sum().backward()
2025-08-14T23:09:11.5922730Z     >>> opt.step()
2025-08-14T23:09:11.5922732Z 
2025-08-14T23:09:11.5922778Z .. warning::
2025-08-14T23:09:11.5922877Z     Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-08-14T23:09:11.5923037Z     passed-in parameters are the same dense type.
2025-08-14T23:09:11.5923040Z 
2025-08-14T23:09:11.5923121Z .. warning::
2025-08-14T23:09:11.5923223Z     If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-08-14T23:09:11.5923316Z     the way that overlapping :class:`DistributedDataParallel` with
2025-08-14T23:09:11.5923443Z     :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-08-14T23:09:11.5923539Z     two or three training iterations do not perform parameter updates in
2025-08-14T23:09:11.5923634Z     the optimizer step, depending on if ``static_graph=False`` or
2025-08-14T23:09:11.5923715Z     ``static_graph=True``, respectively. This is because it needs
2025-08-14T23:09:11.5923804Z     information about the gradient bucketing strategy used by
2025-08-14T23:09:11.5923903Z     :class:`DistributedDataParallel`, which is not finalized until the
2025-08-14T23:09:11.5923998Z     second forward pass if ``static_graph=False`` or until the third
2025-08-14T23:09:11.5924093Z     forward pass if ``static_graph=True``. To adjust for this, one option
2025-08-14T23:09:11.5924151Z     is to prepend dummy inputs.
2025-08-14T23:09:11.5924155Z 
2025-08-14T23:09:11.5924266Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-08-14T23:09:11.5924270Z 
2025-08-14T23:09:11.5925607Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5925612Z 
2025-08-14T23:09:11.5931848Z 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.
2025-08-14T23:09:11.5931960Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5931980Z 
2025-08-14T23:09:11.5932121Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-08-14T23:09:11.5932182Z This optimizer runs local optimizer at every step.
2025-08-14T23:09:11.5932301Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied.
2025-08-14T23:09:11.5932307Z 
2025-08-14T23:09:11.5932351Z Args:
2025-08-14T23:09:11.5932393Z     optim: The local optimizer.
2025-08-14T23:09:11.5932473Z     averager: A model averager instance to run post-localSGD algorithm.
2025-08-14T23:09:11.5932477Z 
2025-08-14T23:09:11.5932513Z Example::
2025-08-14T23:09:11.5932515Z 
2025-08-14T23:09:11.5932565Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.5932601Z     >>> import torch
2025-08-14T23:09:11.5932663Z     >>> import torch.distributed as dist
2025-08-14T23:09:11.5932762Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-08-14T23:09:11.5932804Z     >>> import torch.nn as nn
2025-08-14T23:09:11.5932876Z     >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-08-14T23:09:11.5933109Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-08-14T23:09:11.5933152Z     >>>   PostLocalSGDState,
2025-08-14T23:09:11.5933193Z     >>>   post_localSGD_hook,
2025-08-14T23:09:11.5933274Z     >>> )
2025-08-14T23:09:11.5934677Z     >>>
2025-08-14T23:09:11.5934751Z     >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:11.5934821Z     >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:11.5934857Z     >>> )
2025-08-14T23:09:11.5934890Z     >>>
2025-08-14T23:09:11.5934946Z     >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:11.5935054Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-08-14T23:09:11.5935118Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:11.5935152Z     >>>
2025-08-14T23:09:11.5935225Z     >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-08-14T23:09:11.5935318Z     >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-08-14T23:09:11.5935380Z     >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:11.5935481Z     >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-08-14T23:09:11.5935533Z     >>> opt = PostLocalSGDOptimizer(
2025-08-14T23:09:11.5935575Z     >>>     optim=local_optim,
2025-08-14T23:09:11.5935687Z     >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-08-14T23:09:11.5935721Z     >>> )
2025-08-14T23:09:11.5935751Z     >>>
2025-08-14T23:09:11.5935831Z     >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-08-14T23:09:11.5935937Z     >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-08-14T23:09:11.5936067Z     >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-08-14T23:09:11.5936111Z     >>> for step in range(0, 200):
2025-08-14T23:09:11.5937150Z     >>>    opt.zero_grad()
2025-08-14T23:09:11.5937197Z     >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:11.5937235Z     >>>    loss.backward()
2025-08-14T23:09:11.5937275Z     >>>    opt.step()
2025-08-14T23:09:11.5937278Z 
2025-08-14T23:09:11.5937369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5937375Z 
2025-08-14T23:09:11.5968549Z 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.
2025-08-14T23:09:11.5968655Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5968678Z 
2025-08-14T23:09:11.5968802Z DistributedOptimizer takes remote references to parameters scattered
2025-08-14T23:09:11.5968903Z across workers and applies the given optimizer locally for each parameter.
2025-08-14T23:09:11.5968906Z 
2025-08-14T23:09:11.5968990Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-08-14T23:09:11.5969053Z to retrieve the gradients for specific parameters.
2025-08-14T23:09:11.5969056Z 
2025-08-14T23:09:11.5969095Z Concurrent calls to
2025-08-14T23:09:11.5969183Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-08-14T23:09:11.5969238Z either from the same or different clients, will
2025-08-14T23:09:11.5969324Z be serialized on each worker -- as each worker's optimizer can only work
2025-08-14T23:09:11.5969399Z on one set of gradients at a time. However, there is no guarantee that
2025-08-14T23:09:11.5969487Z the full forward-backward-optimizer sequence will execute for one client
2025-08-14T23:09:11.5969564Z at a time. This means that the gradients being applied may not correspond
2025-08-14T23:09:11.5969642Z to the latest forward pass executed on a given worker. Also, there is no
2025-08-14T23:09:11.5969688Z guaranteed ordering across workers.
2025-08-14T23:09:11.5969690Z 
2025-08-14T23:09:11.5971502Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-08-14T23:09:11.5971591Z by default, so that optimizer updates are not blocked by the Python Global
2025-08-14T23:09:11.5971698Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-08-14T23:09:11.5971853Z Model Parallel). This feature is currently enabled for most optimizers. You
2025-08-14T23:09:11.5971947Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-08-14T23:09:11.5971995Z for your own custom optimizers.
2025-08-14T23:09:11.5971997Z 
2025-08-14T23:09:11.5972035Z Args:
2025-08-14T23:09:11.5972111Z     optimizer_class (optim.Optimizer): the class of optimizer to
2025-08-14T23:09:11.5972155Z         instantiate on each worker.
2025-08-14T23:09:11.5972231Z     params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-08-14T23:09:11.5972271Z         to optimize.
2025-08-14T23:09:11.5972345Z     args: arguments to pass to the optimizer constructor on each worker.
2025-08-14T23:09:11.5972422Z     kwargs: arguments to pass to the optimizer constructor on each worker.
2025-08-14T23:09:11.5972424Z 
2025-08-14T23:09:11.5972460Z Example::
2025-08-14T23:09:11.5972526Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.5972591Z     >>> import torch.distributed.autograd as dist_autograd
2025-08-14T23:09:11.5972640Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.5972698Z     >>> from torch import optim
2025-08-14T23:09:11.5972770Z     >>> from torch.distributed.optim import DistributedOptimizer
2025-08-14T23:09:11.5972802Z     >>>
2025-08-14T23:09:11.5972853Z     >>> with dist_autograd.context() as context_id:
2025-08-14T23:09:11.5974137Z     >>>   # Forward pass.
2025-08-14T23:09:11.5974216Z     >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-08-14T23:09:11.5974285Z     >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-08-14T23:09:11.5974334Z     >>>   loss = rref1.to_here() + rref2.to_here()
2025-08-14T23:09:11.5974365Z     >>>
2025-08-14T23:09:11.5974404Z     >>>   # Backward pass.
2025-08-14T23:09:11.5974462Z     >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-08-14T23:09:11.5974498Z     >>>
2025-08-14T23:09:11.5974535Z     >>>   # Optimizer.
2025-08-14T23:09:11.5974587Z     >>>   dist_optim = DistributedOptimizer(
2025-08-14T23:09:11.5974623Z     >>>      optim.SGD,
2025-08-14T23:09:11.5974665Z     >>>      [rref1, rref2],
2025-08-14T23:09:11.5974699Z     >>>      lr=0.05,
2025-08-14T23:09:11.5974732Z     >>>   )
2025-08-14T23:09:11.5974776Z     >>>   dist_optim.step(context_id)
2025-08-14T23:09:11.5974779Z 
2025-08-14T23:09:11.5974840Z __ https://github.com/pytorch/tutorials/pull/1465
2025-08-14T23:09:11.5974843Z 
2025-08-14T23:09:11.5974932Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5974935Z 
2025-08-14T23:09:11.5988025Z 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.
2025-08-14T23:09:11.5988132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5988145Z 
2025-08-14T23:09:11.5988249Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-08-14T23:09:11.5988364Z on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-08-14T23:09:11.5988369Z 
2025-08-14T23:09:11.5990001Z . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-08-14T23:09:11.5990004Z 
2025-08-14T23:09:11.5990043Z .. warning::
2025-08-14T23:09:11.5990120Z     Current implementation only supports loading Tensors.
2025-08-14T23:09:11.5990138Z 
2025-08-14T23:09:11.5990184Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.5990223Z >>> sd = {"mode": model}
2025-08-14T23:09:11.5990258Z >>> dcp.load(
2025-08-14T23:09:11.5990291Z >>>    sd,
2025-08-14T23:09:11.5990351Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-08-14T23:09:11.5990399Z >>>    planner=DynamicMetaLoadPlanner(),
2025-08-14T23:09:11.5990442Z >>>    checkpoint_id="path_to_model.pt"
2025-08-14T23:09:11.5990475Z >>> )
2025-08-14T23:09:11.5990528Z 
2025-08-14T23:09:11.5990637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5990640Z 
2025-08-14T23:09:11.5990868Z 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.
2025-08-14T23:09:11.5990961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.5990963Z 
2025-08-14T23:09:11.5991089Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-08-14T23:09:11.5991202Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-08-14T23:09:11.5991252Z metadata file, like Torch Save files.
2025-08-14T23:09:11.5991255Z 
2025-08-14T23:09:11.5991326Z . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-08-14T23:09:11.5991328Z 
2025-08-14T23:09:11.5991367Z .. warning::
2025-08-14T23:09:11.5991450Z     Current implementation only supports loading Tensors.
2025-08-14T23:09:11.5991453Z 
2025-08-14T23:09:11.5992614Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.5992676Z >>> sd = {"mode": model}
2025-08-14T23:09:11.5992716Z >>> dcp.load(
2025-08-14T23:09:11.5992748Z >>>    sd,
2025-08-14T23:09:11.5992808Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-08-14T23:09:11.5992853Z >>>    planner=DynamicMetaLoadPlanner(),
2025-08-14T23:09:11.5992981Z >>>    checkpoint_id="path_to_model.pt"
2025-08-14T23:09:11.5993013Z >>> )
2025-08-14T23:09:11.5993016Z 
2025-08-14T23:09:11.5993104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.5993106Z 
2025-08-14T23:09:11.6009871Z 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.
2025-08-14T23:09:11.6010172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6010482Z 
2025-08-14T23:09:11.6010726Z Save a distributed model in SPMD style.
2025-08-14T23:09:11.6010735Z 
2025-08-14T23:09:11.6010843Z This function is different from ``torch.save()`` as it handles
2025-08-14T23:09:11.6010950Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-08-14T23:09:11.6010954Z 
2025-08-14T23:09:11.6011061Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-08-14T23:09:11.6011124Z save will call ``state_dict`` before serialization.
2025-08-14T23:09:11.6011127Z 
2025-08-14T23:09:11.6011191Z .. warning::
2025-08-14T23:09:11.6011283Z     There is no guarantees of Backwards Compatibility across PyTorch versions
2025-08-14T23:09:11.6011333Z     for saved state_dicts.
2025-08-14T23:09:11.6011336Z 
2025-08-14T23:09:11.6011373Z .. warning::
2025-08-14T23:09:11.6014905Z     If using the `process_group` argument, make sure that only its ranks
2025-08-14T23:09:11.6015005Z     call `save_state_dict` and that all data in state_dict belong to it.
2025-08-14T23:09:11.6015048Z 
2025-08-14T23:09:11.6015090Z .. note::
2025-08-14T23:09:11.6015191Z     When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-08-14T23:09:11.6015292Z     the shard_group should be calling `save_state_dict` and the corresponding process
2025-08-14T23:09:11.6015341Z     group needs to be passed in.
2025-08-14T23:09:11.6015343Z 
2025-08-14T23:09:11.6015382Z .. note::
2025-08-14T23:09:11.6015480Z     If no process group is available, this function assumes the intention is to save the
2025-08-14T23:09:11.6015531Z      state_dict in the local process.
2025-08-14T23:09:11.6015534Z 
2025-08-14T23:09:11.6015567Z .. note:
2025-08-14T23:09:11.6015629Z     Rank 0 is assumed to be the coordinator rank.
2025-08-14T23:09:11.6015631Z 
2025-08-14T23:09:11.6015633Z 
2025-08-14T23:09:11.6015664Z Args:
2025-08-14T23:09:11.6015726Z     state_dict (Dict[str, Any]): The state_dict to save.
2025-08-14T23:09:11.6015782Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:11.6016115Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:11.6016190Z         depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:11.6016258Z         It can also be a key if the storage is a key-value store.
2025-08-14T23:09:11.6016298Z         (Default: ``None``)
2025-08-14T23:09:11.6016350Z     storage_writer (Optional[StorageWriter]):
2025-08-14T23:09:11.6016426Z         Instance of StorageWriter used to perform writes. If this is not
2025-08-14T23:09:11.6018105Z         specified, DCP will automatically infer the writer based on the
2025-08-14T23:09:11.6018186Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:11.6018239Z         be raised. (Default: ``None``)
2025-08-14T23:09:11.6018286Z     planner (Optional[SavePlanner]):
2025-08-14T23:09:11.6018359Z         Instance of SavePlanner. If this is not specified, the default
2025-08-14T23:09:11.6018473Z         planner will be used. (Default: ``None``)
2025-08-14T23:09:11.6018525Z     process_group (Optional[ProcessGroup]):
2025-08-14T23:09:11.6018594Z         ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:11.6018675Z         (Default: ``None``)
2025-08-14T23:09:11.6018710Z     no_dist (bool):
2025-08-14T23:09:11.6018779Z         If ``True``, this function will assume the intent is to load
2025-08-14T23:09:11.6018828Z         a checkpoint on a single rank/process.
2025-08-14T23:09:11.6018870Z         (Default: ``False``)
2025-08-14T23:09:11.6018964Z     use_collectives (bool): If ``False``, this function will assume the intent is to save
2025-08-14T23:09:11.6019028Z         a checkpoint without using cross-rank synchronization.
2025-08-14T23:09:11.6019066Z         (Default: ``True``)
2025-08-14T23:09:11.6019144Z         This configuration is experimental and should be used with caution.
2025-08-14T23:09:11.6019241Z         It will change the format of the saved checkpoint and may not be backward compatible.
2025-08-14T23:09:11.6019246Z 
2025-08-14T23:09:11.6019279Z Returns:
2025-08-14T23:09:11.6019343Z     Metadata: Metadata object for the saved checkpoint.
2025-08-14T23:09:11.6019348Z 
2025-08-14T23:09:11.6019380Z Example:
2025-08-14T23:09:11.6019421Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.6019464Z     >>> my_model = MyModule()
2025-08-14T23:09:11.6019466Z 
2025-08-14T23:09:11.6019514Z     >>> state_dict = {"model": my_model}
2025-08-14T23:09:11.6019516Z 
2025-08-14T23:09:11.6019603Z     >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-08-14T23:09:11.6020740Z     ...     "/checkpoint/1"
2025-08-14T23:09:11.6020774Z     ... )
2025-08-14T23:09:11.6020826Z     >>> torch.distributed.checkpoint.save(
2025-08-14T23:09:11.6020868Z     >>>     state_dict=state_dict,
2025-08-14T23:09:11.6020915Z     >>>     storage_writer=fs_storage_writer,
2025-08-14T23:09:11.6020946Z     >>> )
2025-08-14T23:09:11.6020948Z 
2025-08-14T23:09:11.6020983Z .. note::
2025-08-14T23:09:11.6021062Z     save_state_dict uses collectives to coordinate writes across ranks.
2025-08-14T23:09:11.6021141Z     For NCCL-based process groups, internal tensor representations of
2025-08-14T23:09:11.6021222Z     objects must be moved to the GPU device before communication takes place.
2025-08-14T23:09:11.6021301Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-08-14T23:09:11.6021374Z     and it is the user's responsibility to ensure that this is set so that
2025-08-14T23:09:11.6021445Z     each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-08-14T23:09:11.6021447Z 
2025-08-14T23:09:11.6021535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6021537Z 
2025-08-14T23:09:11.6021757Z 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.
2025-08-14T23:09:11.6021850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6021983Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-08-14T23:09:11.6022087Z     staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-08-14T23:09:11.6022090Z 
2025-08-14T23:09:11.6022127Z     .. warning::
2025-08-14T23:09:11.6022188Z         This feature is experimental and subject to change.
2025-08-14T23:09:11.6022244Z         MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED
2025-08-14T23:09:11.6022246Z 
2025-08-14T23:09:11.6023344Z     Args:
2025-08-14T23:09:11.6023411Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-08-14T23:09:11.6023468Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:11.6023543Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:11.6023615Z             depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:11.6023707Z             It can also be a key if the storage is a key-value store.
2025-08-14T23:09:11.6023747Z             (Default: ``None``)
2025-08-14T23:09:11.6023798Z         storage_writer (Optional[StorageWriter]):
2025-08-14T23:09:11.6023893Z             Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-08-14T23:09:11.6023978Z             this is not specified, DCP will automatically infer the writer based on the
2025-08-14T23:09:11.6024050Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:11.6024094Z             be raised. (Default: ``None``)
2025-08-14T23:09:11.6024138Z         planner (Optional[SavePlanner]):
2025-08-14T23:09:11.6024209Z             Instance of SavePlanner. If this is not specified, the default
2025-08-14T23:09:11.6024259Z             planner will be used. (Default: ``None``)
2025-08-14T23:09:11.6024309Z         process_group (Optional[ProcessGroup]):
2025-08-14T23:09:11.6024380Z             ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:11.6024421Z             (Default: ``None``)
2025-08-14T23:09:11.6024483Z         async_checkpointer_type (AsyncCheckpointerType):
2025-08-14T23:09:11.6024549Z             whether to do checkpoint in separate thread or process
2025-08-14T23:09:11.6024606Z             (Default: ``AsyncCheckpointerType.THREAD``)
2025-08-14T23:09:11.6025603Z         async_stager (AsyncStager):
2025-08-14T23:09:11.6025699Z             provides staging implementation. If storage_writer implements AsyncStager
2025-08-14T23:09:11.6025774Z             and async_stager is provided, async_stager will be used for staging
2025-08-14T23:09:11.6025810Z         no_dist (bool):
2025-08-14T23:09:11.6025873Z             If ``True``, this function will assume the intent is to save
2025-08-14T23:09:11.6025921Z             a checkpoint on a single rank/process.
2025-08-14T23:09:11.6025961Z             (Default: ``False``)
2025-08-14T23:09:11.6026069Z         use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``)
2025-08-14T23:09:11.6026149Z             This configuration is experimental and should be used with caution.
2025-08-14T23:09:11.6026246Z             It will change the format of the saved checkpoint and may not be backward compatible.
2025-08-14T23:09:11.6026248Z 
2025-08-14T23:09:11.6026282Z     Returns:
2025-08-14T23:09:11.6026355Z         Future: A future holding the resultant Metadata object from `save`.
2025-08-14T23:09:11.6026358Z 
2025-08-14T23:09:11.6026391Z     Example:
2025-08-14T23:09:11.6026431Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:11.6026470Z         >>> my_model = MyModule()
2025-08-14T23:09:11.6026474Z 
2025-08-14T23:09:11.6026518Z         >>> state_dict = {"model": my_model}
2025-08-14T23:09:11.6026521Z 
2025-08-14T23:09:11.6026606Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-08-14T23:09:11.6026645Z         ...     "/checkpoint/1"
2025-08-14T23:09:11.6026677Z         ... )
2025-08-14T23:09:11.6026799Z         >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-08-14T23:09:11.6026841Z         >>>     state_dict=state_dict,
2025-08-14T23:09:11.6026888Z         >>>     storage_writer=fs_storage_writer,
2025-08-14T23:09:11.6027842Z         >>> )
2025-08-14T23:09:11.6027877Z         >>>
2025-08-14T23:09:11.6027916Z         >>> # ... do some work ...
2025-08-14T23:09:11.6027949Z         >>>
2025-08-14T23:09:11.6027992Z         >>> checkpoint_future.result()
2025-08-14T23:09:11.6027994Z 
2025-08-14T23:09:11.6028025Z     
2025-08-14T23:09:11.6028113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6028116Z 
2025-08-14T23:09:11.6088821Z 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.
2025-08-14T23:09:11.6088937Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6088998Z 
2025-08-14T23:09:11.6089295Z Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-08-14T23:09:11.6089300Z 
2025-08-14T23:09:11.6089422Z SavePlanners are stateful objects that can be used to customize the whole save process.
2025-08-14T23:09:11.6089459Z 
2025-08-14T23:09:11.6089562Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-08-14T23:09:11.6089616Z will be visible to the whole process.
2025-08-14T23:09:11.6089620Z 
2025-08-14T23:09:11.6089754Z A planner subclass can expect the following sequence of calls during save_state_dict:
2025-08-14T23:09:11.6089757Z 
2025-08-14T23:09:11.6089817Z 1) set_up_planner - called on all ranks.
2025-08-14T23:09:11.6089867Z     Signals the start of a checkpoint save.
2025-08-14T23:09:11.6089869Z 
2025-08-14T23:09:11.6089921Z 2) create_local_plan - called on all ranks.
2025-08-14T23:09:11.6090027Z     Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-08-14T23:09:11.6090032Z 
2025-08-14T23:09:11.6090108Z 3) create_global_plan - called on the coordinator rank only.
2025-08-14T23:09:11.6092319Z     Takes the SavePlan from all ranks and make any global decision.
2025-08-14T23:09:11.6092327Z 
2025-08-14T23:09:11.6092396Z 4) finish_plan - called on all ranks.
2025-08-14T23:09:11.6092490Z     This gives each rank a chance to adjust to global planning decisions.
2025-08-14T23:09:11.6092493Z 
2025-08-14T23:09:11.6092560Z 5) resolve_data - called multiple times on each rank
2025-08-14T23:09:11.6092641Z     Lookups a value on the `state_dict` for the storage layer to write.
2025-08-14T23:09:11.6092643Z 
2025-08-14T23:09:11.6092752Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-08-14T23:09:11.6092828Z most changes can be expressed by changes in a single method.
2025-08-14T23:09:11.6092830Z 
2025-08-14T23:09:11.6092877Z There are 3 usual patterns of extension:
2025-08-14T23:09:11.6092880Z 
2025-08-14T23:09:11.6093108Z Rewriting state_dict. This is the simplest way to extend the save process as it
2025-08-14T23:09:11.6093193Z doesn't requite understanding the intrincacies of how SavePlan works:
2025-08-14T23:09:11.6093198Z 
2025-08-14T23:09:11.6093243Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6093297Z >>> class RenamePlanner(DefaultSavePlanner):
2025-08-14T23:09:11.6093343Z >>>     def set_up_planner(
2025-08-14T23:09:11.6093380Z >>>         self,
2025-08-14T23:09:11.6093423Z >>>         state_dict: STATE_DICT_TYPE,
2025-08-14T23:09:11.6093473Z >>>         storage_meta: Optional[StorageMeta],
2025-08-14T23:09:11.6093515Z >>>         is_coordinator: bool,
2025-08-14T23:09:11.6093551Z >>>     ) -> None:
2025-08-14T23:09:11.6093593Z >>> # prefix all keys with `foo_``
2025-08-14T23:09:11.6093705Z >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-08-14T23:09:11.6093708Z 
2025-08-14T23:09:11.6095335Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-08-14T23:09:11.6095392Z 
2025-08-14T23:09:11.6095474Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6095533Z >>> class FP16Planner(DefaultSavePlanner):
2025-08-14T23:09:11.6095577Z >>>     def create_local_plan(self):
2025-08-14T23:09:11.6095626Z >>>         plan = super().create_local_plan()
2025-08-14T23:09:11.6095672Z >>>         for p in plan:
2025-08-14T23:09:11.6095722Z >>>             if p.tensor_data is not None:
2025-08-14T23:09:11.6095789Z >>>                 p.tensor_data.properties.dtype = torch.float16
2025-08-14T23:09:11.6095825Z >>>         return plan
2025-08-14T23:09:11.6095857Z >>>
2025-08-14T23:09:11.6095902Z >>>     def resolve_data(self, write_item):
2025-08-14T23:09:11.6095951Z >>>         item = super().resolve_data(write_item)
2025-08-14T23:09:11.6096052Z >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-08-14T23:09:11.6096055Z 
2025-08-14T23:09:11.6096192Z Using the global planning step to make central decisions that can't be made individually by each rank
2025-08-14T23:09:11.6096196Z 
2025-08-14T23:09:11.6096239Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6096300Z >>> from itertools import zip_longest
2025-08-14T23:09:11.6096343Z >>> from dataclasses import replace
2025-08-14T23:09:11.6096410Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-08-14T23:09:11.6096509Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-08-14T23:09:11.6096561Z >>> # This sample doesn't handle ShardedTensors
2025-08-14T23:09:11.6096610Z >>>     def create_global_plan(self, all_plans):
2025-08-14T23:09:11.6096671Z >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-08-14T23:09:11.6096714Z >>>         items_per_rank = [
2025-08-14T23:09:11.6096772Z >>>             [item for item in items if item is not None]
2025-08-14T23:09:11.6096836Z >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-08-14T23:09:11.6096876Z >>>         ]
2025-08-14T23:09:11.6096917Z >>>         all_plans = [
2025-08-14T23:09:11.6096962Z >>>             replace(plan, items=items)
2025-08-14T23:09:11.6097035Z >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-08-14T23:09:11.6097068Z >>>         ]
2025-08-14T23:09:11.6097126Z >>>         return super().create_global_plan(all_plans)
2025-08-14T23:09:11.6098175Z 
2025-08-14T23:09:11.6098275Z Finally, some planners need to save additional metadata in the checkpoint, this is
2025-08-14T23:09:11.6098372Z accomplished by having each rank contribute their data items in the local plan and
2025-08-14T23:09:11.6098416Z the global planner aggregate them:
2025-08-14T23:09:11.6098418Z 
2025-08-14T23:09:11.6098474Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6098532Z >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-08-14T23:09:11.6098583Z >>>     def create_local_plan(self) -> SavePlan:
2025-08-14T23:09:11.6098631Z >>>         plan = super().create_local_plan()
2025-08-14T23:09:11.6098700Z >>>         return replace(plan, planner_data="per-rank-data")
2025-08-14T23:09:11.6098731Z >>>
2025-08-14T23:09:11.6098840Z >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-08-14T23:09:11.6098914Z >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-08-14T23:09:11.6098976Z >>>         merged_data = [p.planner_data for p in global_plan]
2025-08-14T23:09:11.6099041Z >>>         metadata = replace(metadata, planner_data=merged_data)
2025-08-14T23:09:11.6099086Z >>>         return global_plan, metadata
2025-08-14T23:09:11.6099088Z 
2025-08-14T23:09:11.6099178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6099180Z 
2025-08-14T23:09:11.6099390Z 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.
2025-08-14T23:09:11.6099505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6099522Z 
2025-08-14T23:09:11.6099623Z Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-08-14T23:09:11.6099626Z 
2025-08-14T23:09:11.6099724Z LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-08-14T23:09:11.6099726Z 
2025-08-14T23:09:11.6099824Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-08-14T23:09:11.6099869Z will be visible to the whole process.
2025-08-14T23:09:11.6099871Z 
2025-08-14T23:09:11.6100942Z A planner subclass can expect the following sequence of calls during load_state_dict:
2025-08-14T23:09:11.6100945Z 
2025-08-14T23:09:11.6100992Z 1) set_up_planner - called on all ranks.
2025-08-14T23:09:11.6101044Z     Signals the start of loading a checkpoint.
2025-08-14T23:09:11.6101046Z 
2025-08-14T23:09:11.6101093Z 2) create_local_plan - called on all ranks.
2025-08-14T23:09:11.6101220Z     Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-08-14T23:09:11.6101223Z 
2025-08-14T23:09:11.6101291Z 3) create_global_plan - called on the coordinator rank only.
2025-08-14T23:09:11.6101382Z     Takes the LoadPlan from all ranks and make any global decision.
2025-08-14T23:09:11.6101384Z 
2025-08-14T23:09:11.6101439Z 4) load_bytes - called multiple times on each rank
2025-08-14T23:09:11.6101504Z     This is called once per non-tensor value in state_dict.
2025-08-14T23:09:11.6101506Z 
2025-08-14T23:09:11.6101586Z 5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-08-14T23:09:11.6101654Z     They are called in pair for each Tensor value in state_dict.
2025-08-14T23:09:11.6101657Z 
2025-08-14T23:09:11.6101761Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-08-14T23:09:11.6101829Z most changes can be expressed by changes in a single method.
2025-08-14T23:09:11.6101831Z 
2025-08-14T23:09:11.6101884Z There are two usual patterns of extension:
2025-08-14T23:09:11.6101888Z 
2025-08-14T23:09:11.6101978Z Rewriting state_dict. This is the simplest way to extend the load process as it
2025-08-14T23:09:11.6102067Z doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-08-14T23:09:11.6102150Z to keep a reference to the original state_dict as load happens in place so
2025-08-14T23:09:11.6102197Z we need to be able to perform it in place
2025-08-14T23:09:11.6102199Z 
2025-08-14T23:09:11.6102244Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6102294Z >>> class RenamePlanner(DefaultLoadPlanner):
2025-08-14T23:09:11.6102335Z >>>     def set_up_planner(
2025-08-14T23:09:11.6103402Z >>>         self,
2025-08-14T23:09:11.6103452Z >>>         state_dict: STATE_DICT_TYPE,
2025-08-14T23:09:11.6103493Z >>>         metadata: Metadata,
2025-08-14T23:09:11.6103536Z >>>         is_coordinator: bool,
2025-08-14T23:09:11.6103571Z >>>     ) -> None:
2025-08-14T23:09:11.6103625Z >>>         self.original_state_dict = state_dict
2025-08-14T23:09:11.6103694Z >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-08-14T23:09:11.6103728Z >>>
2025-08-14T23:09:11.6103774Z >>>         if self.flatten_sharded_tensors:
2025-08-14T23:09:11.6103834Z >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-08-14T23:09:11.6103865Z >>>
2025-08-14T23:09:11.6103907Z >>>         if self.flatten_state_dict:
2025-08-14T23:09:11.6103974Z >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-08-14T23:09:11.6104005Z >>>
2025-08-14T23:09:11.6104047Z >>>         self.state_dict = state_dict
2025-08-14T23:09:11.6104088Z >>>         self.metadata = metadata
2025-08-14T23:09:11.6104136Z >>>         self.is_coordinator = is_coordinator
2025-08-14T23:09:11.6104167Z >>>
2025-08-14T23:09:11.6104215Z >>>     def load_bytes(self, read_item, value):
2025-08-14T23:09:11.6104257Z >>> # Remove the "foo_" prefix
2025-08-14T23:09:11.6104400Z >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-08-14T23:09:11.6104423Z 
2025-08-14T23:09:11.6104425Z 
2025-08-14T23:09:11.6105485Z Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-08-14T23:09:11.6105490Z 
2025-08-14T23:09:11.6105535Z >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:11.6105594Z >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-08-14T23:09:11.6105640Z >>>     def resolve_tensor(self, read_item):
2025-08-14T23:09:11.6105691Z >>>         tensor = super().resolve_tensor(read_item)
2025-08-14T23:09:11.6105751Z >>>         return torch.empty_like(tensor, device="cpu")
2025-08-14T23:09:11.6105781Z >>>
2025-08-14T23:09:11.6105831Z >>>     def commit_tensor(self, read_item, tensor):
2025-08-14T23:09:11.6105892Z >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-08-14T23:09:11.6105894Z 
2025-08-14T23:09:11.6106007Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6106011Z 
2025-08-14T23:09:11.6166098Z 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.
2025-08-14T23:09:11.6166448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6166487Z 
2025-08-14T23:09:11.6166574Z Return the model state_dict and optimizers state_dict.
2025-08-14T23:09:11.6166581Z 
2025-08-14T23:09:11.6166692Z ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-08-14T23:09:11.6166805Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-08-14T23:09:11.6166912Z combination of these parallelisms. The main functions of ``get_state_dict``
2025-08-14T23:09:11.6167011Z are: 1.) returning a model and optimizer state_dict that can be resharded
2025-08-14T23:09:11.6167105Z with a different number of trainers and/or different parallelisms.
2025-08-14T23:09:11.6167228Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-08-14T23:09:11.6167276Z these APIs.
2025-08-14T23:09:11.6169656Z 3.) sanity checking the result state_dict.
2025-08-14T23:09:11.6169665Z 
2025-08-14T23:09:11.6169773Z The keys of the result state dictionary are the canonical FQNs (Fully
2025-08-14T23:09:11.6169880Z Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-08-14T23:09:11.6169978Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-08-14T23:09:11.6170073Z parameter is the FQN returned by ``module.named_parameters()`` or
2025-08-14T23:09:11.6170158Z ``module.named_buffers()`` when the module is not distributed by any
2025-08-14T23:09:11.6170270Z parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-08-14T23:09:11.6170358Z a parameter, there will be a conversion from the parameter IDs to the
2025-08-14T23:09:11.6170420Z canonical FQNs when calling this API.
2025-08-14T23:09:11.6170427Z 
2025-08-14T23:09:11.6170520Z ``get_state_dict`` can also process a module that is not parallelized. In
2025-08-14T23:09:11.6170619Z such a case, ``get_state_dict`` only performs one function -- converting the
2025-08-14T23:09:11.6170689Z optimizer parameter IDs to the canonical FQNs.
2025-08-14T23:09:11.6170691Z 
2025-08-14T23:09:11.6170731Z Example:
2025-08-14T23:09:11.6170779Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.6170830Z     >>> import torch
2025-08-14T23:09:11.6170926Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:11.6171013Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-08-14T23:09:11.6171107Z     >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-08-14T23:09:11.6171109Z 
2025-08-14T23:09:11.6171167Z     >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-08-14T23:09:11.6171251Z     >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-08-14T23:09:11.6171311Z     >>> ddp_model = DDP(copy.deepcopy(model))
2025-08-14T23:09:11.6172871Z     >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-08-14T23:09:11.6172874Z 
2025-08-14T23:09:11.6173043Z 
2025-08-14T23:09:11.6173152Z     >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-08-14T23:09:11.6173223Z     >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-08-14T23:09:11.6173274Z     ...     fsdp_model, fsdp_optim
2025-08-14T23:09:11.6173314Z     ... )
2025-08-14T23:09:11.6173316Z 
2025-08-14T23:09:11.6173407Z     >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-08-14T23:09:11.6173455Z     >>> # the asserts will fail.
2025-08-14T23:09:11.6173511Z     >>> assert ddp_state_dict == fsdp_state_dict
2025-08-14T23:09:11.6173576Z     >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-08-14T23:09:11.6173578Z 
2025-08-14T23:09:11.6173580Z 
2025-08-14T23:09:11.6173619Z Args:
2025-08-14T23:09:11.6173678Z     model (nn.Module): the nn.Module to the model.
2025-08-14T23:09:11.6173806Z     optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-08-14T23:09:11.6173875Z         The optimizers that are used to optimize ``model``.
2025-08-14T23:09:11.6174024Z     submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
2025-08-14T23:09:11.6174074Z         that belong to the submodules.
2025-08-14T23:09:11.6174145Z     options (StateDictOptions): the options to control how
2025-08-14T23:09:11.6174236Z         model state_dict and optimizer state_dict should be returned. See
2025-08-14T23:09:11.6174290Z         `StateDictOptions` for the details.
2025-08-14T23:09:11.6174293Z 
2025-08-14T23:09:11.6174332Z Returns:
2025-08-14T23:09:11.6174412Z     ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-08-14T23:09:11.6174417Z 
2025-08-14T23:09:11.6174509Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-08-14T23:09:11.6174513Z 
2025-08-14T23:09:11.6175785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6175789Z 
2025-08-14T23:09:11.6244544Z 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.
2025-08-14T23:09:11.6244699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6244702Z 
2025-08-14T23:09:11.6244782Z Load a checkpoint into a distributed state dict in SPMD style.
2025-08-14T23:09:11.6244784Z 
2025-08-14T23:09:11.6244865Z Each rank must have the same keys in their ``state_dict`` provided to this
2025-08-14T23:09:11.6244948Z API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-08-14T23:09:11.6245027Z the ``utils._assert_same_keys`` API to check (but may incur communication
2025-08-14T23:09:11.6245063Z costs).
2025-08-14T23:09:11.6245065Z 
2025-08-14T23:09:11.6245134Z Each rank will try to read the least amount of data necessary
2025-08-14T23:09:11.6245217Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-08-14T23:09:11.6245306Z or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-08-14T23:09:11.6245310Z 
2025-08-14T23:09:11.6245406Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-08-14T23:09:11.6245499Z load will first call ``state_dict`` before attempting deserialization, followed by
2025-08-14T23:09:11.6245564Z ``load_state_dict`` once the deserialization is complete.
2025-08-14T23:09:11.6245657Z For each non-``Stateful`` object, load will deserialize the object, and then replace
2025-08-14T23:09:11.6245819Z it in the ``state_dict`` with the deserialized object.
2025-08-14T23:09:11.6245823Z 
2025-08-14T23:09:11.6245860Z .. warning::
2025-08-14T23:09:11.6245923Z     All tensors in ``state_dict`` must be allocated on their
2025-08-14T23:09:11.6245987Z     destination device *prior to* calling this function.
2025-08-14T23:09:11.6245990Z 
2025-08-14T23:09:11.6247837Z     All non-tensor data is loaded using `torch.load()` and modified in place
2025-08-14T23:09:11.6247954Z     on state_dict.
2025-08-14T23:09:11.6247958Z 
2025-08-14T23:09:11.6248004Z .. warning::
2025-08-14T23:09:11.6248087Z     Users must call `load_state_dict` on the root module to ensure load
2025-08-14T23:09:11.6248160Z     pos-processing and non-tensor data properly propagates.
2025-08-14T23:09:11.6248163Z 
2025-08-14T23:09:11.6248195Z .. note:
2025-08-14T23:09:11.6248275Z     If no process group is initialized, this function will assume the intent
2025-08-14T23:09:11.6248354Z     is to load a checkpoint into the local process. This can be useful in the
2025-08-14T23:09:11.6248442Z     case of local inference, and when using regular Tensors (as opposed to DTensor
2025-08-14T23:09:11.6248482Z      or ShardedTensor)
2025-08-14T23:09:11.6248484Z 
2025-08-14T23:09:11.6248516Z .. note:
2025-08-14T23:09:11.6248570Z     Rank 0 is assumed to be the coordinator rank.
2025-08-14T23:09:11.6248572Z 
2025-08-14T23:09:11.6248624Z Args:
2025-08-14T23:09:11.6248702Z     state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-08-14T23:09:11.6248757Z     checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:11.6248855Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:11.6248928Z         depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:11.6248993Z         It can also be a key if the storage is a key-value store.
2025-08-14T23:09:11.6249033Z         (Default: ``None``)
2025-08-14T23:09:11.6249085Z     storage_reader (Optional[StorageReader]):
2025-08-14T23:09:11.6249159Z         Instance of StorageWriter used to perform reads. If this is not
2025-08-14T23:09:11.6249232Z         specified, DCP will automatically infer the reader based on the
2025-08-14T23:09:11.6250451Z         checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:11.6250500Z         be raised. (Default: ``None``)
2025-08-14T23:09:11.6250550Z     planner (Optional[LoadPlanner]):
2025-08-14T23:09:11.6250626Z         Instance of LoadPlanner. If this is not specified, the default
2025-08-14T23:09:11.6250675Z         planner will be used. (Default: ``None``)
2025-08-14T23:09:11.6250726Z     process_group (Optional[ProcessGroup]):
2025-08-14T23:09:11.6250794Z         ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:11.6250833Z         (Default: ``None``)
2025-08-14T23:09:11.6250910Z     no_dist (bool): If ``True``, this function will assume the intent is to load
2025-08-14T23:09:11.6251000Z         a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-08-14T23:09:11.6251034Z Returns:
2025-08-14T23:09:11.6251067Z     None.
2025-08-14T23:09:11.6251069Z 
2025-08-14T23:09:11.6251101Z Examples
2025-08-14T23:09:11.6251141Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.6251182Z     >>> my_model = MyModule()
2025-08-14T23:09:11.6251234Z     >>> optimizer = Adagrad(my_model.parameters())
2025-08-14T23:09:11.6251284Z     >>> model_state_dict = my_model.state_dict()
2025-08-14T23:09:11.6251370Z     >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-08-14T23:09:11.6251412Z     ...     "/checkpoint/1"
2025-08-14T23:09:11.6251444Z     ... )
2025-08-14T23:09:11.6251446Z 
2025-08-14T23:09:11.6251507Z     >>> torch.distributed.checkpoint.load_state_dict(
2025-08-14T23:09:11.6251550Z     >>>     state_dict=model_state_dict,
2025-08-14T23:09:11.6252579Z     >>>     storage_reader=fs_storage_reader,
2025-08-14T23:09:11.6252611Z     >>> )
2025-08-14T23:09:11.6252614Z 
2025-08-14T23:09:11.6252686Z     >>> # module.load_state_dict() function might have customized steps
2025-08-14T23:09:11.6252732Z     >>> # to flush the state_dict, must call it to
2025-08-14T23:09:11.6252776Z     >>> # ensure correct behavior.
2025-08-14T23:09:11.6252823Z     >>> my_model.load_state_dict(model_state_dict)
2025-08-14T23:09:11.6252831Z 
2025-08-14T23:09:11.6252864Z .. note::
2025-08-14T23:09:11.6253054Z     load_state_dict uses collectives to coordinate reads across ranks.
2025-08-14T23:09:11.6253180Z     For NCCL-based process groups, internal tensor representations of
2025-08-14T23:09:11.6253262Z     objects must be moved to the GPU device before communication takes place.
2025-08-14T23:09:11.6253340Z     In this case, the device used is given by ``torch.cuda.current_device()``
2025-08-14T23:09:11.6253419Z     and it is the user's responsibility to ensure that this is set so that each
2025-08-14T23:09:11.6253488Z     rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-08-14T23:09:11.6253490Z 
2025-08-14T23:09:11.6253577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6253579Z 
2025-08-14T23:09:11.6314015Z 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.
2025-08-14T23:09:11.6314197Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6314204Z 
2025-08-14T23:09:11.6314291Z Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-08-14T23:09:11.6314313Z 
2025-08-14T23:09:11.6314377Z This is the current recommended way to checkpoint FSDP.
2025-08-14T23:09:11.6314417Z >>> # xdoctest: +SKIP
2025-08-14T23:09:11.6314478Z >>> import torch.distributed.checkpoint as dist_cp
2025-08-14T23:09:11.6316429Z >>> # Save
2025-08-14T23:09:11.6316481Z >>> model: torch.nn.Model
2025-08-14T23:09:11.6316541Z >>> optim_params = model.parameters()
2025-08-14T23:09:11.6316604Z >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-08-14T23:09:11.6316638Z >>> # Save
2025-08-14T23:09:11.6316725Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-08-14T23:09:11.6316762Z >>>     state_dict = {
2025-08-14T23:09:11.6316825Z >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-08-14T23:09:11.6316869Z >>>         "model": model.state_dict()
2025-08-14T23:09:11.6316904Z >>>     }
2025-08-14T23:09:11.6316949Z >>>     dist_cp.save_state_dict(
2025-08-14T23:09:11.6316992Z >>>         state_dict=optim_state,
2025-08-14T23:09:11.6317062Z >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-08-14T23:09:11.6317117Z >>>         planner=dist_cp.DefaultSavePlanner(),
2025-08-14T23:09:11.6317155Z >>>     )
2025-08-14T23:09:11.6317186Z >>>
2025-08-14T23:09:11.6317220Z >>> # Load
2025-08-14T23:09:11.6317302Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-08-14T23:09:11.6317351Z >>>     model_state_dict = model_tp.state_dict()
2025-08-14T23:09:11.6317387Z >>>     checkpoint = {
2025-08-14T23:09:11.6317429Z >>>         "model": model_state_dict
2025-08-14T23:09:11.6318641Z >>>     }
2025-08-14T23:09:11.6318687Z >>>     dist_cp.load_state_dict(
2025-08-14T23:09:11.6318735Z >>>         state_dict=checkpoint,
2025-08-14T23:09:11.6318807Z >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-08-14T23:09:11.6318861Z >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-08-14T23:09:11.6318893Z >>>     )
2025-08-14T23:09:11.6318953Z >>>     model.load_state_dict(checkpoint["model_state"])
2025-08-14T23:09:11.6318985Z >>>
2025-08-14T23:09:11.6319050Z >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-08-14T23:09:11.6319091Z >>>         model_state_dict,
2025-08-14T23:09:11.6319141Z >>>         optimizer_key="optimizer",
2025-08-14T23:09:11.6319207Z >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-08-14T23:09:11.6319238Z >>>     )
2025-08-14T23:09:11.6319269Z >>>
2025-08-14T23:09:11.6319325Z >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:11.6319373Z >>>        model, optim, optim_state["optimizer"]
2025-08-14T23:09:11.6319404Z >>>     )
2025-08-14T23:09:11.6319434Z >>>
2025-08-14T23:09:11.6319482Z >>>     optim.load_state_dict(flattened_osd)
2025-08-14T23:09:11.6319485Z 
2025-08-14T23:09:11.6319579Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6319614Z 
2025-08-14T23:09:11.6686504Z 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.
2025-08-14T23:09:11.6693691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6693709Z 
2025-08-14T23:09:11.6694041Z This configures FSDP-native mixed precision training.
2025-08-14T23:09:11.6694046Z 
2025-08-14T23:09:11.6694150Z Attributes:
2025-08-14T23:09:11.6694424Z     param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-08-14T23:09:11.6694640Z         parameters during forward and backward and thus the dtype for
2025-08-14T23:09:11.6694861Z         forward and backward computation. Outside forward and backward, the
2025-08-14T23:09:11.6695055Z         *sharded* parameters are kept in full precision (e.g. for the
2025-08-14T23:09:11.6695364Z         optimizer step), and for model checkpointing, the parameters are
2025-08-14T23:09:11.6695544Z         always saved in full precision. (Default: ``None``)
2025-08-14T23:09:11.6695765Z     reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-08-14T23:09:11.6695945Z         gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-08-14T23:09:11.6696012Z         ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-08-14T23:09:11.6696084Z         the ``param_dtype`` value, still running gradient reduction in low
2025-08-14T23:09:11.6696162Z         precision. This is permitted to differ from ``param_dtype``, e.g.
2025-08-14T23:09:11.6696235Z         to force gradient reduction to run in full precision. (Default:
2025-08-14T23:09:11.6696270Z         ``None``)
2025-08-14T23:09:11.6696345Z     buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-08-14T23:09:11.6696424Z         buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-08-14T23:09:11.6696500Z         ``buffer_dtype`` in the first forward pass and keeps them in that
2025-08-14T23:09:11.6698536Z         dtype thereafter. For model checkpointing, the buffers are saved
2025-08-14T23:09:11.6698617Z         in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-08-14T23:09:11.6698658Z         ``None``)
2025-08-14T23:09:11.6698732Z     keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-08-14T23:09:11.6698810Z         gradients to full precision after the backward pass in preparation
2025-08-14T23:09:11.6698885Z         for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-08-14T23:09:11.6698958Z         in the dtype used for gradient reduction, which can save memory if
2025-08-14T23:09:11.6699031Z         using a custom optimizer that supports running in low precision.
2025-08-14T23:09:11.6699073Z         (Default: ``False``)
2025-08-14T23:09:11.6699149Z     cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-08-14T23:09:11.6699223Z         its forward args and kwargs to ``param_dtype``. This is to ensure
2025-08-14T23:09:11.6699300Z         that parameter and input dtypes match for forward computation, as
2025-08-14T23:09:11.6699374Z         required by many ops. This may need to be set to ``True`` when only
2025-08-14T23:09:11.6699452Z         applying mixed precision to some but not all FSDP modules, in which
2025-08-14T23:09:11.6699539Z         case a mixed-precision FSDP submodule needs to recast its inputs.
2025-08-14T23:09:11.6699578Z         (Default: ``False``)
2025-08-14T23:09:11.6699655Z     cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-08-14T23:09:11.6699725Z         casts its forward args and kwargs to ``param_dtype``, overriding
2025-08-14T23:09:11.6699791Z         the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-08-14T23:09:11.6699846Z         this does not do anything. (Default: ``True``)
2025-08-14T23:09:11.6699923Z     _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-08-14T23:09:11.6700035Z         module classes to ignore for mixed precision when using an
2025-08-14T23:09:11.6700124Z         ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-08-14T23:09:11.6700201Z         applied to them separately with mixed precision disabled (meaning
2025-08-14T23:09:11.6700277Z         that the final FSDP construction would deviate from the specified
2025-08-14T23:09:11.6700347Z         policy). If ``auto_wrap_policy`` is not specified, then this does
2025-08-14T23:09:11.6700422Z         not do anything. This API is experimental and subject to change.
2025-08-14T23:09:11.6700467Z         (Default: ``(_BatchNorm,)``)
2025-08-14T23:09:11.6700470Z 
2025-08-14T23:09:11.6700540Z .. note:: This API is experimental and subject to change.
2025-08-14T23:09:11.6700542Z 
2025-08-14T23:09:11.6700621Z .. note:: Only floating point tensors are cast to their specified dtypes.
2025-08-14T23:09:11.6700623Z 
2025-08-14T23:09:11.6700710Z .. note:: In ``summon_full_params``, parameters are forced to full
2025-08-14T23:09:11.6700758Z     precision, but buffers are not.
2025-08-14T23:09:11.6700762Z 
2025-08-14T23:09:11.6700840Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-08-14T23:09:11.6702727Z     their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-08-14T23:09:11.6702815Z     Disabling FSDP's mixed precision for those norm modules only means that
2025-08-14T23:09:11.6702997Z     the affine parameters are kept in ``float32``. However, this incurs
2025-08-14T23:09:11.6703081Z     separate all-gathers and reduce-scatters for those norm modules, which
2025-08-14T23:09:11.6703158Z     may be inefficient, so if the workload permits, the user should prefer
2025-08-14T23:09:11.6703216Z     to still apply mixed precision to those modules.
2025-08-14T23:09:11.6703219Z 
2025-08-14T23:09:11.6703294Z .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-08-14T23:09:11.6703372Z     modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-08-14T23:09:11.6703458Z     modules will have FSDP applied to them separately with mixed precision
2025-08-14T23:09:11.6703523Z     disabled. See the ``_module_classes_to_ignore`` argument.
2025-08-14T23:09:11.6703527Z 
2025-08-14T23:09:11.6703602Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-08-14T23:09:11.6703677Z     ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-08-14T23:09:11.6703741Z     its ``cast_root_forward_inputs`` takes precedence over its
2025-08-14T23:09:11.6703806Z     ``cast_forward_inputs``. For non-root FSDP instances, their
2025-08-14T23:09:11.6703881Z     ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-08-14T23:09:11.6703958Z     sufficient for the typical case where each FSDP instance has the same
2025-08-14T23:09:11.6704036Z     ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-08-14T23:09:11.6704104Z     ``param_dtype`` at the beginning of the model's forward pass.
2025-08-14T23:09:11.6704107Z 
2025-08-14T23:09:11.6704183Z .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-08-14T23:09:11.6704264Z     configurations, we recommend setting individual ``cast_forward_inputs``
2025-08-14T23:09:11.6704340Z     values to configure casting inputs or not before each instance's
2025-08-14T23:09:11.6704410Z     forward. In such a case, since the casts happen before each FSDP
2025-08-14T23:09:11.6705482Z     instance's forward, a parent FSDP instance should have its non-FSDP
2025-08-14T23:09:11.6705564Z     submodules run before its FSDP submodules to avoid the activation dtype
2025-08-14T23:09:11.6705638Z     being changed due to a different ``MixedPrecision`` configuration.
2025-08-14T23:09:11.6705640Z 
2025-08-14T23:09:11.6705677Z     Example::
2025-08-14T23:09:11.6705679Z 
2025-08-14T23:09:11.6705732Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6705797Z         >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-08-14T23:09:11.6705882Z         >>> model[1] = FSDP(
2025-08-14T23:09:11.6705939Z         >>>     model[1],
2025-08-14T23:09:11.6706048Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-08-14T23:09:11.6706083Z         >>> )
2025-08-14T23:09:11.6706120Z         >>> model = FSDP(
2025-08-14T23:09:11.6706154Z         >>>     model,
2025-08-14T23:09:11.6706258Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-08-14T23:09:11.6706291Z         >>> )
2025-08-14T23:09:11.6706293Z 
2025-08-14T23:09:11.6706367Z     The above shows a working example. On the other hand, if ``model[1]``
2025-08-14T23:09:11.6706440Z     were replaced with ``model[0]``, meaning that the submodule using
2025-08-14T23:09:11.6706518Z     different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-08-14T23:09:11.6706593Z     would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-08-14T23:09:11.6706644Z     ones.
2025-08-14T23:09:11.6706647Z 
2025-08-14T23:09:11.6706650Z 
2025-08-14T23:09:11.6706740Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6706759Z 
2025-08-14T23:09:11.6706959Z 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.
2025-08-14T23:09:11.6707947Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6707950Z 
2025-08-14T23:09:11.6708026Z ``FullStateDictConfig`` is a config class meant to be used with
2025-08-14T23:09:11.6708098Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-08-14T23:09:11.6708171Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-08-14T23:09:11.6708249Z dicts to save GPU memory and CPU memory, respectively. This config class
2025-08-14T23:09:11.6708324Z is meant to be used via the :func:`state_dict_type` context manager as
2025-08-14T23:09:11.6708360Z follows:
2025-08-14T23:09:11.6708362Z 
2025-08-14T23:09:11.6708413Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6708501Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:11.6708552Z     >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-08-14T23:09:11.6708626Z     >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-08-14T23:09:11.6708706Z     >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-08-14T23:09:11.6708749Z     >>>     state = fsdp.state_dict()
2025-08-14T23:09:11.6708827Z     >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-08-14T23:09:11.6708911Z     >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-08-14T23:09:11.6708996Z     >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-08-14T23:09:11.6709039Z     >>> if dist.get_rank() == 0:
2025-08-14T23:09:11.6709110Z     >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-08-14T23:09:11.6709168Z     >>>     state_dict = torch.load("my_checkpoint.pt")
2025-08-14T23:09:11.6709219Z     >>>     model.load_state_dict(state_dict)
2025-08-14T23:09:11.6709301Z     >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-08-14T23:09:11.6709384Z     >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-08-14T23:09:11.6710282Z     >>> fsdp = FSDP(
2025-08-14T23:09:11.6710316Z     ...     model,
2025-08-14T23:09:11.6710368Z     ...     device_id=torch.cuda.current_device(),
2025-08-14T23:09:11.6710411Z     ...     auto_wrap_policy=...,
2025-08-14T23:09:11.6710452Z     ...     sync_module_states=True,
2025-08-14T23:09:11.6710484Z     ... )
2025-08-14T23:09:11.6710560Z     >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-08-14T23:09:11.6710563Z 
2025-08-14T23:09:11.6710596Z Attributes:
2025-08-14T23:09:11.6710670Z     rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-08-14T23:09:11.6710780Z         dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-08-14T23:09:11.6710843Z         ranks save the full state dict. (Default: ``False``)
2025-08-14T23:09:11.6710845Z 
2025-08-14T23:09:11.6710933Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6710935Z 
2025-08-14T23:09:11.6784517Z 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.
2025-08-14T23:09:11.6784633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6784748Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-08-14T23:09:11.6784751Z 
2025-08-14T23:09:11.6784847Z         Also takes (optional) configuration for the model's and optimizer's state dict.
2025-08-14T23:09:11.6784974Z         The target module does not have to be a FSDP module. If the target
2025-08-14T23:09:11.6785057Z         module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-08-14T23:09:11.6785090Z 
2025-08-14T23:09:11.6785165Z         .. note:: This API should be called for only the top-level (root)
2025-08-14T23:09:11.6786974Z             module.
2025-08-14T23:09:11.6786977Z 
2025-08-14T23:09:11.6787083Z         .. note:: This API enables users to transparently use the conventional
2025-08-14T23:09:11.6787175Z             ``state_dict`` API to take model checkpoints in cases where the
2025-08-14T23:09:11.6787249Z             root FSDP module is wrapped by another ``nn.Module``. For example,
2025-08-14T23:09:11.6787326Z             the following will ensure ``state_dict`` is called on all non-FSDP
2025-08-14T23:09:11.6787410Z             instances, while dispatching into `sharded_state_dict` implementation
2025-08-14T23:09:11.6787448Z             for FSDP:
2025-08-14T23:09:11.6787455Z 
2025-08-14T23:09:11.6787492Z         Example::
2025-08-14T23:09:11.6787495Z 
2025-08-14T23:09:11.6787552Z             >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6787601Z             >>> model = DDP(FSDP(...))
2025-08-14T23:09:11.6787646Z             >>> FSDP.set_state_dict_type(
2025-08-14T23:09:11.6787686Z             >>>     model,
2025-08-14T23:09:11.6787745Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-08-14T23:09:11.6787829Z             >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-08-14T23:09:11.6787912Z             >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-08-14T23:09:11.6787945Z             >>> )
2025-08-14T23:09:11.6787996Z             >>> param_state_dict = model.state_dict()
2025-08-14T23:09:11.6788061Z             >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-08-14T23:09:11.6788064Z 
2025-08-14T23:09:11.6788101Z         Args:
2025-08-14T23:09:11.6788153Z             module (torch.nn.Module): Root module.
2025-08-14T23:09:11.6788238Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-08-14T23:09:11.6789598Z             state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-08-14T23:09:11.6789655Z                 target ``state_dict_type``.
2025-08-14T23:09:11.6789753Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-08-14T23:09:11.6789801Z                 for the optimizer state dict.
2025-08-14T23:09:11.6789804Z 
2025-08-14T23:09:11.6789837Z         Returns:
2025-08-14T23:09:11.6789917Z             A StateDictSettings that include the previous state_dict type and
2025-08-14T23:09:11.6789961Z             configuration for the module.
2025-08-14T23:09:11.6789994Z         
2025-08-14T23:09:11.6790085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6790088Z 
2025-08-14T23:09:11.6790395Z 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.
2025-08-14T23:09:11.6790513Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6790600Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-08-14T23:09:11.6790602Z 
2025-08-14T23:09:11.6790714Z         This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-08-14T23:09:11.6790767Z         :meth:`set_state_dict_type` for the detail.
2025-08-14T23:09:11.6790769Z 
2025-08-14T23:09:11.6790804Z         Example::
2025-08-14T23:09:11.6790806Z 
2025-08-14T23:09:11.6790859Z             >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6790902Z             >>> model = DDP(FSDP(...))
2025-08-14T23:09:11.6790947Z             >>> with FSDP.state_dict_type(
2025-08-14T23:09:11.6791000Z             >>>     model,
2025-08-14T23:09:11.6791055Z             >>>     StateDictType.SHARDED_STATE_DICT,
2025-08-14T23:09:11.6791088Z             >>> ):
2025-08-14T23:09:11.6792129Z             >>>     checkpoint = model.state_dict()
2025-08-14T23:09:11.6792151Z 
2025-08-14T23:09:11.6792186Z         Args:
2025-08-14T23:09:11.6792235Z             module (torch.nn.Module): Root module.
2025-08-14T23:09:11.6792318Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-08-14T23:09:11.6792400Z             state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-08-14T23:09:11.6792462Z                 configuration for the target ``state_dict_type``.
2025-08-14T23:09:11.6792546Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-08-14T23:09:11.6792616Z                ``state_dict`` configuration for the target ``state_dict_type``.
2025-08-14T23:09:11.6792650Z         
2025-08-14T23:09:11.6792741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6792745Z 
2025-08-14T23:09:11.6810744Z 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.
2025-08-14T23:09:11.6810860Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6810863Z 
2025-08-14T23:09:11.6810952Z Transform the state-dict of an optimizer corresponding to a sharded model.
2025-08-14T23:09:11.6810955Z 
2025-08-14T23:09:11.6811028Z The given state-dict can be transformed to one of three types:
2025-08-14T23:09:11.6811156Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-08-14T23:09:11.6811158Z 
2025-08-14T23:09:11.6811243Z For full optimizer state_dict, all states are unflattened and not sharded.
2025-08-14T23:09:11.6811323Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-08-14T23:09:11.6811361Z avoid OOM.
2025-08-14T23:09:11.6811364Z 
2025-08-14T23:09:11.6811448Z For sharded optimizer state_dict, all states are unflattened but sharded.
2025-08-14T23:09:11.6811523Z CPU only can be specified via :meth:`state_dict_type` to further save
2025-08-14T23:09:11.6813801Z memory.
2025-08-14T23:09:11.6813805Z 
2025-08-14T23:09:11.6813908Z For local state_dict, no transformation will be performed. But a state
2025-08-14T23:09:11.6814001Z will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-08-14T23:09:11.6814048Z nature (this is not supported yet).
2025-08-14T23:09:11.6814050Z 
2025-08-14T23:09:11.6814089Z Example::
2025-08-14T23:09:11.6814091Z 
2025-08-14T23:09:11.6814143Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6814231Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:11.6814294Z     >>> from torch.distributed.fsdp import StateDictType
2025-08-14T23:09:11.6814372Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-08-14T23:09:11.6814526Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-08-14T23:09:11.6814571Z     >>> # Save a checkpoint
2025-08-14T23:09:11.6814613Z     >>> model, optim = ...
2025-08-14T23:09:11.6814658Z     >>> FSDP.set_state_dict_type(
2025-08-14T23:09:11.6814692Z     >>>     model,
2025-08-14T23:09:11.6814743Z     >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:11.6814795Z     >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6814853Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6814903Z     >>> )
2025-08-14T23:09:11.6814949Z     >>> state_dict = model.state_dict()
2025-08-14T23:09:11.6815013Z     >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-08-14T23:09:11.6816358Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-08-14T23:09:11.6816402Z     >>> # Load a checkpoint
2025-08-14T23:09:11.6816444Z     >>> model, optim = ...
2025-08-14T23:09:11.6816528Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-08-14T23:09:11.6816577Z     >>> FSDP.set_state_dict_type(
2025-08-14T23:09:11.6816611Z     >>>     model,
2025-08-14T23:09:11.6816687Z     >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:11.6816739Z     >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6816798Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6816831Z     >>> )
2025-08-14T23:09:11.6816876Z     >>> model.load_state_dict(state_dict)
2025-08-14T23:09:11.6816934Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:11.6816978Z     >>>     model, optim, optim_state_dict
2025-08-14T23:09:11.6817010Z     >>> )
2025-08-14T23:09:11.6817059Z     >>> optim.load_state_dict(optim_state_dict)
2025-08-14T23:09:11.6817063Z 
2025-08-14T23:09:11.6817094Z Args:
2025-08-14T23:09:11.6817169Z     model (torch.nn.Module): Root module (which may or may not be a
2025-08-14T23:09:11.6817247Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-08-14T23:09:11.6817301Z         were passed into the optimizer ``optim``.
2025-08-14T23:09:11.6817371Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-08-14T23:09:11.6817408Z         parameters.
2025-08-14T23:09:11.6817487Z     optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-08-14T23:09:11.6818542Z         transform. If the value is None, optim.state_dict() will be used. (
2025-08-14T23:09:11.6818585Z         Default: ``None``)
2025-08-14T23:09:11.6818672Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-08-14T23:09:11.6818742Z         are sharded or ``None`` if using the default process group. (
2025-08-14T23:09:11.6818780Z         Default: ``None``)
2025-08-14T23:09:11.6818782Z 
2025-08-14T23:09:11.6818816Z Returns:
2025-08-14T23:09:11.6818886Z     Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-08-14T23:09:11.6818952Z     ``model``. The sharding of the optimizer state is based on
2025-08-14T23:09:11.6818991Z     ``state_dict_type``.
2025-08-14T23:09:11.6818997Z 
2025-08-14T23:09:11.6819086Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6819090Z 
2025-08-14T23:09:11.6819371Z 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.
2025-08-14T23:09:11.6819464Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.6819466Z 
2025-08-14T23:09:11.6819590Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-08-14T23:09:11.6819592Z 
2025-08-14T23:09:11.6819657Z Given a ``optim_state_dict`` that is transformed through
2025-08-14T23:09:11.6819734Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-08-14T23:09:11.6819811Z state_dict that can be loaded to ``optim`` which is the optimizer for
2025-08-14T23:09:11.6819915Z ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-08-14T23:09:11.6819917Z 
2025-08-14T23:09:11.6819968Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.6820054Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:11.6820114Z     >>> from torch.distributed.fsdp import StateDictType
2025-08-14T23:09:11.6820178Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2025-08-14T23:09:11.6821184Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-08-14T23:09:11.6821224Z     >>> # Save a checkpoint
2025-08-14T23:09:11.6821265Z     >>> model, optim = ...
2025-08-14T23:09:11.6821307Z     >>> FSDP.set_state_dict_type(
2025-08-14T23:09:11.6821341Z     >>>     model,
2025-08-14T23:09:11.6821388Z     >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:11.6821438Z     >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6821510Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6821546Z     >>> )
2025-08-14T23:09:11.6821590Z     >>> state_dict = model.state_dict()
2025-08-14T23:09:11.6821637Z     >>> original_osd = optim.state_dict()
2025-08-14T23:09:11.6821700Z     >>> optim_state_dict = FSDP.optim_state_dict(
2025-08-14T23:09:11.6821733Z     >>>     model,
2025-08-14T23:09:11.6821766Z     >>>     optim,
2025-08-14T23:09:11.6821810Z     >>>     optim_state_dict=original_osd
2025-08-14T23:09:11.6821841Z     >>> )
2025-08-14T23:09:11.6821895Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-08-14T23:09:11.6821935Z     >>> # Load a checkpoint
2025-08-14T23:09:11.6821973Z     >>> model, optim = ...
2025-08-14T23:09:11.6822032Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-08-14T23:09:11.6822073Z     >>> FSDP.set_state_dict_type(
2025-08-14T23:09:11.6822106Z     >>>     model,
2025-08-14T23:09:11.6823154Z     >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:11.6823211Z     >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6823268Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:11.6823301Z     >>> )
2025-08-14T23:09:11.6823348Z     >>> model.load_state_dict(state_dict)
2025-08-14T23:09:11.6823406Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:11.6823450Z     >>>     model, optim, optim_state_dict
2025-08-14T23:09:11.6823482Z     >>> )
2025-08-14T23:09:11.6823531Z     >>> optim.load_state_dict(optim_state_dict)
2025-08-14T23:09:11.6823533Z 
2025-08-14T23:09:11.6823565Z Args:
2025-08-14T23:09:11.6823638Z     model (torch.nn.Module): Root module (which may or may not be a
2025-08-14T23:09:11.6823713Z         :class:`FullyShardedDataParallel` instance) whose parameters
2025-08-14T23:09:11.6823763Z         were passed into the optimizer ``optim``.
2025-08-14T23:09:11.6823832Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-08-14T23:09:11.6823867Z         parameters.
2025-08-14T23:09:11.6823946Z     optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-08-14T23:09:11.6824021Z     is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-08-14T23:09:11.6824093Z         KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-08-14T23:09:11.6824157Z         KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-08-14T23:09:11.6824229Z     load_directly (bool): If this is set to True, this API will also
2025-08-14T23:09:11.6824303Z         call optim.load_state_dict(result) before returning the result.
2025-08-14T23:09:11.6825286Z         Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-08-14T23:09:11.6825329Z         (Default: ``False``)
2025-08-14T23:09:11.6825413Z     group (dist.ProcessGroup): Model's process group across which parameters
2025-08-14T23:09:11.6825481Z         are sharded or ``None`` if using the default process group. (
2025-08-14T23:09:11.6825519Z         Default: ``None``)
2025-08-14T23:09:11.6825523Z 
2025-08-14T23:09:11.6825658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.6825660Z 
2025-08-14T23:09:11.7125368Z 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.
2025-08-14T23:09:11.7125903Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7126036Z 
2025-08-14T23:09:11.7126168Z Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-08-14T23:09:11.7126321Z 
2025-08-14T23:09:11.7126432Z Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-08-14T23:09:11.7126650Z by using different periods concurrently after the warm-up stage.
2025-08-14T23:09:11.7129315Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-08-14T23:09:11.7129694Z that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-08-14T23:09:11.7129955Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-08-14T23:09:11.7130241Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-08-14T23:09:11.7130500Z Similarly, the process groups within this class do not have such an intra-machine process
2025-08-14T23:09:11.7130739Z subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-08-14T23:09:11.7130871Z 
2025-08-14T23:09:11.7130908Z Args:
2025-08-14T23:09:11.7131048Z     period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-08-14T23:09:11.7131255Z                             process group size, used for initializing process groups of
2025-08-14T23:09:11.7131442Z                             different sizes in a hierarchy to average parameters concurrently.
2025-08-14T23:09:11.7131637Z                             Particularly, at each iteration, there will be at most a single
2025-08-14T23:09:11.7131833Z                             process group that runs averaging -- the period of such group should
2025-08-14T23:09:11.7132028Z                             have the largest period which the current step can be divided by.
2025-08-14T23:09:11.7132203Z                             For example, if the dict has three keys: 2, 4, and 8,
2025-08-14T23:09:11.7132374Z                             then this means totally three process groups will be created to
2025-08-14T23:09:11.7132557Z                             average parameters every 2, 4, and 8 iterations, respectively.
2025-08-14T23:09:11.7132735Z                             At the 4th iteration, only the second process group will run
2025-08-14T23:09:11.7133088Z                             averaging, because the first process group should be a
2025-08-14T23:09:11.7133275Z                             subset of the second process group, and no need to execute the first
2025-08-14T23:09:11.7133447Z                             process group redundantly.
2025-08-14T23:09:11.7133606Z                             On the other hand, the third process group can only be triggered
2025-08-14T23:09:11.7133798Z                             every 8 iterations, so it will not be triggered at the 4th iteration.
2025-08-14T23:09:11.7134023Z     warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-08-14T23:09:11.7134320Z     process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-08-14T23:09:11.7134576Z                                             If ``None``, the default process group, which is created
2025-08-14T23:09:11.7134772Z                                             by :func:`torch.distributed.init_process_group`, will be used.
2025-08-14T23:09:11.7134942Z                                             (default: ``None``)
2025-08-14T23:09:11.7135061Z 
2025-08-14T23:09:11.7135102Z Example::
2025-08-14T23:09:11.7135219Z     >>> # xdoctest: +SKIP('undefined rank')
2025-08-14T23:09:11.7135349Z     >>> from collections import OrderedDict
2025-08-14T23:09:11.7135468Z     >>> import torch
2025-08-14T23:09:11.7137177Z     >>> import torch.distributed as dist
2025-08-14T23:09:11.7137365Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-08-14T23:09:11.7137539Z     >>>     PostLocalSGDState,
2025-08-14T23:09:11.7137649Z     >>>     post_localSGD_hook,
2025-08-14T23:09:11.7137747Z     >>> )
2025-08-14T23:09:11.7137922Z     >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-08-14T23:09:11.7138124Z     >>> import torch.nn as nn
2025-08-14T23:09:11.7138224Z     >>>
2025-08-14T23:09:11.7138336Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-08-14T23:09:11.7138480Z     >>> torch.cuda.set_device(rank)
2025-08-14T23:09:11.7138640Z     >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-08-14T23:09:11.7139935Z     >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:11.7140086Z     >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:11.7140229Z     >>> )
2025-08-14T23:09:11.7140332Z     >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:11.7140515Z     >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-08-14T23:09:11.7140690Z     >>> subgroup, _ = dist.new_subgroups()
2025-08-14T23:09:11.7140879Z     >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-08-14T23:09:11.7141084Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:11.7141210Z     >>>
2025-08-14T23:09:11.7141351Z     >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-08-14T23:09:11.7141531Z     >>> # the 16 processes every 16 iterations.
2025-08-14T23:09:11.7142682Z     >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-08-14T23:09:11.7142872Z     >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-08-14T23:09:11.7143196Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:11.7143430Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-08-14T23:09:11.7143616Z     >>> # After 100 steps, run model averaging at two levels.
2025-08-14T23:09:11.7143750Z     >>> for step in range(0, 200):
2025-08-14T23:09:11.7143866Z     >>>    optimizer.zero_grad()
2025-08-14T23:09:11.7143978Z     >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:11.7144095Z     >>>    loss.backward()
2025-08-14T23:09:11.7144196Z     >>>    optimizer.step()
2025-08-14T23:09:11.7144320Z     >>>    # Average parameters after ``optimizer.step()``.
2025-08-14T23:09:11.7145484Z     >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-08-14T23:09:11.7145679Z     >>>    averager.average_parameters(model.parameters())
2025-08-14T23:09:11.7145773Z 
2025-08-14T23:09:11.7145809Z .. warning ::
2025-08-14T23:09:11.7145950Z     The last group size in the dict must be the size of the provided ``process_group``,
2025-08-14T23:09:11.7146156Z     which indicates model averaging at the highest level of the hierarchy.
2025-08-14T23:09:11.7146375Z     If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-08-14T23:09:11.7146516Z 
2025-08-14T23:09:11.7146550Z .. warning ::
2025-08-14T23:09:11.7146683Z     `HierarchicalModelAverager` is experimental and subject to change.
2025-08-14T23:09:11.7146799Z 
2025-08-14T23:09:11.7146887Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7147011Z 
2025-08-14T23:09:11.7147264Z 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.
2025-08-14T23:09:11.7147682Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7148772Z 
2025-08-14T23:09:11.7148846Z Averages parameters periodically after the warm-up stage.
2025-08-14T23:09:11.7148949Z 
2025-08-14T23:09:11.7149041Z This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-08-14T23:09:11.7149237Z by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-08-14T23:09:11.7149423Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-08-14T23:09:11.7149541Z 
2025-08-14T23:09:11.7149574Z Args:
2025-08-14T23:09:11.7149681Z     period (int): The number of steps per model averaging.
2025-08-14T23:09:11.7149869Z                   Usually the period should be greater than ``1`` to reduce the communication cost.
2025-08-14T23:09:11.7150048Z                   Otherwise, only DDP needs to be used.
2025-08-14T23:09:11.7150230Z     warmup_steps (int): The number of warm-up steps. During this stage,
2025-08-14T23:09:11.7150388Z                         model averaging is skipped.
2025-08-14T23:09:11.7151488Z     process_group: The process group to be used for all-reduce.
2025-08-14T23:09:11.7151673Z                    If ``None``, the default process group, which
2025-08-14T23:09:11.7151831Z                    is created by :func:`torch.distributed.init_process_group`,
2025-08-14T23:09:11.7151984Z                    will be used. (default: ``None``)
2025-08-14T23:09:11.7152066Z 
2025-08-14T23:09:11.7152102Z Example::
2025-08-14T23:09:11.7152149Z 
2025-08-14T23:09:11.7152198Z     >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:11.7152315Z     >>> import torch
2025-08-14T23:09:11.7152416Z     >>> import torch.distributed as dist
2025-08-14T23:09:11.7152604Z     >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-08-14T23:09:11.7152845Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-08-14T23:09:11.7153095Z     >>> import torch.nn as nn
2025-08-14T23:09:11.7154153Z     >>>
2025-08-14T23:09:11.7154267Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-08-14T23:09:11.7154411Z     >>> torch.cuda.set_device(rank)
2025-08-14T23:09:11.7154533Z     >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-08-14T23:09:11.7154677Z     >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:11.7154822Z     >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:11.7154940Z     >>> )
2025-08-14T23:09:11.7155040Z     >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:11.7155230Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-08-14T23:09:11.7155428Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:11.7155552Z     >>>
2025-08-14T23:09:11.7156628Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-08-14T23:09:11.7156820Z     >>> # After 100 steps, run model averaging every 4 steps.
2025-08-14T23:09:11.7157019Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:11.7157252Z     >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-08-14T23:09:11.7157418Z     >>> for step in range(0, 200):
2025-08-14T23:09:11.7157530Z     >>>    optimizer.zero_grad()
2025-08-14T23:09:11.7157642Z     >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:11.7157758Z     >>>    loss.backward()
2025-08-14T23:09:11.7157858Z     >>>    optimizer.step()
2025-08-14T23:09:11.7157992Z     >>>    # Will average model parameters globally every 4 steps. Thus,
2025-08-14T23:09:11.7158169Z     >>>    # inter-node communication only occurs every 4 iterations after
2025-08-14T23:09:11.7159335Z     >>>    # the initial ``warmup_steps`` period.
2025-08-14T23:09:11.7159482Z     >>>    averager.average_parameters(model.parameters())
2025-08-14T23:09:11.7159620Z 
2025-08-14T23:09:11.7159725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7159850Z 
2025-08-14T23:09:11.7160108Z 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.
2025-08-14T23:09:11.7160493Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7160620Z 
2025-08-14T23:09:11.7160664Z Run post-localSGD algorithm.
2025-08-14T23:09:11.7160734Z 
2025-08-14T23:09:11.7160816Z This DDP communication hook is used for running post-localSGD algorithm,
2025-08-14T23:09:11.7160991Z by combining with a model averaging component (e.g.,
2025-08-14T23:09:11.7161197Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-08-14T23:09:11.7161393Z that runs after the optimizer step.
2025-08-14T23:09:11.7161469Z 
2025-08-14T23:09:11.7161522Z Args:
2025-08-14T23:09:11.7162968Z     state (PostLocalSGDState): State information to run post-localSGD.
2025-08-14T23:09:11.7163189Z         Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-08-14T23:09:11.7163493Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-08-14T23:09:11.7163760Z         Note that since DDP comm hook only supports single process single device mode,
2025-08-14T23:09:11.7163941Z         only exactly one tensor is stored in this bucket.
2025-08-14T23:09:11.7164031Z 
2025-08-14T23:09:11.7164065Z Returns:
2025-08-14T23:09:11.7164195Z     Future handler of the communication, which updates the gradients in place.
2025-08-14T23:09:11.7164316Z 
2025-08-14T23:09:11.7164351Z Example::
2025-08-14T23:09:11.7164435Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.7164585Z     >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-08-14T23:09:11.7164759Z                               start_localSGD_iter=10)
2025-08-14T23:09:11.7164907Z     >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:11.7165121Z     >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-08-14T23:09:11.7165393Z     >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-08-14T23:09:11.7165547Z 
2025-08-14T23:09:11.7165637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7166820Z 
2025-08-14T23:09:11.7183585Z 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.
2025-08-14T23:09:11.7183960Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7184086Z 
2025-08-14T23:09:11.7184132Z Implement PowerSGD algorithm.
2025-08-14T23:09:11.7184204Z 
2025-08-14T23:09:11.7184288Z This DDP communication hook implements PowerSGD gradient compression
2025-08-14T23:09:11.7184493Z algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-08-14T23:09:11.7184695Z Once gradient tensors are aggregated across all workers, this hook applies
2025-08-14T23:09:11.7184851Z compression as follows:
2025-08-14T23:09:11.7184914Z 
2025-08-14T23:09:11.7185064Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-08-14T23:09:11.7185245Z 
2025-08-14T23:09:11.7185389Z     1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-08-14T23:09:11.7185566Z 
2025-08-14T23:09:11.7185705Z     1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-08-14T23:09:11.7187224Z 
2025-08-14T23:09:11.7187278Z 2. Handles uncompressed tensors:
2025-08-14T23:09:11.7187416Z 
2025-08-14T23:09:11.7187604Z     2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-08-14T23:09:11.7187808Z 
2025-08-14T23:09:11.7187924Z     2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-08-14T23:09:11.7188074Z 
2025-08-14T23:09:11.7188157Z 3. Handles the tensors that should be compressed by PowerSGD compression:
2025-08-14T23:09:11.7188273Z 
2025-08-14T23:09:11.7188360Z     3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-08-14T23:09:11.7188603Z     such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-08-14T23:09:11.7188744Z 
2025-08-14T23:09:11.7188802Z     3.2. Computes each P in Ps, which is equal to MQ;
2025-08-14T23:09:11.7188892Z 
2025-08-14T23:09:11.7188935Z     3.3. Allreduces Ps as a batch;
2025-08-14T23:09:11.7189010Z 
2025-08-14T23:09:11.7189076Z     3.4. Orthogonalizes each P in Ps;
2025-08-14T23:09:11.7189156Z 
2025-08-14T23:09:11.7189228Z     3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-08-14T23:09:11.7189351Z 
2025-08-14T23:09:11.7189392Z     3.6. Allreduces Qs as a batch;
2025-08-14T23:09:11.7190514Z 
2025-08-14T23:09:11.7190619Z     3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-08-14T23:09:11.7190758Z 
2025-08-14T23:09:11.7190896Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-08-14T23:09:11.7191166Z This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-08-14T23:09:11.7191442Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-08-14T23:09:11.7191619Z 
2025-08-14T23:09:11.7191651Z Args:
2025-08-14T23:09:11.7191841Z     state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-08-14T23:09:11.7192145Z         To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-08-14T23:09:11.7192343Z         and ``min_compression_rate``.
2025-08-14T23:09:11.7192561Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-08-14T23:09:11.7192825Z         Note that since DDP comm hook only supports single process single device mode,
2025-08-14T23:09:11.7194087Z         only exactly one tensor is stored in this bucket.
2025-08-14T23:09:11.7194182Z 
2025-08-14T23:09:11.7194215Z Returns:
2025-08-14T23:09:11.7194345Z     Future handler of the communication, which updates the gradients in place.
2025-08-14T23:09:11.7194465Z 
2025-08-14T23:09:11.7194502Z Example::
2025-08-14T23:09:11.7194585Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.7194743Z     >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-08-14T23:09:11.7194934Z                               start_powerSGD_iter=10, min_compression_rate=0.5)
2025-08-14T23:09:11.7195088Z     >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-08-14T23:09:11.7195183Z 
2025-08-14T23:09:11.7195274Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7195397Z 
2025-08-14T23:09:11.7249868Z 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.
2025-08-14T23:09:11.7251844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7251983Z 
2025-08-14T23:09:11.7252070Z Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-08-14T23:09:11.7252244Z Needs to be called on all ranks in an SPMD fashion.
2025-08-14T23:09:11.7252333Z 
2025-08-14T23:09:11.7252366Z Args:
2025-08-14T23:09:11.7252574Z     local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-08-14T23:09:11.7252769Z         of shards that represent the local shards on this rank.
2025-08-14T23:09:11.7253021Z     global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-08-14T23:09:11.7253184Z         shape of the overall sharded tensor.
2025-08-14T23:09:11.7253264Z 
2025-08-14T23:09:11.7253298Z Keyword args:
2025-08-14T23:09:11.7253442Z     process_group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:11.7253622Z         the default process group will be used.
2025-08-14T23:09:11.7253774Z     init_rrefs (bool, optional): Whether or not to initialize
2025-08-14T23:09:11.7253949Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-08-14T23:09:11.7254130Z         Need to initialize the RPC Framework if specified as ``True``.
2025-08-14T23:09:11.7255305Z         Default: ``False``.
2025-08-14T23:09:11.7255373Z 
2025-08-14T23:09:11.7255432Z Returns:
2025-08-14T23:09:11.7255537Z     A :class:`ShardedTensor` object handle on this rank
2025-08-14T23:09:11.7255629Z 
2025-08-14T23:09:11.7255631Z 
2025-08-14T23:09:11.7255687Z Examples:
2025-08-14T23:09:11.7255821Z     Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-08-14T23:09:11.7256012Z     each shard have a (5, 5) local tensor, we can do it like below:
2025-08-14T23:09:11.7256112Z 
2025-08-14T23:09:11.7256147Z     on rank 0:
2025-08-14T23:09:11.7256243Z     >>> # xdoctest: +SKIP("not distributed")
2025-08-14T23:09:11.7256367Z     >>> local_shard_metadata = ShardMetadata(
2025-08-14T23:09:11.7256487Z     >>>     shard_offsets=[0, 0],
2025-08-14T23:09:11.7257537Z     >>>     shard_lengths=[5, 5],
2025-08-14T23:09:11.7257654Z     >>>     placement="rank:0/cuda:0"
2025-08-14T23:09:11.7257759Z     >>> )
2025-08-14T23:09:11.7257874Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-08-14T23:09:11.7258051Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-08-14T23:09:11.7258159Z 
2025-08-14T23:09:11.7258193Z     on rank 1:
2025-08-14T23:09:11.7258288Z     >>> # xdoctest: +SKIP("not distributed")
2025-08-14T23:09:11.7258412Z     >>> local_shard_metadata = ShardMetadata(
2025-08-14T23:09:11.7258528Z     >>>     shard_offsets=[5, 0],
2025-08-14T23:09:11.7258633Z     >>>     shard_lengths=[5, 5],
2025-08-14T23:09:11.7258742Z     >>>     placement="rank:1/cuda:1"
2025-08-14T23:09:11.7259771Z     >>> )
2025-08-14T23:09:11.7259886Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-08-14T23:09:11.7260057Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-08-14T23:09:11.7260161Z 
2025-08-14T23:09:11.7260248Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7260372Z 
2025-08-14T23:09:11.7323654Z 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.
2025-08-14T23:09:11.7324000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7324129Z 
2025-08-14T23:09:11.7324209Z Representation of a sharding plan, describes how to shard a module
2025-08-14T23:09:11.7324422Z across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-08-14T23:09:11.7324652Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-08-14T23:09:11.7324893Z layout of a module with a spec, and when to convert back to data parallel fashion.
2025-08-14T23:09:11.7325013Z 
2025-08-14T23:09:11.7326509Z Args:
2025-08-14T23:09:11.7326660Z     plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-08-14T23:09:11.7326862Z           :class:`torch.distributed._shard.sharder.Sharder`]):
2025-08-14T23:09:11.7327060Z         a dict describes how to shard a module, there're currently two ways to shard a module:
2025-08-14T23:09:11.7327354Z             1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-08-14T23:09:11.7327528Z                a parameter to a `ShardingSpec`.
2025-08-14T23:09:11.7327701Z             2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-08-14T23:09:11.7327870Z                to a `Sharder` object.
2025-08-14T23:09:11.7328058Z     output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-08-14T23:09:11.7328296Z         a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-08-14T23:09:11.7328503Z         keyed by the name of module to ShardingSpec("" in key means the root module).
2025-08-14T23:09:11.7329705Z         Default: `None`
2025-08-14T23:09:11.7329863Z     return_local_tensor (List[str], optional): a list of string, each element enables
2025-08-14T23:09:11.7330100Z         a module's sharded output to be returned as a Tensor from its local shards to
2025-08-14T23:09:11.7330312Z         ensure further processing in a data parallel fashion. ("" in list means the
2025-08-14T23:09:11.7330470Z         root module).
2025-08-14T23:09:11.7330580Z         Default: None
2025-08-14T23:09:11.7330670Z Example:
2025-08-14T23:09:11.7330814Z   Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-08-14T23:09:11.7331044Z   want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-08-14T23:09:11.7331179Z 
2025-08-14T23:09:11.7331246Z     >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-08-14T23:09:11.7331385Z     >>> class MyModule(nn.Module):
2025-08-14T23:09:11.7332488Z     >>>     def __init__(self) -> None:
2025-08-14T23:09:11.7332607Z     >>>        super().__init__()
2025-08-14T23:09:11.7332716Z     >>>        self.fc1 = nn.Linear()
2025-08-14T23:09:11.7332828Z     >>>        self.gelu = nn.GELU()
2025-08-14T23:09:11.7333045Z     >>>        self.fc2 = nn.Linear()
2025-08-14T23:09:11.7333159Z     >>>        self.relu = nn.Linear()
2025-08-14T23:09:11.7333263Z     >>>
2025-08-14T23:09:11.7333345Z     >>>     def forward(self, input):
2025-08-14T23:09:11.7333482Z     >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-08-14T23:09:11.7333581Z 
2025-08-14T23:09:11.7333583Z 
2025-08-14T23:09:11.7333635Z     >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-08-14T23:09:11.7333762Z     >>> sharding_plan = ShardingPlan(
2025-08-14T23:09:11.7334873Z     >>>    plan={
2025-08-14T23:09:11.7334969Z     >>>        "fc1.weight": spec1,
2025-08-14T23:09:11.7335082Z     >>>        "fc2.weight": spec2
2025-08-14T23:09:11.7335183Z     >>>    },
2025-08-14T23:09:11.7335269Z     >>>    output_plan={
2025-08-14T23:09:11.7335370Z     >>>        "fc2": output_spec
2025-08-14T23:09:11.7335470Z     >>>    },
2025-08-14T23:09:11.7335560Z     >>>    return_local_tensor=["fc2"]
2025-08-14T23:09:11.7335663Z     >>> )
2025-08-14T23:09:11.7335708Z 
2025-08-14T23:09:11.7335803Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7335931Z 
2025-08-14T23:09:11.7369031Z 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.
2025-08-14T23:09:11.7371030Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7371166Z 
2025-08-14T23:09:11.7371257Z Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-08-14T23:09:11.7371424Z size and sharding spec on each rank.
2025-08-14T23:09:11.7371500Z 
2025-08-14T23:09:11.7371532Z Args:
2025-08-14T23:09:11.7371653Z     local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-08-14T23:09:11.7371861Z     sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-08-14T23:09:11.7372050Z         The specification describing how to shard the Tensor.
2025-08-14T23:09:11.7372267Z     global_size (Sequence[int]): Size of the sharded tensor.
2025-08-14T23:09:11.7372470Z     process_group (ProcessGroup, optional): The process group to aggregate on.
2025-08-14T23:09:11.7372638Z         Default: None
2025-08-14T23:09:11.7372761Z     init_rrefs (bool, optional): Whether or not to initialize
2025-08-14T23:09:11.7374243Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-08-14T23:09:11.7374434Z         Need to initialize the RPC Framework if specified as ``True``.
2025-08-14T23:09:11.7374577Z         Default: ``False``.
2025-08-14T23:09:11.7374642Z 
2025-08-14T23:09:11.7374677Z Returns:
2025-08-14T23:09:11.7374806Z     A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-08-14T23:09:11.7374970Z         tensor stored in the current rank.
2025-08-14T23:09:11.7375050Z 
2025-08-14T23:09:11.7375084Z Examples:
2025-08-14T23:09:11.7375169Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.7375322Z     >>> # All tensors below are of torch.int64 type.
2025-08-14T23:09:11.7375459Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.7375603Z     >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-08-14T23:09:11.7376803Z     >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-08-14T23:09:11.7376947Z     >>> local_tensor
2025-08-14T23:09:11.7377043Z     tensor([[1, 2, 3, 4]]) # Rank 0
2025-08-14T23:09:11.7377151Z     tensor([[3, 4, 5, 6]]) # Rank 1
2025-08-14T23:09:11.7377256Z     >>> sharding_dim = 0
2025-08-14T23:09:11.7377365Z     >>> sharding_spec = ChunkShardingSpec(
2025-08-14T23:09:11.7377482Z             dim=sharding_dim,
2025-08-14T23:09:11.7377585Z             placements=[
2025-08-14T23:09:11.7377696Z                 "rank:0/cuda:0",
2025-08-14T23:09:11.7377799Z                 "rank:1/cuda:1",
2025-08-14T23:09:11.7377896Z             ],
2025-08-14T23:09:11.7378957Z         )
2025-08-14T23:09:11.7379056Z     >>> st = ShardedTensor._init_from_local_tensor(
2025-08-14T23:09:11.7379192Z     ...     local_tensor, sharding_spec, [2, 4]
2025-08-14T23:09:11.7379305Z     ... )
2025-08-14T23:09:11.7379380Z     >>> st
2025-08-14T23:09:11.7379459Z     ShardedTensor(
2025-08-14T23:09:11.7379557Z         ShardedTensorMetadata(
2025-08-14T23:09:11.7379669Z             shards_metadata=[
2025-08-14T23:09:11.7379833Z                 ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-08-14T23:09:11.7380051Z                 ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-08-14T23:09:11.7380209Z             ],
2025-08-14T23:09:11.7381259Z             size=torch.Size([2, 4])
2025-08-14T23:09:11.7381368Z     )
2025-08-14T23:09:11.7381448Z     >>> st.local_tensor()
2025-08-14T23:09:11.7381548Z     tensor([1, 2, 3, 4]) # Rank 0
2025-08-14T23:09:11.7381652Z     tensor([3, 4, 5, 6]) # Rank 1
2025-08-14T23:09:11.7381721Z 
2025-08-14T23:09:11.7381815Z Warning: This API is experimental and subject to change. It lacks of a fully across
2025-08-14T23:09:11.7382033Z          rank validations, and we only validate the local shard on the current rank.
2025-08-14T23:09:11.7382229Z          We fully rely on the user to ensure local tensor is sharded based on the
2025-08-14T23:09:11.7382377Z          sharding spec.
2025-08-14T23:09:11.7382437Z 
2025-08-14T23:09:11.7382527Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7382651Z 
2025-08-14T23:09:11.7382879Z 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.
2025-08-14T23:09:11.7384314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7384441Z 
2025-08-14T23:09:11.7384531Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-08-14T23:09:11.7384689Z single local shard.
2025-08-14T23:09:11.7384743Z 
2025-08-14T23:09:11.7384825Z If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-08-14T23:09:11.7385069Z If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-08-14T23:09:11.7385228Z we swap local shards directly.
2025-08-14T23:09:11.7385389Z For more generic cases, we merge different shards across different ranks and split
2025-08-14T23:09:11.7385600Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-08-14T23:09:11.7385724Z 
2025-08-14T23:09:11.7385755Z Args:
2025-08-14T23:09:11.7385900Z     resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-08-14T23:09:11.7386100Z         specification describing how the tensor is sharded.
2025-08-14T23:09:11.7387169Z 
2025-08-14T23:09:11.7387203Z Returns:
2025-08-14T23:09:11.7387323Z     A :class:`ShardedTensor` object whose local shards are resharded.
2025-08-14T23:09:11.7387431Z 
2025-08-14T23:09:11.7387464Z Examples:
2025-08-14T23:09:11.7387547Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:11.7387680Z     >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:11.7387827Z     >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-08-14T23:09:11.7387972Z     >>> tensor = torch.stack([tensor, tensor])
2025-08-14T23:09:11.7388100Z     >>> tensor
2025-08-14T23:09:11.7388194Z     tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-08-14T23:09:11.7388318Z     tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-08-14T23:09:11.7388437Z     tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-08-14T23:09:11.7389559Z     tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-08-14T23:09:11.7389676Z     >>> sharding_dim = 0
2025-08-14T23:09:11.7389782Z     >>> spec = ChunkShardingSpec(
2025-08-14T23:09:11.7389893Z             dim=sharding_dim,
2025-08-14T23:09:11.7389996Z             placements=[
2025-08-14T23:09:11.7390095Z                 "rank:0/cuda:0",
2025-08-14T23:09:11.7390198Z                 "rank:1/cuda:1",
2025-08-14T23:09:11.7390298Z                 "rank:2/cuda:2",
2025-08-14T23:09:11.7390400Z                 "rank:3/cuda:3",
2025-08-14T23:09:11.7390495Z             ],
2025-08-14T23:09:11.7390575Z         )
2025-08-14T23:09:11.7391639Z     >>> current_offsets = [0] * 2
2025-08-14T23:09:11.7391751Z     >>> current_offsets[0] = rank * 2
2025-08-14T23:09:11.7391867Z     >>> shard_metadata = ShardMetadata(
2025-08-14T23:09:11.7391999Z             shard_offsets=copy.deepcopy(current_offsets),
2025-08-14T23:09:11.7392128Z             shard_sizes=tensor.size(),
2025-08-14T23:09:11.7392250Z             placement=spec.placements[rank],
2025-08-14T23:09:11.7392360Z         )
2025-08-14T23:09:11.7392438Z     >>> local_shards = [
2025-08-14T23:09:11.7392529Z             Shard(
2025-08-14T23:09:11.7392618Z                 tensor=tensor,
2025-08-14T23:09:11.7392724Z                 metadata=shard_metadata,
2025-08-14T23:09:11.7392830Z             )
2025-08-14T23:09:11.7393907Z         ]
2025-08-14T23:09:11.7394029Z     >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-08-14T23:09:11.7394182Z     >>> sharding_dim = 1
2025-08-14T23:09:11.7394290Z     >>> resharding_spec = ChunkShardingSpec(
2025-08-14T23:09:11.7394407Z             dim=sharding_dim,
2025-08-14T23:09:11.7394510Z             placements=[
2025-08-14T23:09:11.7394605Z                 "rank:0/cuda:0",
2025-08-14T23:09:11.7394706Z                 "rank:1/cuda:1",
2025-08-14T23:09:11.7394804Z                 "rank:2/cuda:2",
2025-08-14T23:09:11.7394901Z                 "rank:3/cuda:3",
2025-08-14T23:09:11.7394995Z             ],
2025-08-14T23:09:11.7395998Z         )
2025-08-14T23:09:11.7396083Z     >>> st.reshard(resharding_spec)
2025-08-14T23:09:11.7396200Z     >>> tensor = st.local_shards()[0].tensor
2025-08-14T23:09:11.7396307Z     >>> tensor
2025-08-14T23:09:11.7396409Z     tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-08-14T23:09:11.7396547Z     tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-08-14T23:09:11.7396682Z     tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-08-14T23:09:11.7396852Z     tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-08-14T23:09:11.7396958Z 
2025-08-14T23:09:11.7397048Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7397173Z 
2025-08-14T23:09:11.7846832Z 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.
2025-08-14T23:09:11.7849329Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7849471Z 
2025-08-14T23:09:11.7849573Z :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-08-14T23:09:11.7849793Z strategies for an operator when the tensor inputs and outputs are DTensor.
2025-08-14T23:09:11.7850000Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-08-14T23:09:11.7850290Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-08-14T23:09:11.7850511Z when users would like to overwrite default sharding strategies of existing operators.
2025-08-14T23:09:11.7850642Z 
2025-08-14T23:09:11.7850699Z Args:
2025-08-14T23:09:11.7850797Z     op (Union[OpOverload, List[OpOverload]]):
2025-08-14T23:09:11.7850951Z         An op or a list of ops to register the customized sharding function.
2025-08-14T23:09:11.7851064Z 
2025-08-14T23:09:11.7851100Z Returns:
2025-08-14T23:09:11.7851241Z     A function decorator which can be used to wrap a function that defines the sharding
2025-08-14T23:09:11.7853766Z     strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-08-14T23:09:11.7853997Z     registered to DTensor and will override the default sharding strategy if DTensor has
2025-08-14T23:09:11.7854236Z     already implemented the operator. The customized sharding function takes the same inputs
2025-08-14T23:09:11.7854465Z     as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-08-14T23:09:11.7854683Z     replaced by a tensor-like object that DTensor uses internally). The function should
2025-08-14T23:09:11.7854907Z     return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-08-14T23:09:11.7855086Z     corresponding input placements.
2025-08-14T23:09:11.7855164Z 
2025-08-14T23:09:11.7855199Z Example:
2025-08-14T23:09:11.7855289Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.7855419Z     >>> @register_sharding(aten._softmax.default)
2025-08-14T23:09:11.7856759Z     >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-08-14T23:09:11.7856922Z     >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-08-14T23:09:11.7857058Z     >>>     acceptable_shardings = []
2025-08-14T23:09:11.7857167Z     >>>
2025-08-14T23:09:11.7857275Z     >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-08-14T23:09:11.7857436Z     >>>     acceptable_shardings.append(all_replicate)
2025-08-14T23:09:11.7857558Z     >>>
2025-08-14T23:09:11.7857650Z     >>>     for sharding_dim in range(x.ndim):
2025-08-14T23:09:11.7857773Z     >>>         if sharding_dim != softmax_dim:
2025-08-14T23:09:11.7857891Z     >>>             all_sharded = (
2025-08-14T23:09:11.7859286Z     >>>                 [Shard(sharding_dim)],
2025-08-14T23:09:11.7859424Z     >>>                 [Shard(sharding_dim), None, None],
2025-08-14T23:09:11.7859547Z     >>>             )
2025-08-14T23:09:11.7859657Z     >>>             acceptable_shardings.append(all_sharded)
2025-08-14T23:09:11.7859775Z     >>>
2025-08-14T23:09:11.7859862Z     >>>     return acceptable_shardings
2025-08-14T23:09:11.7859939Z 
2025-08-14T23:09:11.7860016Z .. note:: This API is currently experimental and subject to change
2025-08-14T23:09:11.7860121Z 
2025-08-14T23:09:11.7860211Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7860336Z 
2025-08-14T23:09:11.7860617Z 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.
2025-08-14T23:09:11.7860968Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.7861096Z 
2025-08-14T23:09:11.7861191Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-08-14T23:09:11.7862510Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-08-14T23:09:11.7862739Z the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-08-14T23:09:11.7863022Z :class:`DTensor` according to the ``out_placements``.
2025-08-14T23:09:11.7863115Z 
2025-08-14T23:09:11.7863147Z Args:
2025-08-14T23:09:11.7863265Z     func (Callable): the function to be applied on each local shard of
2025-08-14T23:09:11.7863412Z         :class:`DTensor` s.
2025-08-14T23:09:11.7863557Z     out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-08-14T23:09:11.7863792Z         the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-08-14T23:09:11.7864002Z         If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-08-14T23:09:11.7864224Z         of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-08-14T23:09:11.7864432Z         values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-08-14T23:09:11.7864602Z         mapping to the flattened ``output``.
2025-08-14T23:09:11.7864756Z         Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-08-14T23:09:11.7865938Z         placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-08-14T23:09:11.7866108Z         should be `None`.
2025-08-14T23:09:11.7866253Z         Note that the only exception is when no :class:`DTensor` argument is passed
2025-08-14T23:09:11.7866450Z         in. In this case, even if `out_placements` is not `None`, the result function
2025-08-14T23:09:11.7866655Z         should ignore the desired placements because the function is not running with
2025-08-14T23:09:11.7866816Z         :class:`DTensor` s.
2025-08-14T23:09:11.7866942Z     in_placements (Tuple[`PlacementType`, ...], optional):
2025-08-14T23:09:11.7867131Z         the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-08-14T23:09:11.7867342Z         If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-08-14T23:09:11.7867541Z         placements of each :class:`DTensor` argument is the same as the required
2025-08-14T23:09:11.7867722Z         placements or not. If the placements are not the same and
2025-08-14T23:09:11.7868825Z         ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-08-14T23:09:11.7869032Z         ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-08-14T23:09:11.7869241Z         the required sharding placements before passing its local tensor to ``func``.
2025-08-14T23:09:11.7869444Z         The only exception is when required placements are not ``None`` and the
2025-08-14T23:09:11.7869640Z         argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-08-14T23:09:11.7869836Z         will be skipped and the argument will be directly passed to ``func``.
2025-08-14T23:09:11.7870024Z         If ``in_placements`` is ``None``, no placements examination will be performed.
2025-08-14T23:09:11.7870172Z         Default: None
2025-08-14T23:09:11.7870295Z     in_grad_placements (Tuple[`PlacementType`, ...], optional):
2025-08-14T23:09:11.7870469Z         the placements hint of the :class:`DTensor` s gradient corresponds
2025-08-14T23:09:11.7870652Z         to the flattened input DTensor. This argument is the hint that user
2025-08-14T23:09:11.7871747Z         can give to :meth:`to_local` in case the gradient layout of the
2025-08-14T23:09:11.7871924Z         local tensor input does not match its :class:`DTensor` input layout.
2025-08-14T23:09:11.7872150Z         If not specified, we will assume the gradient layout of the local
2025-08-14T23:09:11.7872330Z         tensor input remains the same as the original :class:`DTensor` input
2025-08-14T23:09:11.7872503Z         and use that for gradient computation. Default: None.
2025-08-14T23:09:11.7872649Z     device_mesh (:class:`DeviceMesh`, optional):
2025-08-14T23:09:11.7872811Z         the device mesh that the output :class:`DTensor` s are placed on. If not
2025-08-14T23:09:11.7873093Z         specified, this will be inferred from the first input :class:`DTensor`'s device
2025-08-14T23:09:11.7873258Z         mesh. Default: None.
2025-08-14T23:09:11.7873325Z 
2025-08-14T23:09:11.7873360Z Keyword Args:
2025-08-14T23:09:11.7874392Z     redistribute_inputs (bool, optional):
2025-08-14T23:09:11.7874561Z         the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-08-14T23:09:11.7874792Z         their placements are different from the required input placements. If this
2025-08-14T23:09:11.7874998Z         value is ``False`` and some :class:`DTensor` input has a different placement,
2025-08-14T23:09:11.7875166Z         an exception will be raised. Default: False.
2025-08-14T23:09:11.7875268Z 
2025-08-14T23:09:11.7875301Z Returns:
2025-08-14T23:09:11.7875439Z     A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-08-14T23:09:11.7875647Z     and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-08-14T23:09:11.7875766Z 
2025-08-14T23:09:11.7875799Z Raises:
2025-08-14T23:09:11.7875925Z     AssertionError: For any non-DTensor output, we require its corresponding
2025-08-14T23:09:11.7876133Z         output placement in ``out_placements`` be None. An AssertionError will be raised
2025-08-14T23:09:11.7877219Z         if this is not the case.
2025-08-14T23:09:11.7877293Z 
2025-08-14T23:09:11.7877384Z     ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-08-14T23:09:11.7877570Z         a redistribution according to ``in_placements``.
2025-08-14T23:09:11.7877661Z 
2025-08-14T23:09:11.7877694Z Example:
2025-08-14T23:09:11.7877739Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.7877795Z     >>> def mm_allreduce_forward(device_mesh, W, X):
2025-08-14T23:09:11.7877843Z     >>>     partial_sum_tensor = torch.mm(W, X)
2025-08-14T23:09:11.7877931Z     >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-08-14T23:09:11.7877973Z     >>>     return reduced_tensor
2025-08-14T23:09:11.7878005Z     >>>
2025-08-14T23:09:11.7878053Z     >>> W = torch.randn(12, 8, requires_grad=False)
2025-08-14T23:09:11.7878100Z     >>> X = torch.randn(8, 16, requires_grad=False)
2025-08-14T23:09:11.7878139Z     >>> Y = torch.mm(W, X)
2025-08-14T23:09:11.7878209Z     >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-08-14T23:09:11.7878275Z     >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-08-14T23:09:11.7878309Z     >>>
2025-08-14T23:09:11.7878407Z     >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion
2025-08-14T23:09:11.7878455Z     >>> local_mm_allreduce_forward = local_map(
2025-08-14T23:09:11.7878497Z     >>>     mm_allreduce_forward,
2025-08-14T23:09:11.7879457Z     >>>     out_placements=[Replicate()],
2025-08-14T23:09:11.7879506Z     >>>     in_placements=[col_wise, row_wise],
2025-08-14T23:09:11.7879549Z     >>>     device_mesh=device_mesh,
2025-08-14T23:09:11.7879581Z     >>> )
2025-08-14T23:09:11.7879613Z     >>>
2025-08-14T23:09:11.7879652Z     >>> W_dt = distribute_tensor(
2025-08-14T23:09:11.7879695Z     ...     W, device_mesh, (col_wise)
2025-08-14T23:09:11.7879737Z     ... )  # col-wisely sharded W tensor
2025-08-14T23:09:11.7879776Z     >>> X_dt = distribute_tensor(
2025-08-14T23:09:11.7879817Z     ...     X, device_mesh, (row_wise)
2025-08-14T23:09:11.7879857Z     ... )  # row-wisely sharded X tensor
2025-08-14T23:09:11.7879903Z     >>> Y_dt = local_mm_allreduce_forward(
2025-08-14T23:09:11.7879984Z     ...     device_mesh, W_dt, X_dt
2025-08-14T23:09:11.7880043Z     ... )  # apply local_mm_allreduce_forward to DTensors
2025-08-14T23:09:11.7880047Z 
2025-08-14T23:09:11.7880120Z .. note:: This API is currently experimental and subject to change
2025-08-14T23:09:11.7880123Z 
2025-08-14T23:09:11.7880212Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.7880214Z 
2025-08-14T23:09:11.8004531Z 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.
2025-08-14T23:09:11.8004843Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8004872Z 
2025-08-14T23:09:11.8005034Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-08-14T23:09:11.8005215Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-08-14T23:09:11.8005228Z 
2025-08-14T23:09:11.8007132Z Keyword Args:
2025-08-14T23:09:11.8007220Z     input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:11.8007389Z         The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-08-14T23:09:11.8007519Z         DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-08-14T23:09:11.8007564Z         as a placeholder. default: None.
2025-08-14T23:09:11.8007651Z     desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:11.8007781Z         The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:11.8007915Z         have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-08-14T23:09:11.8007973Z     input_kwarg_layouts (Dict[str, Placement]):
2025-08-14T23:09:11.8008108Z         The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-08-14T23:09:11.8008147Z         default: None
2025-08-14T23:09:11.8008209Z     desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-08-14T23:09:11.8008332Z         The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:11.8008386Z         have the desired DTensor layouts. default: None.
2025-08-14T23:09:11.8008436Z     use_local_output (bool, optional):
2025-08-14T23:09:11.8008560Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-08-14T23:09:11.8008599Z Returns:
2025-08-14T23:09:11.8008710Z     A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-08-14T23:09:11.8008713Z 
2025-08-14T23:09:11.8008751Z Example::
2025-08-14T23:09:11.8008796Z     >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:11.8010251Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-08-14T23:09:11.8010329Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:11.8010369Z     >>> ...
2025-08-14T23:09:11.8010486Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:11.8010539Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:11.8010570Z     >>>
2025-08-14T23:09:11.8010695Z     >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-08-14T23:09:11.8010753Z     >>> # and then redistributed to Replicated DTensor.
2025-08-14T23:09:11.8010823Z     >>> parallelize_module(
2025-08-14T23:09:11.8010877Z     >>>     block, # this can be a submodule or module
2025-08-14T23:09:11.8010912Z     >>>     tp_mesh,
2025-08-14T23:09:11.8010955Z     >>>     parallelize_plan={
2025-08-14T23:09:11.8011045Z     >>>         "attn": PrepareModuleInput(
2025-08-14T23:09:11.8011116Z     >>>             input_layouts=(Shard(0), None, None, ...),
2025-08-14T23:09:11.8011182Z     >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-08-14T23:09:11.8011216Z     >>>         ),
2025-08-14T23:09:11.8011249Z     >>>     }
2025-08-14T23:09:11.8011281Z     >>> )
2025-08-14T23:09:11.8011283Z 
2025-08-14T23:09:11.8011371Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8011373Z 
2025-08-14T23:09:11.8011592Z 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.
2025-08-14T23:09:11.8012839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8012842Z 
2025-08-14T23:09:11.8013101Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-08-14T23:09:11.8013223Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-08-14T23:09:11.8013244Z 
2025-08-14T23:09:11.8013278Z Keyword Args:
2025-08-14T23:09:11.8013348Z     output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:11.8013464Z         The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-08-14T23:09:11.8013596Z         DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-08-14T23:09:11.8013654Z         ``None`` need to be specified as a placeholder.
2025-08-14T23:09:11.8013730Z     desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:11.8013861Z         The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-08-14T23:09:11.8013906Z         have the desired DTensor layouts.
2025-08-14T23:09:11.8013954Z     use_local_output (bool, optional):
2025-08-14T23:09:11.8014077Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-08-14T23:09:11.8014111Z Returns:
2025-08-14T23:09:11.8014214Z     A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-08-14T23:09:11.8014217Z 
2025-08-14T23:09:11.8014251Z Example::
2025-08-14T23:09:11.8014294Z     >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:11.8014406Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-08-14T23:09:11.8014478Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:11.8014511Z     >>> ...
2025-08-14T23:09:11.8014617Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:11.8014666Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:11.8014699Z     >>>
2025-08-14T23:09:11.8014838Z     >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-08-14T23:09:11.8014892Z     >>> # and then redistributed to Sharded DTensor.
2025-08-14T23:09:11.8014936Z     >>> parallelize_module(
2025-08-14T23:09:11.8014989Z     >>>     block, # this can be a submodule or module
2025-08-14T23:09:11.8015023Z     >>>     tp_mesh,
2025-08-14T23:09:11.8015078Z     >>>     parallelize_plan = PrepareModuleOutput(
2025-08-14T23:09:11.8015123Z     >>>         output_layouts=Replicate(),
2025-08-14T23:09:11.8015170Z     >>>         desired_output_layouts=Shard(0)
2025-08-14T23:09:11.8015203Z     >>>     )
2025-08-14T23:09:11.8015236Z     >>> )
2025-08-14T23:09:11.8015238Z 
2025-08-14T23:09:11.8015329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8015331Z 
2025-08-14T23:09:11.8016532Z 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.
2025-08-14T23:09:11.8016670Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8016674Z 
2025-08-14T23:09:11.8016823Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module
2025-08-14T23:09:11.8016969Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution
2025-08-14T23:09:11.8017097Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of
2025-08-14T23:09:11.8017177Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`.
2025-08-14T23:09:11.8017180Z 
2025-08-14T23:09:11.8017213Z Keyword Args:
2025-08-14T23:09:11.8017289Z     input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:11.8017435Z         The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-08-14T23:09:11.8017563Z         DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-08-14T23:09:11.8017623Z         as a placeholder. default: None.
2025-08-14T23:09:11.8017706Z     desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:11.8017832Z         The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:11.8017967Z         have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-08-14T23:09:11.8018016Z     input_kwarg_layouts (Dict[str, Placement]):
2025-08-14T23:09:11.8018143Z         The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-08-14T23:09:11.8018178Z         default: None
2025-08-14T23:09:11.8018240Z     desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-08-14T23:09:11.8018367Z         The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:11.8018423Z         have the desired DTensor layouts. default: None.
2025-08-14T23:09:11.8018470Z     use_local_input (bool, optional):
2025-08-14T23:09:11.8019514Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-08-14T23:09:11.8019578Z     output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:11.8019694Z         The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-08-14T23:09:11.8019823Z         DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-08-14T23:09:11.8019879Z         ``None`` need to be specified as a placeholder.
2025-08-14T23:09:11.8019950Z     desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:11.8020085Z         The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-08-14T23:09:11.8020130Z         have the desired DTensor layouts.
2025-08-14T23:09:11.8020177Z     use_local_output (bool, optional):
2025-08-14T23:09:11.8020299Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-08-14T23:09:11.8020332Z Returns:
2025-08-14T23:09:11.8020457Z     A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs.
2025-08-14T23:09:11.8020459Z 
2025-08-14T23:09:11.8020494Z Example::
2025-08-14T23:09:11.8020535Z     >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:11.8020655Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput
2025-08-14T23:09:11.8020726Z     >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:11.8020759Z     >>> ...
2025-08-14T23:09:11.8020900Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:11.8020951Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:11.8020984Z     >>>
2025-08-14T23:09:11.8021099Z     >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor
2025-08-14T23:09:11.8021219Z     >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated
2025-08-14T23:09:11.8022196Z     >>> # as Replicated DTensor and then redistributed to Sharded DTensor.
2025-08-14T23:09:11.8022239Z     >>> parallelize_module(
2025-08-14T23:09:11.8022292Z     >>>     block, # this can be a submodule or module
2025-08-14T23:09:11.8022326Z     >>>     tp_mesh,
2025-08-14T23:09:11.8022368Z     >>>     parallelize_plan={
2025-08-14T23:09:11.8022421Z     >>>         "attn": PrepareModuleInputOutput(
2025-08-14T23:09:11.8022507Z     >>>             input_layouts=(Shard(0), None, None, ...),
2025-08-14T23:09:11.8022574Z     >>>             desired_input_layouts=(Replicate(), None, None, ...),
2025-08-14T23:09:11.8022621Z     >>>             output_layouts=Replicate(),
2025-08-14T23:09:11.8022687Z     >>>             desired_output_layouts=Shard(0),
2025-08-14T23:09:11.8022722Z     >>>         ),
2025-08-14T23:09:11.8022754Z     >>>     }
2025-08-14T23:09:11.8022785Z     >>> )
2025-08-14T23:09:11.8022787Z 
2025-08-14T23:09:11.8022877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8022879Z 
2025-08-14T23:09:11.8254660Z 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.
2025-08-14T23:09:11.8255988Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8256020Z 
2025-08-14T23:09:11.8256256Z Initialize rendezvous event object and record its operations.
2025-08-14T23:09:11.8256300Z 
2025-08-14T23:09:11.8256393Z Args:
2025-08-14T23:09:11.8256559Z     run_id (str): The run id of the rendezvous.
2025-08-14T23:09:11.8256723Z     message (str): The message describing the event.
2025-08-14T23:09:11.8265414Z     node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-08-14T23:09:11.8265658Z     name (str): Event name. (E.g. Current action being performed).
2025-08-14T23:09:11.8265794Z     hostname (str): Hostname of the node.
2025-08-14T23:09:11.8265961Z     pid (Optional[int]): The process id of the node.
2025-08-14T23:09:11.8266210Z     master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-08-14T23:09:11.8266485Z     local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-08-14T23:09:11.8266668Z     rank (Optional[int]): The rank of the node, if known.
2025-08-14T23:09:11.8266764Z Returns:
2025-08-14T23:09:11.8266852Z     None
2025-08-14T23:09:11.8266948Z Example:
2025-08-14T23:09:11.8267097Z     >>> # See DynamicRendezvousHandler class
2025-08-14T23:09:11.8267200Z     >>> def _record(
2025-08-14T23:09:11.8267263Z     ...     self,
2025-08-14T23:09:11.8267307Z     ...     message: str,
2025-08-14T23:09:11.8267367Z     ...     node_state: NodeState = NodeState.RUNNING,
2025-08-14T23:09:11.8267414Z     ...     rank: Optional[int] = None,
2025-08-14T23:09:11.8267456Z     ... ) -> None:
2025-08-14T23:09:11.8267502Z     ...     construct_and_record_rdzv_event(
2025-08-14T23:09:11.8267568Z     ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-08-14T23:09:11.8267614Z     ...         run_id=self._settings.run_id,
2025-08-14T23:09:11.8267662Z     ...         message=message,
2025-08-14T23:09:11.8269101Z     ...         node_state=node_state,
2025-08-14T23:09:11.8269154Z     ...         hostname=self._this_node.addr,
2025-08-14T23:09:11.8269198Z     ...         pid=self._this_node.pid,
2025-08-14T23:09:11.8269245Z     ...         local_id=self._this_node.local_id,
2025-08-14T23:09:11.8269537Z     ...         rank=rank,
2025-08-14T23:09:11.8269570Z     ...     )
2025-08-14T23:09:11.8269620Z 
2025-08-14T23:09:11.8269714Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8269719Z 
2025-08-14T23:09:11.8641920Z 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.
2025-08-14T23:09:11.8642254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8642262Z 
2025-08-14T23:09:11.8642508Z RemoteModule instance can only be created after RPC initialization.
2025-08-14T23:09:11.8642514Z 
2025-08-14T23:09:11.8642723Z It creates a user-specified module on a specified remote node.
2025-08-14T23:09:11.8643159Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-08-14T23:09:11.8643346Z executed on the remote node.
2025-08-14T23:09:11.8643922Z It takes care of autograd recording to ensure the backward pass propagates
2025-08-14T23:09:11.8644127Z gradients back to the corresponding remote module.
2025-08-14T23:09:11.8644683Z It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-08-14T23:09:11.8645080Z without incurring any overheads of copying the actual module,
2025-08-14T23:09:11.8645352Z which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-08-14T23:09:11.8645475Z pointing to the remote module.
2025-08-14T23:09:11.8645480Z 
2025-08-14T23:09:11.8650051Z The arguments of ``forward_async`` and ``forward`` are the same as
2025-08-14T23:09:11.8650148Z the ``forward`` method of the module returned by the ``module_cls``.
2025-08-14T23:09:11.8650151Z 
2025-08-14T23:09:11.8650269Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-08-14T23:09:11.8650272Z 
2025-08-14T23:09:11.8650365Z Particularly, to create a hybrid model, typically the local modules should be
2025-08-14T23:09:11.8650498Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-08-14T23:09:11.8650537Z Hybrid Example:
2025-08-14T23:09:11.8650593Z         >>> class HybridModel(nn.Module):
2025-08-14T23:09:11.8650644Z         >>>     def __init__(self) -> None:
2025-08-14T23:09:11.8650691Z         >>>         nn.Module.__init__(self)
2025-08-14T23:09:11.8650748Z         >>>         self.remote_embedding = RemoteModule(...)
2025-08-14T23:09:11.8650797Z         >>>         self.local_linear = nn.Linear(...)
2025-08-14T23:09:11.8650800Z 
2025-08-14T23:09:11.8650874Z For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-08-14T23:09:11.8650963Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-08-14T23:09:11.8651039Z the generated ``RemoteModule`` will have 2 methods in signature of
2025-08-14T23:09:11.8651091Z ``def forward(input: Tensor) -> Tensor:`` and
2025-08-14T23:09:11.8651159Z ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-08-14T23:09:11.8651164Z 
2025-08-14T23:09:11.8651234Z .. note::
2025-08-14T23:09:11.8651293Z     If the remote module is placed on a cuda device,
2025-08-14T23:09:11.8651379Z     any input CPU tensors will be automatically moved to the same cuda device,
2025-08-14T23:09:11.8651515Z     and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-08-14T23:09:11.8651518Z 
2025-08-14T23:09:11.8651554Z Args:
2025-08-14T23:09:11.8651664Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:11.8651766Z         The device can be a local device or a remote device specified by one of the following remote
2025-08-14T23:09:11.8651805Z         formats:
2025-08-14T23:09:11.8651807Z 
2025-08-14T23:09:11.8651866Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-08-14T23:09:11.8651930Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-08-14T23:09:11.8651983Z 
2025-08-14T23:09:11.8652096Z         In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:11.8652149Z     module_cls (nn.Module): For example,
2025-08-14T23:09:11.8652194Z         >>> class MyModule(nn.Module):
2025-08-14T23:09:11.8652241Z         >>>     def forward(input):
2025-08-14T23:09:11.8653799Z         >>>         return input + 1
2025-08-14T23:09:11.8653844Z         >>>
2025-08-14T23:09:11.8653890Z         >>> module_cls = MyModule
2025-08-14T23:09:11.8653967Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-08-14T23:09:11.8654037Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-08-14T23:09:11.8654136Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-08-14T23:09:11.8654221Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-08-14T23:09:11.8654330Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-08-14T23:09:11.8654414Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-08-14T23:09:11.8654417Z 
2025-08-14T23:09:11.8654472Z Returns:
2025-08-14T23:09:11.8654557Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:11.8654641Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-08-14T23:09:11.8654737Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:11.8654792Z     on the user-provided module on the remote side.
2025-08-14T23:09:11.8654795Z 
2025-08-14T23:09:11.8654829Z Example::
2025-08-14T23:09:11.8654887Z     Run the following code in two different processes:
2025-08-14T23:09:11.8654890Z 
2025-08-14T23:09:11.8654934Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.8654971Z     >>> # On worker 0:
2025-08-14T23:09:11.8655007Z     >>> import torch
2025-08-14T23:09:11.8655060Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8655105Z     >>> from torch import nn, Tensor
2025-08-14T23:09:11.8656230Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:11.8656267Z     >>>
2025-08-14T23:09:11.8656323Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:11.8656371Z     >>> remote_linear_module = RemoteModule(
2025-08-14T23:09:11.8656421Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:11.8656454Z     >>> )
2025-08-14T23:09:11.8656495Z     >>> input = torch.randn(128, 20)
2025-08-14T23:09:11.8656554Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-08-14T23:09:11.8656594Z     >>> ret = ret_fut.wait()
2025-08-14T23:09:11.8656632Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8656634Z 
2025-08-14T23:09:11.8656668Z     >>> # On worker 1:
2025-08-14T23:09:11.8656703Z     >>> import torch
2025-08-14T23:09:11.8656750Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8656786Z     >>>
2025-08-14T23:09:11.8656842Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:11.8656877Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8656879Z 
2025-08-14T23:09:11.8656970Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8656973Z 
2025-08-14T23:09:11.8657234Z 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.
2025-08-14T23:09:11.8657326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8657328Z 
2025-08-14T23:09:11.8657437Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-08-14T23:09:11.8657439Z 
2025-08-14T23:09:11.8657550Z This alternate initialization method can be particularly useful if we want to create multiple
2025-08-14T23:09:11.8657659Z RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-08-14T23:09:11.8657687Z 
2025-08-14T23:09:11.8658860Z Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-08-14T23:09:11.8658929Z which is not supported. The recommended way is as follows:
2025-08-14T23:09:11.8658932Z 
2025-08-14T23:09:11.8658977Z     1. the sender creates a RemoteModule;
2025-08-14T23:09:11.8659032Z     2. the sender sends its ``module_rref`` over RPC;
2025-08-14T23:09:11.8659147Z     3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-08-14T23:09:11.8659150Z 
2025-08-14T23:09:11.8659184Z Example::
2025-08-14T23:09:11.8659241Z     Run the following code in two different processes:
2025-08-14T23:09:11.8659244Z 
2025-08-14T23:09:11.8659287Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.8659322Z     >>> # On worker 0:
2025-08-14T23:09:11.8659356Z     >>> import torch
2025-08-14T23:09:11.8659404Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8659464Z     >>> from torch import nn, Tensor
2025-08-14T23:09:11.8659546Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:11.8659578Z     >>>
2025-08-14T23:09:11.8659646Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:11.8659690Z     >>> remote_module = RemoteModule(
2025-08-14T23:09:11.8659740Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:11.8659771Z     >>> )
2025-08-14T23:09:11.8659803Z     >>>
2025-08-14T23:09:11.8659847Z     >>> remote_module1 = rpc.rpc_sync(
2025-08-14T23:09:11.8659887Z     >>>     "worker1/cpu",
2025-08-14T23:09:11.8660905Z     >>>     RemoteModule.init_from_module_rref,
2025-08-14T23:09:11.8660970Z     >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-08-14T23:09:11.8661002Z     >>> )
2025-08-14T23:09:11.8661039Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8661041Z 
2025-08-14T23:09:11.8661075Z     >>> # On worker 1:
2025-08-14T23:09:11.8661108Z     >>> import torch
2025-08-14T23:09:11.8661159Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8661191Z     >>>
2025-08-14T23:09:11.8661244Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:11.8661281Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8661283Z 
2025-08-14T23:09:11.8661315Z Args:
2025-08-14T23:09:11.8661419Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:11.8661519Z         The device can be a local device or a remote device specified by one of the following remote
2025-08-14T23:09:11.8661553Z         formats:
2025-08-14T23:09:11.8661555Z 
2025-08-14T23:09:11.8661609Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-08-14T23:09:11.8661667Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-08-14T23:09:11.8661669Z 
2025-08-14T23:09:11.8661757Z         In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:11.8661846Z     module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-08-14T23:09:11.8661891Z         the created remote module.
2025-08-14T23:09:11.8661987Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-08-14T23:09:11.8662072Z         to be created. The type object should be decorated by @torch.jit.interface.
2025-08-14T23:09:11.8662148Z         If not provided, the generated RemoteModule is not torchscript-able.
2025-08-14T23:09:11.8663274Z         Warning, this is an experimental API and susceptible to frequent changes.
2025-08-14T23:09:11.8663277Z 
2025-08-14T23:09:11.8663311Z Returns:
2025-08-14T23:09:11.8663398Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:11.8663481Z     user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-08-14T23:09:11.8663574Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:11.8663629Z     on the user-provided module on the remote side.
2025-08-14T23:09:11.8663659Z 
2025-08-14T23:09:11.8663763Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8663766Z 
2025-08-14T23:09:11.8663973Z 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.
2025-08-14T23:09:11.8664063Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:11.8664065Z 
2025-08-14T23:09:11.8664146Z     A RemoteModule instance can only be created after RPC initialization.
2025-08-14T23:09:11.8664149Z 
2025-08-14T23:09:11.8664220Z     It creates a user-specified module on a specified remote node.
2025-08-14T23:09:11.8664300Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-08-14T23:09:11.8664343Z     executed on the remote node.
2025-08-14T23:09:11.8664423Z     It takes care of autograd recording to ensure the backward pass propagates
2025-08-14T23:09:11.8664512Z     gradients back to the corresponding remote module.
2025-08-14T23:09:11.8664515Z 
2025-08-14T23:09:11.8664592Z     It generates two methods ``forward_async`` and ``forward`` based on the
2025-08-14T23:09:11.8664684Z     signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-08-14T23:09:11.8664769Z     runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-08-14T23:09:11.8664840Z     and ``forward`` are the same as the ``forward`` method of the module
2025-08-14T23:09:11.8664883Z     returned by the ``module_cls``.
2025-08-14T23:09:11.8664886Z 
2025-08-14T23:09:11.8664955Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-08-14T23:09:11.8666004Z     that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-08-14T23:09:11.8666087Z     the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-08-14T23:09:11.8666089Z 
2025-08-14T23:09:11.8666142Z     | ``def forward(input: Tensor) -> Tensor:``
2025-08-14T23:09:11.8666207Z     | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-08-14T23:09:11.8666209Z 
2025-08-14T23:09:11.8666240Z Args:
2025-08-14T23:09:11.8666346Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:11.8666464Z         The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-08-14T23:09:11.8666519Z         E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-08-14T23:09:11.8666604Z         In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:11.8666690Z     module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-08-14T23:09:11.8666692Z 
2025-08-14T23:09:11.8666736Z         >>> class MyModule(nn.Module):
2025-08-14T23:09:11.8666777Z         >>>     def forward(input):
2025-08-14T23:09:11.8666817Z         >>>         return input + 1
2025-08-14T23:09:11.8666853Z         >>>
2025-08-14T23:09:11.8666894Z         >>> module_cls = MyModule
2025-08-14T23:09:11.8666898Z 
2025-08-14T23:09:11.8666972Z     args (Sequence, optional): args to be passed to ``module_cls``.
2025-08-14T23:09:11.8667042Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-08-14T23:09:11.8667044Z 
2025-08-14T23:09:11.8667077Z Returns:
2025-08-14T23:09:11.8667162Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:11.8667244Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-08-14T23:09:11.8667337Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:11.8668313Z     on the user-provided module on the remote side.
2025-08-14T23:09:11.8668315Z 
2025-08-14T23:09:11.8668351Z Example::
2025-08-14T23:09:11.8668408Z     Run the following code in two different processes:
2025-08-14T23:09:11.8668410Z 
2025-08-14T23:09:11.8668454Z     >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:11.8668509Z     >>> # On worker 0:
2025-08-14T23:09:11.8668557Z     >>> import torch
2025-08-14T23:09:11.8668606Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8668647Z     >>> from torch import nn, Tensor
2025-08-14T23:09:11.8668729Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:11.8668760Z     >>>
2025-08-14T23:09:11.8668814Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:11.8668861Z     >>> remote_linear_module = RemoteModule(
2025-08-14T23:09:11.8668912Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:11.8668944Z     >>> )
2025-08-14T23:09:11.8668984Z     >>> input = torch.randn(128, 20)
2025-08-14T23:09:11.8669042Z     >>> ret_fut = remote_linear_module.forward_async(input)
2025-08-14T23:09:11.8669082Z     >>> ret = ret_fut.wait()
2025-08-14T23:09:11.8669119Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8669121Z 
2025-08-14T23:09:11.8669154Z     >>> # On worker 1:
2025-08-14T23:09:11.8669203Z     >>> import torch
2025-08-14T23:09:11.8669252Z     >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:11.8669284Z     >>>
2025-08-14T23:09:11.8670260Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:11.8670315Z     >>> rpc.shutdown()
2025-08-14T23:09:11.8670318Z 
2025-08-14T23:09:11.8670388Z     Furthermore, a more practical example that is combined with
2025-08-14T23:09:11.8670562Z     `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-08-14T23:09:11.8670680Z     can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-08-14T23:09:11.8670683Z 
2025-08-14T23:09:11.8670771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:11.8670774Z 
2025-08-14T23:09:12.6688288Z 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.
2025-08-14T23:09:12.6689195Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.6689800Z Return the recommended gain value for the given nonlinearity function.
2025-08-14T23:09:12.6690136Z 
2025-08-14T23:09:12.6690262Z     The values are as follows:
2025-08-14T23:09:12.6690464Z 
2025-08-14T23:09:12.6690609Z     ================= ====================================================
2025-08-14T23:09:12.6697638Z     nonlinearity      gain
2025-08-14T23:09:12.6697871Z     ================= ====================================================
2025-08-14T23:09:12.6698126Z     Linear / Identity :math:`1`
2025-08-14T23:09:12.6698342Z     Conv{1,2,3}D      :math:`1`
2025-08-14T23:09:12.6698541Z     Sigmoid           :math:`1`
2025-08-14T23:09:12.6698750Z     Tanh              :math:`\frac{5}{3}`
2025-08-14T23:09:12.6698987Z     ReLU              :math:`\sqrt{2}`
2025-08-14T23:09:12.6699268Z     Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
2025-08-14T23:09:12.6699562Z     SELU              :math:`\frac{3}{4}`
2025-08-14T23:09:12.6699797Z     ================= ====================================================
2025-08-14T23:09:12.6699967Z 
2025-08-14T23:09:12.6700057Z     .. warning::
2025-08-14T23:09:12.6700303Z         In order to implement `Self-Normalizing Neural Networks`_ ,
2025-08-14T23:09:12.6700678Z         you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``.
2025-08-14T23:09:12.6701041Z         This gives the initial weights a variance of ``1 / N``,
2025-08-14T23:09:12.6701395Z         which is necessary to induce a stable fixed point in the forward pass.
2025-08-14T23:09:12.6701781Z         In contrast, the default gain for ``SELU`` sacrifices the normalization
2025-08-14T23:09:12.6702138Z         effect for more stable gradient flow in rectangular layers.
2025-08-14T23:09:12.6702339Z 
2025-08-14T23:09:12.6702409Z     Args:
2025-08-14T23:09:12.6702644Z         nonlinearity: the non-linear function (`nn.functional` name)
2025-08-14T23:09:12.6703342Z         param: optional parameter for the non-linear function
2025-08-14T23:09:12.6703535Z 
2025-08-14T23:09:12.6703607Z     Examples:
2025-08-14T23:09:12.6703800Z         >>> gain = nn.init.calculate_gain(
2025-08-14T23:09:12.6704033Z         ...     "leaky_relu", 0.2
2025-08-14T23:09:12.6704266Z         ... )  # leaky_relu with negative_slope=0.2
2025-08-14T23:09:12.6704436Z 
2025-08-14T23:09:12.6704785Z     .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html
2025-08-14T23:09:12.6705234Z     
2025-08-14T23:09:12.6707522Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.6707700Z 
2025-08-14T23:09:12.7043181Z 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.
2025-08-14T23:09:12.7044425Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7044807Z 
2025-08-14T23:09:12.7045050Z Context manager for training with uneven inputs across processes in DDP.
2025-08-14T23:09:12.7045392Z 
2025-08-14T23:09:12.7045697Z This context manager will keep track of already-joined DDP processes,
2025-08-14T23:09:12.7046336Z and "shadow" the forward and backward passes by inserting collective
2025-08-14T23:09:12.7046860Z communication operations to match with the ones created by non-joined
2025-08-14T23:09:12.7051602Z DDP processes. This will ensure each collective call has a corresponding
2025-08-14T23:09:12.7052003Z call by already-joined DDP processes, preventing hangs or errors that
2025-08-14T23:09:12.7052375Z would otherwise happen when training with uneven inputs across
2025-08-14T23:09:12.7052796Z processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-08-14T23:09:12.7053526Z specified to be ``True``, all trainers will throw an error once one rank
2025-08-14T23:09:12.7054107Z runs out of inputs, allowing these errors to be caught and handled
2025-08-14T23:09:12.7054567Z according to application logic.
2025-08-14T23:09:12.7054717Z 
2025-08-14T23:09:12.7054876Z Once all DDP processes have joined, the context manager will broadcast
2025-08-14T23:09:12.7055259Z the model corresponding to the last joined process to all processes to
2025-08-14T23:09:12.7055594Z ensure the model is the same across all processes
2025-08-14T23:09:12.7055863Z (which is guaranteed by DDP).
2025-08-14T23:09:12.7056014Z 
2025-08-14T23:09:12.7056162Z To use this to enable training with uneven inputs across processes,
2025-08-14T23:09:12.7056549Z simply wrap this context manager around your training loop. No further
2025-08-14T23:09:12.7056907Z modifications to the model or data loading is required.
2025-08-14T23:09:12.7057060Z 
2025-08-14T23:09:12.7057126Z .. warning::
2025-08-14T23:09:12.7057310Z     If the model or training loop this context manager is wrapped around
2025-08-14T23:09:12.7057566Z     has additional distributed collective operations, such as
2025-08-14T23:09:12.7057818Z     ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-08-14T23:09:12.7058074Z     ``throw_on_early_termination`` must be enabled. This is because this
2025-08-14T23:09:12.7058347Z     context manager is not aware of non-DDP collective communication.
2025-08-14T23:09:12.7058598Z     This flag will cause all ranks to throw when any one rank
2025-08-14T23:09:12.7058852Z     exhausts inputs, allowing these errors to be caught and recovered
2025-08-14T23:09:12.7059073Z     from across all ranks.
2025-08-14T23:09:12.7059170Z 
2025-08-14T23:09:12.7059220Z Args:
2025-08-14T23:09:12.7059383Z     divide_by_initial_world_size (bool): If ``True``, will divide
2025-08-14T23:09:12.7059642Z         gradients by the initial ``world_size`` DDP training was launched
2025-08-14T23:09:12.7059894Z         with. If ``False``, will compute the effective world size
2025-08-14T23:09:12.7060132Z         (number of ranks that have not depleted their inputs yet) and
2025-08-14T23:09:12.7060486Z         divide gradients by that during allreduce. Set
2025-08-14T23:09:12.7060715Z         ``divide_by_initial_world_size=True`` to ensure every input
2025-08-14T23:09:12.7060976Z         sample including the uneven inputs have equal weight in terms of
2025-08-14T23:09:12.7061222Z         how much they contribute to the global gradient. This is
2025-08-14T23:09:12.7061453Z         achieved by always dividing the gradient by the initial
2025-08-14T23:09:12.7063943Z         ``world_size`` even when we encounter uneven inputs. If you set
2025-08-14T23:09:12.7064181Z         this to ``False``, we divide the gradient by the remaining
2025-08-14T23:09:12.7064421Z         number of nodes. This ensures parity with training on a smaller
2025-08-14T23:09:12.7064664Z         ``world_size`` although it also means the uneven inputs would
2025-08-14T23:09:12.7064912Z         contribute more towards the global gradient. Typically, you
2025-08-14T23:09:12.7065198Z         would want to set this to ``True`` for cases where the last few
2025-08-14T23:09:12.7065448Z         inputs of your training job are uneven. In extreme cases, where
2025-08-14T23:09:12.7065690Z         there is a large discrepancy in the number of inputs, setting
2025-08-14T23:09:12.7065943Z         this to ``False`` might provide better results.
2025-08-14T23:09:12.7066174Z     enable (bool): Whether to enable uneven input detection or not. Pass
2025-08-14T23:09:12.7067725Z         in ``enable=False`` to disable in cases where you know that
2025-08-14T23:09:12.7067915Z         inputs are even across participating processes. Default is
2025-08-14T23:09:12.7068066Z         ``True``.
2025-08-14T23:09:12.7068197Z     throw_on_early_termination (bool): Whether to throw an error
2025-08-14T23:09:12.7068379Z         or continue training when at least one rank has exhausted
2025-08-14T23:09:12.7068563Z         inputs. If ``True``, will throw upon the first rank reaching end
2025-08-14T23:09:12.7068747Z         of data. If ``False``, will continue training with a smaller
2025-08-14T23:09:12.7068933Z         effective world size until all ranks are joined. Note that if
2025-08-14T23:09:12.7069101Z         this flag is specified, then the flag
2025-08-14T23:09:12.7069264Z         ``divide_by_initial_world_size`` would be ignored. Default
2025-08-14T23:09:12.7069411Z         is ``False``.
2025-08-14T23:09:12.7069477Z 
2025-08-14T23:09:12.7069479Z 
2025-08-14T23:09:12.7070627Z Example::
2025-08-14T23:09:12.7070685Z 
2025-08-14T23:09:12.7070738Z     >>> # xdoctest: +SKIP("Distributed")
2025-08-14T23:09:12.7070862Z     >>> import torch
2025-08-14T23:09:12.7070977Z     >>> import torch.distributed as dist
2025-08-14T23:09:12.7071098Z     >>> import os
2025-08-14T23:09:12.7071211Z     >>> import torch.multiprocessing as mp
2025-08-14T23:09:12.7071347Z     >>> import torch.nn as nn
2025-08-14T23:09:12.7071469Z     >>> # On each spawned worker
2025-08-14T23:09:12.7071587Z     >>> def worker(rank):
2025-08-14T23:09:12.7071733Z     >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-08-14T23:09:12.7071893Z     >>>     torch.cuda.set_device(rank)
2025-08-14T23:09:12.7073270Z     >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-08-14T23:09:12.7073450Z     >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-08-14T23:09:12.7073624Z     >>>         model, device_ids=[rank], output_device=rank
2025-08-14T23:09:12.7073758Z     >>>     )
2025-08-14T23:09:12.7073867Z     >>>     # Rank 1 gets one more input than rank 0.
2025-08-14T23:09:12.7074036Z     >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-08-14T23:09:12.7074195Z     >>>     with model.join():
2025-08-14T23:09:12.7074312Z     >>>         for _ in range(5):
2025-08-14T23:09:12.7074436Z     >>>             for inp in inputs:
2025-08-14T23:09:12.7074569Z     >>>                 loss = model(inp).sum()
2025-08-14T23:09:12.7074701Z     >>>                 loss.backward()
2025-08-14T23:09:12.7075919Z     >>>     # Without the join() API, the below synchronization will hang
2025-08-14T23:09:12.7076159Z     >>>     # blocking for rank 1's allreduce to complete.
2025-08-14T23:09:12.7076311Z     >>>     torch.cuda.synchronize(device=rank)
2025-08-14T23:09:12.7076405Z 
2025-08-14T23:09:12.7076506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7076646Z 
2025-08-14T23:09:12.7076925Z 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.
2025-08-14T23:09:12.7077288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7077417Z 
2025-08-14T23:09:12.7077522Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-08-14T23:09:12.7077665Z 
2025-08-14T23:09:12.7077739Z Registers an optimizer with DDP such that the optimization for a
2025-08-14T23:09:12.7077935Z parameter will run immediately when that parameter's gradient is
2025-08-14T23:09:12.7078118Z finished with reduction, instead of waiting for all parameters'
2025-08-14T23:09:12.7078303Z gradients to finish reduction. This can result in a training speedup
2025-08-14T23:09:12.7079496Z depending on your workload since the optimizer can run while gradient
2025-08-14T23:09:12.7079687Z reduction for other parameters are still ongoing. In addition, this has
2025-08-14T23:09:12.7079879Z the potential to reduce peak memory consumption during training, as it
2025-08-14T23:09:12.7080063Z only needs to load the per-parameter optimizer states of a single
2025-08-14T23:09:12.7080246Z parameter at a time, instead of loading all per-parameter optimizer
2025-08-14T23:09:12.7080389Z states at once.
2025-08-14T23:09:12.7080441Z 
2025-08-14T23:09:12.7080474Z Args:
2025-08-14T23:09:12.7080590Z     optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-08-14T23:09:12.7080734Z     as a fused optimizer.
2025-08-14T23:09:12.7080862Z     *args (Sequence[Any]): Arguments to forward to `optim`.
2025-08-14T23:09:12.7081035Z     optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-08-14T23:09:12.7082186Z     to optimize, similar to `params` argument of traditional `torch.optim`
2025-08-14T23:09:12.7082378Z     Optimizers. If this is omitted, all DDP model parameters will be
2025-08-14T23:09:12.7082520Z     optimized.
2025-08-14T23:09:12.7082642Z     **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-08-14T23:09:12.7082749Z 
2025-08-14T23:09:12.7082784Z .. warning ::
2025-08-14T23:09:12.7082995Z     _register_fused_optim should only be called once on a DDP instance,
2025-08-14T23:09:12.7083179Z     and registering multiple fused optimizers for the same DDP model
2025-08-14T23:09:12.7083334Z     is not currently supported. Please ping
2025-08-14T23:09:12.7083501Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:12.7083655Z     for your use case.
2025-08-14T23:09:12.7083715Z 
2025-08-14T23:09:12.7083753Z .. warning ::
2025-08-14T23:09:12.7084843Z     _register_fused_optim and register_comm_hook currently do not
2025-08-14T23:09:12.7085023Z     compose together, meaning that custom DDP communication hooks are
2025-08-14T23:09:12.7085198Z     not supported with overlapped optimizers. Please ping
2025-08-14T23:09:12.7085374Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:12.7085523Z     for your use case.
2025-08-14T23:09:12.7085582Z 
2025-08-14T23:09:12.7085616Z .. warning ::
2025-08-14T23:09:12.7085816Z     Gradient accumulation and DDP `no_sync` are currently not supported
2025-08-14T23:09:12.7085976Z     with overlapped optimizer. Please ping
2025-08-14T23:09:12.7086132Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:12.7086280Z     for your use case.
2025-08-14T23:09:12.7086340Z 
2025-08-14T23:09:12.7086375Z Example::
2025-08-14T23:09:12.7087377Z 
2025-08-14T23:09:12.7087433Z     >>> # xdoctest: +SKIP("No rendezvous handler")
2025-08-14T23:09:12.7087676Z     >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-08-14T23:09:12.7087894Z     >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-08-14T23:09:12.7088035Z     >>> lr = 1e-2
2025-08-14T23:09:12.7088126Z     >>> betas = (0.9, 0.99)
2025-08-14T23:09:12.7088224Z     >>> eps = 1e-6
2025-08-14T23:09:12.7088356Z     >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-08-14T23:09:12.7088518Z     >>> # Example with subset of parameters
2025-08-14T23:09:12.7088647Z     >>> params_to_opt = [list(net.parameters())[0]]
2025-08-14T23:09:12.7088776Z     >>> net._register_fused_optim(
2025-08-14T23:09:12.7088930Z     ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-08-14T23:09:12.7090039Z     ... )
2025-08-14T23:09:12.7090086Z 
2025-08-14T23:09:12.7090176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7090326Z 
2025-08-14T23:09:12.7467281Z 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.
2025-08-14T23:09:12.7468341Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7469010Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-08-14T23:09:12.7469416Z 
2025-08-14T23:09:12.7469728Z     For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-08-14T23:09:12.7470251Z     and with 2D inputs, this class
2025-08-14T23:09:12.7470464Z 
2025-08-14T23:09:12.7470769Z         * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-08-14T23:09:12.7471462Z         * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-08-14T23:09:12.7477264Z         * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-08-14T23:09:12.7477712Z 
2025-08-14T23:09:12.7478051Z     However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-08-14T23:09:12.7478593Z     operations.
2025-08-14T23:09:12.7478743Z 
2025-08-14T23:09:12.7478875Z     EmbeddingBag also supports per-sample weights as an argument to the forward
2025-08-14T23:09:12.7479154Z     pass. This scales the output of the Embedding before performing a weighted
2025-08-14T23:09:12.7479364Z     reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-08-14T23:09:12.7479574Z     only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-08-14T23:09:12.7479737Z     :attr:`per_sample_weights`.
2025-08-14T23:09:12.7479811Z 
2025-08-14T23:09:12.7479845Z     Args:
2025-08-14T23:09:12.7479961Z         num_embeddings (int): size of the dictionary of embeddings
2025-08-14T23:09:12.7480129Z         embedding_dim (int): the size of each embedding vector
2025-08-14T23:09:12.7480339Z         max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-08-14T23:09:12.7480541Z                                     is renormalized to have norm :attr:`max_norm`.
2025-08-14T23:09:12.7480753Z         norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-08-14T23:09:12.7481017Z         scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-08-14T23:09:12.7481225Z                                                 the words in the mini-batch. Default ``False``.
2025-08-14T23:09:12.7481390Z                                                 Note: this option is not supported when ``mode="max"``.
2025-08-14T23:09:12.7481582Z         mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-08-14T23:09:12.7481833Z                                  ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-08-14T23:09:12.7482046Z                                  into consideration. ``"mean"`` computes the average of the values
2025-08-14T23:09:12.7482224Z                                  in the bag, ``"max"`` computes the max value over each bag.
2025-08-14T23:09:12.7482371Z                                  Default: ``"mean"``
2025-08-14T23:09:12.7482563Z         sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-08-14T23:09:12.7482795Z                                  Notes for more details regarding sparse gradients. Note: this option is not
2025-08-14T23:09:12.7484249Z                                  supported when ``mode="max"``.
2025-08-14T23:09:12.7484463Z         include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-08-14T23:09:12.7484731Z                                       is equivalent to the size of `indices`. This matches the CSR format.
2025-08-14T23:09:12.7484961Z         padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-08-14T23:09:12.7485224Z                                      gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-08-14T23:09:12.7485445Z                                      during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-08-14T23:09:12.7485657Z                                      EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-08-14T23:09:12.7485929Z                                      zeros, but can be updated to another value to be used as the padding vector.
2025-08-14T23:09:12.7486132Z                                      Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-08-14T23:09:12.7486294Z                                      reduction.
2025-08-14T23:09:12.7486376Z 
2025-08-14T23:09:12.7486413Z     Attributes:
2025-08-14T23:09:12.7487568Z         weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-08-14T23:09:12.7487772Z                          initialized from :math:`\mathcal{N}(0, 1)`.
2025-08-14T23:09:12.7487861Z 
2025-08-14T23:09:12.7487901Z     Examples::
2025-08-14T23:09:12.7487953Z 
2025-08-14T23:09:12.7488017Z         >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-08-14T23:09:12.7488172Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-08-14T23:09:12.7488317Z         >>> # a batch of 2 samples of 4 indices each
2025-08-14T23:09:12.7488470Z         >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-08-14T23:09:12.7488629Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-08-14T23:09:12.7488775Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:12.7488907Z         >>> embedding_sum(input, offsets)
2025-08-14T23:09:12.7489035Z         tensor([[-0.8861, -5.4350, -0.0523],
2025-08-14T23:09:12.7490096Z                 [ 1.1306, -2.5798, -1.0044]])
2025-08-14T23:09:12.7490172Z 
2025-08-14T23:09:12.7490218Z         >>> # Example with padding_idx
2025-08-14T23:09:12.7490366Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-08-14T23:09:12.7490540Z         >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-08-14T23:09:12.7490697Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-08-14T23:09:12.7490828Z         >>> embedding_sum(input, offsets)
2025-08-14T23:09:12.7490945Z         tensor([[ 0.0000,  0.0000,  0.0000],
2025-08-14T23:09:12.7491059Z                 [-0.7082,  3.2145, -2.6251]])
2025-08-14T23:09:12.7491132Z 
2025-08-14T23:09:12.7491196Z         >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-08-14T23:09:12.7491348Z         >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-08-14T23:09:12.7491498Z         >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-08-14T23:09:12.7492613Z                 embedding.weight,
2025-08-14T23:09:12.7492736Z                 padding_idx=embedding.padding_idx,
2025-08-14T23:09:12.7492860Z                 mode='sum')
2025-08-14T23:09:12.7493024Z     
2025-08-14T23:09:12.7493157Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7493281Z 
2025-08-14T23:09:12.7498759Z 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.
2025-08-14T23:09:12.7499089Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7499278Z Take in and process masked source/target sequences.
2025-08-14T23:09:12.7499372Z 
2025-08-14T23:09:12.7499435Z         .. note::
2025-08-14T23:09:12.7499489Z 
2025-08-14T23:09:12.7499667Z             If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are
2025-08-14T23:09:12.7501673Z             not allowed to participate in the attention,
2025-08-14T23:09:12.7501841Z             which is the opposite of the definition for :attr:`attn_mask`
2025-08-14T23:09:12.7502048Z             in :func:`torch.nn.functional.scaled_dot_product_attention`.
2025-08-14T23:09:12.7502153Z 
2025-08-14T23:09:12.7502187Z         Args:
2025-08-14T23:09:12.7502292Z             src: the sequence to the encoder (required).
2025-08-14T23:09:12.7502433Z             tgt: the sequence to the decoder (required).
2025-08-14T23:09:12.7502584Z             src_mask: the additive mask for the src sequence (optional).
2025-08-14T23:09:12.7502749Z             tgt_mask: the additive mask for the tgt sequence (optional).
2025-08-14T23:09:12.7503025Z             memory_mask: the additive mask for the encoder output (optional).
2025-08-14T23:09:12.7503214Z             src_key_padding_mask: the Tensor mask for src keys per batch (optional).
2025-08-14T23:09:12.7504635Z             tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional).
2025-08-14T23:09:12.7504858Z             memory_key_padding_mask: the Tensor mask for memory keys per batch (optional).
2025-08-14T23:09:12.7505062Z             src_is_causal: If specified, applies a causal mask as ``src_mask``.
2025-08-14T23:09:12.7505230Z                 Default: ``None``; try to detect a causal mask.
2025-08-14T23:09:12.7505361Z                 Warning:
2025-08-14T23:09:12.7505482Z                 ``src_is_causal`` provides a hint that ``src_mask`` is
2025-08-14T23:09:12.7505638Z                 the causal mask. Providing incorrect hints can result in
2025-08-14T23:09:12.7505797Z                 incorrect execution, including forward and backward
2025-08-14T23:09:12.7505932Z                 compatibility.
2025-08-14T23:09:12.7506071Z             tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``.
2025-08-14T23:09:12.7506233Z                 Default: ``None``; try to detect a causal mask.
2025-08-14T23:09:12.7506361Z                 Warning:
2025-08-14T23:09:12.7507438Z                 ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
2025-08-14T23:09:12.7507599Z                 the causal mask. Providing incorrect hints can result in
2025-08-14T23:09:12.7507757Z                 incorrect execution, including forward and backward
2025-08-14T23:09:12.7507891Z                 compatibility.
2025-08-14T23:09:12.7508022Z             memory_is_causal: If specified, applies a causal mask as
2025-08-14T23:09:12.7508159Z                 ``memory_mask``.
2025-08-14T23:09:12.7508266Z                 Default: ``False``.
2025-08-14T23:09:12.7508371Z                 Warning:
2025-08-14T23:09:12.7508481Z                 ``memory_is_causal`` provides a hint that
2025-08-14T23:09:12.7508630Z                 ``memory_mask`` is the causal mask. Providing incorrect
2025-08-14T23:09:12.7508782Z                 hints can result in incorrect execution, including
2025-08-14T23:09:12.7509844Z                 forward and backward compatibility.
2025-08-14T23:09:12.7509963Z 
2025-08-14T23:09:12.7510015Z         Shape:
2025-08-14T23:09:12.7510159Z             - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or
2025-08-14T23:09:12.7510327Z               `(N, S, E)` if `batch_first=True`.
2025-08-14T23:09:12.7510491Z             - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
2025-08-14T23:09:12.7510652Z               `(N, T, E)` if `batch_first=True`.
2025-08-14T23:09:12.7510799Z             - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`.
2025-08-14T23:09:12.7510969Z             - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`.
2025-08-14T23:09:12.7511115Z             - memory_mask: :math:`(T, S)`.
2025-08-14T23:09:12.7511278Z             - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
2025-08-14T23:09:12.7511499Z             - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`.
2025-08-14T23:09:12.7512635Z             - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
2025-08-14T23:09:12.7512763Z 
2025-08-14T23:09:12.7512971Z             Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked
2025-08-14T23:09:12.7513189Z             positions. If a BoolTensor is provided, positions with ``True``
2025-08-14T23:09:12.7513391Z             are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
2025-08-14T23:09:12.7513578Z             is provided, it will be added to the attention weight.
2025-08-14T23:09:12.7513779Z             [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
2025-08-14T23:09:12.7513992Z             the attention. If a BoolTensor is provided, the positions with the
2025-08-14T23:09:12.7514210Z             value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
2025-08-14T23:09:12.7514356Z 
2025-08-14T23:09:12.7514448Z             - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
2025-08-14T23:09:12.7514616Z               `(N, T, E)` if `batch_first=True`.
2025-08-14T23:09:12.7514699Z 
2025-08-14T23:09:12.7514786Z             Note: Due to the multi-head attention architecture in the transformer model,
2025-08-14T23:09:12.7515933Z             the output sequence length of a transformer is same as the input sequence
2025-08-14T23:09:12.7516099Z             (i.e. target) length of the decoder.
2025-08-14T23:09:12.7516181Z 
2025-08-14T23:09:12.7516292Z             where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the
2025-08-14T23:09:12.7516489Z             batch size, :math:`E` is the feature number
2025-08-14T23:09:12.7516575Z 
2025-08-14T23:09:12.7516611Z         Examples:
2025-08-14T23:09:12.7516707Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:12.7516825Z             >>> output = transformer_model(
2025-08-14T23:09:12.7516963Z             ...     src, tgt, src_mask=src_mask, tgt_mask=tgt_mask
2025-08-14T23:09:12.7517087Z             ... )
2025-08-14T23:09:12.7517172Z         
2025-08-14T23:09:12.7517305Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7518355Z 
2025-08-14T23:09:12.7576039Z 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.
2025-08-14T23:09:12.7576362Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7576583Z Computes a partial inverse of :class:`MaxPool2d`.
2025-08-14T23:09:12.7576679Z 
2025-08-14T23:09:12.7576775Z     :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-08-14T23:09:12.7576906Z 
2025-08-14T23:09:12.7576988Z     :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-08-14T23:09:12.7577191Z     including the indices of the maximal values and computes a partial inverse
2025-08-14T23:09:12.7577458Z     in which all non-maximal values are set to zero.
2025-08-14T23:09:12.7577547Z 
2025-08-14T23:09:12.7577586Z     Note:
2025-08-14T23:09:12.7577744Z         This operation may behave nondeterministically when the input indices has repeat values.
2025-08-14T23:09:12.7578027Z         See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-08-14T23:09:12.7580071Z 
2025-08-14T23:09:12.7580165Z     .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-08-14T23:09:12.7580360Z               sizes. Hence, the inversion process can get ambiguous.
2025-08-14T23:09:12.7580532Z               To accommodate this, you can provide the needed output size
2025-08-14T23:09:12.7580706Z               as an additional argument :attr:`output_size` in the forward call.
2025-08-14T23:09:12.7580862Z               See the Inputs and Example below.
2025-08-14T23:09:12.7580977Z 
2025-08-14T23:09:12.7581011Z     Args:
2025-08-14T23:09:12.7581127Z         kernel_size (int or tuple): Size of the max pooling window.
2025-08-14T23:09:12.7581292Z         stride (int or tuple): Stride of the max pooling window.
2025-08-14T23:09:12.7581472Z             It is set to :attr:`kernel_size` by default.
2025-08-14T23:09:12.7581627Z         padding (int or tuple): Padding that was added to the input
2025-08-14T23:09:12.7581730Z 
2025-08-14T23:09:12.7581764Z     Inputs:
2025-08-14T23:09:12.7583158Z         - `input`: the input Tensor to invert
2025-08-14T23:09:12.7583315Z         - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-08-14T23:09:12.7583485Z         - `output_size` (optional): the targeted output size
2025-08-14T23:09:12.7583580Z 
2025-08-14T23:09:12.7583614Z     Shape:
2025-08-14T23:09:12.7583729Z         - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-08-14T23:09:12.7583906Z         - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-08-14T23:09:12.7584018Z 
2025-08-14T23:09:12.7584057Z           .. math::
2025-08-14T23:09:12.7584206Z             H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-08-14T23:09:12.7584337Z 
2025-08-14T23:09:12.7584372Z           .. math::
2025-08-14T23:09:12.7584510Z             W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-08-14T23:09:12.7584634Z 
2025-08-14T23:09:12.7585754Z           or as given by :attr:`output_size` in the call operator
2025-08-14T23:09:12.7585855Z 
2025-08-14T23:09:12.7585891Z     Example::
2025-08-14T23:09:12.7585942Z 
2025-08-14T23:09:12.7586006Z         >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-08-14T23:09:12.7586149Z         >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-08-14T23:09:12.7586283Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-08-14T23:09:12.7586419Z                                     [ 5.,  6.,  7.,  8.],
2025-08-14T23:09:12.7586541Z                                     [ 9., 10., 11., 12.],
2025-08-14T23:09:12.7586661Z                                     [13., 14., 15., 16.]]]])
2025-08-14T23:09:12.7586787Z         >>> output, indices = pool(input)
2025-08-14T23:09:12.7586907Z         >>> unpool(output, indices)
2025-08-14T23:09:12.7587020Z         tensor([[[[  0.,   0.,   0.,   0.],
2025-08-14T23:09:12.7588139Z                   [  0.,   6.,   0.,   8.],
2025-08-14T23:09:12.7588253Z                   [  0.,   0.,   0.,   0.],
2025-08-14T23:09:12.7588364Z                   [  0.,  14.,   0.,  16.]]]])
2025-08-14T23:09:12.7588511Z         >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-08-14T23:09:12.7588674Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-08-14T23:09:12.7588806Z                                     [ 6.,  7.,  8.,  9., 10.],
2025-08-14T23:09:12.7588928Z                                     [11., 12., 13., 14., 15.],
2025-08-14T23:09:12.7589113Z                                     [16., 17., 18., 19., 20.]]]])
2025-08-14T23:09:12.7589237Z         >>> output, indices = pool(input)
2025-08-14T23:09:12.7589381Z         >>> # This call will not work without specifying output_size
2025-08-14T23:09:12.7589537Z         >>> unpool(output, indices, output_size=input.size())
2025-08-14T23:09:12.7590663Z         tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-08-14T23:09:12.7590779Z                   [ 0.,  7.,  0.,  9.,  0.],
2025-08-14T23:09:12.7590890Z                   [ 0.,  0.,  0.,  0.,  0.],
2025-08-14T23:09:12.7591001Z                   [ 0., 17.,  0., 19.,  0.]]]])
2025-08-14T23:09:12.7591076Z 
2025-08-14T23:09:12.7591079Z 
2025-08-14T23:09:12.7591110Z     
2025-08-14T23:09:12.7591241Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7591365Z 
2025-08-14T23:09:12.7719583Z 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.
2025-08-14T23:09:12.7720599Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7721223Z Creates a criterion that measures the triplet loss given input
2025-08-14T23:09:12.7721733Z     tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-08-14T23:09:12.7728591Z     positive, and negative examples, respectively), and a nonnegative,
2025-08-14T23:09:12.7728806Z     real-valued function ("distance function") used to compute the relationship
2025-08-14T23:09:12.7729011Z     between the anchor and positive example ("positive distance") and the
2025-08-14T23:09:12.7729187Z     anchor and negative example ("negative distance").
2025-08-14T23:09:12.7729289Z 
2025-08-14T23:09:12.7729370Z     The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-08-14T23:09:12.7729529Z     can be described as:
2025-08-14T23:09:12.7729600Z 
2025-08-14T23:09:12.7729645Z     .. math::
2025-08-14T23:09:12.7729759Z         \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-08-14T23:09:12.7729913Z         l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-08-14T23:09:12.7730008Z 
2025-08-14T23:09:12.7730101Z     where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-08-14T23:09:12.7730326Z     quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-08-14T23:09:12.7730545Z     and :math:`margin` is a nonnegative margin representing the minimum difference
2025-08-14T23:09:12.7730751Z     between the positive and negative distances that is required for the loss to
2025-08-14T23:09:12.7730951Z     be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-08-14T23:09:12.7731111Z     that the distance function can handle.
2025-08-14T23:09:12.7731192Z 
2025-08-14T23:09:12.7731237Z     If :attr:`reduction` is not ``'none'``
2025-08-14T23:09:12.7731355Z     (default ``'mean'``), then:
2025-08-14T23:09:12.7731428Z 
2025-08-14T23:09:12.7731461Z     .. math::
2025-08-14T23:09:12.7731547Z         \ell(x, y) =
2025-08-14T23:09:12.7731639Z         \begin{cases}
2025-08-14T23:09:12.7731771Z             \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-08-14T23:09:12.7731947Z             \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-08-14T23:09:12.7732083Z         \end{cases}
2025-08-14T23:09:12.7735277Z 
2025-08-14T23:09:12.7735382Z     See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-08-14T23:09:12.7735599Z     loss for input tensors using the :math:`l_p` distance as the distance function.
2025-08-14T23:09:12.7735726Z 
2025-08-14T23:09:12.7735760Z     Args:
2025-08-14T23:09:12.7735903Z         distance_function (Callable, optional): A nonnegative, real-valued function that
2025-08-14T23:09:12.7736112Z             quantifies the closeness of two tensors. If not specified,
2025-08-14T23:09:12.7736291Z             `nn.PairwiseDistance` will be used.  Default: ``None``
2025-08-14T23:09:12.7736568Z         margin (float, optional): A nonnegative margin representing the minimum difference
2025-08-14T23:09:12.7736791Z             between the positive and negative distances required for the loss to be 0. Larger
2025-08-14T23:09:12.7737020Z             margins penalize cases where the negative examples are not distant enough from the
2025-08-14T23:09:12.7737224Z             anchors, relative to the positives. Default: :math:`1`.
2025-08-14T23:09:12.7738879Z         swap (bool, optional): Whether to use the distance swap described in the paper
2025-08-14T23:09:12.7739106Z             `Learning shallow convolutional feature descriptors with triplet losses` by
2025-08-14T23:09:12.7739318Z             V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-08-14T23:09:12.7739528Z             negative example than the anchor is, swaps the positive example and the anchor in
2025-08-14T23:09:12.7739734Z             the loss computation. Default: ``False``.
2025-08-14T23:09:12.7739921Z         reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-08-14T23:09:12.7740121Z             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-08-14T23:09:12.7740316Z             ``'mean'``: the sum of the output will be divided by the number of
2025-08-14T23:09:12.7740502Z             elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-08-14T23:09:12.7740626Z 
2025-08-14T23:09:12.7740628Z 
2025-08-14T23:09:12.7740668Z     Shape:
2025-08-14T23:09:12.7740805Z         - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-08-14T23:09:12.7742013Z           as supported by the distance function.
2025-08-14T23:09:12.7742192Z         - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-08-14T23:09:12.7742354Z           otherwise.
2025-08-14T23:09:12.7742413Z 
2025-08-14T23:09:12.7742451Z     Examples:
2025-08-14T23:09:12.7742503Z 
2025-08-14T23:09:12.7742548Z     >>> # Initialize embeddings
2025-08-14T23:09:12.7742666Z     >>> embedding = nn.Embedding(1000, 128)
2025-08-14T23:09:12.7742795Z     >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:12.7743028Z     >>> positive_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:12.7743159Z     >>> negative_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:12.7743284Z     >>> anchor = embedding(anchor_ids)
2025-08-14T23:09:12.7743403Z     >>> positive = embedding(positive_ids)
2025-08-14T23:09:12.7744489Z     >>> negative = embedding(negative_ids)
2025-08-14T23:09:12.7744603Z     >>>
2025-08-14T23:09:12.7744690Z     >>> # Built-in Distance Function
2025-08-14T23:09:12.7744803Z     >>> triplet_loss = \
2025-08-14T23:09:12.7744964Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-08-14T23:09:12.7745158Z     >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:12.7745291Z     >>> output.backward()
2025-08-14T23:09:12.7745385Z     >>>
2025-08-14T23:09:12.7745471Z     >>> # Custom Distance Function
2025-08-14T23:09:12.7745583Z     >>> def l_infinity(x1, x2):
2025-08-14T23:09:12.7745713Z     >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-08-14T23:09:12.7746756Z     >>>
2025-08-14T23:09:12.7746872Z     >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-08-14T23:09:12.7747013Z     >>> triplet_loss = (
2025-08-14T23:09:12.7747172Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-08-14T23:09:12.7747361Z     >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:12.7747489Z     >>> output.backward()
2025-08-14T23:09:12.7747580Z     >>>
2025-08-14T23:09:12.7747667Z     >>> # Custom Distance Function (Lambda)
2025-08-14T23:09:12.7747781Z     >>> triplet_loss = (
2025-08-14T23:09:12.7747893Z     >>>     nn.TripletMarginWithDistanceLoss(
2025-08-14T23:09:12.7748058Z     >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-08-14T23:09:12.7749197Z     >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:12.7749327Z     >>> output.backward()
2025-08-14T23:09:12.7749390Z 
2025-08-14T23:09:12.7749426Z     Reference:
2025-08-14T23:09:12.7749584Z         V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-08-14T23:09:12.7749811Z         https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-08-14T23:09:12.7749957Z     
2025-08-14T23:09:12.7750091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-08-14T23:09:12.7750218Z 
2025-08-14T23:09:12.7750406Z 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.
2025-08-14T23:09:12.7750694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7750899Z The Connectionist Temporal Classification loss.
2025-08-14T23:09:12.7750988Z 
2025-08-14T23:09:12.7752077Z     Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
2025-08-14T23:09:12.7752387Z     probability of possible alignments of input to target, producing a loss value which is differentiable
2025-08-14T23:09:12.7752654Z     with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
2025-08-14T23:09:12.7752984Z     limits the length of the target sequence such that it must be :math:`\leq` the input length.
2025-08-14T23:09:12.7753122Z 
2025-08-14T23:09:12.7753157Z     Args:
2025-08-14T23:09:12.7753265Z         blank (int, optional): blank label. Default :math:`0`.
2025-08-14T23:09:12.7753446Z         reduction (str, optional): Specifies the reduction to apply to the output:
2025-08-14T23:09:12.7753636Z             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-08-14T23:09:12.7753816Z             ``'mean'``: the output losses will be divided by the target lengths and
2025-08-14T23:09:12.7754017Z             then the mean over the batch is taken, ``'sum'``: the output losses will be summed.
2025-08-14T23:09:12.7754184Z             Default: ``'mean'``
2025-08-14T23:09:12.7755249Z         zero_infinity (bool, optional):
2025-08-14T23:09:12.7755401Z             Whether to zero infinite losses and the associated gradients.
2025-08-14T23:09:12.7755545Z             Default: ``False``
2025-08-14T23:09:12.7755679Z             Infinite losses mainly occur when the inputs are too short
2025-08-14T23:09:12.7755823Z             to be aligned to the targets.
2025-08-14T23:09:12.7755898Z 
2025-08-14T23:09:12.7755933Z     Shape:
2025-08-14T23:09:12.7756050Z         - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`,
2025-08-14T23:09:12.7756201Z           where :math:`T = \text{input length}`,
2025-08-14T23:09:12.7756326Z           :math:`N = \text{batch size}`, and
2025-08-14T23:09:12.7756466Z           :math:`C = \text{number of classes (including blank)}`.
2025-08-14T23:09:12.7756641Z           The logarithmized probabilities of the outputs (e.g. obtained with
2025-08-14T23:09:12.7757737Z           :func:`torch.nn.functional.log_softmax`).
2025-08-14T23:09:12.7757874Z         - Targets: Tensor of size :math:`(N, S)` or
2025-08-14T23:09:12.7758018Z           :math:`(\operatorname{sum}(\text{target\_lengths}))`,
2025-08-14T23:09:12.7758157Z           where :math:`N = \text{batch size}` and
2025-08-14T23:09:12.7758299Z           :math:`S = \text{max target length, if shape is } (N, S)`.
2025-08-14T23:09:12.7758464Z           It represents the target sequences. Each element in the target
2025-08-14T23:09:12.7758652Z           sequence is a class index. And the target index cannot be blank (default=0).
2025-08-14T23:09:12.7758830Z           In the :math:`(N, S)` form, targets are padded to the
2025-08-14T23:09:12.7758974Z           length of the longest sequence, and stacked.
2025-08-14T23:09:12.7759128Z           In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form,
2025-08-14T23:09:12.7759328Z           the targets are assumed to be un-padded and
2025-08-14T23:09:12.7760395Z           concatenated within 1 dimension.
2025-08-14T23:09:12.7760552Z         - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`,
2025-08-14T23:09:12.7760732Z           where :math:`N = \text{batch size}`. It represents the lengths of the
2025-08-14T23:09:12.7760910Z           inputs (must each be :math:`\leq T`). And the lengths are specified
2025-08-14T23:09:12.7761097Z           for each sequence to achieve masking under the assumption that sequences
2025-08-14T23:09:12.7761257Z           are padded to equal lengths.
2025-08-14T23:09:12.7761404Z         - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`,
2025-08-14T23:09:12.7761588Z           where :math:`N = \text{batch size}`. It represents lengths of the targets.
2025-08-14T23:09:12.7761798Z           Lengths are specified for each sequence to achieve masking under the
2025-08-14T23:09:12.7762000Z           assumption that sequences are padded to equal lengths. If target shape is
2025-08-14T23:09:12.7762183Z           :math:`(N,S)`, target_lengths are effectively the stop index
2025-08-14T23:09:12.7763409Z           :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for
2025-08-14T23:09:12.7763598Z           each target in a batch. Lengths must each be :math:`\leq S`
2025-08-14T23:09:12.7763781Z           If the targets are given as a 1d tensor that is the concatenation of individual
2025-08-14T23:09:12.7763983Z           targets, the target_lengths must add up to the total length of the tensor.
2025-08-14T23:09:12.7764172Z         - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or
2025-08-14T23:09:12.7764361Z           ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or
2025-08-14T23:09:12.7764550Z           :math:`()` if input is unbatched, where :math:`N = \text{batch size}`.
2025-08-14T23:09:12.7764658Z 
2025-08-14T23:09:12.7764694Z     Examples:
2025-08-14T23:09:12.7764748Z 
2025-08-14T23:09:12.7764793Z         >>> # Target are to be padded
2025-08-14T23:09:12.7764914Z         >>> T = 50  # Input sequence length
2025-08-14T23:09:12.7765044Z         >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:12.7766189Z         >>> N = 16  # Batch size
2025-08-14T23:09:12.7766337Z         >>> S = 30  # Target sequence length of longest target in batch (padding length)
2025-08-14T23:09:12.7766519Z         >>> S_min = 10  # Minimum target length, for demonstration purposes
2025-08-14T23:09:12.7766650Z         >>>
2025-08-14T23:09:12.7766767Z         >>> # Initialize random batch of input vectors, for *size = (T,N,C)
2025-08-14T23:09:12.7766949Z         >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
2025-08-14T23:09:12.7767090Z         >>>
2025-08-14T23:09:12.7767203Z         >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:12.7767387Z         >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)
2025-08-14T23:09:12.7767529Z         >>>
2025-08-14T23:09:12.7767650Z         >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
2025-08-14T23:09:12.7768757Z         >>> target_lengths = torch.randint(
2025-08-14T23:09:12.7768872Z         ...     low=S_min,
2025-08-14T23:09:12.7768970Z         ...     high=S,
2025-08-14T23:09:12.7769065Z         ...     size=(N,),
2025-08-14T23:09:12.7769167Z         ...     dtype=torch.long,
2025-08-14T23:09:12.7769270Z         ... )
2025-08-14T23:09:12.7769359Z         >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:12.7769500Z         >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:12.7769640Z         >>> loss.backward()
2025-08-14T23:09:12.7769736Z         >>>
2025-08-14T23:09:12.7769811Z         >>>
2025-08-14T23:09:12.7770834Z         >>> # Target are to be un-padded
2025-08-14T23:09:12.7770954Z         >>> T = 50  # Input sequence length
2025-08-14T23:09:12.7771131Z         >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:12.7771253Z         >>> N = 16  # Batch size
2025-08-14T23:09:12.7771352Z         >>>
2025-08-14T23:09:12.7771467Z         >>> # Initialize random batch of input vectors, for *size = (T,N,C)
2025-08-14T23:09:12.7771645Z         >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
2025-08-14T23:09:12.7771828Z         >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
2025-08-14T23:09:12.7771969Z         >>>
2025-08-14T23:09:12.7772080Z         >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:12.7772265Z         >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long)
2025-08-14T23:09:12.7773450Z         >>> target = torch.randint(
2025-08-14T23:09:12.7773557Z         ...     low=1,
2025-08-14T23:09:12.7773647Z         ...     high=C,
2025-08-14T23:09:12.7773771Z         ...     size=(sum(target_lengths),),
2025-08-14T23:09:12.7773890Z         ...     dtype=torch.long,
2025-08-14T23:09:12.7773993Z         ... )
2025-08-14T23:09:12.7774080Z         >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:12.7774235Z         >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:12.7774373Z         >>> loss.backward()
2025-08-14T23:09:12.7774468Z         >>>
2025-08-14T23:09:12.7774542Z         >>>
2025-08-14T23:09:12.7775610Z         >>> # Target are to be un-padded and unbatched (effectively N=1)
2025-08-14T23:09:12.7775754Z         >>> T = 50  # Input sequence length
2025-08-14T23:09:12.7775879Z         >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:12.7775992Z         >>>
2025-08-14T23:09:12.7776105Z         >>> # Initialize random batch of input vectors, for *size = (T,C)
2025-08-14T23:09:12.7776263Z         >>> # xdoctest: +SKIP("FIXME: error in doctest")
2025-08-14T23:09:12.7776420Z         >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_()
2025-08-14T23:09:12.7776588Z         >>> input_lengths = torch.tensor(T, dtype=torch.long)
2025-08-14T23:09:12.7776710Z         >>>
2025-08-14T23:09:12.7776821Z         >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:12.7777950Z         >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long)
2025-08-14T23:09:12.7778115Z         >>> target = torch.randint(
2025-08-14T23:09:12.7778221Z         ...     low=1,
2025-08-14T23:09:12.7778310Z         ...     high=C,
2025-08-14T23:09:12.7778408Z         ...     size=(target_lengths,),
2025-08-14T23:09:12.7778522Z         ...     dtype=torch.long,
2025-08-14T23:09:12.7778622Z         ... )
2025-08-14T23:09:12.7778708Z         >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:12.7778845Z         >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:12.7778983Z         >>> loss.backward()
2025-08-14T23:09:12.7779048Z 
2025-08-14T23:09:12.7779083Z     Reference:
2025-08-14T23:09:12.7780140Z         A. Graves et al.: Connectionist Temporal Classification:
2025-08-14T23:09:12.7780325Z         Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
2025-08-14T23:09:12.7780504Z         https://www.cs.toronto.edu/~graves/icml_2006.pdf
2025-08-14T23:09:12.7780597Z 
2025-08-14T23:09:12.7780630Z     Note:
2025-08-14T23:09:12.7780760Z         In order to use CuDNN, the following must be satisfied: :attr:`targets` must be
2025-08-14T23:09:12.7780968Z         in concatenated format, all :attr:`input_lengths` must be `T`.  :math:`blank=0`,
2025-08-14T23:09:12.7781170Z         :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of
2025-08-14T23:09:12.7781325Z         dtype :attr:`torch.int32`.
2025-08-14T23:09:12.7781400Z 
2025-08-14T23:09:12.7781492Z         The regular implementation uses the (more common in PyTorch) `torch.long` dtype.
2025-08-14T23:09:12.7781621Z 
2025-08-14T23:09:12.7781623Z 
2025-08-14T23:09:12.7781655Z     Note:
2025-08-14T23:09:12.7781787Z         In some circumstances when using the CUDA backend with CuDNN, this operator
2025-08-14T23:09:12.7783106Z         may select a nondeterministic algorithm to increase performance. If this is
2025-08-14T23:09:12.7783328Z         undesirable, you can try to make the operation deterministic (potentially at
2025-08-14T23:09:12.7783537Z         a performance cost) by setting ``torch.backends.cudnn.deterministic =
2025-08-14T23:09:12.7783686Z         True``.
2025-08-14T23:09:12.7783808Z         Please see the notes on :doc:`/notes/randomness` for background.
2025-08-14T23:09:12.7783943Z     
2025-08-14T23:09:12.7784073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7784196Z 
2025-08-14T23:09:12.7869107Z 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.
2025-08-14T23:09:12.7869493Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7869783Z Applies Batch Normalization over a N-Dimensional input.
2025-08-14T23:09:12.7869937Z 
2025-08-14T23:09:12.7876419Z     The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-08-14T23:09:12.7876803Z     `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-08-14T23:09:12.7876999Z     Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-08-14T23:09:12.7877112Z 
2025-08-14T23:09:12.7877149Z     .. math::
2025-08-14T23:09:12.7877202Z 
2025-08-14T23:09:12.7877286Z         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-08-14T23:09:12.7877404Z 
2025-08-14T23:09:12.7877488Z     The mean and standard-deviation are calculated per-dimension over all
2025-08-14T23:09:12.7877689Z     mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-08-14T23:09:12.7877900Z     are learnable parameter vectors of size `C` (where `C` is the input size).
2025-08-14T23:09:12.7878100Z     By default, the elements of :math:`\gamma` are sampled from
2025-08-14T23:09:12.7878277Z     :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-08-14T23:09:12.7878482Z     The standard-deviation is calculated via the biased estimator, equivalent to
2025-08-14T23:09:12.7878658Z     `torch.var(input, unbiased=False)`.
2025-08-14T23:09:12.7878737Z 
2025-08-14T23:09:12.7878822Z     Also by default, during training this layer keeps running estimates of its
2025-08-14T23:09:12.7879022Z     computed mean and variance, which are then used for normalization during
2025-08-14T23:09:12.7879221Z     evaluation. The running estimates are kept with a default :attr:`momentum`
2025-08-14T23:09:12.7879374Z     of 0.1.
2025-08-14T23:09:12.7879422Z 
2025-08-14T23:09:12.7879502Z     If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-08-14T23:09:12.7879693Z     keep running estimates, and batch statistics are instead used during
2025-08-14T23:09:12.7879849Z     evaluation time as well.
2025-08-14T23:09:12.7881426Z 
2025-08-14T23:09:12.7881469Z     .. note::
2025-08-14T23:09:12.7881598Z         This :attr:`momentum` argument is different from one used in optimizer
2025-08-14T23:09:12.7881792Z         classes and the conventional notion of momentum. Mathematically, the
2025-08-14T23:09:12.7881954Z         update rule for running statistics here is
2025-08-14T23:09:12.7882135Z         :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-08-14T23:09:12.7882337Z         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-08-14T23:09:12.7882481Z         new observed value.
2025-08-14T23:09:12.7882548Z 
2025-08-14T23:09:12.7882652Z     Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-08-14T23:09:12.7882882Z     statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-08-14T23:09:12.7883171Z     Normalization or Spatio-temporal Batch Normalization.
2025-08-14T23:09:12.7883304Z 
2025-08-14T23:09:12.7883377Z     Currently :class:`SyncBatchNorm` only supports
2025-08-14T23:09:12.7884596Z     :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-08-14T23:09:12.7884814Z     :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-08-14T23:09:12.7885000Z     :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-08-14T23:09:12.7885157Z     Network with DDP.
2025-08-14T23:09:12.7885218Z 
2025-08-14T23:09:12.7885251Z     Args:
2025-08-14T23:09:12.7885363Z         num_features: :math:`C` from an expected input of size
2025-08-14T23:09:12.7885499Z             :math:`(N, C, +)`
2025-08-14T23:09:12.7885633Z         eps: a value added to the denominator for numerical stability.
2025-08-14T23:09:12.7885859Z             Default: ``1e-5``
2025-08-14T23:09:12.7885997Z         momentum: the value used for the running_mean and running_var
2025-08-14T23:09:12.7887195Z             computation. Can be set to ``None`` for cumulative moving average
2025-08-14T23:09:12.7887360Z             (i.e. simple average). Default: 0.1
2025-08-14T23:09:12.7887535Z         affine: a boolean value that when set to ``True``, this module has
2025-08-14T23:09:12.7887701Z             learnable affine parameters. Default: ``True``
2025-08-14T23:09:12.7887866Z         track_running_stats: a boolean value that when set to ``True``, this
2025-08-14T23:09:12.7888056Z             module tracks the running mean and variance, and when set to ``False``,
2025-08-14T23:09:12.7888248Z             this module does not track such statistics, and initializes statistics
2025-08-14T23:09:12.7888436Z             buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-08-14T23:09:12.7888623Z             When these buffers are ``None``, this module always uses batch statistics.
2025-08-14T23:09:12.7888797Z             in both training and eval modes. Default: ``True``
2025-08-14T23:09:12.7888977Z         process_group: synchronization of stats happen within each process group
2025-08-14T23:09:12.7890134Z             individually. Default behavior is synchronization across the whole
2025-08-14T23:09:12.7890286Z             world
2025-08-14T23:09:12.7890340Z 
2025-08-14T23:09:12.7890373Z     Shape:
2025-08-14T23:09:12.7890462Z         - Input: :math:`(N, C, +)`
2025-08-14T23:09:12.7890594Z         - Output: :math:`(N, C, +)` (same shape as input)
2025-08-14T23:09:12.7890686Z 
2025-08-14T23:09:12.7890720Z     .. note::
2025-08-14T23:09:12.7890852Z         Synchronization of batchnorm statistics occurs only while training, i.e.
2025-08-14T23:09:12.7891044Z         synchronization is disabled when ``model.eval()`` is set or if
2025-08-14T23:09:12.7891198Z         ``self.training`` is otherwise ``False``.
2025-08-14T23:09:12.7891280Z 
2025-08-14T23:09:12.7891316Z     Examples::
2025-08-14T23:09:12.7891368Z 
2025-08-14T23:09:12.7891408Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:12.7892460Z         >>> # With Learnable Parameters
2025-08-14T23:09:12.7892586Z         >>> m = nn.SyncBatchNorm(100)
2025-08-14T23:09:12.7892709Z         >>> # creating process group (optional)
2025-08-14T23:09:12.7892844Z         >>> # ranks is a list of int identifying rank ids.
2025-08-14T23:09:12.7893052Z         >>> ranks = list(range(8))
2025-08-14T23:09:12.7893167Z         >>> r1, r2 = ranks[:4], ranks[4:]
2025-08-14T23:09:12.7893296Z         >>> # Note: every rank calls into new_group for every
2025-08-14T23:09:12.7893441Z         >>> # process group created, even if that rank is not
2025-08-14T23:09:12.7893572Z         >>> # part of the group.
2025-08-14T23:09:12.7893731Z         >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-08-14T23:09:12.7893927Z         >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-08-14T23:09:12.7895047Z         >>> # Without Learnable Parameters
2025-08-14T23:09:12.7895199Z         >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-08-14T23:09:12.7895409Z         >>> input = torch.randn(20, 100, 35, 45, 10)
2025-08-14T23:09:12.7895531Z         >>> output = m(input)
2025-08-14T23:09:12.7895598Z 
2025-08-14T23:09:12.7895644Z         >>> # network is nn.BatchNorm layer
2025-08-14T23:09:12.7895818Z         >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-08-14T23:09:12.7896033Z         >>> # only single gpu per process is currently supported
2025-08-14T23:09:12.7896205Z         >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-08-14T23:09:12.7896364Z         >>>                         sync_bn_network,
2025-08-14T23:09:12.7896493Z         >>>                         device_ids=[args.local_rank],
2025-08-14T23:09:12.7896630Z         >>>                         output_device=args.local_rank)
2025-08-14T23:09:12.7897711Z     
2025-08-14T23:09:12.7897842Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7897994Z 
2025-08-14T23:09:12.7898214Z 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.
2025-08-14T23:09:12.7898571Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7898805Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-08-14T23:09:12.7898949Z 
2025-08-14T23:09:12.7898982Z         Args:
2025-08-14T23:09:12.7899116Z             module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-08-14T23:09:12.7899317Z             process_group (optional): process group to scope synchronization,
2025-08-14T23:09:12.7899472Z                 default is the whole world
2025-08-14T23:09:12.7899548Z 
2025-08-14T23:09:12.7899583Z         Returns:
2025-08-14T23:09:12.7899726Z             The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-08-14T23:09:12.7900914Z             layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-08-14T23:09:12.7901100Z             a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-08-14T23:09:12.7901243Z             instead.
2025-08-14T23:09:12.7901299Z 
2025-08-14T23:09:12.7901336Z         Example::
2025-08-14T23:09:12.7901390Z 
2025-08-14T23:09:12.7901437Z             >>> # Network with nn.BatchNorm layer
2025-08-14T23:09:12.7901570Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:12.7901703Z             >>> module = torch.nn.Sequential(
2025-08-14T23:09:12.7901829Z             >>>            torch.nn.Linear(20, 100),
2025-08-14T23:09:12.7901957Z             >>>            torch.nn.BatchNorm1d(100),
2025-08-14T23:09:12.7902076Z             >>>          ).cuda()
2025-08-14T23:09:12.7902190Z             >>> # creating process group (optional)
2025-08-14T23:09:12.7903394Z             >>> # ranks is a list of int identifying rank ids.
2025-08-14T23:09:12.7903526Z             >>> ranks = list(range(8))
2025-08-14T23:09:12.7903644Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-08-14T23:09:12.7903775Z             >>> # Note: every rank calls into new_group for every
2025-08-14T23:09:12.7903925Z             >>> # process group created, even if that rank is not
2025-08-14T23:09:12.7904057Z             >>> # part of the group.
2025-08-14T23:09:12.7904175Z             >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:12.7904343Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-08-14T23:09:12.7904540Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-08-14T23:09:12.7904752Z             >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-08-14T23:09:12.7904893Z 
2025-08-14T23:09:12.7905903Z         
2025-08-14T23:09:12.7906037Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7906161Z 
2025-08-14T23:09:12.7936228Z 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.
2025-08-14T23:09:12.7936698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.7936827Z 
2025-08-14T23:09:12.7936938Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-08-14T23:09:12.7937078Z 
2025-08-14T23:09:12.7937175Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-08-14T23:09:12.7937386Z   be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-08-14T23:09:12.7937500Z 
2025-08-14T23:09:12.7937610Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-08-14T23:09:12.7937839Z   a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-08-14T23:09:12.7938360Z   (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-08-14T23:09:12.7938460Z 
2025-08-14T23:09:12.7938495Z Shape:
2025-08-14T23:09:12.7940106Z     - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-08-14T23:09:12.7940339Z       dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-08-14T23:09:12.7940542Z     - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-08-14T23:09:12.7940712Z       :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-08-14T23:09:12.7940797Z 
2025-08-14T23:09:12.7940829Z Args:
2025-08-14T23:09:12.7940932Z     dim (Union[int, str]): Dimension to be unflattened
2025-08-14T23:09:12.7941140Z     unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-08-14T23:09:12.7941294Z 
2025-08-14T23:09:12.7941328Z Examples:
2025-08-14T23:09:12.7941417Z     >>> input = torch.randn(2, 50)
2025-08-14T23:09:12.7941530Z     >>> # With tuple of ints
2025-08-14T23:09:12.7941640Z     >>> m = nn.Sequential(
2025-08-14T23:09:12.7941744Z     >>>     nn.Linear(50, 50),
2025-08-14T23:09:12.7941850Z     >>>     nn.Unflatten(1, (2, 5, 5))
2025-08-14T23:09:12.7941957Z     >>> )
2025-08-14T23:09:12.7942040Z     >>> output = m(input)
2025-08-14T23:09:12.7942140Z     >>> output.size()
2025-08-14T23:09:12.7942237Z     torch.Size([2, 2, 5, 5])
2025-08-14T23:09:12.7943442Z     >>> # With torch.Size
2025-08-14T23:09:12.7943542Z     >>> m = nn.Sequential(
2025-08-14T23:09:12.7943640Z     >>>     nn.Linear(50, 50),
2025-08-14T23:09:12.7943752Z     >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-08-14T23:09:12.7943864Z     >>> )
2025-08-14T23:09:12.7943945Z     >>> output = m(input)
2025-08-14T23:09:12.7944041Z     >>> output.size()
2025-08-14T23:09:12.7944136Z     torch.Size([2, 2, 5, 5])
2025-08-14T23:09:12.7944245Z     >>> # With namedshape (tuple of tuples)
2025-08-14T23:09:12.7944383Z     >>> input = torch.randn(2, 50, names=("N", "features"))
2025-08-14T23:09:12.7944549Z     >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5)))
2025-08-14T23:09:12.7945648Z     >>> output = unflatten(input)
2025-08-14T23:09:12.7945757Z     >>> output.size()
2025-08-14T23:09:12.7945854Z     torch.Size([2, 2, 5, 5])
2025-08-14T23:09:12.7945918Z 
2025-08-14T23:09:12.7946007Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.7946130Z 
2025-08-14T23:09:12.8056492Z 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.
2025-08-14T23:09:12.8056991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8057178Z Register a parametrization to a tensor in a module.
2025-08-14T23:09:12.8057271Z 
2025-08-14T23:09:12.8057371Z     Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,
2025-08-14T23:09:12.8057600Z     the module will return the parametrized version ``parametrization(module.weight)``.
2025-08-14T23:09:12.8059895Z     If the original tensor requires a gradient, the backward pass will differentiate
2025-08-14T23:09:12.8060144Z     through :attr:`parametrization`, and the optimizer will update the tensor accordingly.
2025-08-14T23:09:12.8060282Z 
2025-08-14T23:09:12.8060391Z     The first time that a module registers a parametrization, this function will add an attribute
2025-08-14T23:09:12.8060617Z     ``parametrizations`` to the module of type :class:`~ParametrizationList`.
2025-08-14T23:09:12.8060737Z 
2025-08-14T23:09:12.8060830Z     The list of parametrizations on the tensor ``weight`` will be accessible under
2025-08-14T23:09:12.8061009Z     ``module.parametrizations.weight``.
2025-08-14T23:09:12.8061095Z 
2025-08-14T23:09:12.8061151Z     The original tensor will be accessible under
2025-08-14T23:09:12.8061293Z     ``module.parametrizations.weight.original``.
2025-08-14T23:09:12.8061386Z 
2025-08-14T23:09:12.8061493Z     Parametrizations may be concatenated by registering several parametrizations
2025-08-14T23:09:12.8061661Z     on the same attribute.
2025-08-14T23:09:12.8061726Z 
2025-08-14T23:09:12.8061809Z     The training mode of a registered parametrization is updated on registration
2025-08-14T23:09:12.8061997Z     to match the training mode of the host module
2025-08-14T23:09:12.8062081Z 
2025-08-14T23:09:12.8062187Z     Parametrized parameters and buffers have an inbuilt caching system that can be activated
2025-08-14T23:09:12.8062374Z     using the context manager :func:`cached`.
2025-08-14T23:09:12.8062454Z 
2025-08-14T23:09:12.8062538Z     A :attr:`parametrization` may optionally implement a method with signature
2025-08-14T23:09:12.8062658Z 
2025-08-14T23:09:12.8064448Z     .. code-block:: python
2025-08-14T23:09:12.8064519Z 
2025-08-14T23:09:12.8064601Z         def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]
2025-08-14T23:09:12.8064713Z 
2025-08-14T23:09:12.8064809Z     This method is called on the unparametrized tensor when the first parametrization
2025-08-14T23:09:12.8065016Z     is registered to compute the initial value of the original tensor.
2025-08-14T23:09:12.8065233Z     If this method is not implemented, the original tensor will be just the unparametrized tensor.
2025-08-14T23:09:12.8065375Z 
2025-08-14T23:09:12.8065486Z     If all the parametrizations registered on a tensor implement `right_inverse` it is possible
2025-08-14T23:09:12.8065726Z     to initialize a parametrized tensor by assigning to it, as shown in the example below.
2025-08-14T23:09:12.8065860Z 
2025-08-14T23:09:12.8065939Z     It is possible for the first parametrization to depend on several inputs.
2025-08-14T23:09:12.8066136Z     This may be implemented returning a tuple of tensors from ``right_inverse``
2025-08-14T23:09:12.8066335Z     (see the example implementation of a ``RankOne`` parametrization below).
2025-08-14T23:09:12.8066454Z 
2025-08-14T23:09:12.8066572Z     In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``
2025-08-14T23:09:12.8067732Z     with names ``original0``, ``original1``,...
2025-08-14T23:09:12.8067819Z 
2025-08-14T23:09:12.8067855Z     .. note::
2025-08-14T23:09:12.8067908Z 
2025-08-14T23:09:12.8068005Z         If unsafe=False (default) both the forward and right_inverse methods will be called
2025-08-14T23:09:12.8068194Z         once to perform a number of consistency checks.
2025-08-14T23:09:12.8068375Z         If unsafe=True, then right_inverse will be called if the tensor is not parametrized,
2025-08-14T23:09:12.8068549Z         and nothing will be called otherwise.
2025-08-14T23:09:12.8068628Z 
2025-08-14T23:09:12.8068662Z     .. note::
2025-08-14T23:09:12.8068712Z 
2025-08-14T23:09:12.8068787Z         In most situations, ``right_inverse`` will be a function such that
2025-08-14T23:09:12.8068941Z         ``forward(right_inverse(X)) == X`` (see
2025-08-14T23:09:12.8069121Z         `right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).
2025-08-14T23:09:12.8069379Z         Sometimes, when the parametrization is not surjective, it may be reasonable
2025-08-14T23:09:12.8070471Z         to relax this.
2025-08-14T23:09:12.8070533Z 
2025-08-14T23:09:12.8070571Z     .. warning::
2025-08-14T23:09:12.8070624Z 
2025-08-14T23:09:12.8070720Z         If a parametrization depends on several inputs, :func:`~register_parametrization`
2025-08-14T23:09:12.8070938Z         will register a number of new parameters. If such parametrization is registered
2025-08-14T23:09:12.8071159Z         after the optimizer is created, these new parameters will need to be added manually
2025-08-14T23:09:12.8071360Z         to the optimizer. See :meth:`torch.Optimizer.add_param_group`.
2025-08-14T23:09:12.8071464Z 
2025-08-14T23:09:12.8071498Z     Args:
2025-08-14T23:09:12.8071619Z         module (nn.Module): module on which to register the parametrization
2025-08-14T23:09:12.8071805Z         tensor_name (str): name of the parameter or buffer on which to register
2025-08-14T23:09:12.8071974Z             the parametrization
2025-08-14T23:09:12.8072123Z         parametrization (nn.Module): the parametrization to register
2025-08-14T23:09:12.8073267Z     Keyword args:
2025-08-14T23:09:12.8073422Z         unsafe (bool): a boolean flag that denotes whether the parametrization
2025-08-14T23:09:12.8073603Z             may change the dtype and shape of the tensor. Default: `False`
2025-08-14T23:09:12.8073798Z             Warning: the parametrization is not checked for consistency upon registration.
2025-08-14T23:09:12.8073971Z             Enable this flag at your own risk.
2025-08-14T23:09:12.8074052Z 
2025-08-14T23:09:12.8074085Z     Raises:
2025-08-14T23:09:12.8074234Z         ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`
2025-08-14T23:09:12.8074370Z 
2025-08-14T23:09:12.8074406Z     Examples:
2025-08-14T23:09:12.8074513Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-08-14T23:09:12.8074639Z         >>> import torch
2025-08-14T23:09:12.8075657Z         >>> import torch.nn as nn
2025-08-14T23:09:12.8075789Z         >>> import torch.nn.utils.parametrize as P
2025-08-14T23:09:12.8075907Z         >>>
2025-08-14T23:09:12.8075995Z         >>> class Symmetric(nn.Module):
2025-08-14T23:09:12.8076114Z         >>>     def forward(self, X):
2025-08-14T23:09:12.8076253Z         >>>         return X.triu() + X.triu(1).T  # Return a symmetric matrix
2025-08-14T23:09:12.8076384Z         >>>
2025-08-14T23:09:12.8076472Z         >>>     def right_inverse(self, A):
2025-08-14T23:09:12.8076586Z         >>>         return A.triu()
2025-08-14T23:09:12.8076687Z         >>>
2025-08-14T23:09:12.8076769Z         >>> m = nn.Linear(5, 5)
2025-08-14T23:09:12.8077783Z         >>> P.register_parametrization(m, "weight", Symmetric())
2025-08-14T23:09:12.8077971Z         >>> print(torch.allclose(m.weight, m.weight.T))  # m.weight is now symmetric
2025-08-14T23:09:12.8078124Z         True
2025-08-14T23:09:12.8078212Z         >>> A = torch.rand(5, 5)
2025-08-14T23:09:12.8078324Z         >>> A = A + A.T  # A is now symmetric
2025-08-14T23:09:12.8078473Z         >>> m.weight = A  # Initialize the weight to be the symmetric matrix A
2025-08-14T23:09:12.8078631Z         >>> print(torch.allclose(m.weight, A))
2025-08-14T23:09:12.8078749Z         True
2025-08-14T23:09:12.8078797Z 
2025-08-14T23:09:12.8078839Z         >>> class RankOne(nn.Module):
2025-08-14T23:09:12.8078953Z         >>>     def forward(self, x, y):
2025-08-14T23:09:12.8079083Z         >>> # Form a rank 1 matrix multiplying two vectors
2025-08-14T23:09:12.8080131Z         >>>         return x.unsqueeze(-1) @ y.unsqueeze(-2)
2025-08-14T23:09:12.8080250Z         >>>
2025-08-14T23:09:12.8080339Z         >>>     def right_inverse(self, Z):
2025-08-14T23:09:12.8080463Z         >>> # Project Z onto the rank 1 matrices
2025-08-14T23:09:12.8080602Z         >>>         U, S, Vh = torch.linalg.svd(Z, full_matrices=False)
2025-08-14T23:09:12.8080737Z         >>> # Return rescaled singular vectors
2025-08-14T23:09:12.8080864Z         >>>         s0_sqrt = S[0].sqrt().unsqueeze(-1)
2025-08-14T23:09:12.8081048Z         >>>         return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt
2025-08-14T23:09:12.8081168Z         >>>
2025-08-14T23:09:12.8081272Z         >>> linear_rank_one = P.register_parametrization(
2025-08-14T23:09:12.8081411Z         ...     nn.Linear(4, 4), "weight", RankOne()
2025-08-14T23:09:12.8082421Z         ... )
2025-08-14T23:09:12.8082547Z         >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())
2025-08-14T23:09:12.8082685Z         1
2025-08-14T23:09:12.8082732Z 
2025-08-14T23:09:12.8082763Z     
2025-08-14T23:09:12.8082979Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8083103Z 
2025-08-14T23:09:12.8106505Z 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.
2025-08-14T23:09:12.8107029Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8107237Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-08-14T23:09:12.8107351Z 
2025-08-14T23:09:12.8107447Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-08-14T23:09:12.8107702Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-08-14T23:09:12.8109888Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-08-14T23:09:12.8110271Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-08-14T23:09:12.8110440Z 
2025-08-14T23:09:12.8110476Z     .. note::
2025-08-14T23:09:12.8110609Z         Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-08-14T23:09:12.8110805Z         than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-08-14T23:09:12.8111002Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-08-14T23:09:12.8111201Z         necessarily benefit from conversion to specified ``memory_format``.
2025-08-14T23:09:12.8111391Z         One place we are confident in is that NHWC(channels_last) conversion for
2025-08-14T23:09:12.8111580Z         convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-08-14T23:09:12.8111762Z         even in cases where we have to apply permutation to input tensors.
2025-08-14T23:09:12.8111869Z 
2025-08-14T23:09:12.8113297Z         Hence our strategy here is to convert only the weight of convolution to
2025-08-14T23:09:12.8113468Z         channels_last. This ensures that;
2025-08-14T23:09:12.8113621Z         1. Fast convolution kernels will be used, the benefit of which could
2025-08-14T23:09:12.8113812Z         outweigh overhead of permutation (if input is not in the same format).
2025-08-14T23:09:12.8114008Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-08-14T23:09:12.8114167Z         from memory_format conversion.
2025-08-14T23:09:12.8114250Z 
2025-08-14T23:09:12.8114334Z         The optimal case is that, layers between convolution layers are channels
2025-08-14T23:09:12.8114528Z         last compatible. Input tensor would be permuted to channels last when it
2025-08-14T23:09:12.8114724Z         encounters the first convolution layer and stay in that memory format.
2025-08-14T23:09:12.8114920Z         Hence following convolutions will not need to permute its input tensor.
2025-08-14T23:09:12.8115038Z 
2025-08-14T23:09:12.8115113Z         In case where a channels last incompatible layer is between convolution
2025-08-14T23:09:12.8116286Z         layers, we need to permute the input tensor back to contiguous format
2025-08-14T23:09:12.8116476Z         for that layer. The input tensor will go through the remaining layers in
2025-08-14T23:09:12.8116665Z         contiguous format and be permuted to channels last when it encounters
2025-08-14T23:09:12.8116856Z         another convolution layer. There's no point in propagating that
2025-08-14T23:09:12.8117094Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-08-14T23:09:12.8117244Z         ``memory_format``.
2025-08-14T23:09:12.8117309Z 
2025-08-14T23:09:12.8117390Z         This claim might change when PyTorch supports fusion of permutation, as
2025-08-14T23:09:12.8117580Z         there might have been a better spot to fuse the permutation other than
2025-08-14T23:09:12.8117736Z         immediately before a convolution.
2025-08-14T23:09:12.8117814Z 
2025-08-14T23:09:12.8117848Z     Args:
2025-08-14T23:09:12.8117968Z         module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-08-14T23:09:12.8119055Z                             ``nn.Module``
2025-08-14T23:09:12.8119190Z         memory_format: user specified ``memory_format``,
2025-08-14T23:09:12.8119346Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-08-14T23:09:12.8119446Z 
2025-08-14T23:09:12.8119481Z     Returns:
2025-08-14T23:09:12.8119609Z         The original module with updated ``nn.Conv2d``
2025-08-14T23:09:12.8119700Z 
2025-08-14T23:09:12.8119733Z     Example:
2025-08-14T23:09:12.8119835Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:12.8119998Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-08-14T23:09:12.8120133Z         >>> input = torch.randint(
2025-08-14T23:09:12.8120262Z         ...     1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda"
2025-08-14T23:09:12.8120386Z         ... )
2025-08-14T23:09:12.8121408Z         >>> model = nn.Sequential(
2025-08-14T23:09:12.8121525Z         >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-08-14T23:09:12.8121645Z         >>> # This is identical to:
2025-08-14T23:09:12.8121803Z         >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-08-14T23:09:12.8121989Z         >>> model = nn.utils.convert_conv2d_weight_memory_format(
2025-08-14T23:09:12.8122128Z         ...     model, torch.channels_last
2025-08-14T23:09:12.8122238Z         ... )
2025-08-14T23:09:12.8122328Z         >>> out = model(input)
2025-08-14T23:09:12.8122425Z     
2025-08-14T23:09:12.8122558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8122684Z 
2025-08-14T23:09:12.8122994Z 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.
2025-08-14T23:09:12.8124291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8124495Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-08-14T23:09:12.8124699Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-08-14T23:09:12.8124923Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-08-14T23:09:12.8125140Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2025-08-14T23:09:12.8125376Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-08-14T23:09:12.8125518Z 
2025-08-14T23:09:12.8125552Z     .. note::
2025-08-14T23:09:12.8125691Z         Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-08-14T23:09:12.8125975Z         than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-08-14T23:09:12.8126164Z         layer with 4d weight will be affected by ``model.to``, which does not
2025-08-14T23:09:12.8126350Z         necessarily benefit from conversion to specified ``memory_format``.
2025-08-14T23:09:12.8127488Z         One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-08-14T23:09:12.8127681Z         convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-08-14T23:09:12.8127863Z         even in cases where we have to apply permutation to input tensors.
2025-08-14T23:09:12.8127970Z 
2025-08-14T23:09:12.8128050Z         Hence our strategy here is to convert only the weight of convolution to
2025-08-14T23:09:12.8128258Z         channels_last_3d. This ensures that;
2025-08-14T23:09:12.8128412Z         1. Fast convolution kernels will be used, the benefit of which could
2025-08-14T23:09:12.8128603Z         outweigh overhead of permutation (if input is not in the same format).
2025-08-14T23:09:12.8128800Z         2. No unnecessary permutations are applied on layers that do not benefit
2025-08-14T23:09:12.8128960Z         from memory_format conversion.
2025-08-14T23:09:12.8129051Z 
2025-08-14T23:09:12.8129176Z         The optimal case is that, layers between convolution layers are channels
2025-08-14T23:09:12.8130975Z         last compatible. Input tensor would be permuted to channels last when it
2025-08-14T23:09:12.8131184Z         encounters the first convolution layer and stay in that memory format.
2025-08-14T23:09:12.8131385Z         Hence following convolutions will not need to permute its input tensor.
2025-08-14T23:09:12.8131546Z 
2025-08-14T23:09:12.8131624Z         In case where a channels last incompatible layer is between convolution
2025-08-14T23:09:12.8131812Z         layers, we need to permute the input tensor back to contiguous format
2025-08-14T23:09:12.8132023Z         for that layer. The input tensor will go through the remaining layers in
2025-08-14T23:09:12.8132213Z         contiguous format and be permuted to channels last when it encounters
2025-08-14T23:09:12.8132401Z         another convolution layer. There's no point in propagating that
2025-08-14T23:09:12.8132583Z         permutation to an earlier layer, as most layers are quite agnostic to
2025-08-14T23:09:12.8132733Z         ``memory_format``.
2025-08-14T23:09:12.8132799Z 
2025-08-14T23:09:12.8132878Z         This claim might change when PyTorch supports fusion of permutation, as
2025-08-14T23:09:12.8134414Z         there might have been a better spot to fuse the permutation other than
2025-08-14T23:09:12.8134576Z         immediately before a convolution.
2025-08-14T23:09:12.8134660Z 
2025-08-14T23:09:12.8134693Z     Args:
2025-08-14T23:09:12.8134816Z         module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-08-14T23:09:12.8134970Z                             ``nn.Module``
2025-08-14T23:09:12.8135106Z         memory_format: user specified ``memory_format``,
2025-08-14T23:09:12.8135259Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-08-14T23:09:12.8135358Z 
2025-08-14T23:09:12.8135393Z     Returns:
2025-08-14T23:09:12.8135496Z         The original module with updated ``nn.Conv3d``
2025-08-14T23:09:12.8135584Z 
2025-08-14T23:09:12.8135618Z     Example:
2025-08-14T23:09:12.8135719Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:12.8135865Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-08-14T23:09:12.8135999Z         >>> input = torch.randint(
2025-08-14T23:09:12.8136127Z         ...     1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda"
2025-08-14T23:09:12.8136250Z         ... )
2025-08-14T23:09:12.8136341Z         >>> model = nn.Sequential(
2025-08-14T23:09:12.8136457Z         >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-08-14T23:09:12.8137539Z         >>> # This is identical to:
2025-08-14T23:09:12.8137704Z         >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-08-14T23:09:12.8137889Z         >>> model = nn.utils.convert_conv3d_weight_memory_format(
2025-08-14T23:09:12.8138030Z         ...     model, torch.channels_last_3d
2025-08-14T23:09:12.8138138Z         ... )
2025-08-14T23:09:12.8138224Z         >>> out = model(input)
2025-08-14T23:09:12.8138321Z     
2025-08-14T23:09:12.8138451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8138576Z 
2025-08-14T23:09:12.8138763Z 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.
2025-08-14T23:09:12.8139061Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8139284Z Pad a packed batch of variable length sequences.
2025-08-14T23:09:12.8139371Z 
2025-08-14T23:09:12.8140368Z     It is an inverse operation to :func:`pack_padded_sequence`.
2025-08-14T23:09:12.8140472Z 
2025-08-14T23:09:12.8140574Z     The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``)
2025-08-14T23:09:12.8140793Z     or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest
2025-08-14T23:09:12.8140953Z     sequence and ``B`` is the batch size.
2025-08-14T23:09:12.8141032Z 
2025-08-14T23:09:12.8141065Z     Example:
2025-08-14T23:09:12.8141199Z         >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
2025-08-14T23:09:12.8141377Z         >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]])
2025-08-14T23:09:12.8141503Z         >>> lens = [2, 1, 3]
2025-08-14T23:09:12.8141637Z         >>> packed = pack_padded_sequence(
2025-08-14T23:09:12.8141777Z         ...     seq, lens, batch_first=True, enforce_sorted=False
2025-08-14T23:09:12.8141900Z         ... )
2025-08-14T23:09:12.8143007Z         >>> packed
2025-08-14T23:09:12.8143172Z         PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]),
2025-08-14T23:09:12.8143368Z                        sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0]))
2025-08-14T23:09:12.8143570Z         >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True)
2025-08-14T23:09:12.8143732Z         >>> seq_unpacked
2025-08-14T23:09:12.8143833Z         tensor([[1, 2, 0],
2025-08-14T23:09:12.8143928Z                 [3, 0, 0],
2025-08-14T23:09:12.8144023Z                 [4, 5, 6]])
2025-08-14T23:09:12.8144123Z         >>> lens_unpacked
2025-08-14T23:09:12.8144218Z         tensor([2, 1, 3])
2025-08-14T23:09:12.8144279Z 
2025-08-14T23:09:12.8144312Z     .. note::
2025-08-14T23:09:12.8145291Z         :attr:`total_length` is useful to implement the
2025-08-14T23:09:12.8145380Z         ``pack sequence -> recurrent network -> unpack sequence`` pattern in a
2025-08-14T23:09:12.8145461Z         :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.
2025-08-14T23:09:12.8145550Z         See :ref:`this FAQ section <pack-rnn-unpack-with-data-parallelism>` for
2025-08-14T23:09:12.8145584Z         details.
2025-08-14T23:09:12.8145587Z 
2025-08-14T23:09:12.8145619Z     Args:
2025-08-14T23:09:12.8145671Z         sequence (PackedSequence): batch to pad
2025-08-14T23:09:12.8145759Z         batch_first (bool, optional): if ``True``, the output will be in ``B x T x *``
2025-08-14T23:09:12.8145804Z             format, ``T x B x *`` otherwise.
2025-08-14T23:09:12.8145876Z         padding_value (float, optional): values for padded elements.
2025-08-14T23:09:12.8145959Z         total_length (int, optional): if not ``None``, the output will be padded to
2025-08-14T23:09:12.8146045Z             have length :attr:`total_length`. This method will throw :class:`ValueError`
2025-08-14T23:09:12.8146117Z             if :attr:`total_length` is less than the max sequence length in
2025-08-14T23:09:12.8146158Z             :attr:`sequence`.
2025-08-14T23:09:12.8146161Z 
2025-08-14T23:09:12.8146194Z     Returns:
2025-08-14T23:09:12.8146265Z         Tuple of Tensor containing the padded sequence, and a Tensor
2025-08-14T23:09:12.8146337Z         containing the list of lengths of each sequence in the batch.
2025-08-14T23:09:12.8146417Z         Batch elements will be re-ordered as they were ordered originally when
2025-08-14T23:09:12.8146492Z         the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``.
2025-08-14T23:09:12.8146525Z     
2025-08-14T23:09:12.8146613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8146615Z 
2025-08-14T23:09:12.8189492Z 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.
2025-08-14T23:09:12.8190381Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8190591Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-08-14T23:09:12.8190622Z 
2025-08-14T23:09:12.8190761Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-08-14T23:09:12.8190891Z     by removing the specified ``amount`` of (currently unpruned) channels
2025-08-14T23:09:12.8190995Z     along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-08-14T23:09:12.8191112Z     Modifies module in place (and also return the modified module)
2025-08-14T23:09:12.8191168Z     by:
2025-08-14T23:09:12.8191173Z 
2025-08-14T23:09:12.8191298Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:12.8191420Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:12.8191547Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:12.8191735Z        original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:12.8191831Z        ``name+'_orig'``.
2025-08-14T23:09:12.8191837Z 
2025-08-14T23:09:12.8191943Z     Args:
2025-08-14T23:09:12.8192048Z         module (nn.Module): module containing the tensor to prune
2025-08-14T23:09:12.8192153Z         name (str): parameter name within ``module`` on which pruning
2025-08-14T23:09:12.8192213Z                 will act.
2025-08-14T23:09:12.8192317Z         amount (int or float): quantity of parameters to prune.
2025-08-14T23:09:12.8195589Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-08-14T23:09:12.8195717Z             fraction of parameters to prune. If ``int``, it represents the
2025-08-14T23:09:12.8195800Z             absolute number of parameters to prune.
2025-08-14T23:09:12.8195941Z         n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-08-14T23:09:12.8196059Z             entries for argument ``p`` in :func:`torch.norm`.
2025-08-14T23:09:12.8196167Z         dim (int): index of the dim along which we define channels to prune.
2025-08-14T23:09:12.8196287Z         importance_scores (torch.Tensor): tensor of importance scores (of same
2025-08-14T23:09:12.8196380Z             shape as module parameter) used to compute mask for pruning.
2025-08-14T23:09:12.8196501Z             The values in this tensor indicate the importance of the corresponding
2025-08-14T23:09:12.8196567Z             elements in the parameter being pruned.
2025-08-14T23:09:12.8196679Z             If unspecified or None, the module parameter will be used in its place.
2025-08-14T23:09:12.8196682Z 
2025-08-14T23:09:12.8196730Z     Returns:
2025-08-14T23:09:12.8196837Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-08-14T23:09:12.8196840Z 
2025-08-14T23:09:12.8196887Z     Examples:
2025-08-14T23:09:12.8196958Z         >>> from torch.nn.utils import prune
2025-08-14T23:09:12.8197021Z         >>> m = prune.ln_structured(
2025-08-14T23:09:12.8197121Z         ...     nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf")
2025-08-14T23:09:12.8197162Z         ... )
2025-08-14T23:09:12.8197204Z     
2025-08-14T23:09:12.8197332Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8197334Z 
2025-08-14T23:09:12.8197590Z 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.
2025-08-14T23:09:12.8197708Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8197711Z 
2025-08-14T23:09:12.8197919Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-08-14T23:09:12.8197925Z 
2025-08-14T23:09:12.8197996Z Modifies modules in place by:
2025-08-14T23:09:12.8197998Z 
2025-08-14T23:09:12.8198103Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:12.8198304Z    binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:12.8198410Z 2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:12.8198514Z    original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:12.8198569Z    ``name+'_orig'``.
2025-08-14T23:09:12.8198571Z 
2025-08-14T23:09:12.8198617Z Args:
2025-08-14T23:09:12.8198723Z     parameters (Iterable of (module, name) tuples): parameters of
2025-08-14T23:09:12.8198819Z         the model to prune in a global fashion, i.e. by aggregating all
2025-08-14T23:09:12.8200444Z         weights prior to deciding which ones to prune. module must be of
2025-08-14T23:09:12.8200526Z         type :class:`nn.Module`, and name must be a string.
2025-08-14T23:09:12.8200635Z     pruning_method (function): a valid pruning function from this module,
2025-08-14T23:09:12.8200724Z         or a custom one implemented by the user that satisfies the
2025-08-14T23:09:12.8200875Z         implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-08-14T23:09:12.8200986Z     importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-08-14T23:09:12.8201123Z         the corresponding parameter's importance scores tensor. The tensor
2025-08-14T23:09:12.8201225Z         should be the same shape as the parameter, and is used for computing
2025-08-14T23:09:12.8201279Z         mask for pruning.
2025-08-14T23:09:12.8201377Z         If unspecified or None, the parameter will be used in place of its
2025-08-14T23:09:12.8201431Z         importance scores.
2025-08-14T23:09:12.8201493Z     kwargs: other keyword arguments such as:
2025-08-14T23:09:12.8201590Z         amount (int or float): quantity of parameters to prune across the
2025-08-14T23:09:12.8201646Z         specified parameters.
2025-08-14T23:09:12.8201738Z         If ``float``, should be between 0.0 and 1.0 and represent the
2025-08-14T23:09:12.8201838Z         fraction of parameters to prune. If ``int``, it represents the
2025-08-14T23:09:12.8201904Z         absolute number of parameters to prune.
2025-08-14T23:09:12.8201907Z 
2025-08-14T23:09:12.8201953Z Raises:
2025-08-14T23:09:12.8202037Z     TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-08-14T23:09:12.8202042Z 
2025-08-14T23:09:12.8202084Z Note:
2025-08-14T23:09:12.8202188Z     Since global structured pruning doesn't make much sense unless the
2025-08-14T23:09:12.8203668Z     norm is normalized by the size of the parameter, we now limit the
2025-08-14T23:09:12.8203748Z     scope of global pruning to unstructured methods.
2025-08-14T23:09:12.8203751Z 
2025-08-14T23:09:12.8203799Z Examples:
2025-08-14T23:09:12.8203861Z     >>> from torch.nn.utils import prune
2025-08-14T23:09:12.8203931Z     >>> from collections import OrderedDict
2025-08-14T23:09:12.8203988Z     >>> net = nn.Sequential(
2025-08-14T23:09:12.8204039Z     ...     OrderedDict(
2025-08-14T23:09:12.8204080Z     ...         [
2025-08-14T23:09:12.8204148Z     ...             ("first", nn.Linear(10, 4)),
2025-08-14T23:09:12.8204207Z     ...             ("second", nn.Linear(4, 1)),
2025-08-14T23:09:12.8204254Z     ...         ]
2025-08-14T23:09:12.8204300Z     ...     )
2025-08-14T23:09:12.8204347Z     ... )
2025-08-14T23:09:12.8204403Z     >>> parameters_to_prune = (
2025-08-14T23:09:12.8204459Z     ...     (net.first, "weight"),
2025-08-14T23:09:12.8204513Z     ...     (net.second, "weight"),
2025-08-14T23:09:12.8204558Z     ... )
2025-08-14T23:09:12.8204614Z     >>> prune.global_unstructured(
2025-08-14T23:09:12.8204667Z     ...     parameters_to_prune,
2025-08-14T23:09:12.8204734Z     ...     pruning_method=prune.L1Unstructured,
2025-08-14T23:09:12.8204780Z     ...     amount=10,
2025-08-14T23:09:12.8204825Z     ... )
2025-08-14T23:09:12.8206189Z     >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-08-14T23:09:12.8206227Z     tensor(10)
2025-08-14T23:09:12.8206231Z 
2025-08-14T23:09:12.8206234Z 
2025-08-14T23:09:12.8206331Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8206364Z 
2025-08-14T23:09:12.8206575Z 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.
2025-08-14T23:09:12.8206675Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:12.8206821Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-08-14T23:09:12.8206823Z 
2025-08-14T23:09:12.8206906Z     Modifies module in place (and also return the modified module) by:
2025-08-14T23:09:12.8206908Z 
2025-08-14T23:09:12.8206986Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:12.8207069Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:12.8207146Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:12.8207263Z        original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:12.8207302Z        ``name+'_orig'``.
2025-08-14T23:09:12.8207304Z 
2025-08-14T23:09:12.8207341Z     Args:
2025-08-14T23:09:12.8207434Z         module (nn.Module): module containing the tensor to prune
2025-08-14T23:09:12.8207504Z         name (str): parameter name within ``module`` on which pruning
2025-08-14T23:09:12.8207543Z             will act.
2025-08-14T23:09:12.8207612Z         mask (Tensor): binary mask to be applied to the parameter.
2025-08-14T23:09:12.8207615Z 
2025-08-14T23:09:12.8207651Z     Returns:
2025-08-14T23:09:12.8207735Z         module (nn.Module): modified (i.e. pruned) version of the input module
2025-08-14T23:09:12.8207737Z 
2025-08-14T23:09:12.8207775Z     Examples:
2025-08-14T23:09:12.8207824Z         >>> from torch.nn.utils import prune
2025-08-14T23:09:12.8208841Z         >>> m = prune.custom_from_mask(
2025-08-14T23:09:12.8208916Z         ...     nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0])
2025-08-14T23:09:12.8208956Z         ... )
2025-08-14T23:09:12.8208998Z         >>> print(m.bias_mask)
2025-08-14T23:09:12.8209041Z         tensor([0., 1., 0.])
2025-08-14T23:09:12.8209043Z 
2025-08-14T23:09:12.8209079Z     
2025-08-14T23:09:12.8209175Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:12.8209177Z 
2025-08-14T23:09:12.8340660Z gathering tests
2025-08-14T23:09:12.8349109Z running 732 test(s)
2025-08-14T23:09:12.8352237Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file
2025-08-14T23:09:12.8359564Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0
2025-08-14T23:09:12.8359797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file
2025-08-14T23:09:12.8361257Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0
2025-08-14T23:09:12.8361569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file
2025-08-14T23:09:12.8362694Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0
2025-08-14T23:09:12.8363235Z * 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
2025-08-14T23:09:12.8363571Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0
2025-08-14T23:09:12.8363941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file
2025-08-14T23:09:12.8364247Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0
2025-08-14T23:09:12.8364997Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file
2025-08-14T23:09:12.8369347Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2025-08-14T23:09:12.8369698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2567 <- wrt source file
2025-08-14T23:09:12.8369979Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0
2025-08-14T23:09:12.8370349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2840 <- wrt source file
2025-08-14T23:09:12.8370674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0
2025-08-14T23:09:12.8371108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file
2025-08-14T23:09:12.8371445Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0
2025-08-14T23:09:12.8371745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file
2025-08-14T23:09:12.8371991Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0
2025-08-14T23:09:12.8372323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file
2025-08-14T23:09:12.8643222Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0
2025-08-14T23:09:12.8644003Z * 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
2025-08-14T23:09:12.8644643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0
2025-08-14T23:09:12.8645381Z * 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
2025-08-14T23:09:12.8646005Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0
2025-08-14T23:09:12.8646295Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file
2025-08-14T23:09:12.8646527Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0
2025-08-14T23:09:12.8646778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 473 <- wrt source file
2025-08-14T23:09:12.8646990Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0
2025-08-14T23:09:12.8647239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 533 <- wrt source file
2025-08-14T23:09:12.8649636Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0
2025-08-14T23:09:12.8649947Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file
2025-08-14T23:09:12.8671111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0
2025-08-14T23:09:12.8673161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file
2025-08-14T23:09:12.8677302Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0
2025-08-14T23:09:12.8677752Z * 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
2025-08-14T23:09:12.8680957Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0
2025-08-14T23:09:12.8681348Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file
2025-08-14T23:09:12.8730277Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0
2025-08-14T23:09:12.8730621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file
2025-08-14T23:09:12.8731490Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0
2025-08-14T23:09:12.8731811Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file
2025-08-14T23:09:12.8734314Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0
2025-08-14T23:09:12.8734680Z * 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
2025-08-14T23:09:12.8736723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2025-08-14T23:09:12.8737136Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file
2025-08-14T23:09:12.8739724Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0
2025-08-14T23:09:12.8741116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file
2025-08-14T23:09:12.8741567Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0
2025-08-14T23:09:12.8747598Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 193 <- wrt source file
2025-08-14T23:09:12.8747973Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0
2025-08-14T23:09:12.8748281Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 307 <- wrt source file
2025-08-14T23:09:12.8754156Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0
2025-08-14T23:09:12.8754526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1041 <- wrt source file
2025-08-14T23:09:12.8758792Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0
2025-08-14T23:09:12.8759062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1316 <- wrt source file
2025-08-14T23:09:12.8764602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0
2025-08-14T23:09:12.8764887Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1400 <- wrt source file
2025-08-14T23:09:12.8768146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0
2025-08-14T23:09:12.8768694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1434 <- wrt source file
2025-08-14T23:09:12.8772199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0
2025-08-14T23:09:12.8772453Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1490 <- wrt source file
2025-08-14T23:09:12.8777831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0
2025-08-14T23:09:12.8778077Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1531 <- wrt source file
2025-08-14T23:09:12.8783401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0
2025-08-14T23:09:12.8783729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1569 <- wrt source file
2025-08-14T23:09:12.8789078Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0
2025-08-14T23:09:12.8789371Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1609 <- wrt source file
2025-08-14T23:09:12.8798059Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0
2025-08-14T23:09:12.8798301Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1784 <- wrt source file
2025-08-14T23:09:12.8806881Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0
2025-08-14T23:09:12.8807124Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1952 <- wrt source file
2025-08-14T23:09:12.8816166Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0
2025-08-14T23:09:12.8816405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2052 <- wrt source file
2025-08-14T23:09:12.8816596Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0
2025-08-14T23:09:12.8816821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2153 <- wrt source file
2025-08-14T23:09:12.8817224Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0
2025-08-14T23:09:12.8817493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file
2025-08-14T23:09:12.8817711Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0
2025-08-14T23:09:12.8817969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file
2025-08-14T23:09:12.8818178Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0
2025-08-14T23:09:12.8818417Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file
2025-08-14T23:09:12.8818609Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0
2025-08-14T23:09:12.8818859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file
2025-08-14T23:09:12.8820028Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0
2025-08-14T23:09:12.8820386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file
2025-08-14T23:09:12.8820572Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0
2025-08-14T23:09:12.8820806Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file
2025-08-14T23:09:12.8826931Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0
2025-08-14T23:09:12.8827226Z * 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
2025-08-14T23:09:12.8828682Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0
2025-08-14T23:09:12.8828998Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file
2025-08-14T23:09:12.8830623Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0
2025-08-14T23:09:12.8830891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 512 <- wrt source file
2025-08-14T23:09:12.8837123Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0
2025-08-14T23:09:12.8837346Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 618 <- wrt source file
2025-08-14T23:09:12.8843796Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0
2025-08-14T23:09:12.8844027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file
2025-08-14T23:09:12.8844463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0
2025-08-14T23:09:12.8844702Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file
2025-08-14T23:09:12.8844900Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0
2025-08-14T23:09:12.8845144Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file
2025-08-14T23:09:12.8882188Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0
2025-08-14T23:09:12.8882419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file
2025-08-14T23:09:12.8951599Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0
2025-08-14T23:09:12.8951848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file
2025-08-14T23:09:12.8955311Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0
2025-08-14T23:09:12.8955555Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 422 <- wrt source file
2025-08-14T23:09:12.8975926Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0
2025-08-14T23:09:12.8976196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1577 <- wrt source file
2025-08-14T23:09:12.8976444Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0
2025-08-14T23:09:12.8976683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1712 <- wrt source file
2025-08-14T23:09:12.8976884Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0
2025-08-14T23:09:12.8977136Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1960 <- wrt source file
2025-08-14T23:09:12.8988748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2025-08-14T23:09:12.8988986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1979 <- wrt source file
2025-08-14T23:09:12.8992314Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0
2025-08-14T23:09:12.8992584Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file
2025-08-14T23:09:12.8992829Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0
2025-08-14T23:09:12.8993131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file
2025-08-14T23:09:12.8993328Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0
2025-08-14T23:09:12.8993591Z * 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
2025-08-14T23:09:12.8998491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0
2025-08-14T23:09:12.8998742Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file
2025-08-14T23:09:12.8998941Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py::_compile_kernel:0
2025-08-14T23:09:12.8999157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 345 <- wrt source file
2025-08-14T23:09:12.8999342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0
2025-08-14T23:09:12.9001437Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 415 <- wrt source file
2025-08-14T23:09:12.9001649Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0
2025-08-14T23:09:12.9001893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 572 <- wrt source file
2025-08-14T23:09:12.9002101Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0
2025-08-14T23:09:12.9002328Z * 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
2025-08-14T23:09:12.9192805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2025-08-14T23:09:12.9284283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file
2025-08-14T23:09:12.9284615Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0
2025-08-14T23:09:12.9285106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file
2025-08-14T23:09:12.9312268Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0
2025-08-14T23:09:12.9312566Z * 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
2025-08-14T23:09:12.9327401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0
2025-08-14T23:09:12.9327701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file
2025-08-14T23:09:12.9335184Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0
2025-08-14T23:09:12.9335479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 125 <- wrt source file
2025-08-14T23:09:12.9335763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0
2025-08-14T23:09:12.9336027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 181 <- wrt source file
2025-08-14T23:09:13.5209201Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0
2025-08-14T23:09:13.5210167Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 411 <- wrt source file
2025-08-14T23:09:13.5211028Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0
2025-08-14T23:09:13.5211840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 443 <- wrt source file
2025-08-14T23:09:13.5212666Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0
2025-08-14T23:09:13.5213634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 464 <- wrt source file
2025-08-14T23:09:13.5214492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0
2025-08-14T23:09:13.5215295Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0, line 482 <- wrt source file
2025-08-14T23:09:13.5216077Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0
2025-08-14T23:09:13.5216749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 497 <- wrt source file
2025-08-14T23:09:13.5217459Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0
2025-08-14T23:09:13.5218148Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 512 <- wrt source file
2025-08-14T23:09:13.5218845Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0
2025-08-14T23:09:13.5219493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file
2025-08-14T23:09:13.5220118Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0
2025-08-14T23:09:13.5221220Z * 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
2025-08-14T23:09:13.5221943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2025-08-14T23:09:13.5222634Z * 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
2025-08-14T23:09:13.5229394Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0
2025-08-14T23:09:13.5229906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file
2025-08-14T23:09:13.5230500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0
2025-08-14T23:09:13.5231018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file
2025-08-14T23:09:13.5231618Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2025-08-14T23:09:13.5232165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file
2025-08-14T23:09:13.6484532Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0
2025-08-14T23:09:13.6485898Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file
2025-08-14T23:09:13.6487102Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0
2025-08-14T23:09:13.6488271Z * 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
2025-08-14T23:09:13.6495147Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0
2025-08-14T23:09:13.6496266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file
2025-08-14T23:09:13.6496783Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0
2025-08-14T23:09:13.6497292Z * 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
2025-08-14T23:09:13.6665841Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2025-08-14T23:09:13.6666426Z * 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
2025-08-14T23:09:13.6667010Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2025-08-14T23:09:13.6667525Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file
2025-08-14T23:09:13.6685360Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2025-08-14T23:09:13.6685980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file
2025-08-14T23:09:13.6715884Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2025-08-14T23:09:13.6716532Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file
2025-08-14T23:09:13.7124607Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2025-08-14T23:09:13.7125923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file
2025-08-14T23:09:13.7152036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2025-08-14T23:09:13.7152485Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file
2025-08-14T23:09:13.7159864Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2025-08-14T23:09:13.7160323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file
2025-08-14T23:09:13.7160821Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2025-08-14T23:09:13.7161259Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file
2025-08-14T23:09:13.7251798Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0
2025-08-14T23:09:13.7252246Z * 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
2025-08-14T23:09:13.7252717Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0
2025-08-14T23:09:13.7253273Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file
2025-08-14T23:09:13.7253696Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2025-08-14T23:09:13.7254164Z * 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
2025-08-14T23:09:13.7254677Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0
2025-08-14T23:09:13.7255178Z * 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
2025-08-14T23:09:13.7255688Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0
2025-08-14T23:09:13.7256151Z * 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
2025-08-14T23:09:13.7256586Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0
2025-08-14T23:09:13.7257026Z * 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
2025-08-14T23:09:13.7257485Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2025-08-14T23:09:13.7257935Z * 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
2025-08-14T23:09:13.7258484Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2025-08-14T23:09:13.7258943Z * 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
2025-08-14T23:09:13.7259434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2025-08-14T23:09:13.7259908Z * 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
2025-08-14T23:09:13.7261360Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2025-08-14T23:09:13.7261886Z * 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
2025-08-14T23:09:13.7263576Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2025-08-14T23:09:13.7264081Z * 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
2025-08-14T23:09:13.7267399Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2025-08-14T23:09:13.7267919Z * 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
2025-08-14T23:09:13.7268367Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2025-08-14T23:09:13.7268852Z * 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
2025-08-14T23:09:13.7269298Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2025-08-14T23:09:13.7269739Z * 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
2025-08-14T23:09:13.7270191Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2025-08-14T23:09:13.7270627Z * 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
2025-08-14T23:09:13.7271071Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2025-08-14T23:09:13.7271540Z * 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
2025-08-14T23:09:13.7273645Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2025-08-14T23:09:13.7274173Z * 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
2025-08-14T23:09:13.7274695Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2025-08-14T23:09:13.7275168Z * 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
2025-08-14T23:09:13.7275699Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0
2025-08-14T23:09:13.7276135Z * 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
2025-08-14T23:09:13.7276580Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2025-08-14T23:09:13.7277020Z * 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
2025-08-14T23:09:13.7277476Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0
2025-08-14T23:09:13.7277953Z * 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
2025-08-14T23:09:13.7278398Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0
2025-08-14T23:09:13.7278863Z * 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
2025-08-14T23:09:13.7279304Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0
2025-08-14T23:09:13.7279730Z * 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
2025-08-14T23:09:13.7280178Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0
2025-08-14T23:09:13.7281831Z * 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
2025-08-14T23:09:13.7282294Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0
2025-08-14T23:09:13.7282748Z * 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
2025-08-14T23:09:13.7283296Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0
2025-08-14T23:09:13.7283744Z * 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
2025-08-14T23:09:13.7284193Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0
2025-08-14T23:09:13.7284674Z * 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
2025-08-14T23:09:13.7285196Z * 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
2025-08-14T23:09:13.7285703Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file
2025-08-14T23:09:13.7286284Z * 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
2025-08-14T23:09:13.7286787Z * 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
2025-08-14T23:09:13.7288436Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0
2025-08-14T23:09:13.7288958Z * 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
2025-08-14T23:09:13.7289477Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0
2025-08-14T23:09:13.7289993Z * 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
2025-08-14T23:09:13.7290524Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0
2025-08-14T23:09:13.7291054Z * 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
2025-08-14T23:09:13.7291548Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2025-08-14T23:09:13.7292093Z * 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
2025-08-14T23:09:13.7292673Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2025-08-14T23:09:13.7293282Z * 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
2025-08-14T23:09:13.7295027Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2025-08-14T23:09:13.7295495Z * 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
2025-08-14T23:09:13.7334433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2025-08-14T23:09:13.7374584Z * 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
2025-08-14T23:09:13.7375493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2025-08-14T23:09:13.7376311Z * 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
2025-08-14T23:09:13.7377188Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2025-08-14T23:09:13.7378095Z * 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
2025-08-14T23:09:13.7379049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2025-08-14T23:09:13.7379964Z * 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
2025-08-14T23:09:13.7384803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2025-08-14T23:09:13.7385863Z * 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
2025-08-14T23:09:13.7386828Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2025-08-14T23:09:13.7387680Z * 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
2025-08-14T23:09:13.7388545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2025-08-14T23:09:13.7389324Z * 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
2025-08-14T23:09:13.7390055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2025-08-14T23:09:13.7390825Z * 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
2025-08-14T23:09:13.7391605Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2025-08-14T23:09:13.7392316Z * 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
2025-08-14T23:09:13.7403795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2025-08-14T23:09:13.7404501Z * 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
2025-08-14T23:09:13.7405195Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2025-08-14T23:09:13.7406023Z * 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
2025-08-14T23:09:13.7406711Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2025-08-14T23:09:13.7407385Z * 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
2025-08-14T23:09:13.7408071Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2025-08-14T23:09:13.7408735Z * 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
2025-08-14T23:09:13.7409420Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2025-08-14T23:09:13.7410114Z * 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
2025-08-14T23:09:13.7410822Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2025-08-14T23:09:13.7411517Z * 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
2025-08-14T23:09:13.7412230Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2025-08-14T23:09:13.7414913Z * 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
2025-08-14T23:09:13.7415691Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2025-08-14T23:09:13.7416415Z * 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
2025-08-14T23:09:13.7417129Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2025-08-14T23:09:13.7417818Z * 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
2025-08-14T23:09:13.7418514Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2025-08-14T23:09:13.7419196Z * 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
2025-08-14T23:09:13.7419938Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2025-08-14T23:09:13.7420644Z * 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
2025-08-14T23:09:13.7421345Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2025-08-14T23:09:13.7422061Z * 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
2025-08-14T23:09:13.7424401Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2025-08-14T23:09:13.7425147Z * 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
2025-08-14T23:09:13.7425909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2025-08-14T23:09:13.7426650Z * 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
2025-08-14T23:09:13.7427404Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2025-08-14T23:09:13.7428104Z * 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
2025-08-14T23:09:13.7428792Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2025-08-14T23:09:13.7429490Z * 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
2025-08-14T23:09:13.7445868Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2025-08-14T23:09:13.7446930Z * 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
2025-08-14T23:09:13.7471196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2025-08-14T23:09:13.7471784Z * 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
2025-08-14T23:09:13.7472363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2025-08-14T23:09:13.7473275Z * 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
2025-08-14T23:09:13.7473806Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2025-08-14T23:09:13.7474287Z * 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
2025-08-14T23:09:13.7474750Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2025-08-14T23:09:13.7475191Z * 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
2025-08-14T23:09:13.7475712Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2025-08-14T23:09:13.7476165Z * 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
2025-08-14T23:09:13.7476675Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2025-08-14T23:09:13.7477135Z * 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
2025-08-14T23:09:13.7477616Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2025-08-14T23:09:13.7478083Z * 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
2025-08-14T23:09:13.7478567Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2025-08-14T23:09:13.7480671Z * 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
2025-08-14T23:09:13.7481148Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2025-08-14T23:09:13.7481580Z * 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
2025-08-14T23:09:13.7481988Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0
2025-08-14T23:09:13.7482387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 280 <- wrt source file
2025-08-14T23:09:13.7482803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2025-08-14T23:09:13.7483424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 388 <- wrt source file
2025-08-14T23:09:13.7483879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2025-08-14T23:09:13.7484289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 491 <- wrt source file
2025-08-14T23:09:13.7484698Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2025-08-14T23:09:13.7491868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 547 <- wrt source file
2025-08-14T23:09:13.7492367Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2025-08-14T23:09:13.7492811Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 608 <- wrt source file
2025-08-14T23:09:13.7493322Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2025-08-14T23:09:13.7493723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 683 <- wrt source file
2025-08-14T23:09:13.7494128Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2025-08-14T23:09:13.7494539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 773 <- wrt source file
2025-08-14T23:09:13.7494994Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0
2025-08-14T23:09:13.7495414Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 971 <- wrt source file
2025-08-14T23:09:13.7495875Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2025-08-14T23:09:13.7496312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1062 <- wrt source file
2025-08-14T23:09:13.7496754Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0
2025-08-14T23:09:13.7497183Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1134 <- wrt source file
2025-08-14T23:09:13.7497622Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2025-08-14T23:09:13.7499872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1803 <- wrt source file
2025-08-14T23:09:13.7500435Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2025-08-14T23:09:13.7500927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1819 <- wrt source file
2025-08-14T23:09:13.7501424Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2025-08-14T23:09:13.7501898Z * 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
2025-08-14T23:09:13.7502371Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0
2025-08-14T23:09:13.7502804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file
2025-08-14T23:09:13.7503325Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0
2025-08-14T23:09:13.7503728Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file
2025-08-14T23:09:13.7504139Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0
2025-08-14T23:09:13.7504543Z * 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
2025-08-14T23:09:13.7506350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0
2025-08-14T23:09:13.7506763Z * 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
2025-08-14T23:09:13.7507171Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1
2025-08-14T23:09:13.7507568Z * 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
2025-08-14T23:09:13.7507975Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2
2025-08-14T23:09:13.7508415Z * 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
2025-08-14T23:09:13.7508874Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0
2025-08-14T23:09:13.7509312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file
2025-08-14T23:09:13.7509699Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0
2025-08-14T23:09:13.7510082Z * 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
2025-08-14T23:09:13.7510483Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_register_buffer:0
2025-08-14T23:09:13.7511961Z * 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
2025-08-14T23:09:13.7512376Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0
2025-08-14T23:09:13.7512753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file
2025-08-14T23:09:13.7513244Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/gds.py::GdsFile:0
2025-08-14T23:09:13.7513625Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file
2025-08-14T23:09:13.7514033Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::Dim:0
2025-08-14T23:09:13.7514449Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file
2025-08-14T23:09:13.7514897Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0
2025-08-14T23:09:13.7515335Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file
2025-08-14T23:09:13.7515777Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1
2025-08-14T23:09:13.7516215Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0, line 815 <- wrt source file
2025-08-14T23:09:13.7517710Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0
2025-08-14T23:09:13.7518155Z * 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
2025-08-14T23:09:13.7518648Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0
2025-08-14T23:09:13.7519087Z * 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
2025-08-14T23:09:13.7823656Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0
2025-08-14T23:09:13.7824103Z * 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
2025-08-14T23:09:13.7862634Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0
2025-08-14T23:09:13.7863243Z * 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
2025-08-14T23:09:13.7863723Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0
2025-08-14T23:09:13.7864203Z * 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
2025-08-14T23:09:13.7938793Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0
2025-08-14T23:09:13.7939278Z * 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
2025-08-14T23:09:13.8014451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0
2025-08-14T23:09:13.8014943Z * 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
2025-08-14T23:09:13.8015425Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0
2025-08-14T23:09:13.8015870Z * 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
2025-08-14T23:09:13.8017861Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0
2025-08-14T23:09:13.8018331Z * 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
2025-08-14T23:09:13.8049681Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0
2025-08-14T23:09:13.8050128Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0, line 102 <- wrt source file
2025-08-14T23:09:13.8052672Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0
2025-08-14T23:09:13.8053209Z * 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
2025-08-14T23:09:13.8053640Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2025-08-14T23:09:13.8054116Z * 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
2025-08-14T23:09:13.8054532Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
2025-08-14T23:09:13.8054992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file
2025-08-14T23:09:13.8055411Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0
2025-08-14T23:09:13.8055801Z * 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
2025-08-14T23:09:13.8056201Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0
2025-08-14T23:09:13.8056593Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file
2025-08-14T23:09:13.8058585Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0
2025-08-14T23:09:13.8059037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 125 <- wrt source file
2025-08-14T23:09:13.8059480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0
2025-08-14T23:09:13.8059910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0, line 49 <- wrt source file
2025-08-14T23:09:13.8060325Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0
2025-08-14T23:09:13.8060717Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0, line 480 <- wrt source file
2025-08-14T23:09:13.8061121Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0
2025-08-14T23:09:13.8061568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file
2025-08-14T23:09:13.8062065Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0
2025-08-14T23:09:13.8062548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 48 <- wrt source file
2025-08-14T23:09:13.8063163Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0
2025-08-14T23:09:13.8063637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file
2025-08-14T23:09:13.8064105Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0
2025-08-14T23:09:13.8064560Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file
2025-08-14T23:09:13.8065030Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0
2025-08-14T23:09:13.8065492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file
2025-08-14T23:09:13.8065969Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0
2025-08-14T23:09:13.8066440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file
2025-08-14T23:09:13.8068063Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0
2025-08-14T23:09:13.8068585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file
2025-08-14T23:09:13.8070301Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0
2025-08-14T23:09:13.8070772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file
2025-08-14T23:09:13.8071241Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0
2025-08-14T23:09:13.8071703Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file
2025-08-14T23:09:13.8072230Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
2025-08-14T23:09:13.8072690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file
2025-08-14T23:09:13.8073274Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0
2025-08-14T23:09:13.8073718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file
2025-08-14T23:09:13.8075734Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0
2025-08-14T23:09:13.8076203Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 37 <- wrt source file
2025-08-14T23:09:13.8076721Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0
2025-08-14T23:09:13.8077216Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 64 <- wrt source file
2025-08-14T23:09:13.8077729Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0
2025-08-14T23:09:13.8078221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 90 <- wrt source file
2025-08-14T23:09:13.8078722Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0
2025-08-14T23:09:13.8079213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 106 <- wrt source file
2025-08-14T23:09:13.8079723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0
2025-08-14T23:09:13.8080214Z * 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
2025-08-14T23:09:13.8082151Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0
2025-08-14T23:09:13.8082666Z * 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
2025-08-14T23:09:13.8083276Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0
2025-08-14T23:09:13.8083847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file
2025-08-14T23:09:13.8084381Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0
2025-08-14T23:09:13.8084879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 178 <- wrt source file
2025-08-14T23:09:13.8085393Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0
2025-08-14T23:09:13.8085956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 204 <- wrt source file
2025-08-14T23:09:13.8086494Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0
2025-08-14T23:09:13.8086983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 221 <- wrt source file
2025-08-14T23:09:13.8088857Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0
2025-08-14T23:09:13.8089373Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file
2025-08-14T23:09:13.8089872Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0
2025-08-14T23:09:13.8090404Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file
2025-08-14T23:09:13.8090971Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0
2025-08-14T23:09:13.8091543Z * 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
2025-08-14T23:09:13.8092137Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0
2025-08-14T23:09:13.8092713Z * 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
2025-08-14T23:09:13.8093375Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0
2025-08-14T23:09:13.8093951Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file
2025-08-14T23:09:13.8094545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0
2025-08-14T23:09:13.8095115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file
2025-08-14T23:09:13.8095683Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2025-08-14T23:09:13.8096232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2025-08-14T23:09:13.8096844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2025-08-14T23:09:13.8098689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file
2025-08-14T23:09:13.8099235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2025-08-14T23:09:13.8099769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file
2025-08-14T23:09:13.8100346Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2025-08-14T23:09:13.8100875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file
2025-08-14T23:09:13.8101425Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2025-08-14T23:09:13.8101949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file
2025-08-14T23:09:13.8102493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2025-08-14T23:09:13.8103122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file
2025-08-14T23:09:13.8103653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2025-08-14T23:09:13.8105213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file
2025-08-14T23:09:13.8105755Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2025-08-14T23:09:13.8106277Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 20 <- wrt source file
2025-08-14T23:09:13.8106808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0
2025-08-14T23:09:13.8107301Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 129 <- wrt source file
2025-08-14T23:09:13.8118287Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0
2025-08-14T23:09:13.8118744Z * 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
2025-08-14T23:09:13.8119180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0
2025-08-14T23:09:13.8119607Z * 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
2025-08-14T23:09:13.8120030Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2025-08-14T23:09:13.8120617Z * 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
2025-08-14T23:09:13.8122686Z * 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
2025-08-14T23:09:13.8123268Z * 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
2025-08-14T23:09:13.8123709Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0
2025-08-14T23:09:13.8124110Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file
2025-08-14T23:09:13.8124545Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0
2025-08-14T23:09:13.8124951Z * 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
2025-08-14T23:09:13.8131243Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0
2025-08-14T23:09:13.8131672Z * 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
2025-08-14T23:09:13.8137406Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0
2025-08-14T23:09:13.8137840Z * 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
2025-08-14T23:09:13.8140012Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0
2025-08-14T23:09:13.8140429Z * 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
2025-08-14T23:09:13.8140831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0
2025-08-14T23:09:13.8141251Z * 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
2025-08-14T23:09:13.8141707Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0
2025-08-14T23:09:13.8142141Z * 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
2025-08-14T23:09:13.8145293Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2025-08-14T23:09:13.8145751Z * 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
2025-08-14T23:09:13.8153042Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2025-08-14T23:09:13.8153477Z * 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
2025-08-14T23:09:13.8153889Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0
2025-08-14T23:09:13.8157350Z * 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
2025-08-14T23:09:13.8157811Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0
2025-08-14T23:09:13.8158295Z * 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
2025-08-14T23:09:13.8158725Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0
2025-08-14T23:09:13.8159126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file
2025-08-14T23:09:13.8159531Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0
2025-08-14T23:09:13.8159922Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file
2025-08-14T23:09:13.8160345Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0
2025-08-14T23:09:13.8160739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file
2025-08-14T23:09:13.8161165Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0
2025-08-14T23:09:13.8161560Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file
2025-08-14T23:09:13.8163523Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0
2025-08-14T23:09:13.8163910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file
2025-08-14T23:09:13.8164308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0
2025-08-14T23:09:13.8164706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file
2025-08-14T23:09:13.8165120Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0
2025-08-14T23:09:13.8165518Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file
2025-08-14T23:09:13.8166018Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0
2025-08-14T23:09:13.8166409Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file
2025-08-14T23:09:13.8166804Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0
2025-08-14T23:09:13.8167189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file
2025-08-14T23:09:13.8168707Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0
2025-08-14T23:09:13.8169157Z * 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
2025-08-14T23:09:13.8169629Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0
2025-08-14T23:09:13.8170087Z * 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
2025-08-14T23:09:13.8170553Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0
2025-08-14T23:09:13.8171048Z * 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
2025-08-14T23:09:13.8171517Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0
2025-08-14T23:09:13.8171982Z * 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
2025-08-14T23:09:13.8172478Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2025-08-14T23:09:13.8173058Z * 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
2025-08-14T23:09:13.8174781Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0
2025-08-14T23:09:13.8175242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file
2025-08-14T23:09:13.8175674Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0
2025-08-14T23:09:13.8176059Z * 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
2025-08-14T23:09:13.8176453Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0
2025-08-14T23:09:13.8176847Z * 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
2025-08-14T23:09:13.8177257Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0
2025-08-14T23:09:13.8177665Z * 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
2025-08-14T23:09:13.8178093Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0
2025-08-14T23:09:13.8178510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 238 <- wrt source file
2025-08-14T23:09:13.8178932Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0
2025-08-14T23:09:13.8180568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 105 <- wrt source file
2025-08-14T23:09:13.8180972Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0
2025-08-14T23:09:13.8181362Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/map.py::map:0, line 77 <- wrt source file
2025-08-14T23:09:13.8181763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/map.py::map:0
2025-08-14T23:09:13.8182184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0, line 80 <- wrt source file
2025-08-14T23:09:13.8182657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0
2025-08-14T23:09:13.8183187Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 133 <- wrt source file
2025-08-14T23:09:13.8183663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0
2025-08-14T23:09:13.8184184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 269 <- wrt source file
2025-08-14T23:09:13.8184687Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0
2025-08-14T23:09:13.8186327Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 155 <- wrt source file
2025-08-14T23:09:13.8186737Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0
2025-08-14T23:09:13.8187155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 423 <- wrt source file
2025-08-14T23:09:13.8187630Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0
2025-08-14T23:09:13.8188036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file
2025-08-14T23:09:13.8188455Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0
2025-08-14T23:09:13.8188851Z * 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
2025-08-14T23:09:13.8189257Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0
2025-08-14T23:09:13.8189651Z * 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
2025-08-14T23:09:13.8190058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0
2025-08-14T23:09:13.8190462Z * 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
2025-08-14T23:09:13.8192013Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0
2025-08-14T23:09:13.8192430Z * 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
2025-08-14T23:09:13.8192828Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0
2025-08-14T23:09:13.8193327Z * 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
2025-08-14T23:09:13.8193748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0
2025-08-14T23:09:13.8194148Z * 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
2025-08-14T23:09:13.8194563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0
2025-08-14T23:09:13.8194955Z * 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
2025-08-14T23:09:13.8195350Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0
2025-08-14T23:09:13.8195752Z * 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
2025-08-14T23:09:13.8196180Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0
2025-08-14T23:09:13.8196669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 547 <- wrt source file
2025-08-14T23:09:13.8198195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2025-08-14T23:09:13.8198654Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 749 <- wrt source file
2025-08-14T23:09:13.8199107Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0
2025-08-14T23:09:13.8199529Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file
2025-08-14T23:09:13.8199986Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0
2025-08-14T23:09:13.8200391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file
2025-08-14T23:09:13.8203513Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0
2025-08-14T23:09:13.8203920Z * 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
2025-08-14T23:09:13.8205848Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_is_leaf:0
2025-08-14T23:09:13.8206234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file
2025-08-14T23:09:13.8208095Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0
2025-08-14T23:09:13.8209920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 93 <- wrt source file
2025-08-14T23:09:13.8215859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0
2025-08-14T23:09:13.8216309Z * 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
2025-08-14T23:09:13.8216800Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2025-08-14T23:09:13.8217306Z * 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
2025-08-14T23:09:13.8217843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0
2025-08-14T23:09:13.8218334Z * 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
2025-08-14T23:09:13.8218803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0
2025-08-14T23:09:13.8219275Z * 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
2025-08-14T23:09:13.8421497Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0
2025-08-14T23:09:13.8426116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 40 <- wrt source file
2025-08-14T23:09:13.8426625Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0
2025-08-14T23:09:13.8427036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file
2025-08-14T23:09:13.8427482Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2025-08-14T23:09:13.8427897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file
2025-08-14T23:09:13.8428317Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0
2025-08-14T23:09:13.8428729Z * 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
2025-08-14T23:09:13.8429161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0
2025-08-14T23:09:13.8429584Z * 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
2025-08-14T23:09:13.8430048Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2025-08-14T23:09:13.8435603Z * 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
2025-08-14T23:09:13.8436138Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0
2025-08-14T23:09:13.8436570Z * 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
2025-08-14T23:09:13.8437045Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2025-08-14T23:09:13.8437503Z * 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
2025-08-14T23:09:13.8437980Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2025-08-14T23:09:13.8438457Z * 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
2025-08-14T23:09:13.8438964Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2025-08-14T23:09:13.8439467Z * 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
2025-08-14T23:09:13.8439981Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2025-08-14T23:09:13.8440486Z * 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
2025-08-14T23:09:13.8441006Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2025-08-14T23:09:13.8441506Z * 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
2025-08-14T23:09:13.8442016Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2025-08-14T23:09:13.8442577Z * 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
2025-08-14T23:09:13.8444627Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2025-08-14T23:09:13.8445154Z * 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
2025-08-14T23:09:13.8445693Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2025-08-14T23:09:13.8446257Z * 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
2025-08-14T23:09:13.8446781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0
2025-08-14T23:09:13.8447268Z * 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
2025-08-14T23:09:13.8447789Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2025-08-14T23:09:13.8448282Z * 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
2025-08-14T23:09:13.8448790Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2025-08-14T23:09:13.8449309Z * 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
2025-08-14T23:09:13.8451177Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2025-08-14T23:09:13.8451686Z * 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
2025-08-14T23:09:13.8452196Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2025-08-14T23:09:13.8452701Z * 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
2025-08-14T23:09:13.8453313Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2025-08-14T23:09:13.8453829Z * 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
2025-08-14T23:09:13.8454362Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2025-08-14T23:09:13.8454882Z * 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
2025-08-14T23:09:13.8455394Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2025-08-14T23:09:13.8455883Z * 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
2025-08-14T23:09:13.8457815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2025-08-14T23:09:13.8458388Z * 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
2025-08-14T23:09:13.8458923Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2025-08-14T23:09:13.8459446Z * 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
2025-08-14T23:09:13.8459978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2025-08-14T23:09:13.8460531Z * 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
2025-08-14T23:09:13.8461075Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2025-08-14T23:09:13.8461627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file
2025-08-14T23:09:13.8462180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2025-08-14T23:09:13.8462702Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 37 <- wrt source file
2025-08-14T23:09:13.8463297Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2025-08-14T23:09:13.8463782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file
2025-08-14T23:09:13.8464261Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2025-08-14T23:09:13.8464735Z * 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
2025-08-14T23:09:13.8465224Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2025-08-14T23:09:13.8465701Z * 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
2025-08-14T23:09:13.8467444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2025-08-14T23:09:13.8467943Z * 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
2025-08-14T23:09:13.8468431Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2025-08-14T23:09:13.8468907Z * 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
2025-08-14T23:09:13.8469389Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0
2025-08-14T23:09:13.8469868Z * 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
2025-08-14T23:09:13.8470440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2025-08-14T23:09:13.8470933Z * 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
2025-08-14T23:09:13.8471440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2025-08-14T23:09:13.8471934Z * 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
2025-08-14T23:09:13.8473601Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2025-08-14T23:09:13.8474109Z * 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
2025-08-14T23:09:13.8474604Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2025-08-14T23:09:13.8475093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file
2025-08-14T23:09:13.8475568Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2025-08-14T23:09:13.8476044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file
2025-08-14T23:09:13.8476535Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2025-08-14T23:09:13.8477028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file
2025-08-14T23:09:13.8477521Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2025-08-14T23:09:13.8478042Z * 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
2025-08-14T23:09:13.8478602Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2025-08-14T23:09:13.8479149Z * 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
2025-08-14T23:09:13.8480854Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2025-08-14T23:09:13.8481390Z * 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
2025-08-14T23:09:13.8481903Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2025-08-14T23:09:13.8482392Z * 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
2025-08-14T23:09:13.8482885Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2025-08-14T23:09:13.8483469Z * 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
2025-08-14T23:09:13.8483959Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2025-08-14T23:09:13.8484427Z * 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
2025-08-14T23:09:13.8484902Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2025-08-14T23:09:13.8485350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py::fullgraph_capture:0, line 2328 <- wrt source file
2025-08-14T23:09:13.8485846Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py::fullgraph_capture:0
2025-08-14T23:09:13.8487361Z * 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
2025-08-14T23:09:13.8487806Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0
2025-08-14T23:09:13.8488283Z * 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
2025-08-14T23:09:13.8488785Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0
2025-08-14T23:09:13.8489219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file
2025-08-14T23:09:13.8489631Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0
2025-08-14T23:09:13.8490062Z * 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
2025-08-14T23:09:13.8490520Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2025-08-14T23:09:13.8490967Z * 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
2025-08-14T23:09:13.8491428Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0
2025-08-14T23:09:13.8493048Z * 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
2025-08-14T23:09:13.8493508Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0
2025-08-14T23:09:13.8493982Z * 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
2025-08-14T23:09:13.8494490Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2025-08-14T23:09:13.8494983Z * 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
2025-08-14T23:09:13.8495484Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2025-08-14T23:09:13.8495974Z * 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
2025-08-14T23:09:13.8496539Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2025-08-14T23:09:13.8497007Z * 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
2025-08-14T23:09:13.8497469Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0
2025-08-14T23:09:13.8498967Z * 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
2025-08-14T23:09:13.8499471Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0
2025-08-14T23:09:13.8500040Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file
2025-08-14T23:09:13.8500611Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0
2025-08-14T23:09:13.8501177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 331 <- wrt source file
2025-08-14T23:09:13.8501745Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0
2025-08-14T23:09:13.8502274Z * DOCTEST : /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
2025-08-14T23:09:13.8502788Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0
2025-08-14T23:09:13.8503307Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 617 <- wrt source file
2025-08-14T23:09:13.8503714Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0
2025-08-14T23:09:13.8505427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file
2025-08-14T23:09:13.8505861Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0
2025-08-14T23:09:13.8506263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 25 <- wrt source file
2025-08-14T23:09:13.8506684Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0
2025-08-14T23:09:13.8507093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file
2025-08-14T23:09:13.8507514Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0
2025-08-14T23:09:13.8507937Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file
2025-08-14T23:09:13.8508386Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0
2025-08-14T23:09:13.8508836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file
2025-08-14T23:09:13.8509350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2025-08-14T23:09:13.8509788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file
2025-08-14T23:09:13.8511369Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0
2025-08-14T23:09:13.8511786Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file
2025-08-14T23:09:13.8512229Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2025-08-14T23:09:13.8512655Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file
2025-08-14T23:09:13.8513201Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0
2025-08-14T23:09:13.8513655Z * 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
2025-08-14T23:09:13.8514161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2025-08-14T23:09:13.8514639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file
2025-08-14T23:09:13.8515134Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0
2025-08-14T23:09:13.8515592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file
2025-08-14T23:09:13.8517069Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0
2025-08-14T23:09:13.8517508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 23 <- wrt source file
2025-08-14T23:09:13.8517947Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0
2025-08-14T23:09:13.8518376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 43 <- wrt source file
2025-08-14T23:09:13.8555279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2025-08-14T23:09:13.8581796Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file
2025-08-14T23:09:13.8582313Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0
2025-08-14T23:09:13.8582720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file
2025-08-14T23:09:13.8583239Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0
2025-08-14T23:09:13.8583649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file
2025-08-14T23:09:13.8586733Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0
2025-08-14T23:09:13.8587232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 103 <- wrt source file
2025-08-14T23:09:13.8587940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2025-08-14T23:09:13.8588392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file
2025-08-14T23:09:13.8588826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0
2025-08-14T23:09:13.8589260Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file
2025-08-14T23:09:13.8589711Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0
2025-08-14T23:09:13.8590140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file
2025-08-14T23:09:13.8590630Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0
2025-08-14T23:09:13.8591102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1065 <- wrt source file
2025-08-14T23:09:13.8591545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0
2025-08-14T23:09:13.8591986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1177 <- wrt source file
2025-08-14T23:09:13.8592442Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0
2025-08-14T23:09:13.8593025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1253 <- wrt source file
2025-08-14T23:09:13.8593553Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2025-08-14T23:09:13.8594036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file
2025-08-14T23:09:13.8594508Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2025-08-14T23:09:13.8594937Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 22 <- wrt source file
2025-08-14T23:09:13.8595353Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0
2025-08-14T23:09:13.8595812Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file
2025-08-14T23:09:13.8597732Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2025-08-14T23:09:13.8598178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 18 <- wrt source file
2025-08-14T23:09:13.8598580Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0
2025-08-14T23:09:13.8598996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 38 <- wrt source file
2025-08-14T23:09:13.8599440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0
2025-08-14T23:09:13.8599890Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 22 <- wrt source file
2025-08-14T23:09:13.8600329Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0
2025-08-14T23:09:13.8600743Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file
2025-08-14T23:09:13.8601175Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2025-08-14T23:09:13.8601588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0, line 114 <- wrt source file
2025-08-14T23:09:13.8602003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0
2025-08-14T23:09:13.8603601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file
2025-08-14T23:09:13.8604035Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2025-08-14T23:09:13.8604457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file
2025-08-14T23:09:13.8604862Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0
2025-08-14T23:09:13.8605290Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 28 <- wrt source file
2025-08-14T23:09:13.8605918Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2025-08-14T23:09:13.8606357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file
2025-08-14T23:09:13.8606788Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2025-08-14T23:09:13.8607208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file
2025-08-14T23:09:13.8607631Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2025-08-14T23:09:13.8609282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file
2025-08-14T23:09:13.8609711Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0
2025-08-14T23:09:13.8610120Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0, line 117 <- wrt source file
2025-08-14T23:09:13.8610540Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0
2025-08-14T23:09:13.8610941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file
2025-08-14T23:09:13.8611342Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0
2025-08-14T23:09:13.8611756Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file
2025-08-14T23:09:13.8612195Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0
2025-08-14T23:09:13.8612611Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file
2025-08-14T23:09:13.8613166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0
2025-08-14T23:09:13.8614770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file
2025-08-14T23:09:13.8615219Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0
2025-08-14T23:09:13.8615635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file
2025-08-14T23:09:13.8630063Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0
2025-08-14T23:09:13.8630549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file
2025-08-14T23:09:13.8630993Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0
2025-08-14T23:09:13.8631437Z * 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
2025-08-14T23:09:13.8636264Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0
2025-08-14T23:09:13.8636715Z * 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
2025-08-14T23:09:13.8637901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0
2025-08-14T23:09:13.8638359Z * 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
2025-08-14T23:09:13.8640311Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0
2025-08-14T23:09:13.8640731Z * 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
2025-08-14T23:09:13.8641146Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0
2025-08-14T23:09:13.8641551Z * 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
2025-08-14T23:09:13.8642169Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0
2025-08-14T23:09:13.8642610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file
2025-08-14T23:09:13.8643149Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0
2025-08-14T23:09:13.8643600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file
2025-08-14T23:09:13.8644059Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0
2025-08-14T23:09:13.8644517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file
2025-08-14T23:09:13.8644997Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0
2025-08-14T23:09:13.8645517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file
2025-08-14T23:09:13.8647330Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0
2025-08-14T23:09:13.8647734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0, line 1530 <- wrt source file
2025-08-14T23:09:13.8651483Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0
2025-08-14T23:09:13.8651900Z * 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
2025-08-14T23:09:13.8652411Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0
2025-08-14T23:09:13.8652898Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 157 <- wrt source file
2025-08-14T23:09:13.8653510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0
2025-08-14T23:09:13.8653989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1386 <- wrt source file
2025-08-14T23:09:13.8654467Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0
2025-08-14T23:09:13.8654907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 431 <- wrt source file
2025-08-14T23:09:13.8656795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0
2025-08-14T23:09:13.8657240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 976 <- wrt source file
2025-08-14T23:09:13.8657718Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0
2025-08-14T23:09:13.8658170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1122 <- wrt source file
2025-08-14T23:09:13.8658617Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0
2025-08-14T23:09:13.8659074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2574 <- wrt source file
2025-08-14T23:09:13.8659551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0
2025-08-14T23:09:13.8660006Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2676 <- wrt source file
2025-08-14T23:09:13.8660463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0
2025-08-14T23:09:13.8660911Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3143 <- wrt source file
2025-08-14T23:09:13.8661374Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0
2025-08-14T23:09:13.8661864Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3372 <- wrt source file
2025-08-14T23:09:13.8662349Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0
2025-08-14T23:09:13.8664166Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3474 <- wrt source file
2025-08-14T23:09:13.8664627Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0
2025-08-14T23:09:13.8665087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3588 <- wrt source file
2025-08-14T23:09:13.8665566Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0
2025-08-14T23:09:13.8666076Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3711 <- wrt source file
2025-08-14T23:09:13.8666580Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0
2025-08-14T23:09:13.8667047Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3920 <- wrt source file
2025-08-14T23:09:13.8667527Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0
2025-08-14T23:09:13.8667994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 4058 <- wrt source file
2025-08-14T23:09:13.8668470Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0
2025-08-14T23:09:13.8671499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 4164 <- wrt source file
2025-08-14T23:09:13.8671949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0
2025-08-14T23:09:13.8672378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 4249 <- wrt source file
2025-08-14T23:09:13.8672826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0
2025-08-14T23:09:13.8673358Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4387 <- wrt source file
2025-08-14T23:09:13.8673839Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0
2025-08-14T23:09:13.8674304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4869 <- wrt source file
2025-08-14T23:09:13.8674770Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0
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2025-08-14T23:09:13.8679180Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 47 <- wrt source file
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2025-08-14T23:09:13.8694426Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py::alltoall:0, line 759 <- wrt source file
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2025-08-14T23:09:13.8695388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py::broadcast:0, line 814 <- wrt source file
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2025-08-14T23:09:13.8696356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py::reduce:0, line 875 <- wrt source file
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2025-08-14T23:09:13.8698328Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 333 <- wrt source file
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2025-08-14T23:09:13.8700342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 83 <- wrt source file
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2025-08-14T23:09:13.8701377Z * 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
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2025-08-14T23:09:13.8702416Z * 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
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2025-08-14T23:09:13.8703310Z * 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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2025-08-14T23:09:13.8704108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::remote:0, line 606 <- wrt source file
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2025-08-14T23:09:13.8706055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 786 <- wrt source file
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2025-08-14T23:09:13.8706888Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 878 <- wrt source file
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2025-08-14T23:09:13.8707790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 43 <- wrt source file
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2025-08-14T23:09:13.8708893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 114 <- wrt source file
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2025-08-14T23:09:13.8709948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 43 <- wrt source file
2025-08-14T23:09:13.8710428Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2025-08-14T23:09:13.8710897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 37 <- wrt source file
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2025-08-14T23:09:13.8713038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1395 <- wrt source file
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2025-08-14T23:09:13.8714049Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1454 <- wrt source file
2025-08-14T23:09:13.8714573Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0
2025-08-14T23:09:13.8715114Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/barriers.py::BarrierConfig:0, line 50 <- wrt source file
2025-08-14T23:09:13.8715653Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/barriers.py::BarrierConfig:0
2025-08-14T23:09:13.8716172Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/builder.py::make_sync_checkpointer:0, line 77 <- wrt source file
2025-08-14T23:09:13.8716712Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/builder.py::make_sync_checkpointer:0
2025-08-14T23:09:13.8717244Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/builder.py::make_async_checkpointer:0, line 138 <- wrt source file
2025-08-14T23:09:13.8717807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/builder.py::make_async_checkpointer:0
2025-08-14T23:09:13.8719287Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/staging.py::DefaultStager.close:0, line 206 <- wrt source file
2025-08-14T23:09:13.8719858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/staging.py::DefaultStager.close:0
2025-08-14T23:09:13.8720385Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::SyncCheckpointer:0, line 104 <- wrt source file
2025-08-14T23:09:13.8720928Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::SyncCheckpointer:0
2025-08-14T23:09:13.8721470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::SyncCheckpointer.save:0, line 142 <- wrt source file
2025-08-14T23:09:13.8722038Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::SyncCheckpointer.save:0
2025-08-14T23:09:13.8722583Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::AsyncCheckpointer:0, line 213 <- wrt source file
2025-08-14T23:09:13.8723213Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::AsyncCheckpointer:0
2025-08-14T23:09:13.8723759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::AsyncCheckpointer.save:0, line 260 <- wrt source file
2025-08-14T23:09:13.8724325Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py::AsyncCheckpointer.save:0
2025-08-14T23:09:13.8726016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 54 <- wrt source file
2025-08-14T23:09:13.8726541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0
2025-08-14T23:09:13.8727022Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 262 <- wrt source file
2025-08-14T23:09:13.8727467Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0
2025-08-14T23:09:13.8727907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file
2025-08-14T23:09:13.8728381Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0
2025-08-14T23:09:13.8728915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 125 <- wrt source file
2025-08-14T23:09:13.8729486Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0
2025-08-14T23:09:13.8730099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1490 <- wrt source file
2025-08-14T23:09:13.8730758Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0
2025-08-14T23:09:13.8731428Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1610 <- wrt source file
2025-08-14T23:09:13.8733166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0
2025-08-14T23:09:13.8733812Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1695 <- wrt source file
2025-08-14T23:09:13.8734459Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0
2025-08-14T23:09:13.8734987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file
2025-08-14T23:09:13.8735433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0
2025-08-14T23:09:13.8735927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file
2025-08-14T23:09:13.8736485Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0
2025-08-14T23:09:13.8737070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file
2025-08-14T23:09:13.8737689Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0
2025-08-14T23:09:13.8738296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file
2025-08-14T23:09:13.8738917Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0
2025-08-14T23:09:13.8740478Z * DOCTEST : /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
2025-08-14T23:09:13.8741036Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0
2025-08-14T23:09:13.8741610Z * 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
2025-08-14T23:09:13.8742194Z * 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
2025-08-14T23:09:13.8742743Z * 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
2025-08-14T23:09:13.8743391Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0
2025-08-14T23:09:13.8743949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 143 <- wrt source file
2025-08-14T23:09:13.8744554Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0
2025-08-14T23:09:13.8745119Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 182 <- wrt source file
2025-08-14T23:09:13.8745728Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0
2025-08-14T23:09:13.8747344Z * DOCTEST : /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
2025-08-14T23:09:13.8747883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0
2025-08-14T23:09:13.8748427Z * 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
2025-08-14T23:09:13.8748992Z * 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
2025-08-14T23:09:13.8749526Z * 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
2025-08-14T23:09:13.8750055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0
2025-08-14T23:09:13.8750582Z * 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
2025-08-14T23:09:13.8751136Z * 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
2025-08-14T23:09:13.8751664Z * 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
2025-08-14T23:09:13.8752198Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0
2025-08-14T23:09:13.8753705Z * 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
2025-08-14T23:09:13.8754187Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0
2025-08-14T23:09:13.8754646Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file
2025-08-14T23:09:13.8755151Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0
2025-08-14T23:09:13.8755619Z * 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
2025-08-14T23:09:13.8756113Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate_with_fsdp.py::replicate:0
2025-08-14T23:09:13.8756581Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file
2025-08-14T23:09:13.8757046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0
2025-08-14T23:09:13.8757536Z * 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
2025-08-14T23:09:13.8758042Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0
2025-08-14T23:09:13.8758573Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 280 <- wrt source file
2025-08-14T23:09:13.8760046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0
2025-08-14T23:09:13.8760522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 837 <- wrt source file
2025-08-14T23:09:13.8760966Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0
2025-08-14T23:09:13.8761428Z * 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
2025-08-14T23:09:13.8761922Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2025-08-14T23:09:13.8762397Z * 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
2025-08-14T23:09:13.8762871Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0
2025-08-14T23:09:13.8763451Z * 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
2025-08-14T23:09:13.8763961Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0
2025-08-14T23:09:13.8764453Z * 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
2025-08-14T23:09:13.8764944Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0
2025-08-14T23:09:13.8766598Z * 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
2025-08-14T23:09:13.8767090Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0
2025-08-14T23:09:13.8767568Z * 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
2025-08-14T23:09:13.8768107Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0
2025-08-14T23:09:13.8768582Z * 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
2025-08-14T23:09:13.8769067Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0
2025-08-14T23:09:13.8769574Z * 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
2025-08-14T23:09:13.8770112Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0
2025-08-14T23:09:13.8770631Z * 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
2025-08-14T23:09:13.8771143Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0
2025-08-14T23:09:13.8772558Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file
2025-08-14T23:09:13.8773103Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0
2025-08-14T23:09:13.8773539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 371 <- wrt source file
2025-08-14T23:09:13.8773977Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/codecache.py::WritableTempFile:0
2025-08-14T23:09:13.8774433Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0, line 45 <- wrt source file
2025-08-14T23:09:13.8774925Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0
2025-08-14T23:09:13.8775409Z * 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 1501 <- wrt source file
2025-08-14T23:09:13.8775906Z * 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
2025-08-14T23:09:13.8776378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3349 <- wrt source file
2025-08-14T23:09:13.8776843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0
2025-08-14T23:09:13.8777252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file
2025-08-14T23:09:13.8778521Z [W814 23:09:13.851548790 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8778764Z [W814 23:09:13.851686108 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8779000Z [W814 23:09:13.851806386 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8779318Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0
2025-08-14T23:09:13.8779695Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file
2025-08-14T23:09:13.8780069Z [W814 23:09:13.852043191 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8780385Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0
2025-08-14T23:09:13.8780758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file
2025-08-14T23:09:13.8781061Z [W814 23:09:13.852246198 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8781299Z [W814 23:09:13.856059069 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8786123Z [W814 23:09:13.856118678 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8786502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0
2025-08-14T23:09:13.8786952Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file
2025-08-14T23:09:13.8787261Z [W814 23:09:13.856353034 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
2025-08-14T23:09:13.8824109Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0
2025-08-14T23:09:13.8824491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file
2025-08-14T23:09:13.8904653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0
2025-08-14T23:09:13.8905061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file
2025-08-14T23:09:13.8923333Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0
2025-08-14T23:09:13.8923723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file
2025-08-14T23:09:13.8925049Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0
2025-08-14T23:09:13.8927621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file
2025-08-14T23:09:13.8928001Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0
2025-08-14T23:09:13.8928376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file
2025-08-14T23:09:13.8928810Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0
2025-08-14T23:09:13.8953835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file
2025-08-14T23:09:13.8954316Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0
2025-08-14T23:09:13.8954700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file
2025-08-14T23:09:13.8955077Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0
2025-08-14T23:09:13.8955436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file
2025-08-14T23:09:13.8958799Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0
2025-08-14T23:09:13.8959184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file
2025-08-14T23:09:13.8959719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0
2025-08-14T23:09:13.8960119Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file
2025-08-14T23:09:13.8960487Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0
2025-08-14T23:09:13.8960864Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file
2025-08-14T23:09:13.8961261Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0
2025-08-14T23:09:13.8961640Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file
2025-08-14T23:09:13.8962072Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0
2025-08-14T23:09:13.8962455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file
2025-08-14T23:09:13.8962991Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0
2025-08-14T23:09:13.8963378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file
2025-08-14T23:09:13.8963775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0
2025-08-14T23:09:13.8964157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file
2025-08-14T23:09:13.8964543Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0
2025-08-14T23:09:13.8964916Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file
2025-08-14T23:09:13.8965293Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0
2025-08-14T23:09:13.8967104Z * 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
2025-08-14T23:09:13.8973731Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2025-08-14T23:09:13.8974260Z * 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
2025-08-14T23:09:13.9328811Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2025-08-14T23:09:13.9345065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2174 <- wrt source file
2025-08-14T23:09:13.9354063Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0
2025-08-14T23:09:13.9356537Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2478 <- wrt source file
2025-08-14T23:09:13.9361588Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0
2025-08-14T23:09:13.9362520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2618 <- wrt source file
2025-08-14T23:09:13.9371213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0
2025-08-14T23:09:13.9375306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file
2025-08-14T23:09:13.9385147Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0
2025-08-14T23:09:13.9385741Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file
2025-08-14T23:09:13.9392134Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0
2025-08-14T23:09:13.9392984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file
2025-08-14T23:09:13.9400916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0
2025-08-14T23:09:13.9401875Z * 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
2025-08-14T23:09:13.9406004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2025-08-14T23:09:13.9406739Z * 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
2025-08-14T23:09:13.9418682Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2025-08-14T23:09:13.9421415Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file
2025-08-14T23:09:13.9424965Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0
2025-08-14T23:09:13.9426128Z * 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
2025-08-14T23:09:13.9426577Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0
2025-08-14T23:09:13.9427343Z * 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
2025-08-14T23:09:13.9455809Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2025-08-14T23:09:13.9456830Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file
2025-08-14T23:09:13.9457807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2025-08-14T23:09:13.9458808Z * 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
2025-08-14T23:09:13.9459831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2025-08-14T23:09:13.9465145Z * 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
2025-08-14T23:09:13.9466110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2025-08-14T23:09:13.9466876Z * 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
2025-08-14T23:09:13.9467656Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2025-08-14T23:09:13.9468617Z * 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
2025-08-14T23:09:13.9469427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2025-08-14T23:09:13.9470099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file
2025-08-14T23:09:13.9470680Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0
2025-08-14T23:09:13.9471269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file
2025-08-14T23:09:13.9471956Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0
2025-08-14T23:09:13.9472551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file
2025-08-14T23:09:13.9473278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0
2025-08-14T23:09:13.9473877Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file
2025-08-14T23:09:14.2966874Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0
2025-08-14T23:09:14.3278568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file
2025-08-14T23:09:14.3286102Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2025-08-14T23:09:14.3286551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file
2025-08-14T23:09:14.3287008Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2025-08-14T23:09:14.3287432Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file
2025-08-14T23:09:14.3316068Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2025-08-14T23:09:14.3316802Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file
2025-08-14T23:09:14.3335457Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2025-08-14T23:09:14.3354055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file
2025-08-14T23:09:14.3354637Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2025-08-14T23:09:14.3355099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file
2025-08-14T23:09:14.3355559Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2025-08-14T23:09:14.3356015Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file
2025-08-14T23:09:14.3356842Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2025-08-14T23:09:14.3362598Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file
2025-08-14T23:09:14.3384048Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2025-08-14T23:09:14.3384505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file
2025-08-14T23:09:14.5544691Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2025-08-14T23:09:14.5868235Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file
2025-08-14T23:09:14.5876670Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0
2025-08-14T23:09:14.5877101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file
2025-08-14T23:09:14.5878457Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2025-08-14T23:09:14.5879668Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file
2025-08-14T23:09:14.6084283Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0
2025-08-14T23:09:14.6090224Z * 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
2025-08-14T23:09:14.6123628Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2025-08-14T23:09:14.6144126Z * 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
2025-08-14T23:09:14.6145741Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0
2025-08-14T23:09:14.6146174Z * 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
2025-08-14T23:09:14.6146603Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0
2025-08-14T23:09:14.6147040Z * 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
2025-08-14T23:09:14.6147508Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2025-08-14T23:09:14.6147961Z * 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
2025-08-14T23:09:14.6153239Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0
2025-08-14T23:09:14.6153698Z * 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
2025-08-14T23:09:14.6154169Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2025-08-14T23:09:14.6154614Z * 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
2025-08-14T23:09:14.6155429Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0
2025-08-14T23:09:14.6155904Z * 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
2025-08-14T23:09:14.6156355Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2025-08-14T23:09:14.6156797Z * 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
2025-08-14T23:09:14.6157250Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0
2025-08-14T23:09:14.6157681Z * 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
2025-08-14T23:09:14.6158223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0
2025-08-14T23:09:14.6158655Z * 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
2025-08-14T23:09:14.6159144Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2025-08-14T23:09:14.6159571Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file
2025-08-14T23:09:14.6159986Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0
2025-08-14T23:09:14.6160393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file
2025-08-14T23:09:14.6160816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2025-08-14T23:09:14.6161223Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file
2025-08-14T23:09:14.6161641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2025-08-14T23:09:14.6162047Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file
2025-08-14T23:09:14.6163863Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2025-08-14T23:09:14.6164285Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file
2025-08-14T23:09:14.6164715Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2025-08-14T23:09:14.6165147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file
2025-08-14T23:09:14.6228785Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2025-08-14T23:09:14.6229734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file
2025-08-14T23:09:14.6235428Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0
2025-08-14T23:09:14.6236266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file
2025-08-14T23:09:14.6238691Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0
2025-08-14T23:09:14.6239583Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file
2025-08-14T23:09:14.6241036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0
2025-08-14T23:09:14.6243659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file
2025-08-14T23:09:14.6244560Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2025-08-14T23:09:14.6244976Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file
2025-08-14T23:09:14.6245452Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0
2025-08-14T23:09:14.6246003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file
2025-08-14T23:09:14.6264358Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2025-08-14T23:09:14.6265250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file
2025-08-14T23:09:14.6265690Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2025-08-14T23:09:14.6266106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file
2025-08-14T23:09:14.6266522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0
2025-08-14T23:09:14.6268617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file
2025-08-14T23:09:14.6269027Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0
2025-08-14T23:09:14.6269424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file
2025-08-14T23:09:14.6269831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0
2025-08-14T23:09:14.6270239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file
2025-08-14T23:09:14.6270669Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0
2025-08-14T23:09:14.6271081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file
2025-08-14T23:09:14.6271491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0
2025-08-14T23:09:14.6271887Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file
2025-08-14T23:09:14.6272293Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0
2025-08-14T23:09:14.6273964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file
2025-08-14T23:09:14.6274380Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0
2025-08-14T23:09:14.6274826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file
2025-08-14T23:09:14.6275274Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0
2025-08-14T23:09:14.6275672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file
2025-08-14T23:09:14.6276081Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0
2025-08-14T23:09:14.6276493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file
2025-08-14T23:09:14.6276930Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2025-08-14T23:09:14.6277368Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file
2025-08-14T23:09:14.6278310Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2025-08-14T23:09:14.6278748Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file
2025-08-14T23:09:14.6282087Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2025-08-14T23:09:14.6283027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file
2025-08-14T23:09:14.6283498Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0
2025-08-14T23:09:14.6283930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file
2025-08-14T23:09:14.6285100Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0
2025-08-14T23:09:14.6285563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file
2025-08-14T23:09:14.6286133Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2025-08-14T23:09:14.6286565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file
2025-08-14T23:09:14.6287740Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0
2025-08-14T23:09:14.6288167Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file
2025-08-14T23:09:14.6289802Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0
2025-08-14T23:09:14.6292333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file
2025-08-14T23:09:14.6292774Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2025-08-14T23:09:14.6293294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file
2025-08-14T23:09:14.6295522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0
2025-08-14T23:09:14.6295942Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file
2025-08-14T23:09:14.6297967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0
2025-08-14T23:09:14.6298392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file
2025-08-14T23:09:14.6300437Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2025-08-14T23:09:14.6300866Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file
2025-08-14T23:09:14.6302901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2025-08-14T23:09:14.6303407Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file
2025-08-14T23:09:14.6303858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2025-08-14T23:09:14.6304292Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file
2025-08-14T23:09:14.6310375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0
2025-08-14T23:09:14.6310824Z * 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
2025-08-14T23:09:14.6313023Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2025-08-14T23:09:14.6313505Z * 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
2025-08-14T23:09:14.6315962Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2025-08-14T23:09:14.6316428Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file
2025-08-14T23:09:15.1721361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0
2025-08-14T23:09:15.1733918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file
2025-08-14T23:09:15.2454036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2025-08-14T23:09:15.2455832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file
2025-08-14T23:09:15.3936302Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2025-08-14T23:09:15.4010860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file
2025-08-14T23:09:15.4254490Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2025-08-14T23:09:15.4284492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file
2025-08-14T23:09:15.6387546Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2025-08-14T23:09:15.6388635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file
2025-08-14T23:09:15.6392011Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2025-08-14T23:09:15.6392497Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file
2025-08-14T23:09:15.6395581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2025-08-14T23:09:15.6396034Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file
2025-08-14T23:09:15.6413467Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2025-08-14T23:09:15.6414064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file
2025-08-14T23:09:15.6414571Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2025-08-14T23:09:15.6415094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file
2025-08-14T23:09:15.6417621Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2025-08-14T23:09:15.6418074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file
2025-08-14T23:09:15.6420575Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2025-08-14T23:09:15.6421021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file
2025-08-14T23:09:15.6465205Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2025-08-14T23:09:15.6465628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file
2025-08-14T23:09:15.7277354Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2025-08-14T23:09:15.7285263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file
2025-08-14T23:09:15.7286113Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2025-08-14T23:09:15.7286681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file
2025-08-14T23:09:15.7907230Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2025-08-14T23:09:15.7907776Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file
2025-08-14T23:09:15.7943410Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2025-08-14T23:09:15.7943840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file
2025-08-14T23:09:15.7944271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2025-08-14T23:09:15.7944689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file
2025-08-14T23:09:15.9426968Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2025-08-14T23:09:15.9464857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file
2025-08-14T23:09:15.9470464Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2025-08-14T23:09:15.9471152Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file
2025-08-14T23:09:15.9815243Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2025-08-14T23:09:15.9816213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file
2025-08-14T23:09:15.9844560Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2025-08-14T23:09:15.9845270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file
2025-08-14T23:09:15.9845989Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2025-08-14T23:09:15.9846599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file
2025-08-14T23:09:16.1199290Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0
2025-08-14T23:09:16.1200008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file
2025-08-14T23:09:16.1224577Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2025-08-14T23:09:16.1225282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file
2025-08-14T23:09:16.1225964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2025-08-14T23:09:16.1226631Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file
2025-08-14T23:09:16.1227305Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2025-08-14T23:09:16.1227983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file
2025-08-14T23:09:16.1228663Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2025-08-14T23:09:16.1229325Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file
2025-08-14T23:09:16.1230000Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2025-08-14T23:09:16.1230662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file
2025-08-14T23:09:16.1241486Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2025-08-14T23:09:16.1245610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file
2025-08-14T23:09:16.1251461Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2025-08-14T23:09:16.1252097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file
2025-08-14T23:09:16.1255770Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0
2025-08-14T23:09:16.1256349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file
2025-08-14T23:09:16.1538030Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2025-08-14T23:09:16.1538787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file
2025-08-14T23:09:16.1544509Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2025-08-14T23:09:16.1545824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file
2025-08-14T23:09:16.1551450Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2025-08-14T23:09:16.1556972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file
2025-08-14T23:09:16.1560865Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2025-08-14T23:09:16.1561334Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file
2025-08-14T23:09:16.1564664Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0
2025-08-14T23:09:16.1565104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file
2025-08-14T23:09:16.1568473Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0
2025-08-14T23:09:16.1568929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file
2025-08-14T23:09:16.1574387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2025-08-14T23:09:16.1574835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file
2025-08-14T23:09:16.1578289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1
2025-08-14T23:09:16.1578752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file
2025-08-14T23:09:16.1582379Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2025-08-14T23:09:16.1582832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file
2025-08-14T23:09:16.1589822Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2025-08-14T23:09:16.1590259Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file
2025-08-14T23:09:16.1590894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1
2025-08-14T23:09:16.1591337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file
2025-08-14T23:09:16.1595803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0
2025-08-14T23:09:16.1596258Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file
2025-08-14T23:09:16.1599843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2025-08-14T23:09:16.1600274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file
2025-08-14T23:09:16.1604335Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2025-08-14T23:09:16.1604782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file
2025-08-14T23:09:16.1612037Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2025-08-14T23:09:16.1612487Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file
2025-08-14T23:09:16.1615660Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2025-08-14T23:09:16.1616103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file
2025-08-14T23:09:16.1618915Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2025-08-14T23:09:16.1619331Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file
2025-08-14T23:09:16.1629572Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0
2025-08-14T23:09:16.1629961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file
2025-08-14T23:09:16.1843093Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0
2025-08-14T23:09:16.1843488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file
2025-08-14T23:09:16.1855517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0
2025-08-14T23:09:16.1857859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file
2025-08-14T23:09:16.1865983Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2025-08-14T23:09:16.1866397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file
2025-08-14T23:09:16.1872419Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2025-08-14T23:09:16.1872846Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file
2025-08-14T23:09:16.1882825Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2025-08-14T23:09:16.1883419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file
2025-08-14T23:09:16.1894080Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2025-08-14T23:09:16.1894540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file
2025-08-14T23:09:16.2026813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2025-08-14T23:09:16.2027375Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file
2025-08-14T23:09:16.3677324Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2025-08-14T23:09:16.3757741Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file
2025-08-14T23:09:16.3760858Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0
2025-08-14T23:09:16.3761267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file
2025-08-14T23:09:16.3795025Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0
2025-08-14T23:09:16.3834021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file
2025-08-14T23:09:16.3873984Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0
2025-08-14T23:09:16.3874470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file
2025-08-14T23:09:16.3879792Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2025-08-14T23:09:16.3880237Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file
2025-08-14T23:09:16.4073480Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2025-08-14T23:09:16.4101074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file
2025-08-14T23:09:16.6433772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2025-08-14T23:09:16.6510407Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file
2025-08-14T23:09:16.6540254Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2025-08-14T23:09:16.6578463Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file
2025-08-14T23:09:16.6582724Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2025-08-14T23:09:16.6584094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file
2025-08-14T23:09:16.6584562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2025-08-14T23:09:16.6585455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file
2025-08-14T23:09:16.6591166Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0
2025-08-14T23:09:16.6591599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file
2025-08-14T23:09:16.6592024Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0
2025-08-14T23:09:16.6592462Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file
2025-08-14T23:09:16.6592993Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0
2025-08-14T23:09:16.6593538Z * 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
2025-08-14T23:09:16.6594018Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.append:0
2025-08-14T23:09:16.6594529Z * 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
2025-08-14T23:09:16.6594988Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.insert:0
2025-08-14T23:09:16.6595442Z * 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
2025-08-14T23:09:16.6597389Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.extend:0
2025-08-14T23:09:16.6597847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file
2025-08-14T23:09:16.6598286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0
2025-08-14T23:09:16.6598711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file
2025-08-14T23:09:16.6599142Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0
2025-08-14T23:09:16.6599572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file
2025-08-14T23:09:16.6600016Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0
2025-08-14T23:09:16.6600452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file
2025-08-14T23:09:16.6600896Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0
2025-08-14T23:09:16.6601306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file
2025-08-14T23:09:16.6613897Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0
2025-08-14T23:09:16.6615947Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file
2025-08-14T23:09:16.6936668Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0
2025-08-14T23:09:16.6938924Z * 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
2025-08-14T23:09:16.6940566Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2025-08-14T23:09:16.6941052Z * 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
2025-08-14T23:09:16.6945090Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2025-08-14T23:09:16.6945565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file
2025-08-14T23:09:16.6946031Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2025-08-14T23:09:16.6946581Z * 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
2025-08-14T23:09:16.6949335Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0
2025-08-14T23:09:16.6954984Z * 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
2025-08-14T23:09:16.6955478Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2025-08-14T23:09:16.6956110Z * 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
2025-08-14T23:09:16.6957804Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0
2025-08-14T23:09:16.6958257Z * 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
2025-08-14T23:09:16.6958738Z * 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
2025-08-14T23:09:16.6959172Z * 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
2025-08-14T23:09:16.6962191Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2025-08-14T23:09:16.6962597Z * 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
2025-08-14T23:09:16.7075103Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2025-08-14T23:09:16.7113638Z * 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
2025-08-14T23:09:16.7117164Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2025-08-14T23:09:16.7117804Z * 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
2025-08-14T23:09:16.7118434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2025-08-14T23:09:16.7119079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file
2025-08-14T23:09:16.7119692Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0
2025-08-14T23:09:16.7120321Z * 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
2025-08-14T23:09:16.7121105Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2025-08-14T23:09:16.7127541Z * 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
2025-08-14T23:09:16.7128118Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2025-08-14T23:09:16.7128560Z * 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
2025-08-14T23:09:16.7129015Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_structured:0
2025-08-14T23:09:16.7129490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file
2025-08-14T23:09:16.7129897Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0
2025-08-14T23:09:16.7130323Z * 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
2025-08-14T23:09:16.7130733Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0
2025-08-14T23:09:16.7131155Z * 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
2025-08-14T23:09:16.7131602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0
2025-08-14T23:09:16.7132036Z * 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
2025-08-14T23:09:16.7132475Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2025-08-14T23:09:16.7133044Z * 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
2025-08-14T23:09:16.7133542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2025-08-14T23:09:16.7134041Z * 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
2025-08-14T23:09:16.7134546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2025-08-14T23:09:16.7135096Z * 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
2025-08-14T23:09:16.7135717Z * 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
2025-08-14T23:09:16.7136065Z ============
2025-08-14T23:09:16.7136181Z Finished doctests
2025-08-14T23:09:16.7136279Z 338 / 732 passed
2025-08-14T23:09:16.7136486Z 
2025-08-14T23:09:16.7136629Z === Found 146 parse-time warnings ===
2025-08-14T23:09:16.7136843Z --- Parse Warning: 1 / 146 ---
2025-08-14T23:09:16.7137224Z     /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.
2025-08-14T23:09:16.7137640Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7137863Z     
2025-08-14T23:09:16.7138009Z     Load a model from a github repo or a local directory.
2025-08-14T23:09:16.7138158Z     
2025-08-14T23:09:16.7138303Z     Note: Loading a model is the typical use case, but this can also be used to
2025-08-14T23:09:16.7138517Z     for loading other objects such as tokenizers, loss functions, etc.
2025-08-14T23:09:16.7138672Z     
2025-08-14T23:09:16.7138794Z     If ``source`` is 'github', ``repo_or_dir`` is expected to be
2025-08-14T23:09:16.7138974Z     of the form ``repo_owner/repo_name[:ref]`` with an optional
2025-08-14T23:09:16.7139132Z     ref (a tag or a branch).
2025-08-14T23:09:16.7139327Z     
2025-08-14T23:09:16.7139552Z     If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2025-08-14T23:09:16.7140008Z     path to a local directory.
2025-08-14T23:09:16.7140163Z     
2025-08-14T23:09:16.7140279Z     Args:
2025-08-14T23:09:16.7140473Z         repo_or_dir (str): If ``source`` is 'github',
2025-08-14T23:09:16.7140754Z             this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2025-08-14T23:09:16.7141103Z             an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2025-08-14T23:09:16.7141458Z             the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2025-08-14T23:09:16.7141748Z             If ``source`` is 'local'  then it should be a path to a local directory.
2025-08-14T23:09:16.7141995Z         model (str): the name of a callable (entrypoint) defined in the
2025-08-14T23:09:16.7142183Z             repo/dir's ``hubconf.py``.
2025-08-14T23:09:16.7142690Z         *args (optional): the corresponding args for callable ``model``.
2025-08-14T23:09:16.7143027Z         source (str, optional): 'github' or 'local'. Specifies how
2025-08-14T23:09:16.7143433Z             ``repo_or_dir`` is to be interpreted. Default is 'github'.
2025-08-14T23:09:16.7143743Z         trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2025-08-14T23:09:16.7144047Z             This parameter was introduced in v1.12 and helps ensuring that users
2025-08-14T23:09:16.7144264Z             only run code from repos that they trust.
2025-08-14T23:09:16.7144430Z     
2025-08-14T23:09:16.7144833Z             - If ``False``, a prompt will ask the user whether the repo should
2025-08-14T23:09:16.7145033Z               be trusted.
2025-08-14T23:09:16.7145227Z             - If ``True``, the repo will be added to the trusted list and loaded
2025-08-14T23:09:16.7145432Z               without requiring explicit confirmation.
2025-08-14T23:09:16.7145675Z             - If ``"check"``, the repo will be checked against the list of
2025-08-14T23:09:16.7145994Z               trusted repos in the cache. If it is not present in that list, the
2025-08-14T23:09:16.7146518Z               behaviour will fall back onto the ``trust_repo=False`` option.
2025-08-14T23:09:16.7146743Z             - If ``None``: this will raise a warning, inviting the user to set
2025-08-14T23:09:16.7146966Z               ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2025-08-14T23:09:16.7147221Z               is only present for backward compatibility and will be removed in
2025-08-14T23:09:16.7147418Z               v2.0.
2025-08-14T23:09:16.7147566Z     
2025-08-14T23:09:16.7147853Z             Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2025-08-14T23:09:16.7148149Z         force_reload (bool, optional): whether to force a fresh download of
2025-08-14T23:09:16.7148953Z             the github repo unconditionally. Does not have any effect if
2025-08-14T23:09:16.7149193Z             ``source = 'local'``. Default is ``False``.
2025-08-14T23:09:16.7149520Z         verbose (bool, optional): If ``False``, mute messages about hitting
2025-08-14T23:09:16.7149830Z             local caches. Note that the message about first download cannot be
2025-08-14T23:09:16.7150059Z             muted. Does not have any effect if ``source = 'local'``.
2025-08-14T23:09:16.7150537Z             Default is ``True``.
2025-08-14T23:09:16.7150778Z         skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2025-08-14T23:09:16.7151768Z             specified by the ``github`` argument properly belongs to the repo owner. This will make
2025-08-14T23:09:16.7152063Z             requests to the GitHub API; you can specify a non-default GitHub token by setting the
2025-08-14T23:09:16.7152356Z             ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2025-08-14T23:09:16.7152578Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-08-14T23:09:16.7152762Z     
2025-08-14T23:09:16.7153124Z     Returns:
2025-08-14T23:09:16.7153287Z         The output of the ``model`` callable when called with the given
2025-08-14T23:09:16.7153516Z         ``*args`` and ``**kwargs``.
2025-08-14T23:09:16.7153651Z     
2025-08-14T23:09:16.7153768Z     Example:
2025-08-14T23:09:16.7153987Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:16.7154165Z         >>> # from a github repo
2025-08-14T23:09:16.7154324Z         >>> repo = "pytorch/vision"
2025-08-14T23:09:16.7154531Z         >>> model = torch.hub.load(
2025-08-14T23:09:16.7154800Z         ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2025-08-14T23:09:16.7155032Z         ... )
2025-08-14T23:09:16.7155179Z         >>> # from a local directory
2025-08-14T23:09:16.7155461Z         >>> path = "/some/local/path/pytorch/vision"
2025-08-14T23:09:16.7155676Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7155935Z         >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2025-08-14T23:09:16.7156155Z     
2025-08-14T23:09:16.7156372Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7156626Z     
2025-08-14T23:09:16.7156802Z       warnings.warn(msg)
2025-08-14T23:09:16.7156987Z     
2025-08-14T23:09:16.7157161Z --- Parse Warning: 2 / 146 ---
2025-08-14T23:09:16.7157632Z     /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.
2025-08-14T23:09:16.7158198Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7158428Z     
2025-08-14T23:09:16.7158640Z     Load a model from a local directory with a ``hubconf.py``.
2025-08-14T23:09:16.7158836Z     
2025-08-14T23:09:16.7159019Z     Args:
2025-08-14T23:09:16.7159538Z         hubconf_dir (str): path to a local directory that contains a
2025-08-14T23:09:16.7159780Z             ``hubconf.py``.
2025-08-14T23:09:16.7160012Z         model (str): name of an entrypoint defined in the directory's
2025-08-14T23:09:16.7160224Z             ``hubconf.py``.
2025-08-14T23:09:16.7160437Z         *args (optional): the corresponding args for callable ``model``.
2025-08-14T23:09:16.7160743Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2025-08-14T23:09:16.7161001Z     
2025-08-14T23:09:16.7161134Z     Returns:
2025-08-14T23:09:16.7161387Z         a single model with corresponding pretrained weights.
2025-08-14T23:09:16.7161653Z     
2025-08-14T23:09:16.7161830Z     Example:
2025-08-14T23:09:16.7162056Z         >>> # xdoctest: +SKIP("stub local path")
2025-08-14T23:09:16.7162341Z         >>> path = "/some/local/path/pytorch/vision"
2025-08-14T23:09:16.7162633Z         >>> model = _load_local(
2025-08-14T23:09:16.7162865Z         ...     path,
2025-08-14T23:09:16.7163210Z         ...     "resnet50",
2025-08-14T23:09:16.7163419Z         ...     weights="ResNet50_Weights.IMAGENET1K_V1",
2025-08-14T23:09:16.7175985Z         ... )
2025-08-14T23:09:16.7176108Z     
2025-08-14T23:09:16.7176262Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7176461Z     
2025-08-14T23:09:16.7176584Z       warnings.warn(msg)
2025-08-14T23:09:16.7176701Z     
2025-08-14T23:09:16.7176908Z --- Parse Warning: 3 / 146 ---
2025-08-14T23:09:16.7177323Z     /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.
2025-08-14T23:09:16.7177780Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7178001Z     Download object at the given URL to a local path.
2025-08-14T23:09:16.7178171Z     
2025-08-14T23:09:16.7178279Z         Args:
2025-08-14T23:09:16.7178386Z             url (str): URL of the object to download
2025-08-14T23:09:16.7178562Z             dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2025-08-14T23:09:16.7178814Z             hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2025-08-14T23:09:16.7179027Z                 Default: None
2025-08-14T23:09:16.7179214Z             progress (bool, optional): whether or not to display a progress bar to stderr
2025-08-14T23:09:16.7179395Z                 Default: True
2025-08-14T23:09:16.7179520Z     
2025-08-14T23:09:16.7179606Z         Example:
2025-08-14T23:09:16.7179723Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:16.7179865Z             >>> # xdoctest: +REQUIRES(POSIX)
2025-08-14T23:09:16.7180006Z             >>> torch.hub.download_url_to_file(
2025-08-14T23:09:16.7180199Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2025-08-14T23:09:16.7180373Z             ...     "/tmp/temporary_file",
2025-08-14T23:09:16.7180488Z             ... )
2025-08-14T23:09:16.7180578Z     
2025-08-14T23:09:16.7180664Z         
2025-08-14T23:09:16.7180806Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7180970Z     
2025-08-14T23:09:16.7181058Z       warnings.warn(msg)
2025-08-14T23:09:16.7181164Z     
2025-08-14T23:09:16.7181284Z --- Parse Warning: 4 / 146 ---
2025-08-14T23:09:16.7181665Z     /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.
2025-08-14T23:09:16.7182115Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7182305Z     Loads the Torch serialized object at the given URL.
2025-08-14T23:09:16.7182433Z     
2025-08-14T23:09:16.7182546Z         If downloaded file is a zip file, it will be automatically
2025-08-14T23:09:16.7182691Z         decompressed.
2025-08-14T23:09:16.7182795Z     
2025-08-14T23:09:16.7183014Z         If the object is already present in `model_dir`, it's deserialized and
2025-08-14T23:09:16.7183170Z         returned.
2025-08-14T23:09:16.7183311Z         The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2025-08-14T23:09:16.7183504Z         ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2025-08-14T23:09:16.7183647Z     
2025-08-14T23:09:16.7183724Z         Args:
2025-08-14T23:09:16.7183828Z             url (str): URL of the object to download
2025-08-14T23:09:16.7183988Z             model_dir (str, optional): directory in which to save the object
2025-08-14T23:09:16.7184228Z             map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2025-08-14T23:09:16.7184490Z             progress (bool, optional): whether or not to display a progress bar to stderr.
2025-08-14T23:09:16.7184666Z                 Default: True
2025-08-14T23:09:16.7184858Z             check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2025-08-14T23:09:16.7185103Z                 ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2025-08-14T23:09:16.7185324Z                 digits of the SHA256 hash of the contents of the file. The hash is used to
2025-08-14T23:09:16.7185589Z                 ensure unique names and to verify the contents of the file.
2025-08-14T23:09:16.7185747Z                 Default: False
2025-08-14T23:09:16.7185942Z             file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2025-08-14T23:09:16.7186211Z             weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2025-08-14T23:09:16.7186461Z                 Recommended for untrusted sources. See :func:`~torch.load` for more details.
2025-08-14T23:09:16.7186622Z     
2025-08-14T23:09:16.7186701Z         Example:
2025-08-14T23:09:16.7186827Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2025-08-14T23:09:16.7186991Z             >>> state_dict = torch.hub.load_state_dict_from_url(
2025-08-14T23:09:16.7187175Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2025-08-14T23:09:16.7187330Z             ... )
2025-08-14T23:09:16.7187438Z     
2025-08-14T23:09:16.7187521Z         
2025-08-14T23:09:16.7187671Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7187962Z     
2025-08-14T23:09:16.7188052Z       warnings.warn(msg)
2025-08-14T23:09:16.7188152Z     
2025-08-14T23:09:16.7188267Z --- Parse Warning: 5 / 146 ---
2025-08-14T23:09:16.7188646Z     /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.
2025-08-14T23:09:16.7189068Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7189235Z     
2025-08-14T23:09:16.7189338Z     dim_order(ambiguity_check=False) -> tuple
2025-08-14T23:09:16.7189466Z     
2025-08-14T23:09:16.7189602Z     Returns the uniquely determined tuple of int describing the dim order or
2025-08-14T23:09:16.7189778Z     physical layout of :attr:`self`.
2025-08-14T23:09:16.7189891Z     
2025-08-14T23:09:16.7190032Z     The dim order represents how dimensions are laid out in memory of dense tensors,
2025-08-14T23:09:16.7190230Z     starting from the outermost to the innermost dimension.
2025-08-14T23:09:16.7190372Z     
2025-08-14T23:09:16.7190491Z     Note that the dim order may not always be uniquely determined.
2025-08-14T23:09:16.7190738Z     If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
2025-08-14T23:09:16.7191052Z     If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted
2025-08-14T23:09:16.7191321Z     into exactly one of the given memory formats, or it cannot be uniquely determined.
2025-08-14T23:09:16.7191570Z     If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness.
2025-08-14T23:09:16.7191774Z     Otherwise, it will raise TypeError.
2025-08-14T23:09:16.7191891Z     
2025-08-14T23:09:16.7191970Z     Args:
2025-08-14T23:09:16.7192135Z         ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order.
2025-08-14T23:09:16.7192319Z     
2025-08-14T23:09:16.7192409Z     Examples::
2025-08-14T23:09:16.7192496Z     
2025-08-14T23:09:16.7192591Z         >>> torch.empty((2, 3, 5, 7)).dim_order()
2025-08-14T23:09:16.7192719Z         (0, 1, 2, 3)
2025-08-14T23:09:16.7192846Z         >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order()
2025-08-14T23:09:16.7193129Z         (0, 2, 1, 3)
2025-08-14T23:09:16.7193272Z         >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order()
2025-08-14T23:09:16.7193425Z         (0, 2, 3, 1)
2025-08-14T23:09:16.7193533Z         >>> torch.empty((1, 2, 3, 4)).dim_order()
2025-08-14T23:09:16.7193648Z         (0, 1, 2, 3)
2025-08-14T23:09:16.7193744Z         >>> try:
2025-08-14T23:09:16.7193874Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True)
2025-08-14T23:09:16.7194055Z         ... except RuntimeError as e:
2025-08-14T23:09:16.7194196Z         ...     print(e)
2025-08-14T23:09:16.7194376Z         The tensor does not have unique dim order, or cannot map to exact one of the given memory formats.
2025-08-14T23:09:16.7194576Z         >>> torch.empty((1, 2, 3, 4)).dim_order(
2025-08-14T23:09:16.7194744Z         ...     ambiguity_check=[torch.contiguous_format, torch.channels_last]
2025-08-14T23:09:16.7194926Z         ... )  # It can be mapped to contiguous format
2025-08-14T23:09:16.7195056Z         (0, 1, 2, 3)
2025-08-14T23:09:16.7195154Z         >>> try:
2025-08-14T23:09:16.7195280Z         ...     torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL")
2025-08-14T23:09:16.7195430Z         ... except TypeError as e:
2025-08-14T23:09:16.7195549Z         ...     print(e)
2025-08-14T23:09:16.7195703Z         The ambiguity_check argument must be a bool or a list of memory formats.
2025-08-14T23:09:16.7195869Z     
2025-08-14T23:09:16.7195973Z     .. warning::
2025-08-14T23:09:16.7196101Z         The dim_order tensor API is experimental and subject to change.
2025-08-14T23:09:16.7196246Z     
2025-08-14T23:09:16.7196411Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7196578Z     
2025-08-14T23:09:16.7196667Z       warnings.warn(msg)
2025-08-14T23:09:16.7196770Z     
2025-08-14T23:09:16.7196888Z --- Parse Warning: 6 / 146 ---
2025-08-14T23:09:16.7197272Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=446.
2025-08-14T23:09:16.7197691Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7197915Z     Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2025-08-14T23:09:16.7198078Z     
2025-08-14T23:09:16.7198203Z             This is helpful when you want to visualize data over some
2025-08-14T23:09:16.7198380Z             range of inputs. See below for a plotting example.
2025-08-14T23:09:16.7198517Z     
2025-08-14T23:09:16.7198639Z             Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2025-08-14T23:09:16.7198820Z             inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2025-08-14T23:09:16.7199005Z             this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2025-08-14T23:09:16.7199179Z             G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2025-08-14T23:09:16.7199355Z             the output :math:`G_i` is constructed by expanding :math:`T_i`
2025-08-14T23:09:16.7199512Z             to the result shape.
2025-08-14T23:09:16.7199628Z     
2025-08-14T23:09:16.7199718Z             .. note::
2025-08-14T23:09:16.7199846Z                 0D inputs are treated equivalently to 1D inputs of a
2025-08-14T23:09:16.7200048Z                 single element.
2025-08-14T23:09:16.7200165Z     
2025-08-14T23:09:16.7200265Z             .. warning::
2025-08-14T23:09:16.7200403Z                 `torch.meshgrid(*tensors)` currently has the same behavior
2025-08-14T23:09:16.7200580Z                 as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2025-08-14T23:09:16.7200722Z     
2025-08-14T23:09:16.7200922Z                 In the future `torch.meshgrid` will transition to
2025-08-14T23:09:16.7201105Z                 `indexing='xy'` as the default.
2025-08-14T23:09:16.7201259Z     
2025-08-14T23:09:16.7201580Z                 https://github.com/pytorch/pytorch/issues/50276 tracks
2025-08-14T23:09:16.7201789Z                 this issue with the goal of migrating to NumPy's behavior.
2025-08-14T23:09:16.7201973Z     
2025-08-14T23:09:16.7202097Z             .. seealso::
2025-08-14T23:09:16.7202230Z     
2025-08-14T23:09:16.7202372Z                 :func:`torch.cartesian_prod` has the same effect but it
2025-08-14T23:09:16.7202576Z                 collects the data in a tensor of vectors.
2025-08-14T23:09:16.7202754Z     
2025-08-14T23:09:16.7202885Z             Args:
2025-08-14T23:09:16.7203171Z                 tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2025-08-14T23:09:16.7203678Z                     treated as tensors of size :math:`(1,)` automatically
2025-08-14T23:09:16.7203882Z     
2025-08-14T23:09:16.7204027Z                 indexing: (str, optional): the indexing mode, either "xy"
2025-08-14T23:09:16.7204236Z                     or "ij", defaults to "ij". See warning for future changes.
2025-08-14T23:09:16.7204413Z     
2025-08-14T23:09:16.7204559Z                     If "xy" is selected, the first dimension corresponds
2025-08-14T23:09:16.7204776Z                     to the cardinality of the second input and the second
2025-08-14T23:09:16.7204970Z                     dimension corresponds to the cardinality of the first
2025-08-14T23:09:16.7205143Z                     input.
2025-08-14T23:09:16.7205293Z     
2025-08-14T23:09:16.7205449Z                     If "ij" is selected, the dimensions are in the same
2025-08-14T23:09:16.7205630Z                     order as the cardinality of the inputs.
2025-08-14T23:09:16.7205872Z     
2025-08-14T23:09:16.7206007Z             Returns:
2025-08-14T23:09:16.7206178Z                 seq (sequence of Tensors): If the input has :math:`N`
2025-08-14T23:09:16.7206382Z                 tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2025-08-14T23:09:16.7206583Z                 output will also have :math:`N` tensors, where each tensor
2025-08-14T23:09:16.7206796Z                 is of shape :math:`(S_0, ..., S_{N-1})`.
2025-08-14T23:09:16.7206951Z     
2025-08-14T23:09:16.7207064Z             Example::
2025-08-14T23:09:16.7207205Z     
2025-08-14T23:09:16.7207902Z                 >>> x = torch.tensor([1, 2, 3])
2025-08-14T23:09:16.7208046Z                 >>> y = torch.tensor([4, 5, 6])
2025-08-14T23:09:16.7208218Z     
2025-08-14T23:09:16.7208381Z                 Observe the element-wise pairings across the grid, (1, 4),
2025-08-14T23:09:16.7208593Z                 (1, 5), ..., (3, 6). This is the same thing as the
2025-08-14T23:09:16.7208774Z                 cartesian product.
2025-08-14T23:09:16.7208958Z                 >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2025-08-14T23:09:16.7209151Z                 >>> grid_x
2025-08-14T23:09:16.7209298Z                 tensor([[1, 1, 1],
2025-08-14T23:09:16.7209438Z                         [2, 2, 2],
2025-08-14T23:09:16.7209613Z                         [3, 3, 3]])
2025-08-14T23:09:16.7209756Z                 >>> grid_y
2025-08-14T23:09:16.7209904Z                 tensor([[4, 5, 6],
2025-08-14T23:09:16.7210056Z                         [4, 5, 6],
2025-08-14T23:09:16.7210187Z                         [4, 5, 6]])
2025-08-14T23:09:16.7210329Z     
2025-08-14T23:09:16.7210483Z                 This correspondence can be seen when these grids are
2025-08-14T23:09:16.7210664Z                 stacked properly.
2025-08-14T23:09:16.7210862Z                 >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2025-08-14T23:09:16.7211068Z                 ...             torch.cartesian_prod(x, y))
2025-08-14T23:09:16.7211223Z                 True
2025-08-14T23:09:16.7211379Z     
2025-08-14T23:09:16.7211523Z                 `torch.meshgrid` is commonly used to produce a grid for
2025-08-14T23:09:16.7211717Z                 plotting.
2025-08-14T23:09:16.7211878Z                 >>> # xdoctest: +REQUIRES(module:matplotlib)
2025-08-14T23:09:16.7212055Z                 >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2025-08-14T23:09:16.7212265Z                 >>> import matplotlib.pyplot as plt
2025-08-14T23:09:16.7212429Z                 >>> xs = torch.linspace(-5, 5, steps=100)
2025-08-14T23:09:16.7212600Z                 >>> ys = torch.linspace(-5, 5, steps=100)
2025-08-14T23:09:16.7212804Z                 >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2025-08-14T23:09:16.7213320Z                 >>> z = torch.sin(torch.sqrt(x * x + y * y))
2025-08-14T23:09:16.7213500Z                 >>> ax = plt.axes(projection='3d')
2025-08-14T23:09:16.7213747Z                 >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2025-08-14T23:09:16.7213926Z                 >>> plt.show()
2025-08-14T23:09:16.7214073Z     
2025-08-14T23:09:16.7214210Z             .. image:: ../_static/img/meshgrid.png
2025-08-14T23:09:16.7214378Z                 :width: 512
2025-08-14T23:09:16.7214523Z     
2025-08-14T23:09:16.7214639Z             
2025-08-14T23:09:16.7214809Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7215031Z     
2025-08-14T23:09:16.7215143Z       warnings.warn(msg)
2025-08-14T23:09:16.7215271Z     
2025-08-14T23:09:16.7215744Z --- Parse Warning: 7 / 146 ---
2025-08-14T23:09:16.7216165Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=842.
2025-08-14T23:09:16.7216644Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7216974Z     unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor]
2025-08-14T23:09:16.7217252Z     
2025-08-14T23:09:16.7236553Z         Returns the unique elements of the input tensor.
2025-08-14T23:09:16.7236721Z     
2025-08-14T23:09:16.7236897Z         .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2025-08-14T23:09:16.7237126Z             this function also eliminates non-consecutive duplicate values.
2025-08-14T23:09:16.7237278Z     
2025-08-14T23:09:16.7237418Z         .. note:: Currently in the CUDA implementation and the CPU implementation,
2025-08-14T23:09:16.7237649Z             `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2025-08-14T23:09:16.7237900Z             Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2025-08-14T23:09:16.7238111Z             :func:`torch.unique_consecutive` which avoids the sorting.
2025-08-14T23:09:16.7238257Z     
2025-08-14T23:09:16.7238345Z         Args:
2025-08-14T23:09:16.7238453Z             input (Tensor): the input tensor
2025-08-14T23:09:16.7238624Z             sorted (bool): Whether to sort the unique elements in ascending order
2025-08-14T23:09:16.7238790Z                 before returning as output.
2025-08-14T23:09:16.7238953Z             return_inverse (bool): Whether to also return the indices for where
2025-08-14T23:09:16.7239151Z                 elements in the original input ended up in the returned unique list.
2025-08-14T23:09:16.7239359Z             return_counts (bool): Whether to also return the counts for each unique
2025-08-14T23:09:16.7239521Z                 element.
2025-08-14T23:09:16.7239670Z             dim (int, optional): the dimension to operate upon. If ``None``, the
2025-08-14T23:09:16.7239860Z                 unique of the flattened input is returned. Otherwise, each of the
2025-08-14T23:09:16.7240057Z                 tensors indexed by the given dimension is treated as one of the
2025-08-14T23:09:16.7240248Z                 elements to apply the unique operation upon. See examples for more
2025-08-14T23:09:16.7240413Z                 details. Default: ``None``
2025-08-14T23:09:16.7240523Z     
2025-08-14T23:09:16.7240602Z         Returns:
2025-08-14T23:09:16.7240770Z             (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2025-08-14T23:09:16.7240948Z     
2025-08-14T23:09:16.7241071Z                 - **output** (*Tensor*): the output list of unique scalar elements.
2025-08-14T23:09:16.7241239Z                 - **inverse_indices** (*Tensor*): (optional) if
2025-08-14T23:09:16.7241405Z                   :attr:`return_inverse` is True, there will be an additional
2025-08-14T23:09:16.7241587Z                   returned tensor (same shape as input) representing the indices
2025-08-14T23:09:16.7241840Z                   for where elements in the original input map to in the output;
2025-08-14T23:09:16.7242044Z                   otherwise, this function will only return a single tensor.
2025-08-14T23:09:16.7242206Z                 - **counts** (*Tensor*): (optional) if
2025-08-14T23:09:16.7242361Z                   :attr:`return_counts` is True, there will be an additional
2025-08-14T23:09:16.7242536Z                   returned tensor (same shape as output or output.size(dim),
2025-08-14T23:09:16.7242715Z                   if dim was specified) representing the number of occurrences
2025-08-14T23:09:16.7242872Z                   for each unique value or tensor.
2025-08-14T23:09:16.7243081Z     
2025-08-14T23:09:16.7243167Z         Example::
2025-08-14T23:09:16.7243261Z     
2025-08-14T23:09:16.7243382Z             >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2025-08-14T23:09:16.7243534Z             >>> output
2025-08-14T23:09:16.7243642Z             tensor([1, 2, 3])
2025-08-14T23:09:16.7243768Z     
2025-08-14T23:09:16.7243868Z             >>> output, inverse_indices = torch.unique(
2025-08-14T23:09:16.7244047Z             ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2025-08-14T23:09:16.7244225Z             >>> output
2025-08-14T23:09:16.7244325Z             tensor([1, 2, 3])
2025-08-14T23:09:16.7244434Z             >>> inverse_indices
2025-08-14T23:09:16.7244544Z             tensor([0, 2, 1, 2])
2025-08-14T23:09:16.7244647Z     
2025-08-14T23:09:16.7244742Z             >>> output, inverse_indices = torch.unique(
2025-08-14T23:09:16.7244914Z             ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2025-08-14T23:09:16.7245070Z             >>> output
2025-08-14T23:09:16.7245166Z             tensor([1, 2, 3])
2025-08-14T23:09:16.7245274Z             >>> inverse_indices
2025-08-14T23:09:16.7245377Z             tensor([[0, 2],
2025-08-14T23:09:16.7245478Z                     [1, 2]])
2025-08-14T23:09:16.7245574Z     
2025-08-14T23:09:16.7245658Z             >>> a = torch.tensor([
2025-08-14T23:09:16.7245825Z             ...     [
2025-08-14T23:09:16.7245921Z             ...         [1, 1, 0, 0],
2025-08-14T23:09:16.7246029Z             ...         [1, 1, 0, 0],
2025-08-14T23:09:16.7246139Z             ...         [0, 0, 1, 1],
2025-08-14T23:09:16.7246242Z             ...     ],
2025-08-14T23:09:16.7246332Z             ...     [
2025-08-14T23:09:16.7246423Z             ...         [0, 0, 1, 1],
2025-08-14T23:09:16.7246528Z             ...         [0, 0, 1, 1],
2025-08-14T23:09:16.7246630Z             ...         [1, 1, 1, 1],
2025-08-14T23:09:16.7246733Z             ...     ],
2025-08-14T23:09:16.7246823Z             ...     [
2025-08-14T23:09:16.7246917Z             ...         [1, 1, 0, 0],
2025-08-14T23:09:16.7247019Z             ...         [1, 1, 0, 0],
2025-08-14T23:09:16.7247121Z             ...         [0, 0, 1, 1],
2025-08-14T23:09:16.7247222Z             ...     ],
2025-08-14T23:09:16.7247313Z             ... ])
2025-08-14T23:09:16.7247397Z     
2025-08-14T23:09:16.7247518Z             >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2025-08-14T23:09:16.7247710Z             >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2025-08-14T23:09:16.7247876Z             >>> # each other, so one of them will be removed.
2025-08-14T23:09:16.7248010Z             >>> (a[0, :, :] == a[2, :, :]).all()
2025-08-14T23:09:16.7248128Z             tensor(True)
2025-08-14T23:09:16.7248241Z             >>> a_unique_dim0 = torch.unique(a, dim=0)
2025-08-14T23:09:16.7248366Z             >>> a_unique_dim0
2025-08-14T23:09:16.7248471Z             tensor([[[0, 0, 1, 1],
2025-08-14T23:09:16.7248582Z                      [0, 0, 1, 1],
2025-08-14T23:09:16.7248688Z                      [1, 1, 1, 1]],
2025-08-14T23:09:16.7248793Z                     [[1, 1, 0, 0],
2025-08-14T23:09:16.7248894Z                      [1, 1, 0, 0],
2025-08-14T23:09:16.7248998Z                      [0, 0, 1, 1]]])
2025-08-14T23:09:16.7249122Z     
2025-08-14T23:09:16.7249264Z             >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2025-08-14T23:09:16.7249420Z             >>> # `a_unique_dim0`:
2025-08-14T23:09:16.7249540Z             >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2025-08-14T23:09:16.7249661Z             tensor(True)
2025-08-14T23:09:16.7249768Z             >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2025-08-14T23:09:16.7249888Z             tensor(True)
2025-08-14T23:09:16.7249981Z     
2025-08-14T23:09:16.7250101Z             >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2025-08-14T23:09:16.7250285Z             >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2025-08-14T23:09:16.7250432Z             >>> # them will be removed.
2025-08-14T23:09:16.7250549Z             >>> (a[:, 0, :] == a[:, 1, :]).all()
2025-08-14T23:09:16.7250660Z             tensor(True)
2025-08-14T23:09:16.7250779Z             >>> torch.unique(a, dim=1)
2025-08-14T23:09:16.7250896Z             tensor([[[0, 0, 1, 1],
2025-08-14T23:09:16.7251002Z                      [1, 1, 0, 0]],
2025-08-14T23:09:16.7251133Z                     [[1, 1, 1, 1],
2025-08-14T23:09:16.7251239Z                      [0, 0, 1, 1]],
2025-08-14T23:09:16.7251340Z                     [[0, 0, 1, 1],
2025-08-14T23:09:16.7251441Z                      [1, 1, 0, 0]]])
2025-08-14T23:09:16.7251541Z     
2025-08-14T23:09:16.7251659Z             >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2025-08-14T23:09:16.7251830Z             >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2025-08-14T23:09:16.7251989Z             >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2025-08-14T23:09:16.7252134Z             >>> # sub-tensors will be removed.
2025-08-14T23:09:16.7252258Z             >>> (a[:, :, 0] == a[:, :, 1]).all()
2025-08-14T23:09:16.7252369Z             tensor(True)
2025-08-14T23:09:16.7252474Z             >>> (a[:, :, 2] == a[:, :, 3]).all()
2025-08-14T23:09:16.7252584Z             tensor(True)
2025-08-14T23:09:16.7252687Z             >>> torch.unique(a, dim=2)
2025-08-14T23:09:16.7252800Z             tensor([[[0, 1],
2025-08-14T23:09:16.7253012Z                      [0, 1],
2025-08-14T23:09:16.7253112Z                      [1, 0]],
2025-08-14T23:09:16.7253210Z                     [[1, 0],
2025-08-14T23:09:16.7253306Z                      [1, 0],
2025-08-14T23:09:16.7253401Z                      [1, 1]],
2025-08-14T23:09:16.7253498Z                     [[0, 1],
2025-08-14T23:09:16.7253592Z                      [0, 1],
2025-08-14T23:09:16.7253688Z                      [1, 0]]])
2025-08-14T23:09:16.7253784Z         
2025-08-14T23:09:16.7253922Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7254080Z     
2025-08-14T23:09:16.7254158Z       warnings.warn(msg)
2025-08-14T23:09:16.7254250Z     
2025-08-14T23:09:16.7254387Z --- Parse Warning: 8 / 146 ---
2025-08-14T23:09:16.7254758Z     /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.
2025-08-14T23:09:16.7255163Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7255411Z     load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args)
2025-08-14T23:09:16.7255593Z     
2025-08-14T23:09:16.7255705Z         Loads an object saved with :func:`torch.save` from a file.
2025-08-14T23:09:16.7255837Z     
2025-08-14T23:09:16.7255961Z         :func:`torch.load` uses Python's unpickling facilities but treats storages,
2025-08-14T23:09:16.7256165Z         which underlie tensors, specially. They are first deserialized on the
2025-08-14T23:09:16.7256362Z         CPU and are then moved to the device they were saved from. If this fails
2025-08-14T23:09:16.7256610Z         (e.g. because the run time system doesn't have certain devices), an exception
2025-08-14T23:09:16.7256813Z         is raised. However, storages can be dynamically remapped to an alternative
2025-08-14T23:09:16.7256999Z         set of devices using the :attr:`map_location` argument.
2025-08-14T23:09:16.7257131Z     
2025-08-14T23:09:16.7257261Z         If :attr:`map_location` is a callable, it will be called once for each serialized
2025-08-14T23:09:16.7257462Z         storage with two arguments: storage and location. The storage argument
2025-08-14T23:09:16.7257659Z         will be the initial deserialization of the storage, residing on the CPU.
2025-08-14T23:09:16.7257857Z         Each serialized storage has a location tag associated with it which
2025-08-14T23:09:16.7258046Z         identifies the device it was saved from, and this tag is the second
2025-08-14T23:09:16.7258248Z         argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2025-08-14T23:09:16.7258479Z         for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2025-08-14T23:09:16.7258672Z         :attr:`map_location` should return either ``None`` or a storage. If
2025-08-14T23:09:16.7258891Z         :attr:`map_location` returns a storage, it will be used as the final deserialized
2025-08-14T23:09:16.7259099Z         object, already moved to the right device. Otherwise, :func:`torch.load` will
2025-08-14T23:09:16.7259312Z         fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2025-08-14T23:09:16.7259470Z     
2025-08-14T23:09:16.7259596Z         If :attr:`map_location` is a :class:`torch.device` object or a string containing
2025-08-14T23:09:16.7259799Z         a device tag, it indicates the location where all tensors should be loaded.
2025-08-14T23:09:16.7259949Z     
2025-08-14T23:09:16.7260083Z         Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2025-08-14T23:09:16.7260287Z         appearing in the file (keys), to ones that specify where to put the
2025-08-14T23:09:16.7260440Z         storages (values).
2025-08-14T23:09:16.7260536Z     
2025-08-14T23:09:16.7260657Z         User extensions can register their own location tags and tagging and
2025-08-14T23:09:16.7260867Z         deserialization methods using :func:`torch.serialization.register_package`.
2025-08-14T23:09:16.7261028Z     
2025-08-14T23:09:16.7261159Z         See :ref:`layout-control` for more advanced tools to manipulate a checkpoint.
2025-08-14T23:09:16.7261315Z     
2025-08-14T23:09:16.7261389Z         Args:
2025-08-14T23:09:16.7261557Z             f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2025-08-14T23:09:16.7261771Z                 or a string or os.PathLike object containing a file name
2025-08-14T23:09:16.7261987Z             map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2025-08-14T23:09:16.7262174Z                 locations
2025-08-14T23:09:16.7262321Z             pickle_module: module used for unpickling metadata and objects (has to
2025-08-14T23:09:16.7262506Z                 match the :attr:`pickle_module` used to serialize file)
2025-08-14T23:09:16.7262683Z             weights_only: Indicates whether unpickler should be restricted to
2025-08-14T23:09:16.7262856Z                 loading only tensors, primitive types, dictionaries
2025-08-14T23:09:16.7263135Z                 and any types added via :func:`torch.serialization.add_safe_globals`.
2025-08-14T23:09:16.7263302Z                 See :ref:`weights-only` for more details.
2025-08-14T23:09:16.7263502Z             mmap: Indicates whether the file should be mapped rather than loading all the storages into memory.
2025-08-14T23:09:16.7263767Z                 Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2025-08-14T23:09:16.7264033Z                 are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2025-08-14T23:09:16.7264348Z                 second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2025-08-14T23:09:16.7264612Z                 tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages
2025-08-14T23:09:16.7264820Z                 will be lazily loaded when their data is accessed.
2025-08-14T23:09:16.7264999Z             pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2025-08-14T23:09:16.7265202Z                 :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2025-08-14T23:09:16.7265357Z                 :attr:`errors=...`.
2025-08-14T23:09:16.7265461Z     
2025-08-14T23:09:16.7265538Z         .. warning::
2025-08-14T23:09:16.7265674Z             :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2025-08-14T23:09:16.7265877Z             uses ``pickle`` module implicitly, which is known to be insecure.
2025-08-14T23:09:16.7266084Z             It is possible to construct malicious pickle data which will execute arbitrary code
2025-08-14T23:09:16.7266300Z             during unpickling. Never load data that could have come from an untrusted
2025-08-14T23:09:16.7266538Z             source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2025-08-14T23:09:16.7266705Z     
2025-08-14T23:09:16.7266778Z         .. note::
2025-08-14T23:09:16.7266924Z             When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2025-08-14T23:09:16.7267144Z             will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2025-08-14T23:09:16.7267369Z             and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2025-08-14T23:09:16.7267530Z     
2025-08-14T23:09:16.7267603Z         .. note::
2025-08-14T23:09:16.7267747Z             By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2025-08-14T23:09:16.7267949Z             case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2025-08-14T23:09:16.7268135Z             when loading files saved by Python 2 in Python 3.  If this default
2025-08-14T23:09:16.7268336Z             is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2025-08-14T23:09:16.7268548Z             these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2025-08-14T23:09:16.7268756Z             to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2025-08-14T23:09:16.7268958Z             as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2025-08-14T23:09:16.7269104Z     
2025-08-14T23:09:16.7269179Z         Example:
2025-08-14T23:09:16.7269285Z             >>> # xdoctest: +SKIP("undefined filepaths")
2025-08-14T23:09:16.7269427Z             >>> torch.load("tensors.pt", weights_only=True)
2025-08-14T23:09:16.7269565Z             # Load all tensors onto the CPU
2025-08-14T23:09:16.7269682Z             >>> torch.load(
2025-08-14T23:09:16.7269789Z             ...     "tensors.pt",
2025-08-14T23:09:16.7269913Z             ...     map_location=torch.device("cpu"),
2025-08-14T23:09:16.7270040Z             ...     weights_only=True,
2025-08-14T23:09:16.7270147Z             ... )
2025-08-14T23:09:16.7270255Z             # Load all tensors onto the CPU, using a function
2025-08-14T23:09:16.7270382Z             >>> torch.load(
2025-08-14T23:09:16.7270483Z             ...     "tensors.pt",
2025-08-14T23:09:16.7270607Z             ...     map_location=lambda storage, loc: storage,
2025-08-14T23:09:16.7270738Z             ...     weights_only=True,
2025-08-14T23:09:16.7270842Z             ... )
2025-08-14T23:09:16.7270936Z             # Load all tensors onto GPU 1
2025-08-14T23:09:16.7271050Z             >>> torch.load(
2025-08-14T23:09:16.7271149Z             ...     "tensors.pt",
2025-08-14T23:09:16.7271280Z             ...     map_location=lambda storage, loc: storage.cuda(1),
2025-08-14T23:09:16.7271450Z             ...     weights_only=True,
2025-08-14T23:09:16.7271570Z             ... )  # type: ignore[attr-defined]
2025-08-14T23:09:16.7271699Z             # Map tensors from GPU 1 to GPU 0
2025-08-14T23:09:16.7271814Z             >>> torch.load(
2025-08-14T23:09:16.7271912Z             ...     "tensors.pt",
2025-08-14T23:09:16.7272031Z             ...     map_location={"cuda:1": "cuda:0"},
2025-08-14T23:09:16.7272153Z             ...     weights_only=True,
2025-08-14T23:09:16.7272257Z             ... )
2025-08-14T23:09:16.7272355Z             # Load tensor from io.BytesIO object
2025-08-14T23:09:16.7272530Z             # Loading from a buffer setting weights_only=False, warning this can be unsafe
2025-08-14T23:09:16.7272704Z             >>> with open("tensor.pt", "rb") as f:
2025-08-14T23:09:16.7272830Z             ...     buffer = io.BytesIO(f.read())
2025-08-14T23:09:16.7273409Z             >>> torch.load(buffer, weights_only=False)
2025-08-14T23:09:16.7273556Z             # Load a module with 'ascii' encoding for unpickling
2025-08-14T23:09:16.7273737Z             # Loading from a module setting weights_only=False, warning this can be unsafe
2025-08-14T23:09:16.7273949Z             >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2025-08-14T23:09:16.7274091Z         
2025-08-14T23:09:16.7274227Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7274387Z     
2025-08-14T23:09:16.7274470Z       warnings.warn(msg)
2025-08-14T23:09:16.7274564Z     
2025-08-14T23:09:16.7274687Z --- Parse Warning: 9 / 146 ---
2025-08-14T23:09:16.7275057Z     /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.
2025-08-14T23:09:16.7275462Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7275675Z     Registers the function implementation as the fallback for the given key.
2025-08-14T23:09:16.7275830Z     
2025-08-14T23:09:16.7275953Z             This function only works for a library with global namespace ("_").
2025-08-14T23:09:16.7280080Z     
2025-08-14T23:09:16.7280167Z             Args:
2025-08-14T23:09:16.7280321Z                 fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2025-08-14T23:09:16.7280504Z                     to register a fallthrough.
2025-08-14T23:09:16.7280699Z                 dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2025-08-14T23:09:16.7280910Z                               the dispatch key that the library was created with.
2025-08-14T23:09:16.7281140Z                 with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2025-08-14T23:09:16.7281420Z                              to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2025-08-14T23:09:16.7281602Z     
2025-08-14T23:09:16.7281685Z             Example::
2025-08-14T23:09:16.7281777Z     
2025-08-14T23:09:16.7287436Z                 >>> my_lib = Library("_", "IMPL")
2025-08-14T23:09:16.7287591Z                 >>> def fallback_kernel(op, *args, **kwargs):
2025-08-14T23:09:16.7287730Z                 >>>     # Handle all autocast ops generically
2025-08-14T23:09:16.7287854Z                 >>>     # ...
2025-08-14T23:09:16.7287974Z                 >>> my_lib.fallback(fallback_kernel, "Autocast")
2025-08-14T23:09:16.7288097Z             
2025-08-14T23:09:16.7288358Z     Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2025-08-14T23:09:16.7288631Z     
2025-08-14T23:09:16.7288726Z     my_lib.fallback(fallback_kernel, "Autocast")
2025-08-14T23:09:16.7288844Z     ^
2025-08-14T23:09:16.7288980Z       warnings.warn(msg)
2025-08-14T23:09:16.7289096Z     
2025-08-14T23:09:16.7290673Z --- Parse Warning: 10 / 146 ---
2025-08-14T23:09:16.7291043Z     /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.
2025-08-14T23:09:16.7291438Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7291647Z     Register a FakeTensor implementation ("fake impl") for this operator.
2025-08-14T23:09:16.7291795Z     
2025-08-14T23:09:16.7291904Z         Also sometimes known as a "meta kernel", "abstract impl".
2025-08-14T23:09:16.7292035Z     
2025-08-14T23:09:16.7292160Z         An "FakeTensor implementation" specifies the behavior of this operator on
2025-08-14T23:09:16.7292359Z         Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2025-08-14T23:09:16.7292584Z         certain properties (sizes/strides/storage_offset/device), it specifies
2025-08-14T23:09:16.7292757Z         what the properties of the output Tensors are.
2025-08-14T23:09:16.7294290Z     
2025-08-14T23:09:16.7294417Z         The FakeTensor implementation has the same signature as the operator.
2025-08-14T23:09:16.7294605Z         It is run for both FakeTensors and meta tensors. To write a FakeTensor
2025-08-14T23:09:16.7294791Z         implementation, assume that all Tensor inputs to the operator are
2025-08-14T23:09:16.7294980Z         regular CPU/CUDA/Meta tensors, but they do not have storage, and
2025-08-14T23:09:16.7295162Z         you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2025-08-14T23:09:16.7295350Z         The FakeTensor implementation must consist of only PyTorch operations
2025-08-14T23:09:16.7295534Z         (and may not directly access the storage or data of any input or
2025-08-14T23:09:16.7295679Z         intermediate Tensors).
2025-08-14T23:09:16.7295781Z     
2025-08-14T23:09:16.7295883Z         This API may be used as a decorator (see examples).
2025-08-14T23:09:16.7297238Z     
2025-08-14T23:09:16.7297335Z         For a detailed guide on custom ops, please see
2025-08-14T23:09:16.7297512Z         https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2025-08-14T23:09:16.7297660Z     
2025-08-14T23:09:16.7297732Z         Args:
2025-08-14T23:09:16.7297862Z             op_name: Operator name (along with the overload) or OpOverload object.
2025-08-14T23:09:16.7298023Z             func: Fake tensor implementation.
2025-08-14T23:09:16.7298174Z             lib (Optional[Library]): Library to register the fake tensor to.
2025-08-14T23:09:16.7298346Z             allow_override: Flag controlling if we want to override an
2025-08-14T23:09:16.7298510Z                             existing registered fake impl. This is by default off,
2025-08-14T23:09:16.7298674Z                             and will error you're trying to register a fake impl to
2025-08-14T23:09:16.7300074Z                             an operator that already has a fake impl. This also only
2025-08-14T23:09:16.7300237Z                             applies if the custom operator was not created via
2025-08-14T23:09:16.7300402Z                             torch.library.custom_op, as overriding and existing fake
2025-08-14T23:09:16.7300555Z                             impl is already allowed.
2025-08-14T23:09:16.7300665Z     
2025-08-14T23:09:16.7300739Z         Examples:
2025-08-14T23:09:16.7300831Z             >>> import torch
2025-08-14T23:09:16.7300939Z             >>> import numpy as np
2025-08-14T23:09:16.7301053Z             >>> from torch import Tensor
2025-08-14T23:09:16.7301160Z             >>>
2025-08-14T23:09:16.7301279Z             >>> # Example 1: an operator without data-dependent output shape
2025-08-14T23:09:16.7302675Z             >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2025-08-14T23:09:16.7302870Z             >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-08-14T23:09:16.7303366Z             >>>     raise NotImplementedError("Implementation goes here")
2025-08-14T23:09:16.7303502Z             >>>
2025-08-14T23:09:16.7303619Z             >>> @torch.library.register_fake("mylib::custom_linear")
2025-08-14T23:09:16.7303758Z             >>> def _(x, weight, bias):
2025-08-14T23:09:16.7303874Z             >>>     assert x.dim() == 2
2025-08-14T23:09:16.7303991Z             >>>     assert weight.dim() == 2
2025-08-14T23:09:16.7304109Z             >>>     assert bias.dim() == 1
2025-08-14T23:09:16.7304235Z             >>>     assert x.shape[1] == weight.shape[1]
2025-08-14T23:09:16.7304370Z             >>>     assert weight.shape[0] == bias.shape[0]
2025-08-14T23:09:16.7305774Z             >>>     assert x.device == weight.device
2025-08-14T23:09:16.7305890Z             >>>
2025-08-14T23:09:16.7305987Z             >>>     return (x @ weight.t()) + bias
2025-08-14T23:09:16.7306099Z             >>>
2025-08-14T23:09:16.7306239Z             >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2025-08-14T23:09:16.7306382Z             >>>     x = torch.randn(2, 3)
2025-08-14T23:09:16.7306499Z             >>>     w = torch.randn(3, 3)
2025-08-14T23:09:16.7306630Z             >>>     b = torch.randn(3)
2025-08-14T23:09:16.7306759Z             >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2025-08-14T23:09:16.7306880Z             >>>
2025-08-14T23:09:16.7306971Z             >>> assert y.shape == (2, 3)
2025-08-14T23:09:16.7308301Z             >>>
2025-08-14T23:09:16.7308418Z             >>> # Example 2: an operator with data-dependent output shape
2025-08-14T23:09:16.7308597Z             >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2025-08-14T23:09:16.7308760Z             >>> def custom_nonzero(x: Tensor) -> Tensor:
2025-08-14T23:09:16.7308889Z             >>>     x_np = x.numpy(force=True)
2025-08-14T23:09:16.7309018Z             >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-08-14T23:09:16.7309158Z             >>>     return torch.tensor(res, device=x.device)
2025-08-14T23:09:16.7309275Z             >>>
2025-08-14T23:09:16.7309389Z             >>> @torch.library.register_fake("mylib::custom_nonzero")
2025-08-14T23:09:16.7309524Z             >>> def _(x):
2025-08-14T23:09:16.7309645Z             >>> # Number of nonzero-elements is data-dependent.
2025-08-14T23:09:16.7311020Z             >>> # Since we cannot peek at the data in an fake impl,
2025-08-14T23:09:16.7311174Z             >>> # we use the ctx object to construct a new symint that
2025-08-14T23:09:16.7311318Z             >>> # represents the data-dependent size.
2025-08-14T23:09:16.7311448Z             >>>     ctx = torch.library.get_ctx()
2025-08-14T23:09:16.7311573Z             >>>     nnz = ctx.new_dynamic_size()
2025-08-14T23:09:16.7311694Z             >>>     shape = [nnz, x.dim()]
2025-08-14T23:09:16.7311827Z             >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-08-14T23:09:16.7311959Z             >>>     return result
2025-08-14T23:09:16.7312060Z             >>>
2025-08-14T23:09:16.7312178Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-08-14T23:09:16.7312310Z             >>>
2025-08-14T23:09:16.7313722Z             >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2025-08-14T23:09:16.7313891Z             >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2025-08-14T23:09:16.7314058Z             >>> trace.print_readable()
2025-08-14T23:09:16.7314163Z             >>>
2025-08-14T23:09:16.7314290Z             >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2025-08-14T23:09:16.7314434Z     
2025-08-14T23:09:16.7314507Z         
2025-08-14T23:09:16.7314727Z     Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2025-08-14T23:09:16.7314963Z     
2025-08-14T23:09:16.7315036Z     _._ = None
2025-08-14T23:09:16.7315116Z     ^
2025-08-14T23:09:16.7316420Z       warnings.warn(msg)
2025-08-14T23:09:16.7316545Z     
2025-08-14T23:09:16.7316690Z --- Parse Warning: 11 / 146 ---
2025-08-14T23:09:16.7317064Z     /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.
2025-08-14T23:09:16.7317474Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7317657Z     Register a backward formula for this custom op.
2025-08-14T23:09:16.7317776Z     
2025-08-14T23:09:16.7317891Z         In order for an operator to work with autograd, you need to register
2025-08-14T23:09:16.7318039Z         a backward formula:
2025-08-14T23:09:16.7318178Z         1. You must tell us how to compute gradients during the backward pass
2025-08-14T23:09:16.7319585Z         by providing us a "backward" function.
2025-08-14T23:09:16.7319766Z         2. If you need any values from the forward to compute gradients, you can
2025-08-14T23:09:16.7319934Z         use `setup_context` to save values for backward.
2025-08-14T23:09:16.7320051Z     
2025-08-14T23:09:16.7320190Z         ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2025-08-14T23:09:16.7320375Z         - ``grads`` is one or more gradients. The number of gradients matches
2025-08-14T23:09:16.7320526Z         the number of outputs of the operator.
2025-08-14T23:09:16.7320682Z         The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2025-08-14T23:09:16.7320880Z         :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2025-08-14T23:09:16.7321062Z         same as :meth:`torch.autograd.Function.backward`.
2025-08-14T23:09:16.7321186Z     
2025-08-14T23:09:16.7322533Z         ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2025-08-14T23:09:16.7322728Z         Please save quantities needed for backward onto the ``ctx`` object via
2025-08-14T23:09:16.7323026Z         either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2025-08-14T23:09:16.7323216Z         or assigning them as attributes of ``ctx``. If your custom op has
2025-08-14T23:09:16.7323399Z         kwarg-only arguments, we expect the signature of ``setup_context``
2025-08-14T23:09:16.7323579Z         to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2025-08-14T23:09:16.7323712Z     
2025-08-14T23:09:16.7323828Z         Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2025-08-14T23:09:16.7324021Z         they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2025-08-14T23:09:16.7324219Z         not depend on or mutate global state. If you need a non-traceable backward,
2025-08-14T23:09:16.7324415Z         you can make it a separate custom_op that you call inside ``backward_fn``.
2025-08-14T23:09:16.7325851Z     
2025-08-14T23:09:16.7325973Z         If you need different autograd behavior on different devices, then we
2025-08-14T23:09:16.7326166Z         recommend creating two different custom operators, one for each device
2025-08-14T23:09:16.7326358Z         that needs different behavior, and switching between them at runtime.
2025-08-14T23:09:16.7326501Z     
2025-08-14T23:09:16.7326575Z         Examples:
2025-08-14T23:09:16.7326668Z             >>> import torch
2025-08-14T23:09:16.7326776Z             >>> import numpy as np
2025-08-14T23:09:16.7326889Z             >>> from torch import Tensor
2025-08-14T23:09:16.7326997Z             >>>
2025-08-14T23:09:16.7327120Z             >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2025-08-14T23:09:16.7328520Z             >>> def numpy_sin(x: Tensor) -> Tensor:
2025-08-14T23:09:16.7328646Z             >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:16.7328761Z             >>>     y_np = np.sin(x_np)
2025-08-14T23:09:16.7328895Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-08-14T23:09:16.7329019Z             >>>
2025-08-14T23:09:16.7329177Z             >>> def setup_context(ctx, inputs, output) -> Tensor:
2025-08-14T23:09:16.7329308Z             >>>     x, = inputs
2025-08-14T23:09:16.7329417Z             >>>     ctx.save_for_backward(x)
2025-08-14T23:09:16.7329526Z             >>>
2025-08-14T23:09:16.7329615Z             >>> def backward(ctx, grad):
2025-08-14T23:09:16.7329730Z             >>>     x, = ctx.saved_tensors
2025-08-14T23:09:16.7331193Z             >>>     return grad * x.cos()
2025-08-14T23:09:16.7331301Z             >>>
2025-08-14T23:09:16.7331396Z             >>> torch.library.register_autograd(
2025-08-14T23:09:16.7331543Z             ...     "mylib::numpy_sin", backward, setup_context=setup_context
2025-08-14T23:09:16.7331674Z             ... )
2025-08-14T23:09:16.7331755Z             >>>
2025-08-14T23:09:16.7331852Z             >>> x = torch.randn(3, requires_grad=True)
2025-08-14T23:09:16.7331974Z             >>> y = numpy_sin(x)
2025-08-14T23:09:16.7332269Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-08-14T23:09:16.7332419Z             >>> assert torch.allclose(grad_x, x.cos())
2025-08-14T23:09:16.7332532Z             >>>
2025-08-14T23:09:16.7333939Z             >>> # Example with a keyword-only arg
2025-08-14T23:09:16.7334122Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-08-14T23:09:16.7334289Z             >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2025-08-14T23:09:16.7334425Z             >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:16.7334539Z             >>>     y_np = x_np * val
2025-08-14T23:09:16.7334668Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2025-08-14T23:09:16.7334792Z             >>>
2025-08-14T23:09:16.7334923Z             >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2025-08-14T23:09:16.7335090Z             >>>     ctx.val = keyword_only_inputs["val"]
2025-08-14T23:09:16.7335203Z             >>>
2025-08-14T23:09:16.7335294Z             >>> def backward(ctx, grad):
2025-08-14T23:09:16.7336656Z             >>>     return grad * ctx.val
2025-08-14T23:09:16.7336762Z             >>>
2025-08-14T23:09:16.7336858Z             >>> torch.library.register_autograd(
2025-08-14T23:09:16.7337004Z             ...     "mylib::numpy_mul", backward, setup_context=setup_context
2025-08-14T23:09:16.7337134Z             ... )
2025-08-14T23:09:16.7337216Z             >>>
2025-08-14T23:09:16.7337311Z             >>> x = torch.randn(3, requires_grad=True)
2025-08-14T23:09:16.7337434Z             >>> y = numpy_mul(x, val=3.14)
2025-08-14T23:09:16.7337573Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2025-08-14T23:09:16.7337736Z             >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2025-08-14T23:09:16.7337865Z     
2025-08-14T23:09:16.7339166Z         
2025-08-14T23:09:16.7339301Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7339456Z     
2025-08-14T23:09:16.7339536Z       warnings.warn(msg)
2025-08-14T23:09:16.7339630Z     
2025-08-14T23:09:16.7339749Z --- Parse Warning: 12 / 146 ---
2025-08-14T23:09:16.7340108Z     /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.
2025-08-14T23:09:16.7340499Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7340699Z     Returns the computed kernel for a given operator and dispatch key.
2025-08-14T23:09:16.7340838Z     
2025-08-14T23:09:16.7340957Z         This function retrieves the kernel that would be executed for a given
2025-08-14T23:09:16.7342390Z         operator and dispatch key combination. The returned SafeKernelFunction
2025-08-14T23:09:16.7342580Z         can be used to call the kernel in a boxed fashion. The intended use
2025-08-14T23:09:16.7342763Z         case for this function is to retrieve the original kernel for a given
2025-08-14T23:09:16.7343119Z         dispatch key and then register another kernel to the same dispatch key
2025-08-14T23:09:16.7343295Z         that calls into the original kernel for certain cases.
2025-08-14T23:09:16.7343423Z     
2025-08-14T23:09:16.7343496Z         Args:
2025-08-14T23:09:16.7343616Z             op: Operator name (along with the overload) or OpOverload object
2025-08-14T23:09:16.7343807Z                 Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef.
2025-08-14T23:09:16.7344018Z             dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for.
2025-08-14T23:09:16.7344213Z                 Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value.
2025-08-14T23:09:16.7345635Z     
2025-08-14T23:09:16.7345710Z         Returns:
2025-08-14T23:09:16.7345847Z             torch._C._SafeKernelFunction: A safe kernel function that can be used to
2025-08-14T23:09:16.7346006Z                 call the kernel.
2025-08-14T23:09:16.7346126Z     
2025-08-14T23:09:16.7346198Z         Raises:
2025-08-14T23:09:16.7346307Z             RuntimeError: If the operator does not exist.
2025-08-14T23:09:16.7346428Z     
2025-08-14T23:09:16.7346521Z         Example:
2025-08-14T23:09:16.7346618Z             >>> # Get the CPU kernel for torch.add
2025-08-14T23:09:16.7346769Z             >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU")
2025-08-14T23:09:16.7348150Z             >>>
2025-08-14T23:09:16.7348251Z             >>> # You can also use DispatchKey enum
2025-08-14T23:09:16.7348428Z             >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU)
2025-08-14T23:09:16.7348585Z             >>>
2025-08-14T23:09:16.7348679Z             >>> # Or use an OpOverload directly
2025-08-14T23:09:16.7348841Z             >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU")
2025-08-14T23:09:16.7348988Z             >>>
2025-08-14T23:09:16.7349114Z             >>> # Example: Using get_kernel in a custom op with conditional dispatch
2025-08-14T23:09:16.7349279Z             >>> # Get the original kernel for torch.sin
2025-08-14T23:09:16.7349439Z             >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU")
2025-08-14T23:09:16.7349582Z             >>>
2025-08-14T23:09:16.7350951Z             >>> # If input has negative values, use original sin, otherwise return zeros
2025-08-14T23:09:16.7351125Z             >>> def conditional_sin_impl(dispatch_keys, x):
2025-08-14T23:09:16.7351254Z             >>>     if (x < 0).any():
2025-08-14T23:09:16.7351394Z             >>>         return original_sin_kernel.call_boxed(dispatch_keys, x)
2025-08-14T23:09:16.7351530Z             >>>     else:
2025-08-14T23:09:16.7351638Z             >>>         return torch.zeros_like(x)
2025-08-14T23:09:16.7351748Z             >>>
2025-08-14T23:09:16.7351850Z             >>> lib = torch.library.Library("aten", "IMPL")
2025-08-14T23:09:16.7352032Z             >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet
2025-08-14T23:09:16.7352228Z             >>> which needs to be the first argument to ``kernel.call_boxed``
2025-08-14T23:09:16.7353753Z             >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True)
2025-08-14T23:09:16.7353895Z             >>>
2025-08-14T23:09:16.7353987Z             >>> # Test the conditional behavior
2025-08-14T23:09:16.7354115Z             >>> x_positive = torch.tensor([1.0, 2.0])
2025-08-14T23:09:16.7354243Z             >>> x_mixed = torch.tensor([-1.0, 2.0])
2025-08-14T23:09:16.7354365Z             >>> torch.sin(x_positive)
2025-08-14T23:09:16.7354478Z             tensor([0., 0.])
2025-08-14T23:09:16.7354583Z             >>> torch.sin(x_mixed)
2025-08-14T23:09:16.7354694Z             tensor([-0.8415, 0.9093])
2025-08-14T23:09:16.7354795Z         
2025-08-14T23:09:16.7355006Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12))
2025-08-14T23:09:16.7356508Z     
2025-08-14T23:09:16.7356642Z     which needs to be the first argument to ``kernel.call_boxed``
2025-08-14T23:09:16.7356776Z           ^
2025-08-14T23:09:16.7356860Z       warnings.warn(msg)
2025-08-14T23:09:16.7356953Z     
2025-08-14T23:09:16.7357071Z --- Parse Warning: 13 / 146 ---
2025-08-14T23:09:16.7357428Z     /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.
2025-08-14T23:09:16.7357822Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7358029Z     Given an operator and some sample arguments, tests if the operator is
2025-08-14T23:09:16.7358183Z         registered correctly.
2025-08-14T23:09:16.7358281Z     
2025-08-14T23:09:16.7359646Z         That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2025-08-14T23:09:16.7359872Z         custom op, you specified metadata (e.g. mutability info) about the custom op
2025-08-14T23:09:16.7360079Z         and these APIs require that the functions you pass them satisfy certain
2025-08-14T23:09:16.7360293Z         properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2025-08-14T23:09:16.7360468Z         ``opcheck`` tests these metadata and properties.
2025-08-14T23:09:16.7360587Z     
2025-08-14T23:09:16.7360675Z         Concretely, we test the following:
2025-08-14T23:09:16.7360782Z     
2025-08-14T23:09:16.7360889Z         - test_schema: If the schema matches the implementation of
2025-08-14T23:09:16.7361069Z           the operator. For example: if the schema specifies a Tensor is mutated,
2025-08-14T23:09:16.7361261Z           then we check the implementation mutates the Tensor. If the schema
2025-08-14T23:09:16.7362721Z           specifies that we return a new Tensor, then we check that the
2025-08-14T23:09:16.7362966Z           implementation returns a new Tensor (instead of an existing one or
2025-08-14T23:09:16.7363122Z           a view of an existing one).
2025-08-14T23:09:16.7363271Z         - test_autograd_registration: If the operator supports training
2025-08-14T23:09:16.7363448Z           (autograd): we check that its autograd formula is registered via
2025-08-14T23:09:16.7363629Z           torch.library.register_autograd or a manual registration to one
2025-08-14T23:09:16.7363813Z           or more DispatchKey::Autograd keys. Any other DispatchKey-based
2025-08-14T23:09:16.7363977Z           registrations may lead to undefined behavior.
2025-08-14T23:09:16.7364132Z         - test_faketensor: If the operator has a FakeTensor kernel
2025-08-14T23:09:16.7364298Z           (and if it is correct). The FakeTensor kernel is necessary (
2025-08-14T23:09:16.7364472Z           but not sufficient) for the operator to work with PyTorch compilation
2025-08-14T23:09:16.7364662Z           APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2025-08-14T23:09:16.7366105Z           (also sometimes known as a meta kernel) was registered for the
2025-08-14T23:09:16.7366282Z           operator and that it is correct. This test takes the result of
2025-08-14T23:09:16.7366456Z           running the operator on real tensors and the result of running
2025-08-14T23:09:16.7366628Z           the operator on FakeTensors and checks that they have the same
2025-08-14T23:09:16.7366788Z           Tensor metadata (sizes/strides/dtype/device/etc).
2025-08-14T23:09:16.7366952Z         - test_aot_dispatch_dynamic: If the operator has correct behavior
2025-08-14T23:09:16.7367124Z           with PyTorch compilation APIs (torch.compile/export/FX).
2025-08-14T23:09:16.7367298Z           This checks that the outputs (and gradients, if applicable) are the
2025-08-14T23:09:16.7367465Z           same under eager-mode PyTorch and torch.compile.
2025-08-14T23:09:16.7367625Z           This test is a superset of ``test_faketensor`` and is an e2e test;
2025-08-14T23:09:16.7367793Z           other things it tests are that the operator supports
2025-08-14T23:09:16.7371209Z           functionalization and that the backward pass (if it exists) also
2025-08-14T23:09:16.7371402Z           supports FakeTensor and functionalization.
2025-08-14T23:09:16.7371535Z     
2025-08-14T23:09:16.7371661Z         For best results, please call ``opcheck`` multiple times with a
2025-08-14T23:09:16.7371849Z         representative set of inputs. If your operator supports
2025-08-14T23:09:16.7372047Z         autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2025-08-14T23:09:16.7372259Z         if your operator supports multiple devices (e.g. CPU and CUDA), please
2025-08-14T23:09:16.7372444Z         use ``opcheck`` with inputs on all supported devices.
2025-08-14T23:09:16.7372580Z     
2025-08-14T23:09:16.7372664Z         Args:
2025-08-14T23:09:16.7372791Z             op: The operator. Must either be a function decorated with
2025-08-14T23:09:16.7375571Z                 :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2025-08-14T23:09:16.7375785Z                 found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2025-08-14T23:09:16.7375955Z             args: The args to the operator
2025-08-14T23:09:16.7376105Z             kwargs: The kwargs to the operator
2025-08-14T23:09:16.7376263Z             test_utils: Tests that we should run. Default: all of them.
2025-08-14T23:09:16.7376429Z                 Example: ("test_schema", "test_faketensor")
2025-08-14T23:09:16.7376594Z             raise_exception: If we should raise an exception on the first
2025-08-14T23:09:16.7376778Z                 error. If False, we will return a dict with information
2025-08-14T23:09:16.7376932Z                 on if each test passed or not.
2025-08-14T23:09:16.7377111Z             rtol (Optional[float]): Relative tolerance for floating point comparisons.
2025-08-14T23:09:16.7377301Z                 If specified ``atol`` must also be specified.
2025-08-14T23:09:16.7378957Z                 If omitted, default values based on the ``dtype`` are selected
2025-08-14T23:09:16.7379145Z                 (see the table in :func:`torch.testing.assert_close`).
2025-08-14T23:09:16.7379333Z             atol (Optional[float]): Absolute tolerance for floating point comparisons.
2025-08-14T23:09:16.7379518Z                 If specified ``rtol`` must also be specified.
2025-08-14T23:09:16.7379681Z                 If omitted, default values based on the ``dtype`` are selected
2025-08-14T23:09:16.7379852Z                 (see the table in :func:`torch.testing.assert_close`).
2025-08-14T23:09:16.7379982Z     
2025-08-14T23:09:16.7380067Z         .. warning::
2025-08-14T23:09:16.7380197Z     
2025-08-14T23:09:16.7380327Z             opcheck and :func:`torch.autograd.gradcheck` test different things;
2025-08-14T23:09:16.7381960Z             opcheck tests if your usage of torch.library APIs is correct while
2025-08-14T23:09:16.7382161Z             :func:`torch.autograd.gradcheck` tests if your autograd formula is
2025-08-14T23:09:16.7382363Z             mathematically correct. Use both to test custom ops that support
2025-08-14T23:09:16.7382528Z             gradient computation.
2025-08-14T23:09:16.7382642Z     
2025-08-14T23:09:16.7382730Z         Example:
2025-08-14T23:09:16.7382827Z     
2025-08-14T23:09:16.7383019Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.7383199Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2025-08-14T23:09:16.7383534Z             >>> def numpy_mul(x: Tensor, y: float) -> Tensor:
2025-08-14T23:09:16.7383702Z             >>>     x_np = x.numpy(force=True)
2025-08-14T23:09:16.7385252Z             >>>     z_np = x_np * y
2025-08-14T23:09:16.7385399Z             >>>     return torch.from_numpy(z_np).to(x.device)
2025-08-14T23:09:16.7385527Z             >>>
2025-08-14T23:09:16.7406390Z             >>> @numpy_mul.register_fake
2025-08-14T23:09:16.7406539Z             >>> def _(x, y):
2025-08-14T23:09:16.7406666Z             >>>     return torch.empty_like(x)
2025-08-14T23:09:16.7406842Z             >>>
2025-08-14T23:09:16.7406974Z             >>> def setup_context(ctx, inputs, output):
2025-08-14T23:09:16.7407102Z             >>>     y, = inputs
2025-08-14T23:09:16.7407217Z             >>>     ctx.y = y
2025-08-14T23:09:16.7407317Z             >>>
2025-08-14T23:09:16.7409401Z             >>> def backward(ctx, grad):
2025-08-14T23:09:16.7409528Z             >>>     return grad * ctx.y, None
2025-08-14T23:09:16.7409646Z             >>>
2025-08-14T23:09:16.7409776Z             >>> numpy_mul.register_autograd(backward, setup_context=setup_context)
2025-08-14T23:09:16.7409926Z             >>>
2025-08-14T23:09:16.7410014Z             >>> sample_inputs = [
2025-08-14T23:09:16.7410130Z             >>>     (torch.randn(3), 3.14),
2025-08-14T23:09:16.7410259Z             >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2025-08-14T23:09:16.7410405Z             >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2025-08-14T23:09:16.7410598Z             >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2025-08-14T23:09:16.7410747Z             >>> ]
2025-08-14T23:09:16.7412033Z             >>>
2025-08-14T23:09:16.7412127Z             >>> for args in sample_inputs:
2025-08-14T23:09:16.7412294Z             >>>     torch.library.opcheck(numpy_mul, args)
2025-08-14T23:09:16.7412415Z     
2025-08-14T23:09:16.7412490Z         
2025-08-14T23:09:16.7412623Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7412779Z     
2025-08-14T23:09:16.7412859Z       warnings.warn(msg)
2025-08-14T23:09:16.7413021Z     
2025-08-14T23:09:16.7413156Z --- Parse Warning: 14 / 146 ---
2025-08-14T23:09:16.7413575Z     /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=1851.
2025-08-14T23:09:16.7415270Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7415484Z     Computes the minimum storage size to hold the given tensor geometry.
2025-08-14T23:09:16.7415632Z     
2025-08-14T23:09:16.7415707Z         Example
2025-08-14T23:09:16.7415789Z         =======
2025-08-14T23:09:16.7415867Z     
2025-08-14T23:09:16.7415951Z         This is the size of a newly allocated tensor's storage, in units of elements
2025-08-14T23:09:16.7415983Z     
2025-08-14T23:09:16.7416024Z         >>> t = torch.empty((10, 20))
2025-08-14T23:09:16.7416110Z         >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset())
2025-08-14T23:09:16.7416143Z         200
2025-08-14T23:09:16.7416174Z     
2025-08-14T23:09:16.7416216Z         >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:16.7416271Z         >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11))
2025-08-14T23:09:16.7416320Z         >>> size = compute_required_storage_length(
2025-08-14T23:09:16.7416377Z         ...     t2.shape, t2.stride(), t2.storage_offset()
2025-08-14T23:09:16.7417574Z         ... )
2025-08-14T23:09:16.7417618Z         >>> size == t.storage().size()
2025-08-14T23:09:16.7417651Z         True
2025-08-14T23:09:16.7417685Z     
2025-08-14T23:09:16.7417758Z         A valid tensor may have a larger storage size, but never smaller
2025-08-14T23:09:16.7417789Z     
2025-08-14T23:09:16.7417834Z         >>> slice = torch.empty(100)[20:40]
2025-08-14T23:09:16.7417875Z         >>> slice.storage().size()
2025-08-14T23:09:16.7417908Z         100
2025-08-14T23:09:16.7417939Z     
2025-08-14T23:09:16.7417985Z         >>> compute_required_storage_length(
2025-08-14T23:09:16.7418048Z         ...     slice.shape, slice.stride(), slice.storage_offset()
2025-08-14T23:09:16.7418080Z         ... )
2025-08-14T23:09:16.7418111Z         40
2025-08-14T23:09:16.7418142Z     
2025-08-14T23:09:16.7418173Z         
2025-08-14T23:09:16.7418264Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7418326Z     
2025-08-14T23:09:16.7418365Z       warnings.warn(msg)
2025-08-14T23:09:16.7418415Z     
2025-08-14T23:09:16.7418490Z --- Parse Warning: 15 / 146 ---
2025-08-14T23:09:16.7418770Z     /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.
2025-08-14T23:09:16.7419998Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7420060Z     Return the sum of each row of the given sparse tensor.
2025-08-14T23:09:16.7420091Z     
2025-08-14T23:09:16.7420176Z         Returns the sum of each row of the sparse tensor :attr:`input` in the given
2025-08-14T23:09:16.7420250Z         dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2025-08-14T23:09:16.7420326Z         reduce over all of them. When sum over all ``sparse_dim``, this method
2025-08-14T23:09:16.7420404Z         returns a dense tensor instead of a sparse tensor.
2025-08-14T23:09:16.7420438Z     
2025-08-14T23:09:16.7420532Z         All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2025-08-14T23:09:16.7420622Z         tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2025-08-14T23:09:16.7420652Z     
2025-08-14T23:09:16.7420732Z         During backward, only gradients at ``nnz`` locations of :attr:`input`
2025-08-14T23:09:16.7420817Z         will propagate back. Note that the gradients of :attr:`input` is coalesced.
2025-08-14T23:09:16.7420850Z     
2025-08-14T23:09:16.7420882Z         Args:
2025-08-14T23:09:16.7420933Z             input (Tensor): the input sparse tensor
2025-08-14T23:09:16.7421030Z             dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2025-08-14T23:09:16.7421070Z                 over all dims.
2025-08-14T23:09:16.7421165Z             dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2025-08-14T23:09:16.7421214Z                 Default: dtype of :attr:`input`.
2025-08-14T23:09:16.7421246Z     
2025-08-14T23:09:16.7422421Z         Example::
2025-08-14T23:09:16.7422453Z     
2025-08-14T23:09:16.7422488Z             >>> nnz = 3
2025-08-14T23:09:16.7422528Z             >>> dims = [5, 5, 2, 3]
2025-08-14T23:09:16.7422590Z             >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2025-08-14T23:09:16.7422660Z                                torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2025-08-14T23:09:16.7422711Z             >>> V = torch.randn(nnz, dims[2], dims[3])
2025-08-14T23:09:16.7422753Z             >>> size = torch.Size(dims)
2025-08-14T23:09:16.7422812Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.7422864Z             >>> S = torch.sparse_coo_tensor(I, V, size)
2025-08-14T23:09:16.7422955Z             >>> S
2025-08-14T23:09:16.7423003Z             tensor(indices=tensor([[2, 0, 3],
2025-08-14T23:09:16.7423046Z                                    [2, 4, 1]]),
2025-08-14T23:09:16.7423102Z                    values=tensor([[[-0.6438, -1.6467,  1.4004],
2025-08-14T23:09:16.7423149Z                                    [ 0.3411,  0.0918, -0.2312]],
2025-08-14T23:09:16.7423182Z     
2025-08-14T23:09:16.7423229Z                                   [[ 0.5348,  0.0634, -2.0494],
2025-08-14T23:09:16.7423274Z                                    [-0.7125, -1.0646,  2.1844]],
2025-08-14T23:09:16.7423305Z     
2025-08-14T23:09:16.7423348Z                                   [[ 0.1276,  0.1874, -0.6334],
2025-08-14T23:09:16.7423395Z                                    [-1.9682, -0.5340,  0.7483]]]),
2025-08-14T23:09:16.7423453Z                    size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2025-08-14T23:09:16.7424627Z     
2025-08-14T23:09:16.7424701Z             # when sum over only part of sparse_dims, return a sparse tensor
2025-08-14T23:09:16.7424747Z             >>> torch.sparse.sum(S, [1, 3])
2025-08-14T23:09:16.7424794Z             tensor(indices=tensor([[0, 2, 3]]),
2025-08-14T23:09:16.7424886Z                    values=tensor([[-1.4512,  0.4073],
2025-08-14T23:09:16.7424930Z                                   [-0.8901,  0.2017],
2025-08-14T23:09:16.7424974Z                                   [-0.3183, -1.7539]]),
2025-08-14T23:09:16.7425027Z                    size=(5, 2), nnz=3, layout=torch.sparse_coo)
2025-08-14T23:09:16.7425058Z     
2025-08-14T23:09:16.7425122Z             # when sum over all sparse dim, return a dense tensor
2025-08-14T23:09:16.7425166Z             # with summed dims squeezed
2025-08-14T23:09:16.7425211Z             >>> torch.sparse.sum(S, [0, 1, 3])
2025-08-14T23:09:16.7425253Z             tensor([-2.6596, -1.1450])
2025-08-14T23:09:16.7425285Z         
2025-08-14T23:09:16.7425376Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7425406Z     
2025-08-14T23:09:16.7425446Z       warnings.warn(msg)
2025-08-14T23:09:16.7425476Z     
2025-08-14T23:09:16.7425566Z --- Parse Warning: 16 / 146 ---
2025-08-14T23:09:16.7425867Z     /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.
2025-08-14T23:09:16.7425978Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7426046Z     Decorate function, to extend gradcheck for sparse tensors.
2025-08-14T23:09:16.7427216Z     
2025-08-14T23:09:16.7427297Z         Decorator for torch.autograd.gradcheck or its functools.partial
2025-08-14T23:09:16.7427376Z         variants that extends the gradcheck function with support to input
2025-08-14T23:09:16.7427438Z         functions that operate on or/and return sparse tensors.
2025-08-14T23:09:16.7427472Z     
2025-08-14T23:09:16.7427546Z         The specified gradcheck function itself is guaranteed to operate
2025-08-14T23:09:16.7427590Z         on strided tensors only.
2025-08-14T23:09:16.7427622Z     
2025-08-14T23:09:16.7427658Z         For example:
2025-08-14T23:09:16.7427689Z     
2025-08-14T23:09:16.7427775Z         >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck)
2025-08-14T23:09:16.7427809Z         >>> x = (
2025-08-14T23:09:16.7427870Z         ...     torch.tensor([[0, 1], [2, 3]], dtype=torch.float64)
2025-08-14T23:09:16.7427909Z         ...     .to_sparse_coo()
2025-08-14T23:09:16.7427951Z         ...     .requires_grad_(True)
2025-08-14T23:09:16.7427984Z         ... )
2025-08-14T23:09:16.7428035Z         >>> gradcheck(lambda x: x.to_sparse_csr(), x)
2025-08-14T23:09:16.7428068Z         True
2025-08-14T23:09:16.7428099Z         
2025-08-14T23:09:16.7428189Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7428222Z     
2025-08-14T23:09:16.7429391Z       warnings.warn(msg)
2025-08-14T23:09:16.7429422Z     
2025-08-14T23:09:16.7429494Z --- Parse Warning: 17 / 146 ---
2025-08-14T23:09:16.7429780Z     /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.
2025-08-14T23:09:16.7429869Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7429916Z     Retrieves the CUDA runtime API module.
2025-08-14T23:09:16.7429947Z     
2025-08-14T23:09:16.7429977Z     
2025-08-14T23:09:16.7430067Z         This function initializes the CUDA runtime environment if it is not already
2025-08-14T23:09:16.7430148Z         initialized and returns the CUDA runtime API module (_cudart). The CUDA
2025-08-14T23:09:16.7430229Z         runtime API module provides access to various CUDA runtime functions.
2025-08-14T23:09:16.7430259Z     
2025-08-14T23:09:16.7430292Z         Args:
2025-08-14T23:09:16.7430324Z             ``None``
2025-08-14T23:09:16.7430355Z     
2025-08-14T23:09:16.7430411Z         Returns:
2025-08-14T23:09:16.7430480Z             module: The CUDA runtime API module (_cudart).
2025-08-14T23:09:16.7430512Z     
2025-08-14T23:09:16.7430545Z         Raises:
2025-08-14T23:09:16.7430629Z             RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2025-08-14T23:09:16.7430757Z             AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2025-08-14T23:09:16.7430789Z     
2025-08-14T23:09:16.7431969Z         Example of CUDA operations with profiling:
2025-08-14T23:09:16.7432010Z             >>> import torch
2025-08-14T23:09:16.7432063Z             >>> from torch.cuda import cudart, check_error
2025-08-14T23:09:16.7432101Z             >>> import os
2025-08-14T23:09:16.7432133Z             >>>
2025-08-14T23:09:16.7432180Z             >>> os.environ["CUDA_PROFILE"] = "1"
2025-08-14T23:09:16.7432211Z             >>>
2025-08-14T23:09:16.7432266Z             >>> def perform_cuda_operations_with_streams():
2025-08-14T23:09:16.7432329Z             >>>     stream = torch.cuda.Stream()
2025-08-14T23:09:16.7432380Z             >>>     with torch.cuda.stream(stream):
2025-08-14T23:09:16.7432432Z             >>>         x = torch.randn(100, 100, device='cuda')
2025-08-14T23:09:16.7432499Z             >>>         y = torch.randn(100, 100, device='cuda')
2025-08-14T23:09:16.7432540Z             >>>         z = torch.mul(x, y)
2025-08-14T23:09:16.7432578Z             >>>     return z
2025-08-14T23:09:16.7432609Z             >>>
2025-08-14T23:09:16.7432655Z             >>> torch.cuda.synchronize()
2025-08-14T23:09:16.7432709Z             >>> print("====== Start nsys profiling ======")
2025-08-14T23:09:16.7432765Z             >>> check_error(cudart().cudaProfilerStart())
2025-08-14T23:09:16.7432822Z             >>> with torch.autograd.profiler.emit_nvtx():
2025-08-14T23:09:16.7432880Z             >>>     result = perform_cuda_operations_with_streams()
2025-08-14T23:09:16.7432996Z             >>>     print("CUDA operations completed.")
2025-08-14T23:09:16.7433060Z             >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2025-08-14T23:09:16.7434270Z             >>> print("====== End nsys profiling ======")
2025-08-14T23:09:16.7434303Z     
2025-08-14T23:09:16.7434376Z         To run this example and save the profiling information, execute:
2025-08-14T23:09:16.7434508Z             >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-08-14T23:09:16.7434540Z     
2025-08-14T23:09:16.7434628Z         This command profiles the CUDA operations in the provided script and saves
2025-08-14T23:09:16.7434701Z         the profiling information to a file named `trace_name.prof`.
2025-08-14T23:09:16.7434786Z         The `--profile-from-start off` option ensures that profiling starts only
2025-08-14T23:09:16.7434843Z         after the `cudaProfilerStart` call in the script.
2025-08-14T23:09:16.7434923Z         The `--csv` and `--print-summary` options format the profiling output as a
2025-08-14T23:09:16.7434977Z         CSV file and print a summary, respectively.
2025-08-14T23:09:16.7435070Z         The `-o` option specifies the output file name, and the `-f` option forces the
2025-08-14T23:09:16.7435129Z         overwrite of the output file if it already exists.
2025-08-14T23:09:16.7435160Z         
2025-08-14T23:09:16.7435385Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2025-08-14T23:09:16.7435416Z     
2025-08-14T23:09:16.7435537Z     $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2025-08-14T23:09:16.7435569Z     ^
2025-08-14T23:09:16.7435607Z       warnings.warn(msg)
2025-08-14T23:09:16.7435638Z     
2025-08-14T23:09:16.7435712Z --- Parse Warning: 18 / 146 ---
2025-08-14T23:09:16.7437167Z     /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.
2025-08-14T23:09:16.7437278Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7437309Z     
2025-08-14T23:09:16.7437392Z     Append the given callback function to this ``Future``, which will be run
2025-08-14T23:09:16.7437469Z     when the ``Future`` is completed.  Multiple callbacks can be added to
2025-08-14T23:09:16.7437544Z     the same ``Future``, but the order in which they will be executed cannot
2025-08-14T23:09:16.7437613Z     be guaranteed (to enforce a certain order consider chaining:
2025-08-14T23:09:16.7437686Z     ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2025-08-14T23:09:16.7437759Z     is the reference to this ``Future``. The callback function can use the
2025-08-14T23:09:16.7437834Z     :meth:`value` method to get the value. Note that if this ``Future`` is
2025-08-14T23:09:16.7437940Z     already completed, the given callback will be run immediately inline.
2025-08-14T23:09:16.7437973Z     
2025-08-14T23:09:16.7438042Z     If the ``Future``'s value contains tensors that reside on GPUs, the
2025-08-14T23:09:16.7438138Z     callback might be invoked while the async kernels that are populating
2025-08-14T23:09:16.7438218Z     those tensors haven't yet finished executing on the device. However, the
2025-08-14T23:09:16.7438291Z     callback will be invoked with some dedicated streams set as current
2025-08-14T23:09:16.7438367Z     (fetched from a global pool) which will be synchronized with those
2025-08-14T23:09:16.7438448Z     kernels. Hence any operation performed by the callback on these tensors
2025-08-14T23:09:16.7438526Z     will be scheduled on the device after the kernels complete. In other
2025-08-14T23:09:16.7438597Z     words, as long as the callback doesn't switch streams, it can safely
2025-08-14T23:09:16.7438678Z     manipulate the result without any additional synchronization. This is
2025-08-14T23:09:16.7438740Z     similar to the non-blocking behavior of :meth:`wait`.
2025-08-14T23:09:16.7439912Z     
2025-08-14T23:09:16.7439987Z     Similarly, if the callback returns a value that contains tensors that
2025-08-14T23:09:16.7440061Z     reside on a GPU, it can do so even if the kernels that are producing
2025-08-14T23:09:16.7440137Z     these tensors are still running on the device, as long as the callback
2025-08-14T23:09:16.7440210Z     didn't change streams during its execution. If one wants to change
2025-08-14T23:09:16.7440287Z     streams, one must be careful to re-synchronize them with the original
2025-08-14T23:09:16.7440363Z     streams, that is, those that were current when the callback was invoked.
2025-08-14T23:09:16.7440396Z     
2025-08-14T23:09:16.7440430Z     Args:
2025-08-14T23:09:16.7440503Z         callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2025-08-14T23:09:16.7440550Z                                 the only argument.
2025-08-14T23:09:16.7440581Z     
2025-08-14T23:09:16.7440614Z     Returns:
2025-08-14T23:09:16.7440678Z         A new ``Future`` object that holds the return value of the
2025-08-14T23:09:16.7440745Z         ``callback`` and will be marked as completed when the given
2025-08-14T23:09:16.7440785Z         ``callback`` finishes.
2025-08-14T23:09:16.7440817Z     
2025-08-14T23:09:16.7440882Z     .. note:: Note that if the callback function throws, either
2025-08-14T23:09:16.7440957Z         through the original future being completed with an exception and
2025-08-14T23:09:16.7441028Z         calling ``fut.wait()``, or through other code in the callback, the
2025-08-14T23:09:16.7441100Z         future returned by ``then`` will be marked appropriately with the
2025-08-14T23:09:16.7442291Z         encountered error. However, if this callback later completes
2025-08-14T23:09:16.7442369Z         additional futures, those futures are not marked as completed with
2025-08-14T23:09:16.7442441Z         an error and the user is responsible for handling completion/waiting
2025-08-14T23:09:16.7442532Z         on those futures independently.
2025-08-14T23:09:16.7442563Z     
2025-08-14T23:09:16.7442598Z     Example::
2025-08-14T23:09:16.7442630Z     
2025-08-14T23:09:16.7442687Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-08-14T23:09:16.7442727Z         >>> def callback(fut):
2025-08-14T23:09:16.7442783Z         ...     print(f"RPC return value is {fut.wait()}.")
2025-08-14T23:09:16.7442829Z         >>> fut = torch.futures.Future()
2025-08-14T23:09:16.7442891Z         >>> # The inserted callback will print the return value when
2025-08-14T23:09:16.7442997Z         >>> # receiving the response from "worker1"
2025-08-14T23:09:16.7443038Z         >>> cb_fut = fut.then(callback)
2025-08-14T23:09:16.7443079Z         >>> chain_cb_fut = cb_fut.then(
2025-08-14T23:09:16.7443136Z         ...     lambda x : print(f"Chained cb done. {x.wait()}")
2025-08-14T23:09:16.7443169Z         ... )
2025-08-14T23:09:16.7443229Z         >>> fut.set_result(5)
2025-08-14T23:09:16.7443271Z         RPC return value is 5.
2025-08-14T23:09:16.7443312Z         Chained cb done. None
2025-08-14T23:09:16.7443343Z     
2025-08-14T23:09:16.7443454Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7444623Z     
2025-08-14T23:09:16.7444662Z       warnings.warn(msg)
2025-08-14T23:09:16.7444692Z     
2025-08-14T23:09:16.7444766Z --- Parse Warning: 19 / 146 ---
2025-08-14T23:09:16.7445066Z     /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.
2025-08-14T23:09:16.7445163Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7445193Z     
2025-08-14T23:09:16.7445269Z     Set the result for this ``Future``, which will mark this ``Future`` as
2025-08-14T23:09:16.7445348Z     completed and trigger all attached callbacks. Note that a ``Future``
2025-08-14T23:09:16.7445394Z     cannot be marked completed twice.
2025-08-14T23:09:16.7445425Z     
2025-08-14T23:09:16.7445502Z     If the result contains tensors that reside on GPUs, this method can be
2025-08-14T23:09:16.7445576Z     called even if the asynchronous kernels that are populating those
2025-08-14T23:09:16.7445655Z     tensors haven't yet completed running on the device, provided that the
2025-08-14T23:09:16.7445782Z     streams on which those kernels were enqueued are set as the current ones
2025-08-14T23:09:16.7445857Z     when this method is called. Put simply, it's safe to call this method
2025-08-14T23:09:16.7445933Z     immediately after launching those kernels, without any additional
2025-08-14T23:09:16.7446012Z     synchronization, as long as one doesn't change streams in between. This
2025-08-14T23:09:16.7446087Z     method will record events on all the relevant current streams and will
2025-08-14T23:09:16.7446161Z     use them to ensure proper scheduling for all the consumers of this
2025-08-14T23:09:16.7446196Z     ``Future``.
2025-08-14T23:09:16.7447371Z     
2025-08-14T23:09:16.7447405Z     Args:
2025-08-14T23:09:16.7447468Z         result (object): the result object of this ``Future``.
2025-08-14T23:09:16.7447499Z     
2025-08-14T23:09:16.7447535Z     Example::
2025-08-14T23:09:16.7447565Z     
2025-08-14T23:09:16.7447623Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2025-08-14T23:09:16.7447662Z         >>> import threading
2025-08-14T23:09:16.7447699Z         >>> import time
2025-08-14T23:09:16.7447745Z         >>> def slow_set_future(fut, value):
2025-08-14T23:09:16.7447785Z         ...     time.sleep(0.5)
2025-08-14T23:09:16.7447826Z         ...     fut.set_result(value)
2025-08-14T23:09:16.7447871Z         >>> fut = torch.futures.Future()
2025-08-14T23:09:16.7447913Z         >>> t = threading.Thread(
2025-08-14T23:09:16.7447953Z         ...     target=slow_set_future,
2025-08-14T23:09:16.7448024Z         ...     args=(fut, torch.ones(2) * 3)
2025-08-14T23:09:16.7448075Z         ... )
2025-08-14T23:09:16.7448111Z         >>> t.start()
2025-08-14T23:09:16.7448150Z         >>> print(fut.wait())
2025-08-14T23:09:16.7448187Z         tensor([3., 3.])
2025-08-14T23:09:16.7448221Z         >>> t.join()
2025-08-14T23:09:16.7448252Z     
2025-08-14T23:09:16.7449479Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7449512Z     
2025-08-14T23:09:16.7449551Z       warnings.warn(msg)
2025-08-14T23:09:16.7449582Z     
2025-08-14T23:09:16.7449654Z --- Parse Warning: 20 / 146 ---
2025-08-14T23:09:16.7449946Z     /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.
2025-08-14T23:09:16.7450051Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7450146Z     Check if the current accelerator is available at runtime: it was build, all the
2025-08-14T23:09:16.7450223Z         required drivers are available and at least one device is visible.
2025-08-14T23:09:16.7450294Z         See :ref:`accelerator<accelerators>` for details.
2025-08-14T23:09:16.7450326Z     
2025-08-14T23:09:16.7450359Z         Returns:
2025-08-14T23:09:16.7450460Z             bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2025-08-14T23:09:16.7450490Z     
2025-08-14T23:09:16.7450579Z         .. note:: This API delegates to the device-specific version of `is_available`.
2025-08-14T23:09:16.7450672Z             On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set,
2025-08-14T23:09:16.7450761Z             this function will NOT poison fork. Otherwise, it will. For more details, see
2025-08-14T23:09:16.7450823Z             :ref:`multiprocessing-poison-fork-note`.
2025-08-14T23:09:16.7450856Z     
2025-08-14T23:09:16.7450893Z         Example::
2025-08-14T23:09:16.7450924Z     
2025-08-14T23:09:16.7451020Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:16.7452185Z         
2025-08-14T23:09:16.7452375Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2025-08-14T23:09:16.7452407Z     
2025-08-14T23:09:16.7452498Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:16.7452540Z                                             ^
2025-08-14T23:09:16.7452578Z       warnings.warn(msg)
2025-08-14T23:09:16.7452609Z     
2025-08-14T23:09:16.7452680Z --- Parse Warning: 21 / 146 ---
2025-08-14T23:09:16.7453031Z     /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.
2025-08-14T23:09:16.7453120Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7453197Z     Wait for all kernels in all streams on the given device to complete.
2025-08-14T23:09:16.7453227Z     
2025-08-14T23:09:16.7453259Z         Args:
2025-08-14T23:09:16.7453373Z             device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2025-08-14T23:09:16.7453456Z                 the current :ref:`accelerator<accelerators>` device type. If not given,
2025-08-14T23:09:16.7453527Z                 use :func:`torch.accelerator.current_device_index` by default.
2025-08-14T23:09:16.7453559Z     
2025-08-14T23:09:16.7453667Z         .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2025-08-14T23:09:16.7453697Z     
2025-08-14T23:09:16.7453732Z         Example::
2025-08-14T23:09:16.7453765Z     
2025-08-14T23:09:16.7454981Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.7455097Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:16.7455155Z             >>> start_event = torch.Event(enable_timing=True)
2025-08-14T23:09:16.7455208Z             >>> end_event = torch.Event(enable_timing=True)
2025-08-14T23:09:16.7455250Z             >>> start_event.record()
2025-08-14T23:09:16.7455337Z             >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2025-08-14T23:09:16.7455378Z             >>> sum = torch.sum(tensor)
2025-08-14T23:09:16.7455420Z             >>> end_event.record()
2025-08-14T23:09:16.7455472Z             >>> torch.accelerator.synchronize()
2025-08-14T23:09:16.7455535Z             >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2025-08-14T23:09:16.7455567Z         
2025-08-14T23:09:16.7455770Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2025-08-14T23:09:16.7455804Z     
2025-08-14T23:09:16.7455894Z     assert torch.accelerator.is_available() "No available accelerators detected."
2025-08-14T23:09:16.7455952Z                                             ^
2025-08-14T23:09:16.7455990Z       warnings.warn(msg)
2025-08-14T23:09:16.7456021Z     
2025-08-14T23:09:16.7456092Z --- Parse Warning: 22 / 146 ---
2025-08-14T23:09:16.7456380Z     /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.
2025-08-14T23:09:16.7456471Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7456551Z     Compiles compute shader from source and allows one to invoke kernels
2025-08-14T23:09:16.7456609Z         defined there from the comfort of Python runtime
2025-08-14T23:09:16.7457780Z         Example::
2025-08-14T23:09:16.7457813Z     
2025-08-14T23:09:16.7457868Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)
2025-08-14T23:09:16.7457915Z             >>> lib = torch.mps.compile_shader(
2025-08-14T23:09:16.7458051Z             ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"
2025-08-14T23:09:16.7458084Z             ...  )
2025-08-14T23:09:16.7458131Z             >>> x = torch.zeros(16, device="mps")
2025-08-14T23:09:16.7458171Z             >>> lib.full(x, 3.14)
2025-08-14T23:09:16.7458203Z         
2025-08-14T23:09:16.7458292Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7458323Z     
2025-08-14T23:09:16.7458361Z       warnings.warn(msg)
2025-08-14T23:09:16.7458392Z     
2025-08-14T23:09:16.7458462Z --- Parse Warning: 23 / 146 ---
2025-08-14T23:09:16.7458737Z     /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.
2025-08-14T23:09:16.7458832Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7458862Z     
2025-08-14T23:09:16.7458942Z     vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2025-08-14T23:09:16.7459015Z     maps ``func`` over some dimension of the inputs. Semantically, vmap
2025-08-14T23:09:16.7459094Z     pushes the map into PyTorch operations called by ``func``, effectively
2025-08-14T23:09:16.7459138Z     vectorizing those operations.
2025-08-14T23:09:16.7460294Z     
2025-08-14T23:09:16.7460371Z     vmap is useful for handling batch dimensions: one can write a function
2025-08-14T23:09:16.7460443Z     ``func`` that runs on examples and then lift it to a function that can
2025-08-14T23:09:16.7460518Z     take batches of examples with ``vmap(func)``. vmap can also be used to
2025-08-14T23:09:16.7460623Z     compute batched gradients when composed with autograd.
2025-08-14T23:09:16.7460654Z     
2025-08-14T23:09:16.7460688Z     .. note::
2025-08-14T23:09:16.7460759Z         :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2025-08-14T23:09:16.7460809Z         convenience. Use whichever one you'd like.
2025-08-14T23:09:16.7460841Z     
2025-08-14T23:09:16.7460873Z     Args:
2025-08-14T23:09:16.7460949Z         func (function): A Python function that takes one or more arguments.
2025-08-14T23:09:16.7460997Z             Must return one or more Tensors.
2025-08-14T23:09:16.7461070Z         in_dims (int or nested structure): Specifies which dimension of the
2025-08-14T23:09:16.7461136Z             inputs should be mapped over. ``in_dims`` should have a
2025-08-14T23:09:16.7461208Z             structure like the inputs. If the ``in_dim`` for a particular
2025-08-14T23:09:16.7461275Z             input is None, then that indicates there is no map dimension.
2025-08-14T23:09:16.7461327Z             Default: 0.
2025-08-14T23:09:16.7461399Z         out_dims (int or Tuple[int]): Specifies where the mapped dimension
2025-08-14T23:09:16.7461467Z             should appear in the outputs. If ``out_dims`` is a Tuple, then
2025-08-14T23:09:16.7461540Z             it should have one element per output. Default: 0.
2025-08-14T23:09:16.7461609Z         randomness (str): Specifies whether the randomness in this
2025-08-14T23:09:16.7462811Z             vmap should be the same or different across batches. If 'different',
2025-08-14T23:09:16.7462884Z             the randomness for each batch will be different. If 'same', the
2025-08-14T23:09:16.7463014Z             randomness will be the same across batches. If 'error', any calls to
2025-08-14T23:09:16.7463090Z             random functions will error. Default: 'error'. WARNING: this flag
2025-08-14T23:09:16.7463162Z             only applies to random PyTorch operations and does not apply to
2025-08-14T23:09:16.7463217Z             Python's random module or numpy randomness.
2025-08-14T23:09:16.7463303Z         chunk_size (None or int): If None (default), apply a single vmap over inputs.
2025-08-14T23:09:16.7463381Z             If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2025-08-14T23:09:16.7463475Z             Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2025-08-14T23:09:16.7463571Z             If you run into memory issues computing the vmap, please try a non-None chunk_size.
2025-08-14T23:09:16.7463603Z     
2025-08-14T23:09:16.7463636Z     Returns:
2025-08-14T23:09:16.7463707Z         Returns a new "batched" function. It takes the same inputs as
2025-08-14T23:09:16.7463778Z         ``func``, except each input has an extra dimension at the index
2025-08-14T23:09:16.7463847Z         specified by ``in_dims``. It takes returns the same outputs as
2025-08-14T23:09:16.7463914Z         ``func``, except each output has an extra dimension at the index
2025-08-14T23:09:16.7463957Z         specified by ``out_dims``.
2025-08-14T23:09:16.7463989Z     
2025-08-14T23:09:16.7464022Z     .. warning:
2025-08-14T23:09:16.7464096Z         :func:`vmap` works best with functional-style code. Please do not
2025-08-14T23:09:16.7464166Z         perform any side-effects in ``func``, with the exception of
2025-08-14T23:09:16.7465390Z         in-place PyTorch operations. Examples of side-effects include mutating
2025-08-14T23:09:16.7465472Z         Python data structures and assigning values to variables not captured
2025-08-14T23:09:16.7465507Z         in ``func``.
2025-08-14T23:09:16.7465538Z     
2025-08-14T23:09:16.7465622Z     One example of using :func:`vmap` is to compute batched dot products. PyTorch
2025-08-14T23:09:16.7465701Z     doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2025-08-14T23:09:16.7465780Z     rummaging through docs, use :func:`vmap` to construct a new function.
2025-08-14T23:09:16.7465811Z     
2025-08-14T23:09:16.7465854Z         >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:16.7465977Z         >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2025-08-14T23:09:16.7466028Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-08-14T23:09:16.7466070Z         >>> batched_dot(x, y)
2025-08-14T23:09:16.7466101Z     
2025-08-14T23:09:16.7466181Z     :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2025-08-14T23:09:16.7466224Z     model authoring experience.
2025-08-14T23:09:16.7466255Z     
2025-08-14T23:09:16.7466299Z         >>> batch_size, feature_size = 3, 5
2025-08-14T23:09:16.7466364Z         >>> weights = torch.randn(feature_size, requires_grad=True)
2025-08-14T23:09:16.7466395Z         >>>
2025-08-14T23:09:16.7466437Z         >>> def model(feature_vec):
2025-08-14T23:09:16.7466489Z         >>> # Very simple linear model with activation
2025-08-14T23:09:16.7466539Z         >>>     return feature_vec.dot(weights).relu()
2025-08-14T23:09:16.7467692Z         >>>
2025-08-14T23:09:16.7467772Z         >>> examples = torch.randn(batch_size, feature_size)
2025-08-14T23:09:16.7467821Z         >>> result = torch.vmap(model)(examples)
2025-08-14T23:09:16.7467856Z     
2025-08-14T23:09:16.7467943Z     :func:`vmap` can also help vectorize computations that were previously difficult
2025-08-14T23:09:16.7468044Z     or impossible to batch. One example is higher-order gradient computation.
2025-08-14T23:09:16.7468126Z     The PyTorch autograd engine computes vjps (vector-Jacobian products).
2025-08-14T23:09:16.7468210Z     Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2025-08-14T23:09:16.7468299Z     requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2025-08-14T23:09:16.7468383Z     we can vectorize the whole computation, computing the Jacobian in a single
2025-08-14T23:09:16.7468424Z     call to ``autograd.grad``.
2025-08-14T23:09:16.7468455Z     
2025-08-14T23:09:16.7468489Z         >>> # Setup
2025-08-14T23:09:16.7468522Z         >>> N = 5
2025-08-14T23:09:16.7468563Z         >>> f = lambda x: x**2
2025-08-14T23:09:16.7468612Z         >>> x = torch.randn(N, requires_grad=True)
2025-08-14T23:09:16.7468647Z         >>> y = f(x)
2025-08-14T23:09:16.7468685Z         >>> I_N = torch.eye(N)
2025-08-14T23:09:16.7468717Z         >>>
2025-08-14T23:09:16.7468760Z         >>> # Sequential approach
2025-08-14T23:09:16.7468838Z         >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2025-08-14T23:09:16.7468885Z         >>>                  for v in I_N.unbind()]
2025-08-14T23:09:16.7470054Z         >>> jacobian = torch.stack(jacobian_rows)
2025-08-14T23:09:16.7470086Z         >>>
2025-08-14T23:09:16.7470135Z         >>> # vectorized gradient computation
2025-08-14T23:09:16.7470173Z         >>> def get_vjp(v):
2025-08-14T23:09:16.7470221Z         >>>     return torch.autograd.grad(y, x, v)
2025-08-14T23:09:16.7470268Z         >>> jacobian = torch.vmap(get_vjp)(I_N)
2025-08-14T23:09:16.7470299Z     
2025-08-14T23:09:16.7470392Z     :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2025-08-14T23:09:16.7470424Z     
2025-08-14T23:09:16.7470467Z         >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:16.7470510Z         >>> batched_dot = torch.vmap(
2025-08-14T23:09:16.7470553Z         ...     torch.vmap(torch.dot)
2025-08-14T23:09:16.7470602Z         ... )  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2025-08-14T23:09:16.7470659Z         >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2025-08-14T23:09:16.7470708Z         >>> batched_dot(x, y)  # tensor of size [2, 3]
2025-08-14T23:09:16.7470740Z     
2025-08-14T23:09:16.7470826Z     If the inputs are not batched along the first dimension, ``in_dims`` specifies
2025-08-14T23:09:16.7470885Z     the dimension that each inputs are batched along as
2025-08-14T23:09:16.7470916Z     
2025-08-14T23:09:16.7470957Z         >>> torch.dot  # [N], [N] -> []
2025-08-14T23:09:16.7471037Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2025-08-14T23:09:16.7472231Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2025-08-14T23:09:16.7472286Z         >>> batched_dot(
2025-08-14T23:09:16.7472321Z         ...     x, y
2025-08-14T23:09:16.7472393Z         ... )  # output is [5] instead of [2] if batched along the 0th dimension
2025-08-14T23:09:16.7472425Z     
2025-08-14T23:09:16.7472516Z     If there are multiple inputs each of which is batched along different dimensions,
2025-08-14T23:09:16.7472592Z     ``in_dims`` must be a tuple with the batch dimension for each input as
2025-08-14T23:09:16.7472623Z     
2025-08-14T23:09:16.7472663Z         >>> torch.dot  # [D], [D] -> []
2025-08-14T23:09:16.7472746Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2025-08-14T23:09:16.7472795Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-08-14T23:09:16.7472831Z         >>> batched_dot(
2025-08-14T23:09:16.7472864Z         ...     x, y
2025-08-14T23:09:16.7473025Z         ... )  # second arg doesn't have a batch dim because in_dim[1] was None
2025-08-14T23:09:16.7473057Z     
2025-08-14T23:09:16.7473143Z     If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2025-08-14T23:09:16.7473208Z     matching the shape of the input:
2025-08-14T23:09:16.7473239Z     
2025-08-14T23:09:16.7473293Z         >>> f = lambda dict: torch.dot(dict["x"], dict["y"])
2025-08-14T23:09:16.7473342Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2025-08-14T23:09:16.7473381Z         >>> input = {"x": x, "y": y}
2025-08-14T23:09:16.7473448Z         >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},))
2025-08-14T23:09:16.7474622Z         >>> batched_dot(input)
2025-08-14T23:09:16.7474654Z     
2025-08-14T23:09:16.7474753Z     By default, the output is batched along the first dimension. However, it can be batched
2025-08-14T23:09:16.7474803Z     along any dimension by using ``out_dims``
2025-08-14T23:09:16.7474833Z     
2025-08-14T23:09:16.7474873Z         >>> f = lambda x: x**2
2025-08-14T23:09:16.7474913Z         >>> x = torch.randn(2, 5)
2025-08-14T23:09:16.7474964Z         >>> batched_pow = torch.vmap(f, out_dims=1)
2025-08-14T23:09:16.7475003Z         >>> batched_pow(x)  # [5, 2]
2025-08-14T23:09:16.7475035Z     
2025-08-14T23:09:16.7475141Z     For any function that uses kwargs, the returned function will not batch the kwargs but will
2025-08-14T23:09:16.7475176Z     accept kwargs
2025-08-14T23:09:16.7475207Z     
2025-08-14T23:09:16.7475247Z         >>> x = torch.randn([2, 5])
2025-08-14T23:09:16.7475287Z         >>> def fn(x, scale=4.):
2025-08-14T23:09:16.7475324Z         >>>   return x * scale
2025-08-14T23:09:16.7475357Z         >>>
2025-08-14T23:09:16.7475399Z         >>> batched_pow = torch.vmap(fn)
2025-08-14T23:09:16.7475455Z         >>> assert torch.allclose(batched_pow(x), x * 4)
2025-08-14T23:09:16.7475536Z         >>> batched_pow(x, scale=x)  # scale is not batched, output has shape [2, 2, 5]
2025-08-14T23:09:16.7475567Z     
2025-08-14T23:09:16.7475600Z     .. note::
2025-08-14T23:09:16.7476799Z         vmap does not provide general autobatching or handle variable-length
2025-08-14T23:09:16.7476842Z         sequences out of the box.
2025-08-14T23:09:16.7476873Z     
2025-08-14T23:09:16.7476965Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7476998Z     
2025-08-14T23:09:16.7477035Z       warnings.warn(msg)
2025-08-14T23:09:16.7477066Z     
2025-08-14T23:09:16.7477141Z --- Parse Warning: 24 / 146 ---
2025-08-14T23:09:16.7477421Z     /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.
2025-08-14T23:09:16.7477514Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7477599Z     ``grad`` operator helps computing gradients of ``func`` with respect to the
2025-08-14T23:09:16.7477671Z         input(s) specified by ``argnums``. This operator can be nested to
2025-08-14T23:09:16.7477756Z         compute higher-order gradients.
2025-08-14T23:09:16.7477787Z     
2025-08-14T23:09:16.7477819Z         Args:
2025-08-14T23:09:16.7477896Z             func (Callable): A Python function that takes one or more arguments.
2025-08-14T23:09:16.7477991Z                 Must return a single-element Tensor. If specified ``has_aux`` equals ``True``,
2025-08-14T23:09:16.7478087Z                 function can return a tuple of single-element Tensor and other auxiliary objects:
2025-08-14T23:09:16.7478127Z                 ``(output, aux)``.
2025-08-14T23:09:16.7478227Z             argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to.
2025-08-14T23:09:16.7478303Z                 ``argnums`` can be single integer or tuple of integers. Default: 0.
2025-08-14T23:09:16.7479510Z             has_aux (bool): Flag indicating that ``func`` returns a tensor and other
2025-08-14T23:09:16.7479589Z                 auxiliary objects: ``(output, aux)``. Default: False.
2025-08-14T23:09:16.7479623Z     
2025-08-14T23:09:16.7479657Z         Returns:
2025-08-14T23:09:16.7479760Z             Function to compute gradients with respect to its inputs. By default, the output of
2025-08-14T23:09:16.7479862Z             the function is the gradient tensor(s) with respect to the first argument.
2025-08-14T23:09:16.7479959Z             If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects
2025-08-14T23:09:16.7480047Z             is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with
2025-08-14T23:09:16.7480103Z             respect to each ``argnums`` value is returned.
2025-08-14T23:09:16.7480134Z     
2025-08-14T23:09:16.7480175Z         Example of using ``grad``:
2025-08-14T23:09:16.7480205Z     
2025-08-14T23:09:16.7480245Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7480289Z             >>> from torch.func import grad
2025-08-14T23:09:16.7480331Z             >>> x = torch.randn([])
2025-08-14T23:09:16.7480384Z             >>> cos_x = grad(lambda x: torch.sin(x))(x)
2025-08-14T23:09:16.7480436Z             >>> assert torch.allclose(cos_x, x.cos())
2025-08-14T23:09:16.7480469Z             >>>
2025-08-14T23:09:16.7480513Z             >>> # Second-order gradients
2025-08-14T23:09:16.7480572Z             >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x)
2025-08-14T23:09:16.7480624Z             >>> assert torch.allclose(neg_sin_x, -x.sin())
2025-08-14T23:09:16.7480655Z     
2025-08-14T23:09:16.7481868Z         When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients:
2025-08-14T23:09:16.7481902Z     
2025-08-14T23:09:16.7481940Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7481989Z             >>> from torch.func import grad, vmap
2025-08-14T23:09:16.7482035Z             >>> batch_size, feature_size = 3, 5
2025-08-14T23:09:16.7482068Z             >>>
2025-08-14T23:09:16.7482113Z             >>> def model(weights, feature_vec):
2025-08-14T23:09:16.7482167Z             >>> # Very simple linear model with activation
2025-08-14T23:09:16.7482213Z             >>>     assert feature_vec.dim() == 1
2025-08-14T23:09:16.7482264Z             >>>     return feature_vec.dot(weights).relu()
2025-08-14T23:09:16.7482297Z             >>>
2025-08-14T23:09:16.7482351Z             >>> def compute_loss(weights, example, target):
2025-08-14T23:09:16.7482396Z             >>>     y = model(weights, example)
2025-08-14T23:09:16.7482452Z             >>>     return ((y - target) ** 2).mean()  # MSELoss
2025-08-14T23:09:16.7482484Z             >>>
2025-08-14T23:09:16.7482549Z             >>> weights = torch.randn(feature_size, requires_grad=True)
2025-08-14T23:09:16.7482606Z             >>> examples = torch.randn(batch_size, feature_size)
2025-08-14T23:09:16.7482657Z             >>> targets = torch.randn(batch_size)
2025-08-14T23:09:16.7482709Z             >>> inputs = (weights, examples, targets)
2025-08-14T23:09:16.7482795Z             >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))(
2025-08-14T23:09:16.7482872Z             ...     *inputs
2025-08-14T23:09:16.7484122Z             ... )
2025-08-14T23:09:16.7484154Z     
2025-08-14T23:09:16.7484224Z         Example of using ``grad`` with ``has_aux`` and ``argnums``:
2025-08-14T23:09:16.7484258Z     
2025-08-14T23:09:16.7484297Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7484342Z             >>> from torch.func import grad
2025-08-14T23:09:16.7484386Z             >>> def my_loss_func(y, y_pred):
2025-08-14T23:09:16.7484439Z             >>>    loss_per_sample = (0.5 * y_pred - y) ** 2
2025-08-14T23:09:16.7484484Z             >>>    loss = loss_per_sample.mean()
2025-08-14T23:09:16.7484536Z             >>>    return loss, (y_pred, loss_per_sample)
2025-08-14T23:09:16.7484568Z             >>>
2025-08-14T23:09:16.7484626Z             >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True)
2025-08-14T23:09:16.7484670Z             >>> y_true = torch.rand(4)
2025-08-14T23:09:16.7484748Z             >>> y_preds = torch.rand(4, requires_grad=True)
2025-08-14T23:09:16.7484793Z             >>> out = fn(y_true, y_preds)
2025-08-14T23:09:16.7484885Z             >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample))
2025-08-14T23:09:16.7484934Z     
2025-08-14T23:09:16.7484968Z         .. note::
2025-08-14T23:09:16.7485033Z             Using PyTorch ``torch.no_grad`` together with ``grad``.
2025-08-14T23:09:16.7485064Z     
2025-08-14T23:09:16.7485121Z             Case 1: Using ``torch.no_grad`` inside a function:
2025-08-14T23:09:16.7485153Z     
2025-08-14T23:09:16.7486379Z                 >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7486419Z                 >>> def f(x):
2025-08-14T23:09:16.7486463Z                 >>>     with torch.no_grad():
2025-08-14T23:09:16.7486503Z                 >>>         c = x ** 2
2025-08-14T23:09:16.7486542Z                 >>>     return x - c
2025-08-14T23:09:16.7486573Z     
2025-08-14T23:09:16.7486653Z             In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``.
2025-08-14T23:09:16.7486685Z     
2025-08-14T23:09:16.7486754Z             Case 2: Using ``grad`` inside ``torch.no_grad`` context manager:
2025-08-14T23:09:16.7486785Z     
2025-08-14T23:09:16.7486826Z                 >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7486871Z                 >>> with torch.no_grad():
2025-08-14T23:09:16.7486910Z                 >>>     grad(f)(x)
2025-08-14T23:09:16.7486940Z     
2025-08-14T23:09:16.7487022Z             In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the
2025-08-14T23:09:16.7487101Z             outer one. This is because ``grad`` is a "function transform": its result
2025-08-14T23:09:16.7487180Z             should not depend on the result of a context manager outside of ``f``.
2025-08-14T23:09:16.7487210Z     
2025-08-14T23:09:16.7487242Z         
2025-08-14T23:09:16.7487332Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7487364Z     
2025-08-14T23:09:16.7487403Z       warnings.warn(msg)
2025-08-14T23:09:16.7488549Z     
2025-08-14T23:09:16.7488627Z --- Parse Warning: 25 / 146 ---
2025-08-14T23:09:16.7488976Z     /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.
2025-08-14T23:09:16.7489069Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7489101Z     
2025-08-14T23:09:16.7489189Z     Config object that specifies the supported data types passed as arguments to
2025-08-14T23:09:16.7489273Z     quantize ops in the reference model spec, for input and output activations,
2025-08-14T23:09:16.7489310Z     weights, and biases.
2025-08-14T23:09:16.7489341Z     
2025-08-14T23:09:16.7489402Z     For example, consider the following reference model:
2025-08-14T23:09:16.7489432Z     
2025-08-14T23:09:16.7489523Z       quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2025-08-14T23:09:16.7489570Z     
2025-08-14T23:09:16.7489649Z     The pattern in the square brackets refers to the reference pattern of
2025-08-14T23:09:16.7489734Z     statically quantized linear. Setting the input dtype as `torch.quint8`
2025-08-14T23:09:16.7489815Z     in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2025-08-14T23:09:16.7489895Z     to the first quantize op (quant1). Similarly, setting the output dtype as
2025-08-14T23:09:16.7489975Z     `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2025-08-14T23:09:16.7490017Z     the second quantize op (quant2).
2025-08-14T23:09:16.7490049Z     
2025-08-14T23:09:16.7490125Z     Note that the dtype here does not refer to the interface dtypes of the
2025-08-14T23:09:16.7491328Z     op. For example, the "input dtype" here is not the dtype of the input
2025-08-14T23:09:16.7491438Z     tensor passed to the quantized linear op. Though it can still be the
2025-08-14T23:09:16.7491514Z     same as the interface dtype, this is not always the case, e.g. the
2025-08-14T23:09:16.7491592Z     interface dtype is fp32 in dynamic quantization but the "input dtype"
2025-08-14T23:09:16.7491684Z     specified in the DTypeConfig would still be quint8. The semantics of
2025-08-14T23:09:16.7491759Z     dtypes here are the same as the semantics of the dtypes specified in
2025-08-14T23:09:16.7491795Z     the observers.
2025-08-14T23:09:16.7491826Z     
2025-08-14T23:09:16.7491901Z     These dtypes are matched against the ones specified in the user's
2025-08-14T23:09:16.7491976Z     QConfig. If there is a match, and the QConfig satisfies the constraints
2025-08-14T23:09:16.7492053Z     specified in the DTypeConfig (if any), then we will quantize the given
2025-08-14T23:09:16.7492133Z     pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2025-08-14T23:09:16.7492177Z     the pattern will not be quantized.
2025-08-14T23:09:16.7492208Z     
2025-08-14T23:09:16.7492246Z     Example usage::
2025-08-14T23:09:16.7492278Z     
2025-08-14T23:09:16.7492320Z         >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:16.7492364Z         >>> dtype_config1 = DTypeConfig(
2025-08-14T23:09:16.7492408Z         ...     input_dtype=torch.quint8,
2025-08-14T23:09:16.7492451Z         ...     output_dtype=torch.quint8,
2025-08-14T23:09:16.7492493Z         ...     weight_dtype=torch.qint8,
2025-08-14T23:09:16.7492535Z         ...     bias_dtype=torch.float)
2025-08-14T23:09:16.7493752Z     
2025-08-14T23:09:16.7493796Z         >>> dtype_config2 = DTypeConfig(
2025-08-14T23:09:16.7493848Z         ...     input_dtype=DTypeWithConstraints(
2025-08-14T23:09:16.7493888Z         ...         dtype=torch.quint8,
2025-08-14T23:09:16.7493931Z         ...         quant_min_lower_bound=0,
2025-08-14T23:09:16.7493978Z         ...         quant_max_upper_bound=255,
2025-08-14T23:09:16.7494010Z         ...     ),
2025-08-14T23:09:16.7494063Z         ...     output_dtype=DTypeWithConstraints(
2025-08-14T23:09:16.7494103Z         ...         dtype=torch.quint8,
2025-08-14T23:09:16.7494147Z         ...         quant_min_lower_bound=0,
2025-08-14T23:09:16.7494191Z         ...         quant_max_upper_bound=255,
2025-08-14T23:09:16.7494224Z         ...     ),
2025-08-14T23:09:16.7494274Z         ...     weight_dtype=DTypeWithConstraints(
2025-08-14T23:09:16.7494314Z         ...         dtype=torch.qint8,
2025-08-14T23:09:16.7494362Z         ...         quant_min_lower_bound=-128,
2025-08-14T23:09:16.7494409Z         ...         quant_max_upper_bound=127,
2025-08-14T23:09:16.7494441Z         ...     ),
2025-08-14T23:09:16.7494481Z         ...     bias_dtype=torch.float)
2025-08-14T23:09:16.7494512Z     
2025-08-14T23:09:16.7494554Z         >>> dtype_config1.input_dtype
2025-08-14T23:09:16.7494589Z         torch.quint8
2025-08-14T23:09:16.7494622Z     
2025-08-14T23:09:16.7495788Z         >>> dtype_config2.input_dtype
2025-08-14T23:09:16.7495824Z         torch.quint8
2025-08-14T23:09:16.7495856Z     
2025-08-14T23:09:16.7495937Z         >>> dtype_config2.input_dtype_with_constraints
2025-08-14T23:09:16.7496140Z         DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2025-08-14T23:09:16.7496172Z     
2025-08-14T23:09:16.7496262Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7496292Z     
2025-08-14T23:09:16.7496329Z       warnings.warn(msg)
2025-08-14T23:09:16.7496361Z     
2025-08-14T23:09:16.7496433Z --- Parse Warning: 26 / 146 ---
2025-08-14T23:09:16.7496845Z     /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.
2025-08-14T23:09:16.7496955Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7496988Z     
2025-08-14T23:09:16.7497085Z     Takes in optional filter values and generates two tables with desired information.
2025-08-14T23:09:16.7497136Z     
2025-08-14T23:09:16.7497212Z     The generated tables are presented in both a list-of-lists format
2025-08-14T23:09:16.7497244Z     
2025-08-14T23:09:16.7497318Z     The reason for the two tables are that they handle different things:
2025-08-14T23:09:16.7497380Z     1.) the first table handles all tensor level information
2025-08-14T23:09:16.7497455Z     2.) the second table handles and displays all channel based information
2025-08-14T23:09:16.7498610Z     
2025-08-14T23:09:16.7498725Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-08-14T23:09:16.7498839Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-08-14T23:09:16.7498962Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-08-14T23:09:16.7498995Z     
2025-08-14T23:09:16.7499034Z     Tensor table columns:
2025-08-14T23:09:16.7499106Z         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:16.7499170Z         ----  ---------  ---------   ---------   ---------        ---------
2025-08-14T23:09:16.7499201Z     
2025-08-14T23:09:16.7499242Z     Per-Channel table columns:
2025-08-14T23:09:16.7499326Z         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:16.7499395Z         ----  ---------  -------  ---------   ---------   ---------        ---------
2025-08-14T23:09:16.7499426Z     
2025-08-14T23:09:16.7499458Z     Args:
2025-08-14T23:09:16.7499552Z         feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:16.7499596Z             contain this filter substring
2025-08-14T23:09:16.7499660Z             Default = "", results in all the features being printed
2025-08-14T23:09:16.7499752Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:16.7499843Z             Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:16.7499875Z     
2025-08-14T23:09:16.7499917Z     Returns a dictionary with two keys:
2025-08-14T23:09:16.7499984Z         (Dict[str, Tuple[List, List]]) A dict containing two keys:
2025-08-14T23:09:16.7501152Z         "tensor_level_info", "channel_level_info"
2025-08-14T23:09:16.7501197Z             Each key maps to a tuple with:
2025-08-14T23:09:16.7501245Z                 A list of the headers of each table
2025-08-14T23:09:16.7501311Z                 A list of lists containing the table information row by row
2025-08-14T23:09:16.7501376Z                 The 0th index row will contain the headers of the columns
2025-08-14T23:09:16.7501426Z                 The rest of the rows will contain data
2025-08-14T23:09:16.7501458Z     
2025-08-14T23:09:16.7501512Z     Example Use:
2025-08-14T23:09:16.7501577Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.7501637Z         >>> mod_report_visualizer.generate_filtered_tables(
2025-08-14T23:09:16.7501709Z         ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:16.7501807Z         ... )  # generates table with per_channel_min info for all modules in block 1 of the model
2025-08-14T23:09:16.7501838Z     
2025-08-14T23:09:16.7501927Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7501958Z     
2025-08-14T23:09:16.7501997Z       warnings.warn(msg)
2025-08-14T23:09:16.7502028Z     
2025-08-14T23:09:16.7502101Z --- Parse Warning: 27 / 146 ---
2025-08-14T23:09:16.7502530Z     /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.
2025-08-14T23:09:16.7502625Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7502683Z     
2025-08-14T23:09:16.7503974Z     Takes in optional filter values and prints out formatted tables of the information.
2025-08-14T23:09:16.7504005Z     
2025-08-14T23:09:16.7504125Z     The reason for the two tables printed out instead of one large one are that they handle different things:
2025-08-14T23:09:16.7504187Z     1.) the first table handles all tensor level information
2025-08-14T23:09:16.7504262Z     2.) the second table handles and displays all channel based information
2025-08-14T23:09:16.7504293Z     
2025-08-14T23:09:16.7504403Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2025-08-14T23:09:16.7504517Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2025-08-14T23:09:16.7504639Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2025-08-14T23:09:16.7504671Z     
2025-08-14T23:09:16.7504710Z     Tensor table columns:
2025-08-14T23:09:16.7504780Z      idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:16.7504841Z     ----  ---------  ---------   ---------   ---------        ---------
2025-08-14T23:09:16.7504871Z     
2025-08-14T23:09:16.7504912Z     Per-Channel table columns:
2025-08-14T23:09:16.7504944Z     
2025-08-14T23:09:16.7505021Z      idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2025-08-14T23:09:16.7505083Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2025-08-14T23:09:16.7505113Z     
2025-08-14T23:09:16.7505146Z     Args:
2025-08-14T23:09:16.7505238Z         feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:16.7505283Z             contain this filter substring
2025-08-14T23:09:16.7506467Z             Default = "", results in all the features being printed
2025-08-14T23:09:16.7506563Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:16.7506651Z             Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:16.7506683Z     
2025-08-14T23:09:16.7506717Z     Example Use:
2025-08-14T23:09:16.7506769Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.7506832Z         >>> mod_report_visualizer.generate_table_visualization(
2025-08-14T23:09:16.7506902Z         ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:16.7506934Z         ... )
2025-08-14T23:09:16.7507005Z         >>> # prints out neatly formatted table with per_channel_min info
2025-08-14T23:09:16.7507055Z         >>> # for all modules in block 1 of the model
2025-08-14T23:09:16.7507086Z     
2025-08-14T23:09:16.7507222Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7507253Z     
2025-08-14T23:09:16.7507291Z       warnings.warn(msg)
2025-08-14T23:09:16.7507323Z     
2025-08-14T23:09:16.7507396Z --- Parse Warning: 28 / 146 ---
2025-08-14T23:09:16.7507801Z     /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.
2025-08-14T23:09:16.7507892Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7507923Z     
2025-08-14T23:09:16.7508006Z     Takes in a feature and optional module_filter and plots of the desired data.
2025-08-14T23:09:16.7509216Z     
2025-08-14T23:09:16.7509332Z     For per channel features, it averages the value across the channels and plots a point
2025-08-14T23:09:16.7509425Z     per module. The reason for this is that for models with hundreds of channels, it can
2025-08-14T23:09:16.7509521Z     be hard to differentiate one channel line from another, and so the point of generating
2025-08-14T23:09:16.7509632Z     a single average point per module is to give a sense of general trends that encourage
2025-08-14T23:09:16.7509671Z     further deep dives.
2025-08-14T23:09:16.7509702Z     
2025-08-14T23:09:16.7509734Z     Note:
2025-08-14T23:09:16.7509828Z         Only features in the report that have tensor value data are plottable by this class
2025-08-14T23:09:16.7509894Z         When the tensor information is plotted, it will plot:
2025-08-14T23:09:16.7509949Z             idx as the x val, feature value as the y_val
2025-08-14T23:09:16.7510013Z         When the channel information is plotted, it will plot:
2025-08-14T23:09:16.7510107Z             the first idx of each module as the x val, feature value as the y_val [for each channel]
2025-08-14T23:09:16.7510191Z             The reason for this is that we want to be able to compare values across the
2025-08-14T23:09:16.7510273Z             channels for same layer, and it will be hard if values are staggered by idx
2025-08-14T23:09:16.7510338Z             This means each module is represented by only 1 x value
2025-08-14T23:09:16.7510370Z     Args:
2025-08-14T23:09:16.7510450Z         feature_filter (str): Filters the features presented to only those that
2025-08-14T23:09:16.7510493Z             contain this filter substring
2025-08-14T23:09:16.7510584Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:16.7510673Z             Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:16.7510705Z     
2025-08-14T23:09:16.7511861Z     Example Use:
2025-08-14T23:09:16.7511913Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.7511975Z         >>> mod_report_visualizer.generate_plot_visualization(
2025-08-14T23:09:16.7512048Z         ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:16.7512080Z         ... )
2025-08-14T23:09:16.7512149Z         >>> # outputs line plot of per_channel_min information for all
2025-08-14T23:09:16.7512217Z         >>> # modules in block1 of model each channel gets it's own line,
2025-08-14T23:09:16.7512281Z         >>> # and it's plotted across the in-order modules on the x-axis
2025-08-14T23:09:16.7512312Z     
2025-08-14T23:09:16.7512400Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7512431Z     
2025-08-14T23:09:16.7512469Z       warnings.warn(msg)
2025-08-14T23:09:16.7512500Z     
2025-08-14T23:09:16.7512572Z --- Parse Warning: 29 / 146 ---
2025-08-14T23:09:16.7513070Z     /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.
2025-08-14T23:09:16.7513179Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7513212Z     
2025-08-14T23:09:16.7513308Z     Takes in a feature and optional module_filter and plots the histogram of desired data.
2025-08-14T23:09:16.7513339Z     
2025-08-14T23:09:16.7513371Z     Note:
2025-08-14T23:09:16.7513465Z         Only features in the report that have tensor value data can be viewed as a histogram
2025-08-14T23:09:16.7513557Z         If you want to plot a histogram from all the channel values of a specific feature for
2025-08-14T23:09:16.7514790Z             a specific model, make sure to specify both the model and the feature properly
2025-08-14T23:09:16.7514876Z             in the filters and you should be able to see a distribution of the channel data
2025-08-14T23:09:16.7514908Z     
2025-08-14T23:09:16.7514940Z     Args:
2025-08-14T23:09:16.7515055Z         feature_filter (str, optional): Filters the features presented to only those that
2025-08-14T23:09:16.7515098Z             contain this filter substring
2025-08-14T23:09:16.7515177Z             Default = "", results in all the features being printed
2025-08-14T23:09:16.7515267Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2025-08-14T23:09:16.7515355Z             Default = "", results in all the modules in the reports to be visible in the table
2025-08-14T23:09:16.7515434Z         num_bins (int, optional): The number of bins to create the histogram with
2025-08-14T23:09:16.7515502Z             Default = 10, the values will be split into 10 equal sized bins
2025-08-14T23:09:16.7515532Z     
2025-08-14T23:09:16.7515567Z     Example Use:
2025-08-14T23:09:16.7515608Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7515712Z         >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2025-08-14T23:09:16.7515783Z         ...     feature_filter="per_channel_min", module_fqn_filter="block1"
2025-08-14T23:09:16.7515816Z         ... )
2025-08-14T23:09:16.7515913Z         # outputs histogram of per_channel_min information for all modules in block1 of model
2025-08-14T23:09:16.7516003Z             information is gathered across all channels for all modules in block 1 for the
2025-08-14T23:09:16.7516081Z             per_channel_min and is displayed in a histogram of equally sized bins
2025-08-14T23:09:16.7516112Z     
2025-08-14T23:09:16.7517310Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7517341Z     
2025-08-14T23:09:16.7517379Z       warnings.warn(msg)
2025-08-14T23:09:16.7517412Z     
2025-08-14T23:09:16.7517485Z --- Parse Warning: 30 / 146 ---
2025-08-14T23:09:16.7517827Z     /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.
2025-08-14T23:09:16.7517920Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7517952Z     
2025-08-14T23:09:16.7518058Z     The ModelReport class aims to provide users an easy way to diagnose issues that they run into
2025-08-14T23:09:16.7518162Z     with their models. The class works with all traceable GraphModules to help diagnose issues,
2025-08-14T23:09:16.7518269Z     though the requirements on the type of model more-so depends on the specific report the user
2025-08-14T23:09:16.7518376Z     is trying to generate. With respect to the reports, the ModelReport class is initialized with
2025-08-14T23:09:16.7518478Z     a set of Detector classes, each of which generate reports on quantization configuration
2025-08-14T23:09:16.7518518Z     issues a use might have.
2025-08-14T23:09:16.7518550Z     
2025-08-14T23:09:16.7518602Z     Currently supports generating reports on:
2025-08-14T23:09:16.7518722Z     - Suggestions for per-channel vs. per-tensor quantization (nn.Module)
2025-08-14T23:09:16.7518816Z     - Suggestions for dynamic vs static quantization for linear layers (Graph Modules)
2025-08-14T23:09:16.7518918Z     - Suggestions for input-weight equalization for linear and conv layers (Graph Modules)
2025-08-14T23:09:16.7518994Z     - Suggestions for outlier detection for all layers (Graph Modules)
2025-08-14T23:09:16.7519026Z     
2025-08-14T23:09:16.7519161Z     The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver)
2025-08-14T23:09:16.7520413Z     where needed for each detector to gather the information it needs, and then after calibration, the ModelReport
2025-08-14T23:09:16.7520540Z     class compiles the report generated by each Detector class into a single report to return to the user. It also
2025-08-14T23:09:16.7520618Z     has the capability to remove all the observers it inserted as well.
2025-08-14T23:09:16.7520666Z     
2025-08-14T23:09:16.7520769Z     * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule
2025-08-14T23:09:16.7520800Z     
2025-08-14T23:09:16.7520949Z     * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class
2025-08-14T23:09:16.7521059Z         Make sure that these are all unique types of detectors [do not have more than 1 of the same class]
2025-08-14T23:09:16.7521091Z     
2025-08-14T23:09:16.7521189Z     * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors.
2025-08-14T23:09:16.7521270Z         This set is generated by calling the get_detector_name() of each detector
2025-08-14T23:09:16.7521300Z     
2025-08-14T23:09:16.7521415Z     * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest
2025-08-14T23:09:16.7521524Z         The purpose of this is to keep track of what observers were inserted for each detector, so that they
2025-08-14T23:09:16.7521574Z         can be removed at the end if desired
2025-08-14T23:09:16.7521604Z     
2025-08-14T23:09:16.7521714Z     * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not
2025-08-14T23:09:16.7521798Z         This is to ensure we only insert observers once with the ModelReport instance
2025-08-14T23:09:16.7521829Z     
2025-08-14T23:09:16.7521918Z     * :attr:`_removed_observers` A boolean to track if we have removed observers already
2025-08-14T23:09:16.7522019Z         The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport
2025-08-14T23:09:16.7523328Z         instance. This also allows the functionality where we can generate the report multiple times
2025-08-14T23:09:16.7523388Z         as long as we haven't removed the observers yet.
2025-08-14T23:09:16.7523418Z     
2025-08-14T23:09:16.7523451Z     Note:
2025-08-14T23:09:16.7523556Z         This class was initially designed to work with the Fx Graph Mode workflow in mind. However,
2025-08-14T23:09:16.7523664Z         full functionality is available as long as there is a traceable GraphModule that is being used.
2025-08-14T23:09:16.7523767Z         One method to get a traceable GraphModule without going through the Fx workflow is to use
2025-08-14T23:09:16.7523813Z         the QuantizationTracer class.
2025-08-14T23:09:16.7523844Z     
2025-08-14T23:09:16.7523885Z     General Flow for Fx workflow:
2025-08-14T23:09:16.7524020Z     1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model
2025-08-14T23:09:16.7524067Z     2.) Prepare your model with prepare_fx
2025-08-14T23:09:16.7524153Z     3.) Call model_report.prepare_detailed_calibration to add relevant observers
2025-08-14T23:09:16.7524197Z     4.) Calibrate your model with data
2025-08-14T23:09:16.7524317Z     5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
2025-08-14T23:09:16.7524380Z     Optional
2025-08-14T23:09:16.7524493Z         6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance
2025-08-14T23:09:16.7524580Z         7.) To help in parsing report information and debugging, view report info as a:
2025-08-14T23:09:16.7524617Z             - Table
2025-08-14T23:09:16.7524655Z             - Histogram
2025-08-14T23:09:16.7524693Z             - Line plot
2025-08-14T23:09:16.7524805Z     8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions
2025-08-14T23:09:16.7526051Z     
2025-08-14T23:09:16.7526098Z     Example (with QuantizationTracer):
2025-08-14T23:09:16.7526141Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7526183Z         >>> # get the necessary qconfig
2025-08-14T23:09:16.7526231Z         >>> config = PrepareCustomConfig()
2025-08-14T23:09:16.7526292Z         >>> skipped_module_names, skipped_module_classes = (
2025-08-14T23:09:16.7526389Z         ...     get_skipped_module_name_and_classes(config, False)
2025-08-14T23:09:16.7526422Z         ... )
2025-08-14T23:09:16.7526455Z     
2025-08-14T23:09:16.7526508Z         >>> # initialize our model and get GraphModule
2025-08-14T23:09:16.7526570Z         >>> model = SomeModel()
2025-08-14T23:09:16.7526661Z         >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)
2025-08-14T23:09:16.7526726Z         >>> graph_module = GraphModule(model, tracer.trace(model))
2025-08-14T23:09:16.7526757Z     
2025-08-14T23:09:16.7526815Z         >>> # get our set of detectors and ModelReport instance
2025-08-14T23:09:16.7526855Z         >>> detector_set = set(
2025-08-14T23:09:16.7526887Z         ...     [
2025-08-14T23:09:16.7526944Z         ...         DynamicStaticDetector(tolerance=0.5),
2025-08-14T23:09:16.7527019Z         ...         InputWeightEqualizationDetector(ratio_threshold=0.7),
2025-08-14T23:09:16.7527052Z         ...     ]
2025-08-14T23:09:16.7527083Z         ... )
2025-08-14T23:09:16.7528304Z         >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set)
2025-08-14T23:09:16.7528336Z     
2025-08-14T23:09:16.7528397Z         >>> # now we insert the observers and calibrate the model
2025-08-14T23:09:16.7528490Z         >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration()
2025-08-14T23:09:16.7528544Z         >>> for i in range(num_callibration_batches):
2025-08-14T23:09:16.7528597Z         >>>     example_input = get_callibration_input()
2025-08-14T23:09:16.7528651Z         >>>     tracer_model_with_observers(example_input)
2025-08-14T23:09:16.7528682Z     
2025-08-14T23:09:16.7528775Z         >>> # finally we generate the reports and optionally remove the observers we inserted
2025-08-14T23:09:16.7528834Z         >>> reports = tracer_reporter.generate_model_report(
2025-08-14T23:09:16.7528881Z         ...     remove_inserted_observers=True
2025-08-14T23:09:16.7528912Z         ... )
2025-08-14T23:09:16.7528943Z     
2025-08-14T23:09:16.7529025Z         >>> # Optional: we can generate the qconfig mapping based on the suggestions
2025-08-14T23:09:16.7529088Z         >>> qconfigs = model_report.generate_qconfig_mapping()
2025-08-14T23:09:16.7529119Z     
2025-08-14T23:09:16.7529204Z         >>> # Optional: we can generate the equalization mapping based on the suggestions
2025-08-14T23:09:16.7529270Z         >>> qconfigs = model_report.generate_equalization_mapping()
2025-08-14T23:09:16.7529301Z     
2025-08-14T23:09:16.7529399Z         >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired
2025-08-14T23:09:16.7529473Z         >>> model_report_visualizer = tracer_reporter.generate_visualizer()
2025-08-14T23:09:16.7529505Z     
2025-08-14T23:09:16.7530673Z     
2025-08-14T23:09:16.7530768Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7530798Z     
2025-08-14T23:09:16.7530837Z       warnings.warn(msg)
2025-08-14T23:09:16.7530867Z     
2025-08-14T23:09:16.7530942Z --- Parse Warning: 31 / 146 ---
2025-08-14T23:09:16.7531365Z     /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.
2025-08-14T23:09:16.7531462Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7531493Z     
2025-08-14T23:09:16.7531585Z     The Activation sparsifier class aims to sparsify/prune activations in a neural
2025-08-14T23:09:16.7531667Z     network. The idea is to attach the sparsifier to a layer (or layers) and it
2025-08-14T23:09:16.7531756Z     zeroes out the activations based on the mask_fn (or sparsification function)
2025-08-14T23:09:16.7531793Z     input by the user.
2025-08-14T23:09:16.7531872Z     The mask_fn is applied once all the inputs are aggregated and reduced i.e.
2025-08-14T23:09:16.7531946Z     mask = mask_fn(reduce_fn(aggregate_fn(activations)))
2025-08-14T23:09:16.7531979Z     
2025-08-14T23:09:16.7532013Z     Note::
2025-08-14T23:09:16.7532125Z         The sparsification mask is computed on the input **before it goes through the attached layer**.
2025-08-14T23:09:16.7532174Z     
2025-08-14T23:09:16.7532206Z     Args:
2025-08-14T23:09:16.7532246Z         model (nn.Module):
2025-08-14T23:09:16.7533563Z             The model whose layers will be sparsified. The layers that needs to be
2025-08-14T23:09:16.7533652Z             sparsified should be added separately using the register_layer() function
2025-08-14T23:09:16.7533700Z         aggregate_fn (Optional, Callable):
2025-08-14T23:09:16.7533790Z             default aggregate_fn that is used if not specified while registering the layer.
2025-08-14T23:09:16.7533854Z             specifies how inputs should be aggregated over time.
2025-08-14T23:09:16.7533953Z             The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor.
2025-08-14T23:09:16.7533990Z             Example
2025-08-14T23:09:16.7534056Z                 def add_agg_fn(tensor1, tensor2):  return tensor1 + tensor2
2025-08-14T23:09:16.7534103Z                 reduce_fn (Optional, Callable):
2025-08-14T23:09:16.7534191Z                     default reduce_fn that is used if not specified while registering the layer.
2025-08-14T23:09:16.7534281Z                     reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after
2025-08-14T23:09:16.7534329Z                     calling agg_fn() on all inputs.
2025-08-14T23:09:16.7534367Z                     Example
2025-08-14T23:09:16.7534438Z                 def mean_reduce_fn(agg_tensor):    return agg_tensor.mean(dim=0)
2025-08-14T23:09:16.7534485Z                 mask_fn (Optional, Callable):
2025-08-14T23:09:16.7534591Z                     default mask_fn that is used to create the sparsification mask using the tensor obtained after
2025-08-14T23:09:16.7534681Z                     calling the reduce_fn(). This is used by default if a custom one is passed in the
2025-08-14T23:09:16.7534724Z                     register_layer().
2025-08-14T23:09:16.7534856Z                     Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config
2025-08-14T23:09:16.7534896Z                     arguments.
2025-08-14T23:09:16.7534940Z                 features (Optional, list):
2025-08-14T23:09:16.7534992Z                     default selected features to sparsify.
2025-08-14T23:09:16.7536716Z                     If this is non-empty, then the mask_fn will be applied for each feature of the input.
2025-08-14T23:09:16.7536784Z                     For example,
2025-08-14T23:09:16.7536931Z                 mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features]
2025-08-14T23:09:16.7537003Z                 feature_dim (Optional, int):
2025-08-14T23:09:16.7537163Z                     default dimension of input features. Again, features along this dim will be chosen
2025-08-14T23:09:16.7537278Z                     for sparsification.
2025-08-14T23:09:16.7537363Z                 sparse_config (Dict):
2025-08-14T23:09:16.7537491Z                     Default configuration for the mask_fn. This config will be passed
2025-08-14T23:09:16.7537555Z                     with the mask_fn()
2025-08-14T23:09:16.7537605Z     
2025-08-14T23:09:16.7537657Z     Example:
2025-08-14T23:09:16.7537718Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7537781Z         >>> model = SomeModel()
2025-08-14T23:09:16.7537921Z         >>> act_sparsifier = ActivationSparsifier(...)  # init activation sparsifier
2025-08-14T23:09:16.7537984Z         >>> # Initialize aggregate_fn
2025-08-14T23:09:16.7538045Z         >>> def agg_fn(x, y):
2025-08-14T23:09:16.7538103Z         >>>     return x + y
2025-08-14T23:09:16.7538154Z         >>>
2025-08-14T23:09:16.7538218Z         >>> # Initialize reduce_fn
2025-08-14T23:09:16.7538275Z         >>> def reduce_fn(x):
2025-08-14T23:09:16.7538367Z         >>>     return torch.mean(x, dim=0)
2025-08-14T23:09:16.7538418Z         >>>
2025-08-14T23:09:16.7540367Z         >>> # Initialize mask_fn
2025-08-14T23:09:16.7540427Z         >>> def mask_fn(data):
2025-08-14T23:09:16.7540548Z         >>>     return torch.eye(data.shape).to(data.device)
2025-08-14T23:09:16.7540598Z         >>>
2025-08-14T23:09:16.7540648Z         >>>
2025-08-14T23:09:16.7540719Z         >>> act_sparsifier.register_layer(
2025-08-14T23:09:16.7540783Z         ...     model.some_layer,
2025-08-14T23:09:16.7540845Z         ...     aggregate_fn=agg_fn,
2025-08-14T23:09:16.7540908Z         ...     reduce_fn=reduce_fn,
2025-08-14T23:09:16.7540968Z         ...     mask_fn=mask_fn,
2025-08-14T23:09:16.7541019Z         ... )
2025-08-14T23:09:16.7541070Z         >>>
2025-08-14T23:09:16.7541135Z         >>> # start training process
2025-08-14T23:09:16.7541193Z         >>> for _ in [...]:
2025-08-14T23:09:16.7541251Z         >>> # epoch starts
2025-08-14T23:09:16.7541357Z         >>> # model.forward(), compute_loss() and model.backwards()
2025-08-14T23:09:16.7541414Z         >>> # epoch ends
2025-08-14T23:09:16.7541482Z         >>>     act_sparsifier.step()
2025-08-14T23:09:16.7541544Z         >>> # end training process
2025-08-14T23:09:16.7541612Z         >>> sparsifier.squash_mask()
2025-08-14T23:09:16.7541660Z     
2025-08-14T23:09:16.7543724Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7543776Z     
2025-08-14T23:09:16.7543837Z       warnings.warn(msg)
2025-08-14T23:09:16.7543886Z     
2025-08-14T23:09:16.7544007Z --- Parse Warning: 32 / 146 ---
2025-08-14T23:09:16.7544664Z     /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.
2025-08-14T23:09:16.7544817Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7544867Z     
2025-08-14T23:09:16.7544989Z     Abstract method that needs to be implemented by the child class.
2025-08-14T23:09:16.7545136Z     The expected return type should is a dictionary of name to schedule_param value
2025-08-14T23:09:16.7545290Z     The returned values will be updated in sparsifier when the scheduler step() function
2025-08-14T23:09:16.7545342Z     is called.
2025-08-14T23:09:16.7545392Z     
2025-08-14T23:09:16.7545444Z     Example:
2025-08-14T23:09:16.7545512Z         >>> def get_schedule_param(self):
2025-08-14T23:09:16.7545572Z         ...     new_param = {}
2025-08-14T23:09:16.7545668Z         ...     for name in self.sparsifier.data_groups.keys():
2025-08-14T23:09:16.7545731Z         ...         new_param[name] = (
2025-08-14T23:09:16.7545853Z         ...             self.sparsifier.data_groups[name][self.schedule_param] * 0.5
2025-08-14T23:09:16.7545907Z         ...         )
2025-08-14T23:09:16.7545968Z         ...     return new_param
2025-08-14T23:09:16.7546056Z     
2025-08-14T23:09:16.7548140Z     When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param]
2025-08-14T23:09:16.7548200Z     would be halved
2025-08-14T23:09:16.7548249Z     
2025-08-14T23:09:16.7548396Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7548445Z     
2025-08-14T23:09:16.7548505Z       warnings.warn(msg)
2025-08-14T23:09:16.7548554Z     
2025-08-14T23:09:16.7548668Z --- Parse Warning: 33 / 146 ---
2025-08-14T23:09:16.7549257Z     /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.
2025-08-14T23:09:16.7549408Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7549534Z     Squashes the sparse masks into the appropriate tensors.
2025-08-14T23:09:16.7549586Z     
2025-08-14T23:09:16.7549706Z             If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2025-08-14T23:09:16.7549837Z             the module will have a `sparse_params` dict attached to it.
2025-08-14T23:09:16.7549886Z     
2025-08-14T23:09:16.7549939Z             Args:
2025-08-14T23:09:16.7550048Z                 params_to_keep: List of keys to save in the module or a dict
2025-08-14T23:09:16.7550144Z                                 representing the modules and keys that will have
2025-08-14T23:09:16.7550220Z                                 sparsity parameters saved
2025-08-14T23:09:16.7550344Z                 params_to_keep_per_layer: Dict to specify the params that should be
2025-08-14T23:09:16.7550435Z                                 saved for specific layers. The keys in the dict
2025-08-14T23:09:16.7552372Z                                 should be the module fqn, while the values should
2025-08-14T23:09:16.7552468Z                                 be a list of strings with the names of the variables
2025-08-14T23:09:16.7552549Z                                 to save in the `sparse_params`
2025-08-14T23:09:16.7552599Z     
2025-08-14T23:09:16.7552656Z             Examples:
2025-08-14T23:09:16.7552737Z                 >>> # xdoctest: +SKIP("locals are undefined")
2025-08-14T23:09:16.7552808Z                 >>> # Don't save any sparse params
2025-08-14T23:09:16.7552880Z                 >>> sparsifier.squash_mask()
2025-08-14T23:09:16.7553036Z                 >>> hasattr(model.submodule1, "sparse_params")
2025-08-14T23:09:16.7553091Z                 False
2025-08-14T23:09:16.7553140Z     
2025-08-14T23:09:16.7553213Z                 >>> # Keep sparse params per layer
2025-08-14T23:09:16.7553286Z                 >>> sparsifier.squash_mask(
2025-08-14T23:09:16.7553356Z                 ...     params_to_keep_per_layer={
2025-08-14T23:09:16.7553438Z                 ...         "submodule1.linear1": ("foo", "bar"),
2025-08-14T23:09:16.7553520Z                 ...         "submodule2.linear42": ("baz",),
2025-08-14T23:09:16.7553578Z                 ...     }
2025-08-14T23:09:16.7553631Z                 ... )
2025-08-14T23:09:16.7553728Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:16.7553791Z                 {'foo': 42, 'bar': 24}
2025-08-14T23:09:16.7553888Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:16.7553947Z                 {'baz': 0.1}
2025-08-14T23:09:16.7555864Z     
2025-08-14T23:09:16.7555947Z                 >>> # Keep sparse params for all layers
2025-08-14T23:09:16.7556055Z                 >>> sparsifier.squash_mask(params_to_keep=("foo", "bar"))
2025-08-14T23:09:16.7556152Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:16.7556216Z                 {'foo': 42, 'bar': 24}
2025-08-14T23:09:16.7556316Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:16.7556381Z                 {'foo': 42, 'bar': 24}
2025-08-14T23:09:16.7556467Z     
2025-08-14T23:09:16.7556631Z                 >>> # Keep some sparse params for all layers, and specific ones for
2025-08-14T23:09:16.7556696Z                 >>> # some other layers
2025-08-14T23:09:16.7556769Z                 >>> sparsifier.squash_mask(
2025-08-14T23:09:16.7556844Z                 ...     params_to_keep=("foo", "bar"),
2025-08-14T23:09:16.7556961Z                 ...     params_to_keep_per_layer={"submodule2.linear42": ("baz",)},
2025-08-14T23:09:16.7557014Z                 ... )
2025-08-14T23:09:16.7557114Z                 >>> print(model.submodule1.linear1.sparse_params)
2025-08-14T23:09:16.7557174Z                 {'foo': 42, 'bar': 24}
2025-08-14T23:09:16.7557273Z                 >>> print(model.submodule2.linear42.sparse_params)
2025-08-14T23:09:16.7557342Z                 {'foo': 42, 'bar': 24, 'baz': 0.1}
2025-08-14T23:09:16.7557395Z             
2025-08-14T23:09:16.7557568Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7557623Z     
2025-08-14T23:09:16.7557685Z       warnings.warn(msg)
2025-08-14T23:09:16.7559632Z     
2025-08-14T23:09:16.7559748Z --- Parse Warning: 34 / 146 ---
2025-08-14T23:09:16.7560287Z     /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.
2025-08-14T23:09:16.7560442Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7560523Z     A quantized long short-term memory (LSTM).
2025-08-14T23:09:16.7560574Z     
2025-08-14T23:09:16.7560739Z         For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2025-08-14T23:09:16.7560789Z     
2025-08-14T23:09:16.7560846Z         Attributes:
2025-08-14T23:09:16.7560922Z             layers : instances of the `_LSTMLayer`
2025-08-14T23:09:16.7560972Z     
2025-08-14T23:09:16.7561033Z         .. note::
2025-08-14T23:09:16.7561161Z             To access the weights and biases, you need to access them per layer.
2025-08-14T23:09:16.7561269Z             See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2025-08-14T23:09:16.7561321Z     
2025-08-14T23:09:16.7561379Z         Examples::
2025-08-14T23:09:16.7561445Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7561513Z             >>> custom_module_config = {
2025-08-14T23:09:16.7561599Z             ...     'float_to_observed_custom_module_class': {
2025-08-14T23:09:16.7561681Z             ...         nn.LSTM: nn.quantizable.LSTM,
2025-08-14T23:09:16.7561736Z             ...     },
2025-08-14T23:09:16.7563789Z             ...     'observed_to_quantized_custom_module_class': {
2025-08-14T23:09:16.7563882Z             ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2025-08-14T23:09:16.7563937Z             ...     }
2025-08-14T23:09:16.7563989Z             ... }
2025-08-14T23:09:16.7564125Z             >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2025-08-14T23:09:16.7564253Z             >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2025-08-14T23:09:16.7564308Z         
2025-08-14T23:09:16.7564456Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7564508Z     
2025-08-14T23:09:16.7564570Z       warnings.warn(msg)
2025-08-14T23:09:16.7564620Z     
2025-08-14T23:09:16.7564736Z --- Parse Warning: 35 / 146 ---
2025-08-14T23:09:16.7565255Z     /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.
2025-08-14T23:09:16.7565414Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7565543Z     Applies a 1D convolution over a quantized input signal composed of
2025-08-14T23:09:16.7565655Z         several quantized input planes.
2025-08-14T23:09:16.7565799Z     
2025-08-14T23:09:16.7565949Z         For details on input arguments, parameters, and implementation see
2025-08-14T23:09:16.7566021Z         :class:`~torch.nn.Conv1d`.
2025-08-14T23:09:16.7566074Z     
2025-08-14T23:09:16.7566130Z         .. note::
2025-08-14T23:09:16.7566256Z             Only `zeros` is supported for the :attr:`padding_mode` argument.
2025-08-14T23:09:16.7568263Z     
2025-08-14T23:09:16.7568321Z         .. note::
2025-08-14T23:09:16.7568430Z             Only `torch.quint8` is supported for the input data type.
2025-08-14T23:09:16.7568483Z     
2025-08-14T23:09:16.7568534Z     
2025-08-14T23:09:16.7568592Z         Attributes:
2025-08-14T23:09:16.7568718Z             weight (Tensor):     packed tensor derived from the learnable weight
2025-08-14T23:09:16.7568787Z                                  parameter.
2025-08-14T23:09:16.7568879Z             scale (Tensor):      scalar for the output scale
2025-08-14T23:09:16.7569015Z             zero_point (Tensor): scalar for the output zero point
2025-08-14T23:09:16.7569068Z     
2025-08-14T23:09:16.7569165Z         See :class:`~torch.nn.Conv1d` for other attributes.
2025-08-14T23:09:16.7569240Z     
2025-08-14T23:09:16.7569298Z         Examples::
2025-08-14T23:09:16.7569350Z     
2025-08-14T23:09:16.7569443Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2025-08-14T23:09:16.7569529Z             >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2025-08-14T23:09:16.7569603Z             >>> input = torch.randn(20, 16, 100)
2025-08-14T23:09:16.7569676Z             >>> # quantize input to quint8
2025-08-14T23:09:16.7569741Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7569871Z             >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2025-08-14T23:09:16.7569953Z             ...                                     dtype=torch.quint8)
2025-08-14T23:09:16.7571950Z             >>> output = m(q_input)
2025-08-14T23:09:16.7572001Z     
2025-08-14T23:09:16.7572058Z         
2025-08-14T23:09:16.7572214Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7572267Z     
2025-08-14T23:09:16.7572331Z       warnings.warn(msg)
2025-08-14T23:09:16.7572383Z     
2025-08-14T23:09:16.7572500Z --- Parse Warning: 36 / 146 ---
2025-08-14T23:09:16.7573080Z     /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.
2025-08-14T23:09:16.7573238Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7573458Z     Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2025-08-14T23:09:16.7573509Z     
2025-08-14T23:09:16.7573655Z         Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2025-08-14T23:09:16.7573788Z         Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2025-08-14T23:09:16.7573924Z         Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2025-08-14T23:09:16.7573980Z         (UAI 2018).
2025-08-14T23:09:16.7574032Z     
2025-08-14T23:09:16.7574165Z         Exponential Moving Average is a variation of `Polyak averaging`_,
2025-08-14T23:09:16.7574311Z         but using exponential weights instead of equal weights across iterations.
2025-08-14T23:09:16.7574364Z     
2025-08-14T23:09:16.7574506Z         AveragedModel class creates a copy of the provided module :attr:`model`
2025-08-14T23:09:16.7576585Z         on the device :attr:`device` and allows to compute running averages of the
2025-08-14T23:09:16.7576658Z         parameters of the :attr:`model`.
2025-08-14T23:09:16.7576712Z     
2025-08-14T23:09:16.7576764Z         Args:
2025-08-14T23:09:16.7576861Z             model (torch.nn.Module): model to use with SWA/EMA
2025-08-14T23:09:16.7577004Z             device (torch.device, optional): if provided, the averaged model will be
2025-08-14T23:09:16.7577134Z                 stored on the :attr:`device`
2025-08-14T23:09:16.7577259Z             avg_fn (function, optional): the averaging function used to update
2025-08-14T23:09:16.7577383Z                 parameters; the function must take in the current value of the
2025-08-14T23:09:16.7577516Z                 :class:`AveragedModel` parameter, the current value of :attr:`model`
2025-08-14T23:09:16.7577634Z                 parameter, and the number of models already averaged; if None,
2025-08-14T23:09:16.7577733Z                 an equally weighted average is used (default: None)
2025-08-14T23:09:16.7577869Z             multi_avg_fn (function, optional): the averaging function used to update
2025-08-14T23:09:16.7578012Z                 parameters inplace; the function must take in the current values of the
2025-08-14T23:09:16.7578198Z                 :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2025-08-14T23:09:16.7578336Z                 parameters as a list, and the number of models already averaged; if None,
2025-08-14T23:09:16.7578434Z                 an equally weighted average is used (default: None)
2025-08-14T23:09:16.7578580Z             use_buffers (bool): if ``True``, it will compute running averages for
2025-08-14T23:09:16.7578716Z                 both the parameters and the buffers of the model. (default: ``False``)
2025-08-14T23:09:16.7578768Z     
2025-08-14T23:09:16.7578823Z         Example:
2025-08-14T23:09:16.7578906Z             >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.7580919Z             >>> loader, optimizer, model, loss_fn = ...
2025-08-14T23:09:16.7581025Z             >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2025-08-14T23:09:16.7581163Z             >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2025-08-14T23:09:16.7581237Z             >>>                                     T_max=300)
2025-08-14T23:09:16.7581303Z             >>> swa_start = 160
2025-08-14T23:09:16.7581393Z             >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2025-08-14T23:09:16.7581460Z             >>> for i in range(300):
2025-08-14T23:09:16.7581535Z             >>>      for input, target in loader:
2025-08-14T23:09:16.7581609Z             >>>          optimizer.zero_grad()
2025-08-14T23:09:16.7581695Z             >>>          loss_fn(model(input), target).backward()
2025-08-14T23:09:16.7581764Z             >>>          optimizer.step()
2025-08-14T23:09:16.7581831Z             >>>      if i > swa_start:
2025-08-14T23:09:16.7581913Z             >>>          swa_model.update_parameters(model)
2025-08-14T23:09:16.7581987Z             >>>          swa_scheduler.step()
2025-08-14T23:09:16.7582045Z             >>>      else:
2025-08-14T23:09:16.7582113Z             >>>          scheduler.step()
2025-08-14T23:09:16.7582165Z             >>>
2025-08-14T23:09:16.7582261Z             >>> # Update bn statistics for the swa_model at the end
2025-08-14T23:09:16.7582363Z             >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2025-08-14T23:09:16.7582417Z     
2025-08-14T23:09:16.7582594Z         You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2025-08-14T23:09:16.7584882Z         If no averaging function is provided, the default is to compute
2025-08-14T23:09:16.7584980Z         equally-weighted average of the weights (SWA).
2025-08-14T23:09:16.7585032Z     
2025-08-14T23:09:16.7585087Z         Example:
2025-08-14T23:09:16.7585172Z             >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.7585300Z             >>> # Compute exponential moving averages of the weights and buffers
2025-08-14T23:09:16.7585406Z             >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2025-08-14T23:09:16.7585537Z             >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2025-08-14T23:09:16.7585590Z     
2025-08-14T23:09:16.7585644Z         .. note::
2025-08-14T23:09:16.7585835Z             When using SWA/EMA with models containing Batch Normalization you may
2025-08-14T23:09:16.7585957Z             need to update the activation statistics for Batch Normalization.
2025-08-14T23:09:16.7586099Z             This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2025-08-14T23:09:16.7586234Z             or by setting :attr:`use_buffers` to `True`. The first approach updates the
2025-08-14T23:09:16.7586381Z             statistics in a post-training step by passing data through the model. The
2025-08-14T23:09:16.7586523Z             second does it during the parameter update phase by averaging all buffers.
2025-08-14T23:09:16.7586671Z             Empirical evidence has shown that updating the statistics in normalization
2025-08-14T23:09:16.7586803Z             layers increases accuracy, but you may wish to empirically test which
2025-08-14T23:09:16.7586899Z             approach yields the best results in your problem.
2025-08-14T23:09:16.7586972Z     
2025-08-14T23:09:16.7587026Z         .. note::
2025-08-14T23:09:16.7587178Z             :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2025-08-14T23:09:16.7591004Z     
2025-08-14T23:09:16.7591068Z         .. note::
2025-08-14T23:09:16.7591191Z             When :meth:`update_parameters` is called for the first time (i.e.
2025-08-14T23:09:16.7591303Z             :attr:`n_averaged` is `0`) the parameters of `model` are copied
2025-08-14T23:09:16.7591423Z             to the parameters of :class:`AveragedModel`. For every subsequent
2025-08-14T23:09:16.7591535Z             call of :meth:`update_parameters` the function `avg_fn` is used
2025-08-14T23:09:16.7591604Z             to update the parameters.
2025-08-14T23:09:16.7591655Z     
2025-08-14T23:09:16.7591788Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-08-14T23:09:16.7591873Z             https://arxiv.org/abs/1803.05407
2025-08-14T23:09:16.7592017Z         .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2025-08-14T23:09:16.7592076Z             Average:
2025-08-14T23:09:16.7592150Z             https://arxiv.org/abs/1806.05594
2025-08-14T23:09:16.7592276Z         .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2025-08-14T23:09:16.7592350Z             https://arxiv.org/abs/1904.11943
2025-08-14T23:09:16.7592483Z         .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2025-08-14T23:09:16.7592547Z             Generalizes Well:
2025-08-14T23:09:16.7592619Z             https://arxiv.org/abs/2001.02312
2025-08-14T23:09:16.7592681Z         .. _Polyak averaging:
2025-08-14T23:09:16.7592788Z             https://paperswithcode.com/method/polyak-averaging
2025-08-14T23:09:16.7592839Z         
2025-08-14T23:09:16.7596047Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7596102Z     
2025-08-14T23:09:16.7596166Z       warnings.warn(msg)
2025-08-14T23:09:16.7596216Z     
2025-08-14T23:09:16.7596354Z --- Parse Warning: 37 / 146 ---
2025-08-14T23:09:16.7596836Z     /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.
2025-08-14T23:09:16.7596991Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7597118Z     Anneals the learning rate in each parameter group to a fixed value.
2025-08-14T23:09:16.7597168Z     
2025-08-14T23:09:16.7597305Z         This learning rate scheduler is meant to be used with Stochastic Weight
2025-08-14T23:09:16.7597434Z         Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2025-08-14T23:09:16.7597492Z     
2025-08-14T23:09:16.7597544Z         Args:
2025-08-14T23:09:16.7597651Z             optimizer (torch.optim.Optimizer): wrapped optimizer
2025-08-14T23:09:16.7597773Z             swa_lrs (float or list): the learning rate value for all param groups
2025-08-14T23:09:16.7597919Z                 together or separately for each group.
2025-08-14T23:09:16.7598039Z             annealing_epochs (int): number of epochs in the annealing phase
2025-08-14T23:09:16.7598101Z                 (default: 10)
2025-08-14T23:09:16.7598227Z             annealing_strategy (str): "cos" or "linear"; specifies the annealing
2025-08-14T23:09:16.7598354Z                 strategy: "cos" for cosine annealing, "linear" for linear annealing
2025-08-14T23:09:16.7598416Z                 (default: "cos")
2025-08-14T23:09:16.7598525Z             last_epoch (int): the index of the last epoch (default: -1)
2025-08-14T23:09:16.7600476Z     
2025-08-14T23:09:16.7600587Z         The :class:`SWALR` scheduler can be used together with other
2025-08-14T23:09:16.7600714Z         schedulers to switch to a constant learning rate late in the training
2025-08-14T23:09:16.7600779Z         as in the example below.
2025-08-14T23:09:16.7600828Z     
2025-08-14T23:09:16.7600912Z         Example:
2025-08-14T23:09:16.7600996Z             >>> # xdoctest: +SKIP("Undefined variables")
2025-08-14T23:09:16.7601071Z             >>> loader, optimizer, model = ...
2025-08-14T23:09:16.7601163Z             >>> lr_lambda = lambda epoch: 0.9
2025-08-14T23:09:16.7601297Z             >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2025-08-14T23:09:16.7601365Z             >>>        lr_lambda=lr_lambda)
2025-08-14T23:09:16.7601468Z             >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2025-08-14T23:09:16.7601574Z             >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2025-08-14T23:09:16.7601634Z             >>> swa_start = 160
2025-08-14T23:09:16.7601698Z             >>> for i in range(300):
2025-08-14T23:09:16.7601770Z             >>>      for input, target in loader:
2025-08-14T23:09:16.7601842Z             >>>          optimizer.zero_grad()
2025-08-14T23:09:16.7601926Z             >>>          loss_fn(model(input), target).backward()
2025-08-14T23:09:16.7601996Z             >>>          optimizer.step()
2025-08-14T23:09:16.7602061Z             >>>      if i > swa_start:
2025-08-14T23:09:16.7602131Z             >>>          swa_scheduler.step()
2025-08-14T23:09:16.7602190Z             >>>      else:
2025-08-14T23:09:16.7604216Z             >>>          scheduler.step()
2025-08-14T23:09:16.7604267Z     
2025-08-14T23:09:16.7604401Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2025-08-14T23:09:16.7604476Z             https://arxiv.org/abs/1803.05407
2025-08-14T23:09:16.7604529Z         
2025-08-14T23:09:16.7604676Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7604727Z     
2025-08-14T23:09:16.7604788Z       warnings.warn(msg)
2025-08-14T23:09:16.7604840Z     
2025-08-14T23:09:16.7604955Z --- Parse Warning: 38 / 146 ---
2025-08-14T23:09:16.7605484Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/optimizer.py line=867.
2025-08-14T23:09:16.7605638Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7605813Z     Load the optimizer state.
2025-08-14T23:09:16.7605864Z     
2025-08-14T23:09:16.7605917Z             Args:
2025-08-14T23:09:16.7606036Z                 state_dict (dict): optimizer state. Should be an object returned
2025-08-14T23:09:16.7606113Z                     from a call to :meth:`state_dict`.
2025-08-14T23:09:16.7606165Z     
2025-08-14T23:09:16.7606224Z             .. warning::
2025-08-14T23:09:16.7606425Z                 Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`,
2025-08-14T23:09:16.7606548Z                 as calling it beforehand will overwrite the loaded learning rates.
2025-08-14T23:09:16.7608490Z     
2025-08-14T23:09:16.7608549Z             .. note::
2025-08-14T23:09:16.7608786Z                 The names of the parameters (if they exist under the "param_names" key of each param group
2025-08-14T23:09:16.7608891Z                 in :meth:`state_dict`) will not affect the loading process.
2025-08-14T23:09:16.7609082Z                 To use the parameters' names for custom cases (such as when the parameters in the loaded state dict
2025-08-14T23:09:16.7609176Z                 differ from those initialized in the optimizer),
2025-08-14T23:09:16.7609355Z                 a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict
2025-08-14T23:09:16.7609416Z                 accordingly.
2025-08-14T23:09:16.7609587Z                 If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override
2025-08-14T23:09:16.7609769Z                 the current names, if present, in the optimizer state. If they do not exist in loaded state dict,
2025-08-14T23:09:16.7609889Z                 the optimizer ``param_names`` will remain unchanged.
2025-08-14T23:09:16.7609941Z     
2025-08-14T23:09:16.7609999Z             Example:
2025-08-14T23:09:16.7610064Z                 >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7610160Z                 >>> model = torch.nn.Linear(10, 10)
2025-08-14T23:09:16.7610262Z                 >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4)
2025-08-14T23:09:16.7610362Z                 >>> scheduler1 = torch.optim.lr_scheduler.LinearLR(
2025-08-14T23:09:16.7610420Z                 ...     optim,
2025-08-14T23:09:16.7610485Z                 ...     start_factor=0.1,
2025-08-14T23:09:16.7610546Z                 ...     end_factor=1,
2025-08-14T23:09:16.7610609Z                 ...     total_iters=20,
2025-08-14T23:09:16.7610662Z                 ... )
2025-08-14T23:09:16.7612658Z                 >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR(
2025-08-14T23:09:16.7612714Z                 ...     optim,
2025-08-14T23:09:16.7612777Z                 ...     T_max=80,
2025-08-14T23:09:16.7612839Z                 ...     eta_min=3e-5,
2025-08-14T23:09:16.7612893Z                 ... )
2025-08-14T23:09:16.7613049Z                 >>> lr = torch.optim.lr_scheduler.SequentialLR(
2025-08-14T23:09:16.7613108Z                 ...     optim,
2025-08-14T23:09:16.7613196Z                 ...     schedulers=[scheduler1, scheduler2],
2025-08-14T23:09:16.7613260Z                 ...     milestones=[20],
2025-08-14T23:09:16.7613314Z                 ... )
2025-08-14T23:09:16.7613405Z                 >>> lr.load_state_dict(torch.load("./save_seq.pt"))
2025-08-14T23:09:16.7613529Z                 >>> # now load the optimizer checkpoint after loading the LRScheduler
2025-08-14T23:09:16.7613630Z                 >>> optim.load_state_dict(torch.load("./save_optim.pt"))
2025-08-14T23:09:16.7613681Z     
2025-08-14T23:09:16.7613732Z             
2025-08-14T23:09:16.7613883Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7613933Z     
2025-08-14T23:09:16.7613996Z       warnings.warn(msg)
2025-08-14T23:09:16.7614045Z     
2025-08-14T23:09:16.7614164Z --- Parse Warning: 39 / 146 ---
2025-08-14T23:09:16.7614669Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=808.
2025-08-14T23:09:16.7616709Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7616899Z     Contains a list of schedulers expected to be called sequentially during the optimization process.
2025-08-14T23:09:16.7616949Z     
2025-08-14T23:09:16.7617159Z         Specifically, the schedulers will be called according to the milestone points, which should provide exact
2025-08-14T23:09:16.7617288Z         intervals by which each scheduler should be called at a given epoch.
2025-08-14T23:09:16.7617338Z     
2025-08-14T23:09:16.7617391Z         Args:
2025-08-14T23:09:16.7617512Z             optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:16.7617623Z             schedulers (list): List of chained schedulers.
2025-08-14T23:09:16.7617750Z             milestones (list): List of integers that reflects milestone points.
2025-08-14T23:09:16.7617854Z             last_epoch (int): The index of last epoch. Default: -1.
2025-08-14T23:09:16.7617903Z     
2025-08-14T23:09:16.7617957Z         Example:
2025-08-14T23:09:16.7618022Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7618115Z             >>> # Assuming optimizer uses lr = 0.05 for all groups
2025-08-14T23:09:16.7618184Z             >>> # lr = 0.005     if epoch == 0
2025-08-14T23:09:16.7618249Z             >>> # lr = 0.005     if epoch == 1
2025-08-14T23:09:16.7618315Z             >>> # lr = 0.005     if epoch == 2
2025-08-14T23:09:16.7618367Z             >>> # ...
2025-08-14T23:09:16.7618435Z             >>> # lr = 0.05      if epoch == 20
2025-08-14T23:09:16.7618520Z             >>> # lr = 0.045     if epoch == 21
2025-08-14T23:09:16.7618589Z             >>> # lr = 0.0405    if epoch == 22
2025-08-14T23:09:16.7620581Z             >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20)
2025-08-14T23:09:16.7620710Z             >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
2025-08-14T23:09:16.7620778Z             >>> scheduler = SequentialLR(
2025-08-14T23:09:16.7620838Z             ...     optimizer,
2025-08-14T23:09:16.7620919Z             ...     schedulers=[scheduler1, scheduler2],
2025-08-14T23:09:16.7620984Z             ...     milestones=[20],
2025-08-14T23:09:16.7621035Z             ... )
2025-08-14T23:09:16.7621102Z             >>> for epoch in range(100):
2025-08-14T23:09:16.7621160Z             >>>     train(...)
2025-08-14T23:09:16.7621222Z             >>>     validate(...)
2025-08-14T23:09:16.7621285Z             >>>     scheduler.step()
2025-08-14T23:09:16.7621335Z     
2025-08-14T23:09:16.7621444Z         .. image:: ../scripts/lr_scheduler_images/SequentialLR.png
2025-08-14T23:09:16.7621497Z         
2025-08-14T23:09:16.7621646Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7621696Z     
2025-08-14T23:09:16.7621758Z       warnings.warn(msg)
2025-08-14T23:09:16.7621807Z     
2025-08-14T23:09:16.7621921Z --- Parse Warning: 40 / 146 ---
2025-08-14T23:09:16.7622441Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1233.
2025-08-14T23:09:16.7622586Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7622695Z     Reduce learning rate when a metric has stopped improving.
2025-08-14T23:09:16.7624676Z     
2025-08-14T23:09:16.7624797Z         Models often benefit from reducing the learning rate by a factor
2025-08-14T23:09:16.7624914Z         of 2-10 once learning stagnates. This scheduler reads a metrics
2025-08-14T23:09:16.7625033Z         quantity and if no improvement is seen for a 'patience' number
2025-08-14T23:09:16.7625111Z         of epochs, the learning rate is reduced.
2025-08-14T23:09:16.7625162Z     
2025-08-14T23:09:16.7625215Z         Args:
2025-08-14T23:09:16.7625298Z             optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:16.7625394Z             mode (str): One of `min`, `max`. In `min` mode, lr will
2025-08-14T23:09:16.7625489Z                 be reduced when the quantity monitored has stopped
2025-08-14T23:09:16.7625586Z                 decreasing; in `max` mode it will be reduced when the
2025-08-14T23:09:16.7625699Z                 quantity monitored has stopped increasing. Default: 'min'.
2025-08-14T23:09:16.7625804Z             factor (float): Factor by which the learning rate will be
2025-08-14T23:09:16.7625889Z                 reduced. new_lr = lr * factor. Default: 0.1.
2025-08-14T23:09:16.7626019Z             patience (int): The number of allowed epochs with no improvement after
2025-08-14T23:09:16.7626157Z                 which the learning rate will be reduced.
2025-08-14T23:09:16.7626295Z                 For example, consider the case of having no patience (`patience = 0`).
2025-08-14T23:09:16.7626502Z                 In the first epoch, a baseline is established and is always considered good as there's no previous baseline.
2025-08-14T23:09:16.7626624Z                 In the second epoch, if the performance is worse than the baseline,
2025-08-14T23:09:16.7626714Z                 we have what is considered an intolerable epoch.
2025-08-14T23:09:16.7626869Z                 Since the count of intolerable epochs (1) is greater than the patience level (0),
2025-08-14T23:09:16.7628824Z                 the learning rate is reduced at the end of this epoch.
2025-08-14T23:09:16.7629014Z                 From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch
2025-08-14T23:09:16.7629233Z                 if the performance is worse than the baseline. If the performance improves or remains the same,
2025-08-14T23:09:16.7629312Z                 the learning rate is not adjusted.
2025-08-14T23:09:16.7629393Z                 Default: 10.
2025-08-14T23:09:16.7629507Z             threshold (float): Threshold for measuring the new optimum,
2025-08-14T23:09:16.7629604Z                 to only focus on significant changes. Default: 1e-4.
2025-08-14T23:09:16.7629709Z             threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
2025-08-14T23:09:16.7629804Z                 dynamic_threshold = best * ( 1 + threshold ) in 'max'
2025-08-14T23:09:16.7629891Z                 mode or best * ( 1 - threshold ) in `min` mode.
2025-08-14T23:09:16.7629984Z                 In `abs` mode, dynamic_threshold = best + threshold in
2025-08-14T23:09:16.7630090Z                 `max` mode or best - threshold in `min` mode. Default: 'rel'.
2025-08-14T23:09:16.7630193Z             cooldown (int): Number of epochs to wait before resuming
2025-08-14T23:09:16.7630299Z                 normal operation after lr has been reduced. Default: 0.
2025-08-14T23:09:16.7630397Z             min_lr (float or list): A scalar or a list of scalars. A
2025-08-14T23:09:16.7630493Z                 lower bound on the learning rate of all param groups
2025-08-14T23:09:16.7630577Z                 or each group respectively. Default: 0.
2025-08-14T23:09:16.7630681Z             eps (float): Minimal decay applied to lr. If the difference
2025-08-14T23:09:16.7630789Z                 between new and old lr is smaller than eps, the update is
2025-08-14T23:09:16.7630856Z                 ignored. Default: 1e-8.
2025-08-14T23:09:16.7630907Z     
2025-08-14T23:09:16.7630960Z         Example:
2025-08-14T23:09:16.7632898Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7633097Z             >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
2025-08-14T23:09:16.7633193Z             >>> scheduler = ReduceLROnPlateau(optimizer, "min")
2025-08-14T23:09:16.7633261Z             >>> for epoch in range(10):
2025-08-14T23:09:16.7633324Z             >>>     train(...)
2025-08-14T23:09:16.7633392Z             >>>     val_loss = validate(...)
2025-08-14T23:09:16.7633487Z             >>> # Note that step should be called after validate()
2025-08-14T23:09:16.7633556Z             >>>     scheduler.step(val_loss)
2025-08-14T23:09:16.7633607Z     
2025-08-14T23:09:16.7633722Z         .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png
2025-08-14T23:09:16.7633774Z         
2025-08-14T23:09:16.7634012Z     Original Error: IndentationError('unexpected indent', ('<source_block>', 8, 4, '    scheduler.step(val_loss)\n', 8, -1))
2025-08-14T23:09:16.7634063Z     
2025-08-14T23:09:16.7634129Z         scheduler.step(val_loss)
2025-08-14T23:09:16.7634179Z        ^
2025-08-14T23:09:16.7634241Z       warnings.warn(msg)
2025-08-14T23:09:16.7634290Z     
2025-08-14T23:09:16.7634410Z --- Parse Warning: 41 / 146 ---
2025-08-14T23:09:16.7634942Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1430.
2025-08-14T23:09:16.7635118Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7635305Z     Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).
2025-08-14T23:09:16.7637239Z     
2025-08-14T23:09:16.7637405Z         The policy cycles the learning rate between two boundaries with a constant frequency,
2025-08-14T23:09:16.7637558Z         as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_.
2025-08-14T23:09:16.7637695Z         The distance between the two boundaries can be scaled on a per-iteration
2025-08-14T23:09:16.7637759Z         or per-cycle basis.
2025-08-14T23:09:16.7637809Z     
2025-08-14T23:09:16.7637979Z         Cyclical learning rate policy changes the learning rate after every batch.
2025-08-14T23:09:16.7638100Z         `step` should be called after a batch has been used for training.
2025-08-14T23:09:16.7638151Z     
2025-08-14T23:09:16.7638273Z         This class has three built-in policies, as put forth in the paper:
2025-08-14T23:09:16.7638344Z     
2025-08-14T23:09:16.7638465Z         * "triangular": A basic triangular cycle without amplitude scaling.
2025-08-14T23:09:16.7638639Z         * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
2025-08-14T23:09:16.7638824Z         * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
2025-08-14T23:09:16.7638890Z           at each cycle iteration.
2025-08-14T23:09:16.7638940Z     
2025-08-14T23:09:16.7639080Z         This implementation was adapted from the github repo: `bckenstler/CLR`_
2025-08-14T23:09:16.7639129Z     
2025-08-14T23:09:16.7639182Z         Args:
2025-08-14T23:09:16.7639267Z             optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:16.7639376Z             base_lr (float or list): Initial learning rate which is the
2025-08-14T23:09:16.7639478Z                 lower boundary in the cycle for each parameter group.
2025-08-14T23:09:16.7641447Z             max_lr (float or list): Upper learning rate boundaries in the cycle
2025-08-14T23:09:16.7643848Z                 for each parameter group. Functionally,
2025-08-14T23:09:16.7643944Z                 it defines the cycle amplitude (max_lr - base_lr).
2025-08-14T23:09:16.7644026Z                 The lr at any cycle is the sum of base_lr
2025-08-14T23:09:16.7644114Z                 and some scaling of the amplitude; therefore
2025-08-14T23:09:16.7644202Z                 max_lr may not actually be reached depending on
2025-08-14T23:09:16.7644267Z                 scaling function.
2025-08-14T23:09:16.7644369Z             step_size_up (int): Number of training iterations in the
2025-08-14T23:09:16.7644450Z                 increasing half of a cycle. Default: 2000
2025-08-14T23:09:16.7644561Z             step_size_down (int): Number of training iterations in the
2025-08-14T23:09:16.7644663Z                 decreasing half of a cycle. If step_size_down is None,
2025-08-14T23:09:16.7644744Z                 it is set to step_size_up. Default: None
2025-08-14T23:09:16.7644842Z             mode (str): One of {triangular, triangular2, exp_range}.
2025-08-14T23:09:16.7644967Z                 Values correspond to policies detailed above.
2025-08-14T23:09:16.7645060Z                 If scale_fn is not None, this argument is ignored.
2025-08-14T23:09:16.7645125Z                 Default: 'triangular'
2025-08-14T23:09:16.7645227Z             gamma (float): Constant in 'exp_range' scaling function:
2025-08-14T23:09:16.7645294Z                 gamma**(cycle iterations)
2025-08-14T23:09:16.7645356Z                 Default: 1.0
2025-08-14T23:09:16.7647843Z             scale_fn (function): Custom scaling policy defined by a single
2025-08-14T23:09:16.7647924Z                 argument lambda function, where
2025-08-14T23:09:16.7648070Z                 0 <= scale_fn(x) <= 1 for all x >= 0.
2025-08-14T23:09:16.7648146Z                 If specified, then 'mode' is ignored.
2025-08-14T23:09:16.7648209Z                 Default: None
2025-08-14T23:09:16.7648289Z             scale_mode (str): {'cycle', 'iterations'}.
2025-08-14T23:09:16.7648372Z                 Defines whether scale_fn is evaluated on
2025-08-14T23:09:16.7648454Z                 cycle number or cycle iterations (training
2025-08-14T23:09:16.7648528Z                 iterations since start of cycle).
2025-08-14T23:09:16.7648590Z                 Default: 'cycle'
2025-08-14T23:09:16.7648708Z             cycle_momentum (bool): If ``True``, momentum is cycled inversely
2025-08-14T23:09:16.7648819Z                 to learning rate between 'base_momentum' and 'max_momentum'.
2025-08-14T23:09:16.7648878Z                 Default: True
2025-08-14T23:09:16.7649009Z             base_momentum (float or list): Lower momentum boundaries in the cycle
2025-08-14T23:09:16.7649155Z                 for each parameter group. Note that momentum is cycled inversely
2025-08-14T23:09:16.7649255Z                 to learning rate; at the peak of a cycle, momentum is
2025-08-14T23:09:16.7649342Z                 'base_momentum' and learning rate is 'max_lr'.
2025-08-14T23:09:16.7649403Z                 Default: 0.8
2025-08-14T23:09:16.7649528Z             max_momentum (float or list): Upper momentum boundaries in the cycle
2025-08-14T23:09:16.7649612Z                 for each parameter group. Functionally,
2025-08-14T23:09:16.7649726Z                 it defines the cycle amplitude (max_momentum - base_momentum).
2025-08-14T23:09:16.7649833Z                 The momentum at any cycle is the difference of max_momentum
2025-08-14T23:09:16.7651844Z                 and some scaling of the amplitude; therefore
2025-08-14T23:09:16.7651949Z                 base_momentum may not actually be reached depending on
2025-08-14T23:09:16.7652058Z                 scaling function. Note that momentum is cycled inversely
2025-08-14T23:09:16.7652186Z                 to learning rate; at the start of a cycle, momentum is 'max_momentum'
2025-08-14T23:09:16.7652257Z                 and learning rate is 'base_lr'
2025-08-14T23:09:16.7652317Z                 Default: 0.9
2025-08-14T23:09:16.7652449Z             last_epoch (int): The index of the last batch. This parameter is used when
2025-08-14T23:09:16.7652672Z                 resuming a training job. Since `step()` should be invoked after each
2025-08-14T23:09:16.7652793Z                 batch instead of after each epoch, this number represents the total
2025-08-14T23:09:16.7652998Z                 number of *batches* computed, not the total number of epochs computed.
2025-08-14T23:09:16.7653112Z                 When last_epoch=-1, the schedule is started from the beginning.
2025-08-14T23:09:16.7653173Z                 Default: -1
2025-08-14T23:09:16.7653223Z     
2025-08-14T23:09:16.7653277Z         Example:
2025-08-14T23:09:16.7653341Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7653480Z             >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
2025-08-14T23:09:16.7653574Z             >>> scheduler = torch.optim.lr_scheduler.CyclicLR(
2025-08-14T23:09:16.7653635Z             ...     optimizer,
2025-08-14T23:09:16.7653694Z             ...     base_lr=0.01,
2025-08-14T23:09:16.7653754Z             ...     max_lr=0.1,
2025-08-14T23:09:16.7653820Z             ...     step_size_up=10,
2025-08-14T23:09:16.7655774Z             ... )
2025-08-14T23:09:16.7655870Z             >>> data_loader = torch.utils.data.DataLoader(...)
2025-08-14T23:09:16.7655936Z             >>> for epoch in range(10):
2025-08-14T23:09:16.7656009Z             >>>     for batch in data_loader:
2025-08-14T23:09:16.7656075Z             >>>         train_batch(...)
2025-08-14T23:09:16.7656144Z             >>>         scheduler.step()
2025-08-14T23:09:16.7656193Z     
2025-08-14T23:09:16.7656291Z         .. image:: ../scripts/lr_scheduler_images/CyclicLR.png
2025-08-14T23:09:16.7656377Z     
2025-08-14T23:09:16.7656577Z         .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
2025-08-14T23:09:16.7656675Z         .. _bckenstler/CLR: https://github.com/bckenstler/CLR
2025-08-14T23:09:16.7656728Z         
2025-08-14T23:09:16.7656875Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7656926Z     
2025-08-14T23:09:16.7656987Z       warnings.warn(msg)
2025-08-14T23:09:16.7657038Z     
2025-08-14T23:09:16.7657170Z --- Parse Warning: 42 / 146 ---
2025-08-14T23:09:16.7657737Z     /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=1722.
2025-08-14T23:09:16.7657889Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7658057Z     Set the learning rate of each parameter group using a cosine annealing schedule.
2025-08-14T23:09:16.7658109Z     
2025-08-14T23:09:16.7658219Z         The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
2025-08-14T23:09:16.7660238Z         is the number of epochs since the last restart and :math:`T_{i}` is the number
2025-08-14T23:09:16.7660327Z         of epochs between two warm restarts in SGDR:
2025-08-14T23:09:16.7660377Z     
2025-08-14T23:09:16.7660432Z         .. math::
2025-08-14T23:09:16.7660540Z             \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
2025-08-14T23:09:16.7660627Z             \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
2025-08-14T23:09:16.7660676Z     
2025-08-14T23:09:16.7660776Z         When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
2025-08-14T23:09:16.7660888Z         When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
2025-08-14T23:09:16.7660937Z     
2025-08-14T23:09:16.7661003Z         It has been proposed in
2025-08-14T23:09:16.7661109Z         `SGDR: Stochastic Gradient Descent with Warm Restarts`_.
2025-08-14T23:09:16.7661160Z     
2025-08-14T23:09:16.7661212Z         Args:
2025-08-14T23:09:16.7661296Z             optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:16.7661392Z             T_0 (int): Number of iterations until the first restart.
2025-08-14T23:09:16.7661595Z             T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1.
2025-08-14T23:09:16.7661705Z             eta_min (float, optional): Minimum learning rate. Default: 0.
2025-08-14T23:09:16.7661831Z             last_epoch (int, optional): The index of the last epoch. Default: -1.
2025-08-14T23:09:16.7661880Z     
2025-08-14T23:09:16.7661983Z         .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
2025-08-14T23:09:16.7662057Z             https://arxiv.org/abs/1608.03983
2025-08-14T23:09:16.7664062Z     
2025-08-14T23:09:16.7664117Z         Example:
2025-08-14T23:09:16.7664184Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7664297Z             >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
2025-08-14T23:09:16.7664436Z             >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
2025-08-14T23:09:16.7664499Z             ...     optimizer, T_0=20
2025-08-14T23:09:16.7664551Z             ... )
2025-08-14T23:09:16.7664618Z             >>> for epoch in range(100):
2025-08-14T23:09:16.7664678Z             >>>     train(...)
2025-08-14T23:09:16.7664739Z             >>>     validate(...)
2025-08-14T23:09:16.7664802Z             >>>     scheduler.step()
2025-08-14T23:09:16.7664852Z     
2025-08-14T23:09:16.7664991Z         .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png
2025-08-14T23:09:16.7665043Z         
2025-08-14T23:09:16.7665191Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7665242Z     
2025-08-14T23:09:16.7665301Z       warnings.warn(msg)
2025-08-14T23:09:16.7665353Z     
2025-08-14T23:09:16.7665514Z --- Parse Warning: 43 / 146 ---
2025-08-14T23:09:16.7666037Z     /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=1872.
2025-08-14T23:09:16.7666191Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7668258Z     Sets the learning rate of each parameter group according to the 1cycle learning rate policy.
2025-08-14T23:09:16.7668309Z     
2025-08-14T23:09:16.7668481Z         The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
2025-08-14T23:09:16.7668649Z         learning rate and then from that maximum learning rate to some minimum learning rate much
2025-08-14T23:09:16.7668726Z         lower than the initial learning rate.
2025-08-14T23:09:16.7668884Z         This policy was initially described in the paper `Super-Convergence:
2025-08-14T23:09:16.7669012Z         Very Fast Training of Neural Networks Using Large Learning Rates`_.
2025-08-14T23:09:16.7669061Z     
2025-08-14T23:09:16.7669206Z         The 1cycle learning rate policy changes the learning rate after every batch.
2025-08-14T23:09:16.7669323Z         `step` should be called after a batch has been used for training.
2025-08-14T23:09:16.7669376Z     
2025-08-14T23:09:16.7669443Z         This scheduler is not chainable.
2025-08-14T23:09:16.7669494Z     
2025-08-14T23:09:16.7669633Z         Note also that the total number of steps in the cycle can be determined in one
2025-08-14T23:09:16.7669718Z         of two ways (listed in order of precedence):
2025-08-14T23:09:16.7669767Z     
2025-08-14T23:09:16.7669856Z         #. A value for total_steps is explicitly provided.
2025-08-14T23:09:16.7669966Z         #. A number of epochs (epochs) and a number of steps per epoch
2025-08-14T23:09:16.7670036Z            (steps_per_epoch) are provided.
2025-08-14T23:09:16.7670138Z            In this case, the number of total steps is inferred by
2025-08-14T23:09:16.7670215Z            total_steps = epochs * steps_per_epoch
2025-08-14T23:09:16.7670266Z     
2025-08-14T23:09:16.7672275Z         You must either provide a value for total_steps or provide a value for both
2025-08-14T23:09:16.7672346Z         epochs and steps_per_epoch.
2025-08-14T23:09:16.7672424Z     
2025-08-14T23:09:16.7672603Z         The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
2025-08-14T23:09:16.7672773Z         claims that "unpublished work has shown even better results by using only two phases". To
2025-08-14T23:09:16.7672986Z         mimic the behaviour of the original paper instead, set ``three_phase=True``.
2025-08-14T23:09:16.7673036Z     
2025-08-14T23:09:16.7673089Z         Args:
2025-08-14T23:09:16.7673172Z             optimizer (Optimizer): Wrapped optimizer.
2025-08-14T23:09:16.7673291Z             max_lr (float or list): Upper learning rate boundaries in the cycle
2025-08-14T23:09:16.7673360Z                 for each parameter group.
2025-08-14T23:09:16.7673485Z             total_steps (int): The total number of steps in the cycle. Note that
2025-08-14T23:09:16.7673607Z                 if a value is not provided here, then it must be inferred by providing
2025-08-14T23:09:16.7673690Z                 a value for epochs and steps_per_epoch.
2025-08-14T23:09:16.7673752Z                 Default: None
2025-08-14T23:09:16.7673869Z             epochs (int): The number of epochs to train for. This is used along
2025-08-14T23:09:16.7674013Z                 with steps_per_epoch in order to infer the total number of steps in the cycle
2025-08-14T23:09:16.7674098Z                 if a value for total_steps is not provided.
2025-08-14T23:09:16.7674157Z                 Default: None
2025-08-14T23:09:16.7674289Z             steps_per_epoch (int): The number of steps per epoch to train for. This is
2025-08-14T23:09:16.7674424Z                 used along with epochs in order to infer the total number of steps in the
2025-08-14T23:09:16.7676457Z                 cycle if a value for total_steps is not provided.
2025-08-14T23:09:16.7676518Z                 Default: None
2025-08-14T23:09:16.7676653Z             pct_start (float): The percentage of the cycle (in number of steps) spent
2025-08-14T23:09:16.7676728Z                 increasing the learning rate.
2025-08-14T23:09:16.7676788Z                 Default: 0.3
2025-08-14T23:09:16.7676865Z             anneal_strategy (str): {'cos', 'linear'}
2025-08-14T23:09:16.7677012Z                 Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
2025-08-14T23:09:16.7677073Z                 linear annealing.
2025-08-14T23:09:16.7677133Z                 Default: 'cos'
2025-08-14T23:09:16.7677251Z             cycle_momentum (bool): If ``True``, momentum is cycled inversely
2025-08-14T23:09:16.7677363Z                 to learning rate between 'base_momentum' and 'max_momentum'.
2025-08-14T23:09:16.7677443Z                 Default: True
2025-08-14T23:09:16.7677572Z             base_momentum (float or list): Lower momentum boundaries in the cycle
2025-08-14T23:09:16.7677693Z                 for each parameter group. Note that momentum is cycled inversely
2025-08-14T23:09:16.7677792Z                 to learning rate; at the peak of a cycle, momentum is
2025-08-14T23:09:16.7677882Z                 'base_momentum' and learning rate is 'max_lr'.
2025-08-14T23:09:16.7677940Z                 Default: 0.85
2025-08-14T23:09:16.7678064Z             max_momentum (float or list): Upper momentum boundaries in the cycle
2025-08-14T23:09:16.7678145Z                 for each parameter group. Functionally,
2025-08-14T23:09:16.7678259Z                 it defines the cycle amplitude (max_momentum - base_momentum).
2025-08-14T23:09:16.7678336Z                 Note that momentum is cycled inversely
2025-08-14T23:09:16.7678460Z                 to learning rate; at the start of a cycle, momentum is 'max_momentum'
2025-08-14T23:09:16.7680394Z                 and learning rate is 'base_lr'
2025-08-14T23:09:16.7680457Z                 Default: 0.95
2025-08-14T23:09:16.7680566Z             div_factor (float): Determines the initial learning rate via
2025-08-14T23:09:16.7680636Z                 initial_lr = max_lr/div_factor
2025-08-14T23:09:16.7680695Z                 Default: 25
2025-08-14T23:09:16.7680844Z             final_div_factor (float): Determines the minimum learning rate via
2025-08-14T23:09:16.7680920Z                 min_lr = initial_lr/final_div_factor
2025-08-14T23:09:16.7680980Z                 Default: 1e4
2025-08-14T23:09:16.7681124Z             three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
2025-08-14T23:09:16.7681274Z                 learning rate according to 'final_div_factor' instead of modifying the second
2025-08-14T23:09:16.7681414Z                 phase (the first two phases will be symmetrical about the step indicated by
2025-08-14T23:09:16.7681474Z                 'pct_start').
2025-08-14T23:09:16.7681607Z             last_epoch (int): The index of the last batch. This parameter is used when
2025-08-14T23:09:16.7681736Z                 resuming a training job. Since `step()` should be invoked after each
2025-08-14T23:09:16.7681862Z                 batch instead of after each epoch, this number represents the total
2025-08-14T23:09:16.7681994Z                 number of *batches* computed, not the total number of epochs computed.
2025-08-14T23:09:16.7682107Z                 When last_epoch=-1, the schedule is started from the beginning.
2025-08-14T23:09:16.7682168Z                 Default: -1
2025-08-14T23:09:16.7682217Z     
2025-08-14T23:09:16.7682270Z         Example:
2025-08-14T23:09:16.7682333Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7684384Z             >>> data_loader = torch.utils.data.DataLoader(...)
2025-08-14T23:09:16.7684523Z             >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
2025-08-14T23:09:16.7684624Z             >>> scheduler = torch.optim.lr_scheduler.OneCycleLR(
2025-08-14T23:09:16.7684799Z             ...     optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10
2025-08-14T23:09:16.7684851Z             ... )
2025-08-14T23:09:16.7684919Z             >>> for epoch in range(10):
2025-08-14T23:09:16.7684988Z             >>>     for batch in data_loader:
2025-08-14T23:09:16.7685055Z             >>>         train_batch(...)
2025-08-14T23:09:16.7685120Z             >>>         optimizer.step()
2025-08-14T23:09:16.7685185Z             >>>         scheduler.step()
2025-08-14T23:09:16.7685235Z     
2025-08-14T23:09:16.7685337Z         .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png
2025-08-14T23:09:16.7685386Z     
2025-08-14T23:09:16.7685559Z         .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
2025-08-14T23:09:16.7685633Z             https://arxiv.org/abs/1708.07120
2025-08-14T23:09:16.7685685Z         
2025-08-14T23:09:16.7685978Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7686031Z     
2025-08-14T23:09:16.7686092Z       warnings.warn(msg)
2025-08-14T23:09:16.7686144Z     
2025-08-14T23:09:16.7686261Z --- Parse Warning: 44 / 146 ---
2025-08-14T23:09:16.7686803Z     /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.
2025-08-14T23:09:16.7688857Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7688909Z     
2025-08-14T23:09:16.7689043Z     Registers a fake kernel based on the given operator profiles. This fake
2025-08-14T23:09:16.7689187Z     kernel registration will override any existing fake kernel registrations.
2025-08-14T23:09:16.7689236Z     
2025-08-14T23:09:16.7689363Z     The input is a dictionary mapping operator names to a set of operator
2025-08-14T23:09:16.7689503Z     profiles, which we will use to generate fake kernels. The operator profiles
2025-08-14T23:09:16.7689621Z     are a record of the input and output tensor metadata. Based on this
2025-08-14T23:09:16.7689759Z     information we will match a given input to the recorded profile, and return
2025-08-14T23:09:16.7689887Z     an output with the same metadata as in the recorded profile. If a profile
2025-08-14T23:09:16.7690001Z     doesn't exist then an exception will be thrown.
2025-08-14T23:09:16.7690051Z     
2025-08-14T23:09:16.7690184Z     The fake kernel generation is considered unsafe because it relies on the
2025-08-14T23:09:16.7690316Z     rigid, pre-defined operator profiles that do not account for potential
2025-08-14T23:09:16.7690460Z     variations in output behavior. Specifically, the generated kernels assume a
2025-08-14T23:09:16.7690605Z     fixed relationship between input and output ranks. However, in reality, it's
2025-08-14T23:09:16.7690746Z     possible that data-dependent operations may produce outputs of different
2025-08-14T23:09:16.7690877Z     ranks even when given inputs of the same rank. The generated fake kernels
2025-08-14T23:09:16.7691004Z     are inflexible and unable to accommodate these nuances, making them
2025-08-14T23:09:16.7691064Z     potentially unsafe.
2025-08-14T23:09:16.7691115Z     
2025-08-14T23:09:16.7691168Z     Args:
2025-08-14T23:09:16.7693246Z         op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator
2025-08-14T23:09:16.7693364Z             name to a set of operator profiles from which we will generate fake
2025-08-14T23:09:16.7693419Z             kernels.
2025-08-14T23:09:16.7693468Z     
2025-08-14T23:09:16.7693523Z     Examples:
2025-08-14T23:09:16.7693572Z     
2025-08-14T23:09:16.7693677Z         >>> # Example: Registering an op-profile from draft-export
2025-08-14T23:09:16.7693735Z         >>> import torch
2025-08-14T23:09:16.7693835Z         >>> from torch.export._draft_export import draft_export
2025-08-14T23:09:16.7693917Z         >>>
2025-08-14T23:09:16.7694047Z         >>> @torch.library.custom_op("mylib::foo", mutates_args=())
2025-08-14T23:09:16.7694127Z         >>> def foo(x: Tensor, y: Tensor) -> Tensor:
2025-08-14T23:09:16.7694188Z         >>>     return x + y
2025-08-14T23:09:16.7694240Z         >>>
2025-08-14T23:09:16.7694307Z         >>> class M(torch.nn.Module):
2025-08-14T23:09:16.7694375Z         >>>     def forward(self, a, b):
2025-08-14T23:09:16.7694466Z         >>>         res = torch.ops.mylib.foo(a, b)  # no fake impl
2025-08-14T23:09:16.7694526Z         >>>         return res
2025-08-14T23:09:16.7694576Z         >>>
2025-08-14T23:09:16.7694678Z         >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4))
2025-08-14T23:09:16.7694729Z         >>>
2025-08-14T23:09:16.7696782Z         >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles):
2025-08-14T23:09:16.7696859Z         >>>     decomp = ep.run_decompositions()
2025-08-14T23:09:16.7696910Z     
2025-08-14T23:09:16.7696986Z     
2025-08-14T23:09:16.7697136Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7697185Z     
2025-08-14T23:09:16.7697246Z       warnings.warn(msg)
2025-08-14T23:09:16.7697295Z     
2025-08-14T23:09:16.7697411Z --- Parse Warning: 45 / 146 ---
2025-08-14T23:09:16.7697952Z     /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.
2025-08-14T23:09:16.7698097Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.7698203Z     Register a FakeTensor implementation for this custom op.
2025-08-14T23:09:16.7698254Z     
2025-08-14T23:09:16.7698397Z             This is necessary to get the operator to work efficiently with torch.compile.
2025-08-14T23:09:16.7698446Z     
2025-08-14T23:09:16.7698577Z             The Fake impl (sometimes also known as a meta kernel or abstract impl)
2025-08-14T23:09:16.7698712Z             specifies the behavior of this operator on Tensors that carry no data.
2025-08-14T23:09:16.7698805Z             Given some input Tensors with certain properties
2025-08-14T23:09:16.7698957Z             (sizes/strides/storage_offset/device), it specifies what the properties of
2025-08-14T23:09:16.7699053Z             the output Tensors are.
2025-08-14T23:09:16.7699103Z     
2025-08-14T23:09:16.7699224Z             Please see :func:`torch.library.register_fake` for more details.
2025-08-14T23:09:16.7701136Z     
2025-08-14T23:09:16.7701190Z             Args:
2025-08-14T23:09:16.7701296Z                 fn (Callable): The function to register as the FakeTensor
2025-08-14T23:09:16.7701360Z                     implementation.
2025-08-14T23:09:16.7701410Z     
2025-08-14T23:09:16.7701466Z             Examples:
2025-08-14T23:09:16.7701527Z                 >>> import torch
2025-08-14T23:09:16.7701598Z                 >>> import numpy as np
2025-08-14T23:09:16.7701671Z                 >>> from torch import Tensor
2025-08-14T23:09:16.7701724Z                 >>>
2025-08-14T23:09:16.7701837Z                 >>> # Example 1: an operator without data-dependent output shape
2025-08-14T23:09:16.7701950Z                 >>> @torch.library.custom_op("mylib::linear", mutates_args=())
2025-08-14T23:09:16.7702064Z                 >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2025-08-14T23:09:16.7702141Z                 >>>     return (x @ weight.t()) + bias
2025-08-14T23:09:16.7702192Z                 >>>
2025-08-14T23:09:16.7702261Z                 >>> @linear.register_fake
2025-08-14T23:09:16.7702328Z                 >>> def _(x, weight, bias):
2025-08-14T23:09:16.7702392Z                 >>>     assert x.dim() == 2
2025-08-14T23:09:16.7702463Z                 >>>     assert weight.dim() == 2
2025-08-14T23:09:16.7702531Z                 >>>     assert bias.dim() == 1
2025-08-14T23:09:16.7702635Z                 >>>     assert x.shape[1] == weight.shape[1]
2025-08-14T23:09:16.7702731Z                 >>>     assert weight.shape[0] == bias.shape[0]
2025-08-14T23:09:16.7704749Z                 >>>     assert x.device == weight.device
2025-08-14T23:09:16.7704839Z                 >>>     return x.new_empty(x.size(0), weight.size(0))
2025-08-14T23:09:16.7704893Z                 >>>
2025-08-14T23:09:16.7704957Z                 >>> x = torch.randn(2, 2)
2025-08-14T23:09:16.7705029Z                 >>> weight = torch.randn(2, 2)
2025-08-14T23:09:16.7705093Z                 >>> bias = torch.randn(2)
2025-08-14T23:09:16.7705177Z                 >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:16.7705290Z                 >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias)
2025-08-14T23:09:16.7705373Z                 >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:16.7705549Z                 >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias))
2025-08-14T23:09:16.7705604Z                 >>>
2025-08-14T23:09:16.7705708Z                 >>> # Example 2: an operator with data-dependent output shape
2025-08-14T23:09:16.7705824Z                 >>> @torch.library.custom_op("mylib::nonzero", mutates_args=())
2025-08-14T23:09:16.7705900Z                 >>> def nonzero(x: Tensor) -> Tensor:
2025-08-14T23:09:16.7705969Z                 >>>     x_np = x.cpu().numpy()
2025-08-14T23:09:16.7706049Z                 >>>     res = np.stack(np.nonzero(x_np), axis=1)
2025-08-14T23:09:16.7706134Z                 >>>     return torch.tensor(res, device=x.device)
2025-08-14T23:09:16.7706186Z                 >>>
2025-08-14T23:09:16.7706256Z                 >>> @nonzero.register_fake
2025-08-14T23:09:16.7706316Z                 >>> def _(x):
2025-08-14T23:09:16.7706409Z                 >>> # Number of nonzero-elements is data-dependent.
2025-08-14T23:09:16.7708370Z                 >>> # Since we cannot peek at the data in an abstract impl,
2025-08-14T23:09:16.7708469Z                 >>> # we use the ctx object to construct a new symint that
2025-08-14T23:09:16.7708551Z                 >>> # represents the data-dependent size.
2025-08-14T23:09:16.7708628Z                 >>>     ctx = torch.library.get_ctx()
2025-08-14T23:09:16.7708701Z                 >>>     nnz = ctx.new_dynamic_size()
2025-08-14T23:09:16.7708801Z                 >>>     shape = [nnz, x.dim()]
2025-08-14T23:09:16.7708893Z                 >>>     result = x.new_empty(shape, dtype=torch.int64)
2025-08-14T23:09:16.7708957Z                 >>>     return result
2025-08-14T23:09:16.7709008Z                 >>>
2025-08-14T23:09:16.7709080Z                 >>> x = torch.tensor([0, 1, 2, 0, 0, 1])
2025-08-14T23:09:16.7709164Z                 >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:16.7709253Z                 >>> out = torch.compile(nonzero, fullgraph=True)(x)
2025-08-14T23:09:16.7709335Z                 >>> # xdoctest: +SKIP("Requires Python <= 3.11")
2025-08-14T23:09:16.7709418Z                 >>> assert torch.allclose(out, x.nonzero())
2025-08-14T23:09:16.7709471Z     
2025-08-14T23:09:16.7709522Z             
2025-08-14T23:09:16.7709826Z     Original Error: IndentationError('expected an indented block after function definition on line 36', ('<source_block>', 37, 1, '_._ = None\n', 37, 2))
2025-08-14T23:09:16.7709878Z     
2025-08-14T23:09:16.7709931Z     _._ = None
2025-08-14T23:09:16.7709981Z     ^
2025-08-14T23:09:16.7710043Z       warnings.warn(msg)
2025-08-14T23:09:16.7710092Z     
2025-08-14T23:09:16.7712076Z --- Parse Warning: 46 / 146 ---
2025-08-14T23:09:16.7712555Z     /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.
2025-08-14T23:09:16.7712710Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7712859Z     Create a custom operator whose implementation is backed by 1+ triton kernels.
2025-08-14T23:09:16.7712989Z     
2025-08-14T23:09:16.7713132Z         This is a more structured way of using triton kernels with PyTorch.
2025-08-14T23:09:16.7713283Z         Prefer using triton kernels with no ``torch.library`` custom operator wrappers
2025-08-14T23:09:16.7713429Z         (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because
2025-08-14T23:09:16.7713492Z         that is simpler;
2025-08-14T23:09:16.7713639Z         only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you
2025-08-14T23:09:16.7713772Z         want to create an operator that behaves like PyTorch built-in operators.
2025-08-14T23:09:16.7713894Z         For example, you may use a ``torch.library`` wrapper API to define the
2025-08-14T23:09:16.7714021Z         behavior of the triton kernel when passed a tensor subclass or under
2025-08-14T23:09:16.7714085Z         a TorchDispatchMode.
2025-08-14T23:09:16.7714136Z     
2025-08-14T23:09:16.7714307Z         Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op`
2025-08-14T23:09:16.7714372Z         when the implementation
2025-08-14T23:09:16.7714500Z         consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2025-08-14T23:09:16.7714604Z         custom operators as opaque (:func:`torch.compile` and
2025-08-14T23:09:16.7714743Z         :func:`torch.export.export` will never trace into them), but ``triton_op``
2025-08-14T23:09:16.7714871Z         makes the implementation visible to these subsystems, allowing them
2025-08-14T23:09:16.7716822Z         to optimize the triton kernel(s).
2025-08-14T23:09:16.7716873Z     
2025-08-14T23:09:16.7716986Z         Note that ``fn`` must only consist of calls to PyTorch-understood
2025-08-14T23:09:16.7717117Z         operators and triton kernels. Any triton kernels called inside ``fn``
2025-08-14T23:09:16.7717233Z         must be wrapped in a call to :func:`torch.library.wrap_triton`.
2025-08-14T23:09:16.7717283Z     
2025-08-14T23:09:16.7717339Z         Args:
2025-08-14T23:09:16.7717471Z             name (str): A name for the custom op that looks like "{namespace}::{name}",
2025-08-14T23:09:16.7717596Z                 e.g. "mylib::my_linear". The name is used as the op's stable identifier
2025-08-14T23:09:16.7717695Z                 in PyTorch subsystems (e.g. torch.export, FX graphs).
2025-08-14T23:09:16.7717863Z                 To avoid name collisions, please use your project name as the namespace;
2025-08-14T23:09:16.7717984Z                 e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2025-08-14T23:09:16.7718145Z             mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2025-08-14T23:09:16.7718283Z                 This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2025-08-14T23:09:16.7718438Z                 it pessimistically assumes that all inputs to the operator are being mutated.
2025-08-14T23:09:16.7718549Z             schema (None | str): A schema string for the operator. If None
2025-08-14T23:09:16.7718674Z                 (recommended) we'll infer a schema for the operator from its type
2025-08-14T23:09:16.7718792Z                 annotations. We recommend letting us infer a schema unless you
2025-08-14T23:09:16.7718864Z                 have a specific reason not to.
2025-08-14T23:09:16.7718956Z                 Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2025-08-14T23:09:16.7719007Z     
2025-08-14T23:09:16.7719063Z         Example::
2025-08-14T23:09:16.7720974Z     
2025-08-14T23:09:16.7721060Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.7721121Z             >>> import torch
2025-08-14T23:09:16.7721212Z             >>> from torch.library import triton_op, wrap_triton
2025-08-14T23:09:16.7721265Z             >>>
2025-08-14T23:09:16.7721324Z             >>> import triton
2025-08-14T23:09:16.7721399Z             >>> from triton import language as tl
2025-08-14T23:09:16.7721453Z             >>>
2025-08-14T23:09:16.7721540Z             >>> @triton.jit
2025-08-14T23:09:16.7721618Z             >>> def add_kernel(
2025-08-14T23:09:16.7721675Z             >>>     in_ptr0,
2025-08-14T23:09:16.7721732Z             >>>     in_ptr1,
2025-08-14T23:09:16.7721787Z             >>>     out_ptr,
2025-08-14T23:09:16.7721846Z             >>>     n_elements,
2025-08-14T23:09:16.7721919Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-08-14T23:09:16.7721971Z             >>> ):
2025-08-14T23:09:16.7722042Z             >>>     pid = tl.program_id(axis=0)
2025-08-14T23:09:16.7722114Z             >>>     block_start = pid * BLOCK_SIZE
2025-08-14T23:09:16.7722206Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-08-14T23:09:16.7722274Z             >>>     mask = offsets < n_elements
2025-08-14T23:09:16.7722355Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-08-14T23:09:16.7724357Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-08-14T23:09:16.7724454Z             >>>     output = x + y
2025-08-14T23:09:16.7724547Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-08-14T23:09:16.7724598Z             >>>
2025-08-14T23:09:16.7724682Z             >>> @triton_op("mylib::add", mutates_args={})
2025-08-14T23:09:16.7724790Z             >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2025-08-14T23:09:16.7724864Z             >>>     output = torch.empty_like(x)
2025-08-14T23:09:16.7724936Z             >>>     n_elements = output.numel()
2025-08-14T23:09:16.7724988Z             >>>
2025-08-14T23:09:16.7725052Z             >>>     def grid(meta):
2025-08-14T23:09:16.7725156Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-08-14T23:09:16.7725209Z             >>>
2025-08-14T23:09:16.7725317Z             >>>     # NB: we need to wrap the triton kernel in a call to wrap_triton
2025-08-14T23:09:16.7725428Z             >>>     wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2025-08-14T23:09:16.7725492Z             >>>     return output
2025-08-14T23:09:16.7725545Z             >>>
2025-08-14T23:09:16.7725606Z             >>> @torch.compile
2025-08-14T23:09:16.7725665Z             >>> def f(x, y):
2025-08-14T23:09:16.7725815Z             >>>     return add(x, y)
2025-08-14T23:09:16.7725868Z             >>>
2025-08-14T23:09:16.7725965Z             >>> x = torch.randn(3, device="cuda")
2025-08-14T23:09:16.7726036Z             >>> y = torch.randn(3, device="cuda")
2025-08-14T23:09:16.7727969Z             >>>
2025-08-14T23:09:16.7728028Z             >>> z = f(x, y)
2025-08-14T23:09:16.7728099Z             >>> assert torch.allclose(z, x + y)
2025-08-14T23:09:16.7728150Z     
2025-08-14T23:09:16.7728200Z         
2025-08-14T23:09:16.7728348Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7728398Z     
2025-08-14T23:09:16.7728459Z       warnings.warn(msg)
2025-08-14T23:09:16.7728508Z     
2025-08-14T23:09:16.7728626Z --- Parse Warning: 47 / 146 ---
2025-08-14T23:09:16.7729114Z     /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.
2025-08-14T23:09:16.7729267Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7729382Z     Allows capture of a triton kernel into a graph via make_fx or
2025-08-14T23:09:16.7729450Z         non-strict ``torch.export``.
2025-08-14T23:09:16.7729500Z     
2025-08-14T23:09:16.7729610Z         These technologies perform Dispatcher-based tracing (via
2025-08-14T23:09:16.7729726Z         ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2025-08-14T23:09:16.7729845Z         The ``wrap_triton`` API wraps a triton kernel into a callable that
2025-08-14T23:09:16.7729918Z         can actually be traced into a graph.
2025-08-14T23:09:16.7729968Z     
2025-08-14T23:09:16.7730093Z         Please use this API together with :func:`torch.library.triton_op`.
2025-08-14T23:09:16.7730191Z     
2025-08-14T23:09:16.7732111Z         Examples:
2025-08-14T23:09:16.7732161Z     
2025-08-14T23:09:16.7732225Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7732283Z             >>> import torch
2025-08-14T23:09:16.7732344Z             >>> import triton
2025-08-14T23:09:16.7732418Z             >>> from triton import language as tl
2025-08-14T23:09:16.7732528Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2025-08-14T23:09:16.7732607Z             >>> from torch.library import wrap_triton
2025-08-14T23:09:16.7732659Z             >>>
2025-08-14T23:09:16.7732716Z             >>> @triton.jit
2025-08-14T23:09:16.7732775Z             >>> def add_kernel(
2025-08-14T23:09:16.7732832Z             >>>     in_ptr0,
2025-08-14T23:09:16.7732889Z             >>>     in_ptr1,
2025-08-14T23:09:16.7733008Z             >>>     out_ptr,
2025-08-14T23:09:16.7733105Z             >>>     n_elements,
2025-08-14T23:09:16.7733179Z             >>>     BLOCK_SIZE: "tl.constexpr",
2025-08-14T23:09:16.7733232Z             >>> ):
2025-08-14T23:09:16.7733302Z             >>>     pid = tl.program_id(axis=0)
2025-08-14T23:09:16.7733373Z             >>>     block_start = pid * BLOCK_SIZE
2025-08-14T23:09:16.7733466Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2025-08-14T23:09:16.7733534Z             >>>     mask = offsets < n_elements
2025-08-14T23:09:16.7735493Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2025-08-14T23:09:16.7735573Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2025-08-14T23:09:16.7735636Z             >>>     output = x + y
2025-08-14T23:09:16.7735724Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2025-08-14T23:09:16.7735776Z             >>>
2025-08-14T23:09:16.7735835Z             >>> def add(x, y):
2025-08-14T23:09:16.7735906Z             >>>     output = torch.empty_like(x)
2025-08-14T23:09:16.7735975Z             >>>     n_elements = output.numel()
2025-08-14T23:09:16.7736029Z             >>>
2025-08-14T23:09:16.7736093Z             >>>     def grid_fn(meta):
2025-08-14T23:09:16.7736194Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2025-08-14T23:09:16.7736244Z             >>>
2025-08-14T23:09:16.7736357Z             >>>     wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2025-08-14T23:09:16.7736451Z             >>>     return output
2025-08-14T23:09:16.7736504Z             >>>
2025-08-14T23:09:16.7736576Z             >>> x = torch.randn(3, device="cuda")
2025-08-14T23:09:16.7736645Z             >>> y = torch.randn(3, device="cuda")
2025-08-14T23:09:16.7736711Z             >>> gm = make_fx(add)(x, y)
2025-08-14T23:09:16.7736773Z             >>> print(gm.code)
2025-08-14T23:09:16.7736844Z             >>> # def forward(self, x_1, y_1):
2025-08-14T23:09:16.7736982Z             >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2025-08-14T23:09:16.7737124Z             >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2025-08-14T23:09:16.7739073Z             >>> #         kernel_idx = 0, constant_args_idx = 0,
2025-08-14T23:09:16.7739149Z             >>> #         grid = [(1, 1, 1)], kwargs = {
2025-08-14T23:09:16.7739243Z             >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2025-08-14T23:09:16.7739323Z             >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2025-08-14T23:09:16.7739379Z             >>> #         })
2025-08-14T23:09:16.7739443Z             >>> #     return empty_like
2025-08-14T23:09:16.7739493Z     
2025-08-14T23:09:16.7739545Z         
2025-08-14T23:09:16.7739691Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7739742Z     
2025-08-14T23:09:16.7739802Z       warnings.warn(msg)
2025-08-14T23:09:16.7739853Z     
2025-08-14T23:09:16.7739969Z --- Parse Warning: 48 / 146 ---
2025-08-14T23:09:16.7740581Z     /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.
2025-08-14T23:09:16.7740734Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7740847Z     Return a new dict with new, potentially nested, key value pair
2025-08-14T23:09:16.7740897Z     
2025-08-14T23:09:16.7740956Z         >>> purchase = {
2025-08-14T23:09:16.7741017Z         ...     "name": "Alice",
2025-08-14T23:09:16.7741120Z         ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:16.7741196Z         ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:16.7741247Z         ... }
2025-08-14T23:09:16.7743383Z         >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2025-08-14T23:09:16.7743482Z         {'credit card': '5555-1234-1234-1234',
2025-08-14T23:09:16.7743544Z          'name': 'Alice',
2025-08-14T23:09:16.7743646Z          'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2025-08-14T23:09:16.7743698Z         
2025-08-14T23:09:16.7743843Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7743895Z     
2025-08-14T23:09:16.7743954Z       warnings.warn(msg)
2025-08-14T23:09:16.7744005Z     
2025-08-14T23:09:16.7744118Z --- Parse Warning: 49 / 146 ---
2025-08-14T23:09:16.7744684Z     /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.
2025-08-14T23:09:16.7744834Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7744925Z     Update value in a (potentially) nested dictionary
2025-08-14T23:09:16.7744978Z     
2025-08-14T23:09:16.7745031Z         inputs:
2025-08-14T23:09:16.7745104Z         d - dictionary on which to operate
2025-08-14T23:09:16.7745230Z         keys - list or tuple giving the location of the value to be changed in d
2025-08-14T23:09:16.7745307Z         func - function to operate on that value
2025-08-14T23:09:16.7745380Z     
2025-08-14T23:09:16.7745491Z         If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2025-08-14T23:09:16.7745626Z         original dictionary with v replaced by func(v), but does not mutate the
2025-08-14T23:09:16.7747610Z         original dictionary.
2025-08-14T23:09:16.7747664Z     
2025-08-14T23:09:16.7747784Z         If k0 is not a key in d, update_in creates nested dictionaries to the depth
2025-08-14T23:09:16.7747911Z         specified by the keys, with the innermost value set to func(default).
2025-08-14T23:09:16.7747962Z     
2025-08-14T23:09:16.7748025Z         >>> inc = lambda x: x + 1
2025-08-14T23:09:16.7748095Z         >>> update_in({"a": 0}, ["a"], inc)
2025-08-14T23:09:16.7748148Z         {'a': 1}
2025-08-14T23:09:16.7748201Z     
2025-08-14T23:09:16.7748260Z         >>> transaction = {
2025-08-14T23:09:16.7748321Z         ...     "name": "Alice",
2025-08-14T23:09:16.7748433Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:16.7748508Z         ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:16.7748559Z         ... }
2025-08-14T23:09:16.7748687Z         >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2025-08-14T23:09:16.7748755Z         {'credit card': '5555-1234-1234-1234',
2025-08-14T23:09:16.7748813Z          'name': 'Alice',
2025-08-14T23:09:16.7748914Z          'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2025-08-14T23:09:16.7748964Z     
2025-08-14T23:09:16.7749038Z         >>> # updating a value when k0 is not in d
2025-08-14T23:09:16.7749119Z         >>> update_in({}, [1, 2, 3], str, default="bar")
2025-08-14T23:09:16.7749214Z         {1: {2: {3: 'bar'}}}
2025-08-14T23:09:16.7751174Z         >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2025-08-14T23:09:16.7751236Z         {1: 'foo', 2: {3: {4: 1}}}
2025-08-14T23:09:16.7751287Z         
2025-08-14T23:09:16.7751438Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7751488Z     
2025-08-14T23:09:16.7751550Z       warnings.warn(msg)
2025-08-14T23:09:16.7751599Z     
2025-08-14T23:09:16.7751713Z --- Parse Warning: 50 / 146 ---
2025-08-14T23:09:16.7752267Z     /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.
2025-08-14T23:09:16.7752421Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7752522Z     Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2025-08-14T23:09:16.7752594Z     
2025-08-14T23:09:16.7752702Z         If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2025-08-14T23:09:16.7752823Z         ``no_default`` is specified, then it raises KeyError or IndexError.
2025-08-14T23:09:16.7752873Z     
2025-08-14T23:09:16.7753064Z         ``get_in`` is a generalization of ``operator.getitem`` for nested data
2025-08-14T23:09:16.7753145Z         structures such as dictionaries and lists.
2025-08-14T23:09:16.7753196Z     
2025-08-14T23:09:16.7753254Z         >>> transaction = {
2025-08-14T23:09:16.7753316Z         ...     "name": "Alice",
2025-08-14T23:09:16.7753429Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2025-08-14T23:09:16.7755444Z         ...     "credit card": "5555-1234-1234-1234",
2025-08-14T23:09:16.7755497Z         ... }
2025-08-14T23:09:16.7755586Z         >>> get_in(["purchase", "items", 0], transaction)
2025-08-14T23:09:16.7755642Z         'Apple'
2025-08-14T23:09:16.7755715Z         >>> get_in(["name"], transaction)
2025-08-14T23:09:16.7755768Z         'Alice'
2025-08-14T23:09:16.7755852Z         >>> get_in(["purchase", "total"], transaction)
2025-08-14T23:09:16.7755946Z         >>> get_in(["purchase", "items", "apple"], transaction)
2025-08-14T23:09:16.7756030Z         >>> get_in(["purchase", "items", 10], transaction)
2025-08-14T23:09:16.7756146Z         >>> get_in(["purchase", "total"], transaction, 0)
2025-08-14T23:09:16.7756197Z         0
2025-08-14T23:09:16.7756267Z         >>> get_in(["y"], {}, no_default=True)
2025-08-14T23:09:16.7756337Z         Traceback (most recent call last):
2025-08-14T23:09:16.7756395Z             ...
2025-08-14T23:09:16.7756451Z         KeyError: 'y'
2025-08-14T23:09:16.7756521Z     
2025-08-14T23:09:16.7756578Z         See Also:
2025-08-14T23:09:16.7756642Z             itertoolz.get
2025-08-14T23:09:16.7756706Z             operator.getitem
2025-08-14T23:09:16.7756758Z         
2025-08-14T23:09:16.7756909Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7759674Z     
2025-08-14T23:09:16.7759736Z       warnings.warn(msg)
2025-08-14T23:09:16.7759787Z     
2025-08-14T23:09:16.7759906Z --- Parse Warning: 51 / 146 ---
2025-08-14T23:09:16.7760476Z     /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.
2025-08-14T23:09:16.7760634Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7760712Z     Group a collection by a key function
2025-08-14T23:09:16.7760761Z     
2025-08-14T23:09:16.7760862Z         >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2025-08-14T23:09:16.7760937Z         >>> groupby(len, names)  # doctest: +SKIP
2025-08-14T23:09:16.7761035Z         {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2025-08-14T23:09:16.7761118Z     
2025-08-14T23:09:16.7761204Z         >>> iseven = lambda x: x % 2 == 0
2025-08-14T23:09:16.7761307Z         >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2025-08-14T23:09:16.7761376Z         {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2025-08-14T23:09:16.7761426Z     
2025-08-14T23:09:16.7761515Z         Non-callable keys imply grouping on a member.
2025-08-14T23:09:16.7761583Z     
2025-08-14T23:09:16.7761640Z         >>> groupby(
2025-08-14T23:09:16.7761696Z         ...     "gender",
2025-08-14T23:09:16.7764361Z         ...     [
2025-08-14T23:09:16.7764440Z         ...         {"name": "Alice", "gender": "F"},
2025-08-14T23:09:16.7764514Z         ...         {"name": "Bob", "gender": "M"},
2025-08-14T23:09:16.7764592Z         ...         {"name": "Charlie", "gender": "M"},
2025-08-14T23:09:16.7764646Z         ...     ],
2025-08-14T23:09:16.7764707Z         ... )  # doctest:+SKIP
2025-08-14T23:09:16.7764777Z         {'F': [{'gender': 'F', 'name': 'Alice'}],
2025-08-14T23:09:16.7764900Z          'M': [{'gender': 'M', 'name': 'Bob'},
2025-08-14T23:09:16.7764971Z                {'gender': 'M', 'name': 'Charlie'}]}
2025-08-14T23:09:16.7765021Z     
2025-08-14T23:09:16.7765106Z         Not to be confused with ``itertools.groupby``
2025-08-14T23:09:16.7765156Z     
2025-08-14T23:09:16.7765209Z         See Also:
2025-08-14T23:09:16.7765264Z             countby
2025-08-14T23:09:16.7765315Z         
2025-08-14T23:09:16.7765463Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7765511Z     
2025-08-14T23:09:16.7765572Z       warnings.warn(msg)
2025-08-14T23:09:16.7765621Z     
2025-08-14T23:09:16.7765830Z --- Parse Warning: 52 / 146 ---
2025-08-14T23:09:16.7772225Z     /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.
2025-08-14T23:09:16.7772386Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7772612Z     Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
2025-08-14T23:09:16.7772723Z         Label will only appear if CPU activity tracing is enabled.
2025-08-14T23:09:16.7772811Z     
2025-08-14T23:09:16.7772891Z         It is useful when tracing the code profile.
2025-08-14T23:09:16.7773021Z     
2025-08-14T23:09:16.7773073Z         Args:
2025-08-14T23:09:16.7773164Z             name (str): Label assigned to the block of code.
2025-08-14T23:09:16.7773272Z             node_id (int): ID of node, for distributed profiling. Unset in
2025-08-14T23:09:16.7773340Z             non-distributed cases.
2025-08-14T23:09:16.7773389Z     
2025-08-14T23:09:16.7773444Z         Example:
2025-08-14T23:09:16.7773552Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
2025-08-14T23:09:16.7773635Z             >>> x = torch.randn((1, 1), requires_grad=True)
2025-08-14T23:09:16.7773737Z             >>> with torch.autograd.profiler.profile() as prof:
2025-08-14T23:09:16.7773797Z             ...     y = x**2
2025-08-14T23:09:16.7773896Z             ...     with torch.autograd.profiler.record_function(
2025-08-14T23:09:16.7773958Z             ...         "label-z"
2025-08-14T23:09:16.7774024Z             ...     ):  # label the block
2025-08-14T23:09:16.7777483Z             ...         z = y**3
2025-08-14T23:09:16.7777543Z             ...     y.backward()
2025-08-14T23:09:16.7777613Z             >>> # xdoctest: +IGNORE_WANT
2025-08-14T23:09:16.7777702Z             >>> # NOTE: some columns were removed for brevity
2025-08-14T23:09:16.7777821Z             >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
2025-08-14T23:09:16.7777944Z             -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.7778055Z             Name                                 Self CPU total %  CPU time avg     Number of Calls
2025-08-14T23:09:16.7778248Z             -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.7778338Z             pow                                  60.77%           47.470us         3
2025-08-14T23:09:16.7778422Z             mul                                  21.73%           25.465us         2
2025-08-14T23:09:16.7778527Z             PowBackward0                         12.03%           121.891us        1
2025-08-14T23:09:16.7778659Z             torch::autograd::AccumulateGrad      2.70%            6.324us          1
2025-08-14T23:09:16.7778745Z             label-z                              2.13%            12.421us         1
2025-08-14T23:09:16.7778864Z             torch::autograd::GraphRoot           0.64%            1.503us          1
2025-08-14T23:09:16.7778977Z             -----------------------------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.7779075Z             Self CPU time total: 234.344us
2025-08-14T23:09:16.7779142Z             CUDA time total: 0.000us
2025-08-14T23:09:16.7779195Z     
2025-08-14T23:09:16.7779245Z         
2025-08-14T23:09:16.7779395Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7782723Z     
2025-08-14T23:09:16.7782790Z       warnings.warn(msg)
2025-08-14T23:09:16.7782847Z     
2025-08-14T23:09:16.7783082Z --- Parse Warning: 53 / 146 ---
2025-08-14T23:09:16.7783599Z     /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.
2025-08-14T23:09:16.7783753Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7783841Z     Register a container-like type as pytree node.
2025-08-14T23:09:16.7783895Z     
2025-08-14T23:09:16.7783951Z         Args:
2025-08-14T23:09:16.7784068Z             cls (type): A Python type to treat as an internal pytree node.
2025-08-14T23:09:16.7784229Z             flatten_fn (callable): A function to be used during flattening, taking an instance of
2025-08-14T23:09:16.7784375Z                 ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2025-08-14T23:09:16.7784590Z                 recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2025-08-14T23:09:16.7784663Z                 passed to the ``unflatten_fn``.
2025-08-14T23:09:16.7784824Z             unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2025-08-14T23:09:16.7784978Z                 returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2025-08-14T23:09:16.7785072Z                 The function should return an instance of ``cls``.
2025-08-14T23:09:16.7785228Z             serialized_type_name (str, optional): A keyword argument used to specify the fully
2025-08-14T23:09:16.7785329Z                 qualified name used when serializing the tree spec.
2025-08-14T23:09:16.7785508Z             to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2025-08-14T23:09:16.7785663Z                 to convert the context of the pytree to a custom json dumpable representation. This is
2025-08-14T23:09:16.7788460Z                 used for json serialization, which is being used in :mod:`torch.export` right now.
2025-08-14T23:09:16.7788633Z             from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2025-08-14T23:09:16.7788796Z                 how to convert the custom json dumpable representation of the context back to the
2025-08-14T23:09:16.7788947Z                 original context. This is used for json deserialization, which is being used in
2025-08-14T23:09:16.7789021Z                 :mod:`torch.export` right now.
2025-08-14T23:09:16.7789071Z     
2025-08-14T23:09:16.7789130Z         Example::
2025-08-14T23:09:16.7789217Z     
2025-08-14T23:09:16.7789308Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7789403Z             >>> # Registry a Python type with lambda functions
2025-08-14T23:09:16.7789472Z             >>> register_pytree_node(
2025-08-14T23:09:16.7789528Z             ...     set,
2025-08-14T23:09:16.7789606Z             ...     lambda s: (sorted(s), None, None),
2025-08-14T23:09:16.7789684Z             ...     lambda children, _: set(children),
2025-08-14T23:09:16.7789735Z             ... )
2025-08-14T23:09:16.7789787Z         
2025-08-14T23:09:16.7789935Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7789985Z     
2025-08-14T23:09:16.7790045Z       warnings.warn(msg)
2025-08-14T23:09:16.7790096Z     
2025-08-14T23:09:16.7790215Z --- Parse Warning: 54 / 146 ---
2025-08-14T23:09:16.7793240Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1218.
2025-08-14T23:09:16.7793410Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7793462Z     
2025-08-14T23:09:16.7793588Z     Context passed to policy function during selective checkpointing.
2025-08-14T23:09:16.7793640Z     
2025-08-14T23:09:16.7793770Z     This class is used to pass relevant metadata to the policy function during
2025-08-14T23:09:16.7793922Z     selective checkpointing. The metadata includes whether the current invocation
2025-08-14T23:09:16.7794019Z     of the policy function is during recomputation or not.
2025-08-14T23:09:16.7794069Z     
2025-08-14T23:09:16.7794123Z     Example:
2025-08-14T23:09:16.7794191Z         >>> # xdoctest: +SKIP(stub)
2025-08-14T23:09:16.7794242Z         >>>
2025-08-14T23:09:16.7794324Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-08-14T23:09:16.7794394Z         >>>    print(ctx.is_recompute)
2025-08-14T23:09:16.7794446Z         >>>
2025-08-14T23:09:16.7794611Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-08-14T23:09:16.7794662Z         >>>
2025-08-14T23:09:16.7794748Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-08-14T23:09:16.7794805Z         >>>     fn, x, y,
2025-08-14T23:09:16.7794897Z         >>>     use_reentrant=False,
2025-08-14T23:09:16.7794961Z         >>>     context_fn=context_fn,
2025-08-14T23:09:16.7795013Z         >>> )
2025-08-14T23:09:16.7797320Z     
2025-08-14T23:09:16.7797480Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7797529Z     
2025-08-14T23:09:16.7797590Z       warnings.warn(msg)
2025-08-14T23:09:16.7797639Z     
2025-08-14T23:09:16.7797756Z --- Parse Warning: 55 / 146 ---
2025-08-14T23:09:16.7798306Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1358.
2025-08-14T23:09:16.7798456Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7798504Z     
2025-08-14T23:09:16.7798642Z     Helper to avoid recomputing certain ops during activation checkpointing.
2025-08-14T23:09:16.7798692Z     
2025-08-14T23:09:16.7798821Z     Use this with `torch.utils.checkpoint.checkpoint` to control which
2025-08-14T23:09:16.7798919Z     operations are recomputed during the backward pass.
2025-08-14T23:09:16.7798969Z     
2025-08-14T23:09:16.7799019Z     Args:
2025-08-14T23:09:16.7799093Z         policy_fn_or_list (Callable or List):
2025-08-14T23:09:16.7799190Z           - If a policy function is provided, it should accept a
2025-08-14T23:09:16.7799331Z             :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2025-08-14T23:09:16.7799453Z             kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2025-08-14T23:09:16.7799646Z             indicating whether the execution of the op should be recomputed or not.
2025-08-14T23:09:16.7799763Z           - If a list of operations is provided, it is equivalent to a policy
2025-08-14T23:09:16.7799873Z             returning `CheckpointPolicy.MUST_SAVE` for the specified
2025-08-14T23:09:16.7802271Z             operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2025-08-14T23:09:16.7802332Z             operations.
2025-08-14T23:09:16.7802462Z         allow_cache_entry_mutation (bool, optional): By default, an error is
2025-08-14T23:09:16.7802580Z             raised if any tensors cached by selective activation checkpoint are
2025-08-14T23:09:16.7802703Z             mutated in order to ensure correctness. If set to `True`, this check
2025-08-14T23:09:16.7802759Z             is disabled.
2025-08-14T23:09:16.7802812Z     Returns:
2025-08-14T23:09:16.7802882Z         A tuple of two context managers.
2025-08-14T23:09:16.7803055Z     
2025-08-14T23:09:16.7803107Z     Example:
2025-08-14T23:09:16.7803176Z         >>> # xdoctest: +REQUIRES(LINUX)
2025-08-14T23:09:16.7803238Z         >>> import functools
2025-08-14T23:09:16.7803290Z         >>>
2025-08-14T23:09:16.7803366Z         >>> x = torch.rand(10, 10, requires_grad=True)
2025-08-14T23:09:16.7803442Z         >>> y = torch.rand(10, 10, requires_grad=True)
2025-08-14T23:09:16.7803493Z         >>>
2025-08-14T23:09:16.7803551Z         >>> ops_to_save = [
2025-08-14T23:09:16.7803626Z         >>>    torch.ops.aten.mm.default,
2025-08-14T23:09:16.7803676Z         >>> ]
2025-08-14T23:09:16.7803727Z         >>>
2025-08-14T23:09:16.7803804Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2025-08-14T23:09:16.7806395Z         >>>    if op in ops_to_save:
2025-08-14T23:09:16.7806481Z         >>>        return CheckpointPolicy.MUST_SAVE
2025-08-14T23:09:16.7806536Z         >>>    else:
2025-08-14T23:09:16.7806632Z         >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2025-08-14T23:09:16.7806685Z         >>>
2025-08-14T23:09:16.7806887Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2025-08-14T23:09:16.7806938Z         >>>
2025-08-14T23:09:16.7806999Z         >>> # or equivalently
2025-08-14T23:09:16.7807158Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2025-08-14T23:09:16.7807250Z         >>>
2025-08-14T23:09:16.7807308Z         >>> def fn(x, y):
2025-08-14T23:09:16.7807423Z         >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2025-08-14T23:09:16.7807475Z         >>>
2025-08-14T23:09:16.7807564Z         >>> out = torch.utils.checkpoint.checkpoint(
2025-08-14T23:09:16.7807621Z         >>>     fn, x, y,
2025-08-14T23:09:16.7807682Z         >>>     use_reentrant=False,
2025-08-14T23:09:16.7807748Z         >>>     context_fn=context_fn,
2025-08-14T23:09:16.7807800Z         >>> )
2025-08-14T23:09:16.7807849Z     
2025-08-14T23:09:16.7807996Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7808048Z     
2025-08-14T23:09:16.7808108Z       warnings.warn(msg)
2025-08-14T23:09:16.7810408Z     
2025-08-14T23:09:16.7810532Z --- Parse Warning: 56 / 146 ---
2025-08-14T23:09:16.7811078Z     /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.
2025-08-14T23:09:16.7811234Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7811283Z     
2025-08-14T23:09:16.7811454Z     This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2025-08-14T23:09:16.7811504Z     
2025-08-14T23:09:16.7811676Z     This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2025-08-14T23:09:16.7811820Z     for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2025-08-14T23:09:16.7812030Z     server like load. It can emulate multiple calling threads to a single module
2025-08-14T23:09:16.7812169Z     provided. In the future we plan to enhance this component to support inter and
2025-08-14T23:09:16.7812309Z     intra-op parallelism as well as multiple models running in a single process.
2025-08-14T23:09:16.7812360Z     
2025-08-14T23:09:16.7812505Z     Please note that even though nn.Module is supported, it might incur an overhead
2025-08-14T23:09:16.7812631Z     from the need to hold GIL every time we execute Python code or pass around
2025-08-14T23:09:16.7812769Z     inputs as Python objects. As soon as you have a ScriptModule version of your
2025-08-14T23:09:16.7812903Z     model for inference deployment it is better to switch to using it in this
2025-08-14T23:09:16.7813056Z     benchmark.
2025-08-14T23:09:16.7813106Z     
2025-08-14T23:09:16.7813162Z     Example::
2025-08-14T23:09:16.7813211Z     
2025-08-14T23:09:16.7815540Z         >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.7815632Z         >>> from torch.utils import ThroughputBenchmark
2025-08-14T23:09:16.7815719Z         >>> bench = ThroughputBenchmark(my_module)
2025-08-14T23:09:16.7815821Z         >>> # Pre-populate benchmark's data set with the inputs
2025-08-14T23:09:16.7815888Z         >>> for input in inputs:
2025-08-14T23:09:16.7816017Z         ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2025-08-14T23:09:16.7816096Z         ...     bench.add_input(input[0], x2=input[1])
2025-08-14T23:09:16.7816210Z         >>> # Inputs supplied above are randomly used during the execution
2025-08-14T23:09:16.7816272Z         >>> stats = bench.benchmark(
2025-08-14T23:09:16.7816337Z         ...     num_calling_threads=4,
2025-08-14T23:09:16.7816401Z         ...     num_warmup_iters = 100,
2025-08-14T23:09:16.7816464Z         ...     num_iters = 1000,
2025-08-14T23:09:16.7816516Z         ... )
2025-08-14T23:09:16.7816627Z         >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2025-08-14T23:09:16.7816733Z         >>> print("Number of iterations: {}".format(stats.num_iters))
2025-08-14T23:09:16.7816784Z     
2025-08-14T23:09:16.7816929Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7817008Z     
2025-08-14T23:09:16.7817068Z       warnings.warn(msg)
2025-08-14T23:09:16.7817119Z     
2025-08-14T23:09:16.7817234Z --- Parse Warning: 57 / 146 ---
2025-08-14T23:09:16.7817736Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1147.
2025-08-14T23:09:16.7820131Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7820184Z     
2025-08-14T23:09:16.7820279Z     Create a :class:`setuptools.Extension` for C++.
2025-08-14T23:09:16.7820333Z     
2025-08-14T23:09:16.7820476Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:16.7820606Z     bare minimum (but often sufficient) arguments to build a C++ extension.
2025-08-14T23:09:16.7820655Z     
2025-08-14T23:09:16.7820780Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:16.7820869Z     constructor. Full list arguments can be found at
2025-08-14T23:09:16.7821063Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-08-14T23:09:16.7821113Z     
2025-08-14T23:09:16.7821167Z     .. warning::
2025-08-14T23:09:16.7821300Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:16.7821420Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:16.7821537Z         the user's responsibility in their library to not use APIs from
2025-08-14T23:09:16.7821671Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:16.7821870Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:16.7821993Z         example, to give access to custom ops from python, the library should
2025-08-14T23:09:16.7822072Z         register the ops through the dispatcher.
2025-08-14T23:09:16.7822123Z     
2025-08-14T23:09:16.7822251Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:16.7822371Z         as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:16.7825071Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:16.7825200Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:16.7825326Z         safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:16.7825451Z         different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:16.7825556Z         CPython version of choice.
2025-08-14T23:09:16.7825606Z     
2025-08-14T23:09:16.7825661Z     Example:
2025-08-14T23:09:16.7825723Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7825816Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:16.7825887Z         >>> from setuptools import setup
2025-08-14T23:09:16.7826016Z         >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2025-08-14T23:09:16.7826069Z         >>> setup(
2025-08-14T23:09:16.7826133Z         ...     name='extension',
2025-08-14T23:09:16.7826230Z         ...     ext_modules=[
2025-08-14T23:09:16.7826293Z         ...         CppExtension(
2025-08-14T23:09:16.7826357Z         ...             name='extension',
2025-08-14T23:09:16.7826431Z         ...             sources=['extension.cpp'],
2025-08-14T23:09:16.7826505Z         ...             extra_compile_args=['-g'],
2025-08-14T23:09:16.7826600Z         ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2025-08-14T23:09:16.7826655Z         ...     ],
2025-08-14T23:09:16.7826713Z         ...     cmdclass={
2025-08-14T23:09:16.7829012Z         ...         'build_ext': BuildExtension
2025-08-14T23:09:16.7829065Z         ...     })
2025-08-14T23:09:16.7829115Z     
2025-08-14T23:09:16.7829267Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7829355Z     
2025-08-14T23:09:16.7829415Z       warnings.warn(msg)
2025-08-14T23:09:16.7829465Z     
2025-08-14T23:09:16.7829584Z --- Parse Warning: 58 / 146 ---
2025-08-14T23:09:16.7830089Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1217.
2025-08-14T23:09:16.7830240Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7830290Z     
2025-08-14T23:09:16.7830388Z     Create a :class:`setuptools.Extension` for CUDA/C++.
2025-08-14T23:09:16.7830439Z     
2025-08-14T23:09:16.7830579Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:16.7830700Z     bare minimum (but often sufficient) arguments to build a CUDA/C++
2025-08-14T23:09:16.7830833Z     extension. This includes the CUDA include path, library path and runtime
2025-08-14T23:09:16.7830888Z     library.
2025-08-14T23:09:16.7830937Z     
2025-08-14T23:09:16.7831057Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:16.7831146Z     constructor. Full list arguments can be found at
2025-08-14T23:09:16.7831333Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2025-08-14T23:09:16.7831384Z     
2025-08-14T23:09:16.7833758Z     .. warning::
2025-08-14T23:09:16.7833901Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:16.7834022Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:16.7834199Z         the user's responsibility in their library to not use APIs from
2025-08-14T23:09:16.7834333Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:16.7834459Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:16.7834582Z         example, to give access to custom ops from python, the library should
2025-08-14T23:09:16.7834660Z         register the ops through the dispatcher.
2025-08-14T23:09:16.7834713Z     
2025-08-14T23:09:16.7834841Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:16.7834958Z         as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:16.7835079Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:16.7835201Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:16.7835343Z         safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:16.7835470Z         different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:16.7835538Z         CPython version of choice.
2025-08-14T23:09:16.7835588Z     
2025-08-14T23:09:16.7835641Z     Example:
2025-08-14T23:09:16.7835703Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7835796Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:16.7835865Z         >>> from setuptools import setup
2025-08-14T23:09:16.7838316Z         >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2025-08-14T23:09:16.7838372Z         >>> setup(
2025-08-14T23:09:16.7838444Z         ...     name='cuda_extension',
2025-08-14T23:09:16.7838503Z         ...     ext_modules=[
2025-08-14T23:09:16.7838566Z         ...         CUDAExtension(
2025-08-14T23:09:16.7838636Z         ...                 name='cuda_extension',
2025-08-14T23:09:16.7838735Z         ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:16.7838822Z         ...                 extra_compile_args={'cxx': ['-g'],
2025-08-14T23:09:16.7838896Z         ...                                     'nvcc': ['-O2']},
2025-08-14T23:09:16.7838989Z         ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2025-08-14T23:09:16.7839074Z         ...     ],
2025-08-14T23:09:16.7839133Z         ...     cmdclass={
2025-08-14T23:09:16.7839204Z         ...         'build_ext': BuildExtension
2025-08-14T23:09:16.7839256Z         ...     })
2025-08-14T23:09:16.7839305Z     
2025-08-14T23:09:16.7839369Z     Compute capabilities:
2025-08-14T23:09:16.7839418Z     
2025-08-14T23:09:16.7839595Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-08-14T23:09:16.7839765Z     building process of the extension, plus PTX. If down the road a new card is installed the
2025-08-14T23:09:16.7839937Z     extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2025-08-14T23:09:16.7840111Z     newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
2025-08-14T23:09:16.7840277Z     will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2025-08-14T23:09:16.7842617Z     support (see below for details on PTX).
2025-08-14T23:09:16.7842674Z     
2025-08-14T23:09:16.7842873Z     You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2025-08-14T23:09:16.7843022Z     CCs you want the extension to support:
2025-08-14T23:09:16.7843071Z     
2025-08-14T23:09:16.7843184Z     ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2025-08-14T23:09:16.7843316Z     ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2025-08-14T23:09:16.7843366Z     
2025-08-14T23:09:16.7843555Z     The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2025-08-14T23:09:16.7843800Z     CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2025-08-14T23:09:16.7843973Z     the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2025-08-14T23:09:16.7844141Z     CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2025-08-14T23:09:16.7844328Z     provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2025-08-14T23:09:16.7844485Z     those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2025-08-14T23:09:16.7844667Z     off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2025-08-14T23:09:16.7844848Z     "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2025-08-14T23:09:16.7844908Z     "8.0 8.6" would be better.
2025-08-14T23:09:16.7844959Z     
2025-08-14T23:09:16.7845149Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-08-14T23:09:16.7845318Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-08-14T23:09:16.7845369Z     
2025-08-14T23:09:16.7848263Z     Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2025-08-14T23:09:16.7848399Z     To workaround the issue, move python binding logic to pure C++ file.
2025-08-14T23:09:16.7848454Z     
2025-08-14T23:09:16.7848508Z     Example use:
2025-08-14T23:09:16.7848571Z         #include <ATen/ATen.h>
2025-08-14T23:09:16.7848671Z         at::Tensor SigmoidAlphaBlendForwardCuda(....)
2025-08-14T23:09:16.7848720Z     
2025-08-14T23:09:16.7848774Z     Instead of:
2025-08-14T23:09:16.7848840Z         #include <torch/extension.h>
2025-08-14T23:09:16.7848938Z         torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2025-08-14T23:09:16.7848986Z     
2025-08-14T23:09:16.7849148Z     Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2025-08-14T23:09:16.7849445Z     Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2025-08-14T23:09:16.7849495Z     
2025-08-14T23:09:16.7849561Z     Relocatable device code linking:
2025-08-14T23:09:16.7849611Z     
2025-08-14T23:09:16.7849808Z     If you want to reference device symbols across compilation units (across object files),
2025-08-14T23:09:16.7849957Z     the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2025-08-14T23:09:16.7850161Z     An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2025-08-14T23:09:16.7850349Z     `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2025-08-14T23:09:16.7850531Z     Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2025-08-14T23:09:16.7852848Z     helps reduce the protentional perf degradation of `-rdc`.
2025-08-14T23:09:16.7853029Z     Note that it needs to be used at both steps to be useful.
2025-08-14T23:09:16.7853081Z     
2025-08-14T23:09:16.7853290Z     If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2025-08-14T23:09:16.7853435Z     There is also a case where `-dlink` is used without `-rdc`:
2025-08-14T23:09:16.7853583Z     when an extension is linked against a static lib containing rdc-compiled objects
2025-08-14T23:09:16.7853709Z     like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2025-08-14T23:09:16.7853757Z     
2025-08-14T23:09:16.7853877Z     Note: Ninja is required to build a CUDA Extension with RDC linking.
2025-08-14T23:09:16.7853927Z     
2025-08-14T23:09:16.7853981Z     Example:
2025-08-14T23:09:16.7854078Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7854169Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:16.7854275Z         >>> CUDAExtension(
2025-08-14T23:09:16.7854364Z         ...        name='cuda_extension',
2025-08-14T23:09:16.7854463Z         ...        sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:16.7854522Z         ...        dlink=True,
2025-08-14T23:09:16.7854597Z         ...        dlink_libraries=["dlink_lib"],
2025-08-14T23:09:16.7854676Z         ...        extra_compile_args={'cxx': ['-g'],
2025-08-14T23:09:16.7854753Z         ...                            'nvcc': ['-O2', '-rdc=true']})
2025-08-14T23:09:16.7854802Z     
2025-08-14T23:09:16.7854950Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7857166Z     
2025-08-14T23:09:16.7857237Z       warnings.warn(msg)
2025-08-14T23:09:16.7857287Z     
2025-08-14T23:09:16.7857411Z --- Parse Warning: 59 / 146 ---
2025-08-14T23:09:16.7857956Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1408.
2025-08-14T23:09:16.7858110Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7858159Z     
2025-08-14T23:09:16.7858260Z     Creates a :class:`setuptools.Extension` for SYCL/C++.
2025-08-14T23:09:16.7858311Z     
2025-08-14T23:09:16.7858454Z     Convenience method that creates a :class:`setuptools.Extension` with the
2025-08-14T23:09:16.7858572Z     bare minimum (but often sufficient) arguments to build a SYCL/C++
2025-08-14T23:09:16.7858626Z     extension.
2025-08-14T23:09:16.7858675Z     
2025-08-14T23:09:16.7858792Z     All arguments are forwarded to the :class:`setuptools.Extension`
2025-08-14T23:09:16.7858848Z     constructor.
2025-08-14T23:09:16.7858897Z     
2025-08-14T23:09:16.7858952Z     .. warning::
2025-08-14T23:09:16.7859083Z         The PyTorch python API (as provided in libtorch_python) cannot be built
2025-08-14T23:09:16.7859210Z         with the flag ``py_limited_api=True``.  When this flag is passed, it is
2025-08-14T23:09:16.7859328Z         the user's responsibility in their library to not use APIs from
2025-08-14T23:09:16.7859459Z         libtorch_python (in particular pytorch/python bindings) and to only use
2025-08-14T23:09:16.7859584Z         APIs from libtorch (aten objects, operators and the dispatcher). For
2025-08-14T23:09:16.7861921Z         example, to give access to custom ops from python, the library should
2025-08-14T23:09:16.7862000Z         register the ops through the dispatcher.
2025-08-14T23:09:16.7862051Z     
2025-08-14T23:09:16.7862180Z         Contrary to CPython setuptools, who does not define -DPy_LIMITED_API
2025-08-14T23:09:16.7862300Z         as a compile flag when py_limited_api is specified as an option for
2025-08-14T23:09:16.7862420Z         the "bdist_wheel" command in ``setup``, PyTorch does! We will specify
2025-08-14T23:09:16.7862546Z         -DPy_LIMITED_API=min_supported_cpython to best enforce consistency,
2025-08-14T23:09:16.7862671Z         safety, and sanity in order to encourage best practices. To target a
2025-08-14T23:09:16.7862798Z         different version, set min_supported_cpython to the hexcode of the
2025-08-14T23:09:16.7862865Z         CPython version of choice.
2025-08-14T23:09:16.7862984Z     
2025-08-14T23:09:16.7863039Z     Example:
2025-08-14T23:09:16.7863102Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7863194Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:16.7863322Z         >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension
2025-08-14T23:09:16.7863375Z         >>> setup(
2025-08-14T23:09:16.7863443Z         ...     name='xpu_extension',
2025-08-14T23:09:16.7863501Z         ...     ext_modules=[
2025-08-14T23:09:16.7863562Z         ...     SyclExtension(
2025-08-14T23:09:16.7863634Z         ...                 name='xpu_extension',
2025-08-14T23:09:16.7863732Z         ...                 sources=['extension.cpp', 'extension_kernel.cpp'],
2025-08-14T23:09:16.7866033Z         ...                 extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
2025-08-14T23:09:16.7866090Z         ...     ],
2025-08-14T23:09:16.7866190Z         ...     cmdclass={
2025-08-14T23:09:16.7866265Z         ...         'build_ext': BuildExtension
2025-08-14T23:09:16.7866320Z         ...     })
2025-08-14T23:09:16.7866371Z     
2025-08-14T23:09:16.7866544Z     By default the extension will be compiled to run on all archs of the cards visible during the
2025-08-14T23:09:16.7866693Z     building process of the extension. If down the road a new card is installed the
2025-08-14T23:09:16.7866839Z     extension may need to be recompiled. You can override the default behavior using
2025-08-14T23:09:16.7867013Z     `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension
2025-08-14T23:09:16.7867068Z     to support:
2025-08-14T23:09:16.7867118Z     
2025-08-14T23:09:16.7867260Z     ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py``
2025-08-14T23:09:16.7867312Z     
2025-08-14T23:09:16.7867479Z     Note that while it's possible to include all supported archs, the more archs get included the
2025-08-14T23:09:16.7867646Z     slower the building process will be, as it will build a separate kernel image for each arch.
2025-08-14T23:09:16.7867698Z     
2025-08-14T23:09:16.7867783Z     Note: Ninja is required to build SyclExtension.
2025-08-14T23:09:16.7867833Z     
2025-08-14T23:09:16.7867979Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7868029Z     
2025-08-14T23:09:16.7868089Z       warnings.warn(msg)
2025-08-14T23:09:16.7870282Z     
2025-08-14T23:09:16.7870407Z --- Parse Warning: 60 / 146 ---
2025-08-14T23:09:16.7870886Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1585.
2025-08-14T23:09:16.7871041Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7871092Z     
2025-08-14T23:09:16.7871181Z     Load a PyTorch C++ extension just-in-time (JIT).
2025-08-14T23:09:16.7871230Z     
2025-08-14T23:09:16.7871352Z     To load an extension, a Ninja build file is emitted, which is used to
2025-08-14T23:09:16.7871500Z     compile the given sources into a dynamic library. This library is
2025-08-14T23:09:16.7871632Z     subsequently loaded into the current Python process as a module and
2025-08-14T23:09:16.7871709Z     returned from this function, ready for use.
2025-08-14T23:09:16.7871760Z     
2025-08-14T23:09:16.7871881Z     By default, the directory to which the build file is emitted and the
2025-08-14T23:09:16.7872017Z     resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2025-08-14T23:09:16.7872132Z     ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2025-08-14T23:09:16.7872256Z     the name of the extension. This location can be overridden in two ways.
2025-08-14T23:09:16.7872378Z     First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2025-08-14T23:09:16.7872506Z     replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2025-08-14T23:09:16.7872632Z     into subfolders of this directory. Second, if the ``build_directory``
2025-08-14T23:09:16.7872766Z     argument to this function is supplied, it overrides the entire path, i.e.
2025-08-14T23:09:16.7872861Z     the library will be compiled into that folder directly.
2025-08-14T23:09:16.7875122Z     
2025-08-14T23:09:16.7875257Z     To compile the sources, the default system compiler (``c++``) is used,
2025-08-14T23:09:16.7875397Z     which can be overridden by setting the ``CXX`` environment variable. To pass
2025-08-14T23:09:16.7875528Z     additional arguments to the compilation process, ``extra_cflags`` or
2025-08-14T23:09:16.7875658Z     ``extra_ldflags`` can be provided. For example, to compile your extension
2025-08-14T23:09:16.7875836Z     with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2025-08-14T23:09:16.7875932Z     ``extra_cflags`` to pass further include directories.
2025-08-14T23:09:16.7875981Z     
2025-08-14T23:09:16.7876117Z     CUDA support with mixed compilation is provided. Simply pass CUDA source
2025-08-14T23:09:16.7876227Z     files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2025-08-14T23:09:16.7876367Z     detected and compiled with nvcc rather than the C++ compiler. This includes
2025-08-14T23:09:16.7876489Z     passing the CUDA lib64 directory as a library directory, and linking
2025-08-14T23:09:16.7876581Z     ``cudart``. You can pass additional flags to nvcc via
2025-08-14T23:09:16.7876698Z     ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2025-08-14T23:09:16.7876833Z     heuristics for finding the CUDA install directory are used, which usually
2025-08-14T23:09:16.7876977Z     work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2025-08-14T23:09:16.7877036Z     safest option.
2025-08-14T23:09:16.7877086Z     
2025-08-14T23:09:16.7877220Z     SYCL support with mixed compilation is provided. Simply pass SYCL source
2025-08-14T23:09:16.7877337Z     files (``.sycl``) along with other sources. Such files will be detected
2025-08-14T23:09:16.7877462Z     and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather
2025-08-14T23:09:16.7877584Z     than the C++ compiler. You can pass additional flags to SYCL compiler
2025-08-14T23:09:16.7879798Z     via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++.
2025-08-14T23:09:16.7879927Z     SYCL compiler is expected to be found via system PATH environment
2025-08-14T23:09:16.7879980Z     variable.
2025-08-14T23:09:16.7880030Z     
2025-08-14T23:09:16.7880082Z     Args:
2025-08-14T23:09:16.7880207Z         name: The name of the extension to build. This MUST be the same as the
2025-08-14T23:09:16.7880277Z             name of the pybind11 module!
2025-08-14T23:09:16.7880399Z         sources: A list of relative or absolute paths to C++ source files.
2025-08-14T23:09:16.7880529Z         extra_cflags: optional list of compiler flags to forward to the build.
2025-08-14T23:09:16.7880655Z         extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2025-08-14T23:09:16.7880753Z             when building CUDA sources.
2025-08-14T23:09:16.7880879Z         extra_sycl_cflags: optional list of compiler flags to forward to SYCL
2025-08-14T23:09:16.7880953Z             compiler when building SYCL sources.
2025-08-14T23:09:16.7881078Z         extra_ldflags: optional list of linker flags to forward to the build.
2025-08-14T23:09:16.7881205Z         extra_include_paths: optional list of include directories to forward
2025-08-14T23:09:16.7881264Z             to the build.
2025-08-14T23:09:16.7881370Z         build_directory: optional path to use as build workspace.
2025-08-14T23:09:16.7881479Z         verbose: If ``True``, turns on verbose logging of load steps.
2025-08-14T23:09:16.7881608Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-08-14T23:09:16.7881704Z             the build. If set to ``None`` (default), this value is
2025-08-14T23:09:16.7881823Z             automatically determined based on the existence of ``.cu`` or
2025-08-14T23:09:16.7884139Z             ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2025-08-14T23:09:16.7884209Z             and libraries to be included.
2025-08-14T23:09:16.7884340Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-08-14T23:09:16.7884434Z             the build. If set to ``None`` (default), this value is
2025-08-14T23:09:16.7884560Z             automatically determined based on the existence of ``.sycl`` in
2025-08-14T23:09:16.7884656Z             ``sources``. Set it to `True`` to force SYCL headers and
2025-08-14T23:09:16.7884724Z             libraries to be included.
2025-08-14T23:09:16.7884843Z         is_python_module: If ``True`` (default), imports the produced shared
2025-08-14T23:09:16.7885006Z             library as a Python module. If ``False``, behavior depends on
2025-08-14T23:09:16.7885069Z             ``is_standalone``.
2025-08-14T23:09:16.7885189Z         is_standalone: If ``False`` (default) loads the constructed extension
2025-08-14T23:09:16.7885303Z             into the process as a plain dynamic library. If ``True``, build a
2025-08-14T23:09:16.7885369Z             standalone executable.
2025-08-14T23:09:16.7885419Z     
2025-08-14T23:09:16.7885472Z     Returns:
2025-08-14T23:09:16.7885544Z         If ``is_python_module`` is ``True``:
2025-08-14T23:09:16.7885649Z             Returns the loaded PyTorch extension as a Python module.
2025-08-14T23:09:16.7885700Z     
2025-08-14T23:09:16.7885939Z         If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2025-08-14T23:09:16.7886062Z             Returns nothing. (The shared library is loaded into the process as
2025-08-14T23:09:16.7886142Z             a side effect.)
2025-08-14T23:09:16.7886193Z     
2025-08-14T23:09:16.7888341Z         If ``is_standalone`` is ``True``.
2025-08-14T23:09:16.7888460Z             Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2025-08-14T23:09:16.7888562Z             added to the PATH environment variable as a side effect.)
2025-08-14T23:09:16.7888614Z     
2025-08-14T23:09:16.7888666Z     Example:
2025-08-14T23:09:16.7888729Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7888813Z         >>> from torch.utils.cpp_extension import load
2025-08-14T23:09:16.7888872Z         >>> module = load(
2025-08-14T23:09:16.7888934Z         ...     name='extension',
2025-08-14T23:09:16.7889032Z         ...     sources=['extension.cpp', 'extension_kernel.cu'],
2025-08-14T23:09:16.7889095Z         ...     extra_cflags=['-O2'],
2025-08-14T23:09:16.7889153Z         ...     verbose=True)
2025-08-14T23:09:16.7889203Z     
2025-08-14T23:09:16.7889356Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7889408Z     
2025-08-14T23:09:16.7889468Z       warnings.warn(msg)
2025-08-14T23:09:16.7889519Z     
2025-08-14T23:09:16.7889636Z --- Parse Warning: 61 / 146 ---
2025-08-14T23:09:16.7890134Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1890.
2025-08-14T23:09:16.7890321Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7890371Z     
2025-08-14T23:09:16.7890493Z     Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2025-08-14T23:09:16.7892670Z     
2025-08-14T23:09:16.7892809Z     This function behaves exactly like :func:`load`, but takes its sources as
2025-08-14T23:09:16.7893019Z     strings rather than filenames. These strings are stored to files in the
2025-08-14T23:09:16.7893144Z     build directory, after which the behavior of :func:`load_inline` is
2025-08-14T23:09:16.7893211Z     identical to :func:`load`.
2025-08-14T23:09:16.7893259Z     
2025-08-14T23:09:16.7893311Z     See `the
2025-08-14T23:09:16.7893500Z     tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2025-08-14T23:09:16.7893578Z     for good examples of using this function.
2025-08-14T23:09:16.7893629Z     
2025-08-14T23:09:16.7893764Z     Sources may omit two required parts of a typical non-inline C++ extension:
2025-08-14T23:09:16.7893903Z     the necessary header includes, as well as the (pybind11) binding code. More
2025-08-14T23:09:16.7894042Z     precisely, strings passed to ``cpp_sources`` are first concatenated into a
2025-08-14T23:09:16.7894153Z     single ``.cpp`` file. This file is then prepended with ``#include
2025-08-14T23:09:16.7894216Z     <torch/extension.h>``
2025-08-14T23:09:16.7894265Z     
2025-08-14T23:09:16.7894396Z     Furthermore, if the ``functions`` argument is supplied, bindings will be
2025-08-14T23:09:16.7894590Z     automatically generated for each function specified. ``functions`` can
2025-08-14T23:09:16.7894717Z     either be a list of function names, or a dictionary mapping from function
2025-08-14T23:09:16.7894845Z     names to docstrings. If a list is given, the name of each function is used
2025-08-14T23:09:16.7894904Z     as its docstring.
2025-08-14T23:09:16.7897107Z     
2025-08-14T23:09:16.7897235Z     The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2025-08-14T23:09:16.7897343Z     file and  prepended with ``torch/types.h``, ``cuda.h`` and
2025-08-14T23:09:16.7897464Z     ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2025-08-14T23:09:16.7897594Z     separately, but ultimately linked into a single library. Note that no
2025-08-14T23:09:16.7897725Z     bindings are generated for functions in ``cuda_sources`` per se. To bind
2025-08-14T23:09:16.7897895Z     to a CUDA kernel, you must create a C++ function that calls it, and either
2025-08-14T23:09:16.7898021Z     declare or define this C++ function in one of the ``cpp_sources`` (and
2025-08-14T23:09:16.7898093Z     include its name in ``functions``).
2025-08-14T23:09:16.7898142Z     
2025-08-14T23:09:16.7898268Z     The sources in ``sycl_sources`` are concatenated into a separate ``.sycl``
2025-08-14T23:09:16.7898394Z     file and  prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes.
2025-08-14T23:09:16.7898510Z     The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately
2025-08-14T23:09:16.7898633Z     linked into a single library. Note that no bindings are generated for
2025-08-14T23:09:16.7898761Z     functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must
2025-08-14T23:09:16.7898885Z     create a C++ function that calls it, and either declare or define this
2025-08-14T23:09:16.7898996Z     C++ function in one of the ``cpp_sources`` (and include its name
2025-08-14T23:09:16.7899054Z     in ``functions``).
2025-08-14T23:09:16.7899108Z     
2025-08-14T23:09:16.7899157Z     
2025-08-14T23:09:16.7899208Z     
2025-08-14T23:09:16.7899317Z     See :func:`load` for a description of arguments omitted below.
2025-08-14T23:09:16.7901483Z     
2025-08-14T23:09:16.7901539Z     Args:
2025-08-14T23:09:16.7901672Z         cpp_sources: A string, or list of strings, containing C++ source code.
2025-08-14T23:09:16.7901833Z         cuda_sources: A string, or list of strings, containing CUDA source code.
2025-08-14T23:09:16.7901960Z         sycl_sources: A string, or list of strings, containing SYCL source code.
2025-08-14T23:09:16.7902085Z         functions: A list of function names for which to generate function
2025-08-14T23:09:16.7902207Z             bindings. If a dictionary is given, it should map function names to
2025-08-14T23:09:16.7902317Z             docstrings (which are otherwise just the function names).
2025-08-14T23:09:16.7902443Z         with_cuda: Determines whether CUDA headers and libraries are added to
2025-08-14T23:09:16.7902542Z             the build. If set to ``None`` (default), this value is
2025-08-14T23:09:16.7902661Z             automatically determined based on whether ``cuda_sources`` is
2025-08-14T23:09:16.7902755Z             provided. Set it to ``True`` to force CUDA headers
2025-08-14T23:09:16.7902823Z             and libraries to be included.
2025-08-14T23:09:16.7903045Z         with_sycl: Determines whether SYCL headers and libraries are added to
2025-08-14T23:09:16.7903139Z             the build. If set to ``None`` (default), this value is
2025-08-14T23:09:16.7903258Z             automatically determined based on whether ``sycl_sources`` is
2025-08-14T23:09:16.7903385Z             provided. Set it to ``True`` to force SYCL headers
2025-08-14T23:09:16.7903454Z             and libraries to be included.
2025-08-14T23:09:16.7903575Z         with_pytorch_error_handling: Determines whether pytorch error and
2025-08-14T23:09:16.7903692Z             warning macros are handled by pytorch instead of pybind. To do
2025-08-14T23:09:16.7903868Z             this, each function ``foo`` is called via an intermediary ``_safe_foo``
2025-08-14T23:09:16.7906340Z             function. This redirection might cause issues in obscure cases
2025-08-14T23:09:16.7906449Z             of cpp. This flag should be set to ``False`` when this redirect
2025-08-14T23:09:16.7906515Z             causes issues.
2025-08-14T23:09:16.7906663Z         no_implicit_headers: If ``True``, skips automatically adding headers, most notably
2025-08-14T23:09:16.7906797Z             ``#include <torch/extension.h>`` and ``#include <torch/types.h>`` lines.
2025-08-14T23:09:16.7906893Z             Use this option to improve cold start times when you
2025-08-14T23:09:16.7907042Z             already include the necessary headers in your source code. Default: ``False``.
2025-08-14T23:09:16.7907092Z     
2025-08-14T23:09:16.7907146Z     Example:
2025-08-14T23:09:16.7907236Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2025-08-14T23:09:16.7907361Z         >>> from torch.utils.cpp_extension import load_inline
2025-08-14T23:09:16.7907421Z         >>> source = """
2025-08-14T23:09:16.7907511Z         at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2025-08-14T23:09:16.7907576Z           return x.sin() + y.sin();
2025-08-14T23:09:16.7907628Z         }
2025-08-14T23:09:16.7907679Z         """
2025-08-14T23:09:16.7907765Z         >>> module = load_inline(name='inline_extension',
2025-08-14T23:09:16.7907841Z         ...                      cpp_sources=[source],
2025-08-14T23:09:16.7907917Z         ...                      functions=['sin_add'])
2025-08-14T23:09:16.7907968Z     
2025-08-14T23:09:16.7908021Z     .. note::
2025-08-14T23:09:16.7908163Z         Since load_inline will just-in-time compile the source code, please ensure
2025-08-14T23:09:16.7910434Z         that you have the right toolchains installed in the runtime. For example,
2025-08-14T23:09:16.7910571Z         when loading C++, make sure a C++ compiler is available. If you're loading
2025-08-14T23:09:16.7910716Z         a CUDA extension, you will need to additionally install the corresponding CUDA
2025-08-14T23:09:16.7910860Z         toolkit (nvcc and any other dependencies your code has). Compiling toolchains
2025-08-14T23:09:16.7910997Z         are not included when you install torch and must be additionally installed.
2025-08-14T23:09:16.7911080Z     
2025-08-14T23:09:16.7911229Z         During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
2025-08-14T23:09:16.7911357Z         the extension. This may use up too many resources on some systems. One
2025-08-14T23:09:16.7911484Z         can control the number of workers by setting the `MAX_JOBS` environment
2025-08-14T23:09:16.7911557Z         variable to a non-negative number.
2025-08-14T23:09:16.7911607Z     
2025-08-14T23:09:16.7911752Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7911802Z     
2025-08-14T23:09:16.7911863Z       warnings.warn(msg)
2025-08-14T23:09:16.7911913Z     
2025-08-14T23:09:16.7912034Z --- Parse Warning: 62 / 146 ---
2025-08-14T23:09:16.7912565Z     /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.
2025-08-14T23:09:16.7912719Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7912865Z     Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights).
2025-08-14T23:09:16.7912987Z     
2025-08-14T23:09:16.7913039Z         Args:
2025-08-14T23:09:16.7915303Z             weights (sequence)   : a sequence of weights, not necessary summing up to one
2025-08-14T23:09:16.7915400Z             num_samples (int): number of samples to draw
2025-08-14T23:09:16.7915524Z             replacement (bool): if ``True``, samples are drawn with replacement.
2025-08-14T23:09:16.7915643Z                 If not, they are drawn without replacement, which means that when a
2025-08-14T23:09:16.7915829Z                 sample index is drawn for a row, it cannot be drawn again for that row.
2025-08-14T23:09:16.7915924Z             generator (Generator): Generator used in sampling.
2025-08-14T23:09:16.7915974Z     
2025-08-14T23:09:16.7916027Z         Example:
2025-08-14T23:09:16.7916118Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.7916171Z             >>> list(
2025-08-14T23:09:16.7916243Z             ...     WeightedRandomSampler(
2025-08-14T23:09:16.7916328Z             ...         [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True
2025-08-14T23:09:16.7916380Z             ...     )
2025-08-14T23:09:16.7916431Z             ... )
2025-08-14T23:09:16.7916487Z             [4, 4, 1, 4, 5]
2025-08-14T23:09:16.7916540Z             >>> list(
2025-08-14T23:09:16.7916609Z             ...     WeightedRandomSampler(
2025-08-14T23:09:16.7916715Z             ...         [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False
2025-08-14T23:09:16.7916768Z             ...     )
2025-08-14T23:09:16.7916822Z             ... )
2025-08-14T23:09:16.7916876Z             [0, 1, 4, 3, 2]
2025-08-14T23:09:16.7916928Z         
2025-08-14T23:09:16.7919197Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7919256Z     
2025-08-14T23:09:16.7919316Z       warnings.warn(msg)
2025-08-14T23:09:16.7919366Z     
2025-08-14T23:09:16.7919482Z --- Parse Warning: 63 / 146 ---
2025-08-14T23:09:16.7919979Z     /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.
2025-08-14T23:09:16.7920129Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7920232Z     Wraps another sampler to yield a mini-batch of indices.
2025-08-14T23:09:16.7920281Z     
2025-08-14T23:09:16.7920334Z         Args:
2025-08-14T23:09:16.7920468Z             sampler (Sampler or Iterable): Base sampler. Can be any iterable object
2025-08-14T23:09:16.7920542Z             batch_size (int): Size of mini-batch.
2025-08-14T23:09:16.7920661Z             drop_last (bool): If ``True``, the sampler will drop the last batch if
2025-08-14T23:09:16.7920781Z                 its size would be less than ``batch_size``
2025-08-14T23:09:16.7920832Z     
2025-08-14T23:09:16.7920884Z         Example:
2025-08-14T23:09:16.7920938Z             >>> list(
2025-08-14T23:09:16.7921000Z             ...     BatchSampler(
2025-08-14T23:09:16.7921120Z             ...         SequentialSampler(range(10)), batch_size=3, drop_last=False
2025-08-14T23:09:16.7921171Z             ...     )
2025-08-14T23:09:16.7921223Z             ... )
2025-08-14T23:09:16.7921286Z             [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
2025-08-14T23:09:16.7923538Z             >>> list(
2025-08-14T23:09:16.7923738Z             ...     BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True)
2025-08-14T23:09:16.7923797Z             ... )
2025-08-14T23:09:16.7923860Z             [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
2025-08-14T23:09:16.7923911Z         
2025-08-14T23:09:16.7924057Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7924109Z     
2025-08-14T23:09:16.7924168Z       warnings.warn(msg)
2025-08-14T23:09:16.7924217Z     
2025-08-14T23:09:16.7924331Z --- Parse Warning: 64 / 146 ---
2025-08-14T23:09:16.7924856Z     /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.
2025-08-14T23:09:16.7925007Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7925126Z     Sampler that restricts data loading to a subset of the dataset.
2025-08-14T23:09:16.7925215Z     
2025-08-14T23:09:16.7925315Z         It is especially useful in conjunction with
2025-08-14T23:09:16.7925462Z         :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2025-08-14T23:09:16.7925619Z         process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2025-08-14T23:09:16.7925884Z         :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2025-08-14T23:09:16.7925962Z         original dataset that is exclusive to it.
2025-08-14T23:09:16.7926013Z     
2025-08-14T23:09:16.7926067Z         .. note::
2025-08-14T23:09:16.7928532Z             Dataset is assumed to be of constant size and that any instance of it always
2025-08-14T23:09:16.7928624Z             returns the same elements in the same order.
2025-08-14T23:09:16.7928679Z     
2025-08-14T23:09:16.7928730Z         Args:
2025-08-14T23:09:16.7928804Z             dataset: Dataset used for sampling.
2025-08-14T23:09:16.7928966Z             num_replicas (int, optional): Number of processes participating in
2025-08-14T23:09:16.7929113Z                 distributed training. By default, :attr:`world_size` is retrieved from the
2025-08-14T23:09:16.7929183Z                 current distributed group.
2025-08-14T23:09:16.7929326Z             rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2025-08-14T23:09:16.7929444Z                 By default, :attr:`rank` is retrieved from the current distributed
2025-08-14T23:09:16.7929500Z                 group.
2025-08-14T23:09:16.7929631Z             shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2025-08-14T23:09:16.7929687Z                 indices.
2025-08-14T23:09:16.7929797Z             seed (int, optional): random seed used to shuffle the sampler if
2025-08-14T23:09:16.7929912Z                 :attr:`shuffle=True`. This number should be identical across all
2025-08-14T23:09:16.7930009Z                 processes in the distributed group. Default: ``0``.
2025-08-14T23:09:16.7930137Z             drop_last (bool, optional): if ``True``, then the sampler will drop the
2025-08-14T23:09:16.7930252Z                 tail of the data to make it evenly divisible across the number of
2025-08-14T23:09:16.7930367Z                 replicas. If ``False``, the sampler will add extra indices to make
2025-08-14T23:09:16.7930487Z                 the data evenly divisible across the replicas. Default: ``False``.
2025-08-14T23:09:16.7930561Z     
2025-08-14T23:09:16.7930616Z         .. warning::
2025-08-14T23:09:16.7932854Z             In distributed mode, calling the :meth:`set_epoch` method at
2025-08-14T23:09:16.7933074Z             the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2025-08-14T23:09:16.7933227Z             is necessary to make shuffling work properly across multiple epochs. Otherwise,
2025-08-14T23:09:16.7933302Z             the same ordering will be always used.
2025-08-14T23:09:16.7933352Z     
2025-08-14T23:09:16.7933405Z         Example::
2025-08-14T23:09:16.7933457Z     
2025-08-14T23:09:16.7933522Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7933650Z             >>> sampler = DistributedSampler(dataset) if is_distributed else None
2025-08-14T23:09:16.7933753Z             >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2025-08-14T23:09:16.7933828Z             ...                     sampler=sampler)
2025-08-14T23:09:16.7933912Z             >>> for epoch in range(start_epoch, n_epochs):
2025-08-14T23:09:16.7933977Z             ...     if is_distributed:
2025-08-14T23:09:16.7934048Z             ...         sampler.set_epoch(epoch)
2025-08-14T23:09:16.7934108Z             ...     train(loader)
2025-08-14T23:09:16.7934159Z         
2025-08-14T23:09:16.7934303Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7934354Z     
2025-08-14T23:09:16.7934413Z       warnings.warn(msg)
2025-08-14T23:09:16.7934464Z     
2025-08-14T23:09:16.7934580Z --- Parse Warning: 65 / 146 ---
2025-08-14T23:09:16.7937317Z     /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.
2025-08-14T23:09:16.7937475Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7937527Z     
2025-08-14T23:09:16.7937594Z     Iterable-style DataPipe.
2025-08-14T23:09:16.7937644Z     
2025-08-14T23:09:16.7937792Z     All DataPipes that represent an iterable of data samples should subclass this.
2025-08-14T23:09:16.7937941Z     This style of DataPipes is particularly useful when data come from a stream, or
2025-08-14T23:09:16.7938143Z     when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its
2025-08-14T23:09:16.7938314Z     elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``.
2025-08-14T23:09:16.7938384Z     
2025-08-14T23:09:16.7938519Z     All subclasses should overwrite :meth:`__iter__`, which would return an
2025-08-14T23:09:16.7938718Z     iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its
2025-08-14T23:09:16.7938928Z     method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should
2025-08-14T23:09:16.7939087Z     override ``reset()`` if necessary. The common usages include resetting buffers, pointers,
2025-08-14T23:09:16.7939205Z     and various state variables within the custom ``IterDataPipe``.
2025-08-14T23:09:16.7939254Z     
2025-08-14T23:09:16.7939304Z     Note:
2025-08-14T23:09:16.7939427Z         Only `one` iterator can be valid for each ``IterDataPipe`` at a time,
2025-08-14T23:09:16.7939624Z         and the creation a second iterator will invalidate the first one. This constraint is necessary because
2025-08-14T23:09:16.7939837Z         some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators.
2025-08-14T23:09:16.7939992Z         The code example below presents details on how this constraint looks in practice.
2025-08-14T23:09:16.7940199Z         If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_.
2025-08-14T23:09:16.7942398Z     
2025-08-14T23:09:16.7942569Z     These DataPipes can be invoked in two ways, using the class constructor or applying their
2025-08-14T23:09:16.7942774Z     functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes).
2025-08-14T23:09:16.7943031Z     You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple
2025-08-14T23:09:16.7943098Z     operations in succession.
2025-08-14T23:09:16.7943148Z     
2025-08-14T23:09:16.7943230Z     .. _GitHub IterDataPipe Single Iterator Issue:
2025-08-14T23:09:16.7943314Z         https://github.com/pytorch/data/issues/45
2025-08-14T23:09:16.7943366Z     
2025-08-14T23:09:16.7943418Z     Note:
2025-08-14T23:09:16.7943553Z         When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
2025-08-14T23:09:16.7943709Z         item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader`
2025-08-14T23:09:16.7943835Z         iterator. When :attr:`num_workers > 0`, each worker process will have a
2025-08-14T23:09:16.7943975Z         different copy of the DataPipe object, so it is often desired to configure
2025-08-14T23:09:16.7944111Z         each copy independently to avoid having duplicate data returned from the
2025-08-14T23:09:16.7944251Z         workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
2025-08-14T23:09:16.7944394Z         process, returns information about the worker. It can be used in either the
2025-08-14T23:09:16.7944539Z         dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
2025-08-14T23:09:16.7944649Z         :attr:`worker_init_fn` option to modify each copy's behavior.
2025-08-14T23:09:16.7944739Z     
2025-08-14T23:09:16.7944810Z     Examples:
2025-08-14T23:09:16.7947013Z         General Usage:
2025-08-14T23:09:16.7947076Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7947206Z             >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
2025-08-14T23:09:16.7947282Z             >>> dp = IterableWrapper(range(10))
2025-08-14T23:09:16.7947393Z             >>> map_dp_1 = Mapper(dp, lambda x: x + 1)  # Using class constructor
2025-08-14T23:09:16.7947456Z             >>> map_dp_2 = dp.map(
2025-08-14T23:09:16.7947519Z             ...     lambda x: x + 1
2025-08-14T23:09:16.7947600Z             ... )  # Using functional form (recommended)
2025-08-14T23:09:16.7947662Z             >>> list(map_dp_1)
2025-08-14T23:09:16.7947724Z             [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
2025-08-14T23:09:16.7947783Z             >>> list(map_dp_2)
2025-08-14T23:09:16.7947844Z             [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
2025-08-14T23:09:16.7947965Z             >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0)
2025-08-14T23:09:16.7948028Z             >>> list(filter_dp)
2025-08-14T23:09:16.7948085Z             [2, 4, 6, 8, 10]
2025-08-14T23:09:16.7948160Z         Single Iterator Constraint Example:
2025-08-14T23:09:16.7948285Z             >>> from torchdata.datapipes.iter import IterableWrapper, Mapper
2025-08-14T23:09:16.7954487Z             >>> source_dp = IterableWrapper(range(10))
2025-08-14T23:09:16.7954552Z             >>> it1 = iter(source_dp)
2025-08-14T23:09:16.7954610Z             >>> list(it1)
2025-08-14T23:09:16.7954670Z             [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2025-08-14T23:09:16.7954733Z             >>> it1 = iter(source_dp)
2025-08-14T23:09:16.7954790Z             >>> it2 = iter(
2025-08-14T23:09:16.7954849Z             ...     source_dp
2025-08-14T23:09:16.7954943Z             ... )  # The creation of a new iterator invalidates `it1`
2025-08-14T23:09:16.7955001Z             >>> next(it2)
2025-08-14T23:09:16.7955053Z             0
2025-08-14T23:09:16.7955167Z             >>> next(it1)  # Further usage of `it1` will raise a `RunTimeError`
2025-08-14T23:09:16.7955217Z     
2025-08-14T23:09:16.7955363Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7955413Z     
2025-08-14T23:09:16.7955473Z       warnings.warn(msg)
2025-08-14T23:09:16.7955558Z     
2025-08-14T23:09:16.7955679Z --- Parse Warning: 66 / 146 ---
2025-08-14T23:09:16.7956254Z     /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.
2025-08-14T23:09:16.7956412Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7956464Z     
2025-08-14T23:09:16.7956707Z     Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``.
2025-08-14T23:09:16.7956764Z     
2025-08-14T23:09:16.7956836Z     (functional name: ``groupby``).
2025-08-14T23:09:16.7956892Z     
2025-08-14T23:09:16.7957112Z     The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group
2025-08-14T23:09:16.7957287Z     will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full,
2025-08-14T23:09:16.7957463Z     the DataPipe will yield the largest batch with the same key, provided that its size is larger
2025-08-14T23:09:16.7957652Z     than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``.
2025-08-14T23:09:16.7957703Z     
2025-08-14T23:09:16.7957925Z     After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity
2025-08-14T23:09:16.7958107Z     will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``.
2025-08-14T23:09:16.7958190Z     
2025-08-14T23:09:16.7958244Z     Args:
2025-08-14T23:09:16.7958351Z         datapipe: Iterable datapipe to be grouped
2025-08-14T23:09:16.7958513Z         group_key_fn: Function used to generate group key from the data of the source datapipe
2025-08-14T23:09:16.7958653Z         keep_key: Option to yield the matching key along with the items in a tuple,
2025-08-14T23:09:16.7958763Z             resulting in `(key, [items])` otherwise returning [items]
2025-08-14T23:09:16.7958860Z         buffer_size: The size of buffer for ungrouped data
2025-08-14T23:09:16.7959021Z         group_size: The max size of each group, a batch is yielded as soon as it reaches this size
2025-08-14T23:09:16.7959218Z         guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full
2025-08-14T23:09:16.7959423Z         drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer
2025-08-14T23:09:16.7959513Z             when the buffer is full
2025-08-14T23:09:16.7959566Z     
2025-08-14T23:09:16.7959623Z     Example:
2025-08-14T23:09:16.7959682Z         >>> import os
2025-08-14T23:09:16.7959747Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7959863Z         >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:16.7959928Z         >>> def group_fn(file):
2025-08-14T23:09:16.7960028Z         ...     return os.path.basename(file).split(".")[0]
2025-08-14T23:09:16.7960103Z         >>> source_dp = IterableWrapper(
2025-08-14T23:09:16.7960209Z         ...     ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"]
2025-08-14T23:09:16.7960262Z         ... )
2025-08-14T23:09:16.7960354Z         >>> dp0 = source_dp.groupby(group_key_fn=group_fn)
2025-08-14T23:09:16.7960412Z         >>> list(dp0)
2025-08-14T23:09:16.7960513Z         [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']]
2025-08-14T23:09:16.7960624Z         >>> # A group is yielded as soon as its size equals to `group_size`
2025-08-14T23:09:16.7960742Z         >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2)
2025-08-14T23:09:16.7960799Z         >>> list(dp1)
2025-08-14T23:09:16.7960900Z         [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
2025-08-14T23:09:16.7961098Z         >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size`
2025-08-14T23:09:16.7961188Z         >>> dp2 = source_dp.groupby(
2025-08-14T23:09:16.7961255Z         ...     group_key_fn=group_fn,
2025-08-14T23:09:16.7961320Z         ...     buffer_size=3,
2025-08-14T23:09:16.7961381Z         ...     group_size=3,
2025-08-14T23:09:16.7961454Z         ...     guaranteed_group_size=2,
2025-08-14T23:09:16.7961506Z         ... )
2025-08-14T23:09:16.7961561Z         >>> list(dp2)
2025-08-14T23:09:16.7964119Z         [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']]
2025-08-14T23:09:16.7964174Z     
2025-08-14T23:09:16.7964331Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7964382Z     
2025-08-14T23:09:16.7964444Z       warnings.warn(msg)
2025-08-14T23:09:16.7964493Z     
2025-08-14T23:09:16.7964610Z --- Parse Warning: 67 / 146 ---
2025-08-14T23:09:16.7965233Z     /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.
2025-08-14T23:09:16.7965388Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7965437Z     
2025-08-14T23:09:16.7965647Z     Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``).
2025-08-14T23:09:16.7965696Z     
2025-08-14T23:09:16.7965832Z     Args:
2025-08-14T23:09:16.7965930Z         datapipe: Iterable datapipe that provides pathnames
2025-08-14T23:09:16.7966034Z         mode: An optional string that specifies the mode in which
2025-08-14T23:09:16.7966223Z             the file is opened by ``open()``. It defaults to ``r``, other options are
2025-08-14T23:09:16.7966318Z             ``b`` for reading in binary mode and ``t`` for text mode.
2025-08-14T23:09:16.7966440Z         encoding: An optional string that specifies the encoding of the
2025-08-14T23:09:16.7966594Z             underlying file. It defaults to ``None`` to match the default encoding of ``open``.
2025-08-14T23:09:16.7966670Z         length: Nominal length of the datapipe
2025-08-14T23:09:16.7966719Z     
2025-08-14T23:09:16.7966772Z     Note:
2025-08-14T23:09:16.7966923Z         The opened file handles will be closed by Python's GC periodically. Users can choose
2025-08-14T23:09:16.7966988Z         to close them explicitly.
2025-08-14T23:09:16.7967038Z     
2025-08-14T23:09:16.7967091Z     Example:
2025-08-14T23:09:16.7967153Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.7967236Z         >>> from torchdata.datapipes.iter import (
2025-08-14T23:09:16.7967325Z         ...     FileLister,
2025-08-14T23:09:16.7967385Z         ...     FileOpener,
2025-08-14T23:09:16.7967443Z         ...     StreamReader,
2025-08-14T23:09:16.7967500Z         ... )
2025-08-14T23:09:16.7967624Z         >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt"))
2025-08-14T23:09:16.7967689Z         >>> dp = FileOpener(dp)
2025-08-14T23:09:16.7967751Z         >>> dp = StreamReader(dp)
2025-08-14T23:09:16.7967804Z         >>> list(dp)
2025-08-14T23:09:16.7967864Z         [('./abc.txt', 'abc')]
2025-08-14T23:09:16.7967912Z     
2025-08-14T23:09:16.7968059Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7968108Z     
2025-08-14T23:09:16.7968168Z       warnings.warn(msg)
2025-08-14T23:09:16.7968217Z     
2025-08-14T23:09:16.7968331Z --- Parse Warning: 68 / 146 ---
2025-08-14T23:09:16.7968915Z     /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.
2025-08-14T23:09:16.7969068Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7969117Z     
2025-08-14T23:09:16.7969374Z     Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``).
2025-08-14T23:09:16.7969444Z     
2025-08-14T23:09:16.7969556Z     A list of the child DataPipes is returned from this operation.
2025-08-14T23:09:16.7969605Z     
2025-08-14T23:09:16.7969657Z     Args:
2025-08-14T23:09:16.7969737Z         datapipe: Iterable DataPipe being filtered
2025-08-14T23:09:16.7969851Z         num_instances: number of instances of the DataPipe to create
2025-08-14T23:09:16.7970065Z         classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None``
2025-08-14T23:09:16.7970252Z         drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None``
2025-08-14T23:09:16.7970433Z         buffer_size: this defines the maximum number of inputs that the buffer can hold across all child
2025-08-14T23:09:16.7970540Z             DataPipes while waiting for their values to be yielded.
2025-08-14T23:09:16.7970643Z             Defaults to ``1000``. Use ``-1`` for the unlimited buffer.
2025-08-14T23:09:16.7970692Z     
2025-08-14T23:09:16.7970747Z     Examples:
2025-08-14T23:09:16.7970823Z         >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:16.7970929Z         >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:16.7970988Z         >>> def odd_or_even(n):
2025-08-14T23:09:16.7971048Z         ...     return n % 2
2025-08-14T23:09:16.7971123Z         >>> source_dp = IterableWrapper(range(5))
2025-08-14T23:09:16.7971247Z         >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even)
2025-08-14T23:09:16.7971332Z         >>> list(dp1)
2025-08-14T23:09:16.7971384Z         [0, 2, 4]
2025-08-14T23:09:16.7971457Z         >>> list(dp2)
2025-08-14T23:09:16.7971509Z         [1, 3]
2025-08-14T23:09:16.7971685Z         >>> # It can also filter out any element that gets `None` from the `classifier_fn`
2025-08-14T23:09:16.7971752Z         >>> def odd_or_even_no_zero(n):
2025-08-14T23:09:16.7971824Z         ...     return n % 2 if n != 0 else None
2025-08-14T23:09:16.7971892Z         >>> dp1, dp2 = source_dp.demux(
2025-08-14T23:09:16.7972020Z         ...     num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True
2025-08-14T23:09:16.7972073Z         ... )
2025-08-14T23:09:16.7972132Z         >>> list(dp1)
2025-08-14T23:09:16.7972184Z         [2, 4]
2025-08-14T23:09:16.7972244Z         >>> list(dp2)
2025-08-14T23:09:16.7972294Z         [1, 3]
2025-08-14T23:09:16.7972350Z     
2025-08-14T23:09:16.7972498Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7972566Z     
2025-08-14T23:09:16.7972626Z       warnings.warn(msg)
2025-08-14T23:09:16.7972681Z     
2025-08-14T23:09:16.7972795Z --- Parse Warning: 69 / 146 ---
2025-08-14T23:09:16.7973433Z     /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.
2025-08-14T23:09:16.7973587Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7973642Z     
2025-08-14T23:09:16.7973820Z     Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``).
2025-08-14T23:09:16.7973875Z     
2025-08-14T23:09:16.7974072Z     As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration,
2025-08-14T23:09:16.7974189Z     and so on. It ends when the shortest input DataPipe is exhausted.
2025-08-14T23:09:16.7974244Z     
2025-08-14T23:09:16.7974297Z     Args:
2025-08-14T23:09:16.7974532Z         datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted
2025-08-14T23:09:16.7974583Z     
2025-08-14T23:09:16.7974640Z     Example:
2025-08-14T23:09:16.7974718Z         >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:16.7974859Z         >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:16.7974923Z         >>> dp1, dp2, dp3 = (
2025-08-14T23:09:16.7974998Z         ...     IterableWrapper(range(3)),
2025-08-14T23:09:16.7975073Z         ...     IterableWrapper(range(10, 15)),
2025-08-14T23:09:16.7975151Z         ...     IterableWrapper(range(20, 25)),
2025-08-14T23:09:16.7975203Z         ... )
2025-08-14T23:09:16.7975273Z         >>> list(dp1.mux(dp2, dp3))
2025-08-14T23:09:16.7975334Z         [0, 10, 20, 1, 11, 21, 2, 12, 22]
2025-08-14T23:09:16.7975386Z     
2025-08-14T23:09:16.7975536Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7977769Z     
2025-08-14T23:09:16.7977830Z       warnings.warn(msg)
2025-08-14T23:09:16.7977880Z     
2025-08-14T23:09:16.7977992Z --- Parse Warning: 70 / 146 ---
2025-08-14T23:09:16.7978560Z     /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.
2025-08-14T23:09:16.7978712Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7978763Z     
2025-08-14T23:09:16.7978939Z     Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``).
2025-08-14T23:09:16.7978990Z     
2025-08-14T23:09:16.7979122Z     The output is stopped as soon as the shortest input DataPipe is exhausted.
2025-08-14T23:09:16.7979172Z     
2025-08-14T23:09:16.7979224Z     Args:
2025-08-14T23:09:16.7979369Z         *datapipes: Iterable DataPipes being aggregated
2025-08-14T23:09:16.7979419Z     
2025-08-14T23:09:16.7979471Z     Example:
2025-08-14T23:09:16.7979549Z         >>> # xdoctest: +REQUIRES(module:torchdata)
2025-08-14T23:09:16.7979652Z         >>> from torchdata.datapipes.iter import IterableWrapper
2025-08-14T23:09:16.7979716Z         >>> dp1, dp2, dp3 = (
2025-08-14T23:09:16.7979786Z         ...     IterableWrapper(range(5)),
2025-08-14T23:09:16.7979860Z         ...     IterableWrapper(range(10, 15)),
2025-08-14T23:09:16.7979931Z         ...     IterableWrapper(range(20, 25)),
2025-08-14T23:09:16.7981971Z         ... )
2025-08-14T23:09:16.7982040Z         >>> list(dp1.zip(dp2, dp3))
2025-08-14T23:09:16.7982122Z         [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)]
2025-08-14T23:09:16.7982172Z     
2025-08-14T23:09:16.7982320Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.7982369Z     
2025-08-14T23:09:16.7982461Z       warnings.warn(msg)
2025-08-14T23:09:16.7982510Z     
2025-08-14T23:09:16.7982624Z --- Parse Warning: 71 / 146 ---
2025-08-14T23:09:16.7983183Z     /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.
2025-08-14T23:09:16.7983338Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.7983429Z     Asserts that ``actual`` and ``expected`` are close.
2025-08-14T23:09:16.7983479Z     
2025-08-14T23:09:16.7983687Z         If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2025-08-14T23:09:16.7983738Z     
2025-08-14T23:09:16.7983792Z         .. math::
2025-08-14T23:09:16.7983840Z     
2025-08-14T23:09:16.7984052Z             \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2025-08-14T23:09:16.7984102Z     
2025-08-14T23:09:16.7984301Z         Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2025-08-14T23:09:16.7984421Z         only considered equal to each other if ``equal_nan`` is ``True``.
2025-08-14T23:09:16.7984499Z     
2025-08-14T23:09:16.7986699Z         In addition, they are only considered close if they have the same
2025-08-14T23:09:16.7986753Z     
2025-08-14T23:09:16.7986909Z         - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2025-08-14T23:09:16.7986994Z         - ``dtype`` (if ``check_dtype`` is ``True``),
2025-08-14T23:09:16.7987082Z         - ``layout`` (if ``check_layout`` is ``True``), and
2025-08-14T23:09:16.7987159Z         - stride (if ``check_stride`` is ``True``).
2025-08-14T23:09:16.7987208Z     
2025-08-14T23:09:16.7987389Z         If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2025-08-14T23:09:16.7987440Z     
2025-08-14T23:09:16.7987646Z         If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2025-08-14T23:09:16.7987860Z         checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2025-08-14T23:09:16.7987996Z         or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2025-08-14T23:09:16.7988216Z         are always checked for equality whereas the values are checked for closeness according to the definition above.
2025-08-14T23:09:16.7988267Z     
2025-08-14T23:09:16.7988429Z         If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2025-08-14T23:09:16.7988629Z         :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2025-08-14T23:09:16.7988690Z         definition above.
2025-08-14T23:09:16.7988741Z     
2025-08-14T23:09:16.7988972Z         ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2025-08-14T23:09:16.7989187Z         :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2025-08-14T23:09:16.7989398Z         have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2025-08-14T23:09:16.7991707Z         or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2025-08-14T23:09:16.7991850Z         their elements are considered close according to the above definition.
2025-08-14T23:09:16.7991901Z     
2025-08-14T23:09:16.7991954Z         .. note::
2025-08-14T23:09:16.7992005Z     
2025-08-14T23:09:16.7992193Z             Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2025-08-14T23:09:16.7992407Z             :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2025-08-14T23:09:16.7992571Z             Python scalars of different types can be checked, but require ``check_dtype=False``.
2025-08-14T23:09:16.7992622Z     
2025-08-14T23:09:16.7992673Z         Args:
2025-08-14T23:09:16.7992741Z             actual (Any): Actual input.
2025-08-14T23:09:16.7992814Z             expected (Any): Expected input.
2025-08-14T23:09:16.7993109Z             allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2025-08-14T23:09:16.7993208Z                 are allowed. Otherwise type equality is required.
2025-08-14T23:09:16.7993415Z             rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2025-08-14T23:09:16.7993567Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-08-14T23:09:16.7993770Z             atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2025-08-14T23:09:16.7993923Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2025-08-14T23:09:16.7994068Z             equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2025-08-14T23:09:16.7994260Z             check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2025-08-14T23:09:16.7994407Z                 :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2025-08-14T23:09:16.7994543Z                 :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2025-08-14T23:09:16.7996867Z             check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2025-08-14T23:09:16.7997068Z                 check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2025-08-14T23:09:16.7997170Z                 :func:`torch.promote_types`) before being compared.
2025-08-14T23:09:16.7997372Z             check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2025-08-14T23:09:16.7997563Z                 check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2025-08-14T23:09:16.7997622Z                 compared.
2025-08-14T23:09:16.7997825Z             check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2025-08-14T23:09:16.7998025Z             msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2025-08-14T23:09:16.7998226Z                 the comparison. Can also passed as callable in which case it will be called with the generated message and
2025-08-14T23:09:16.7998298Z                 should return the new message.
2025-08-14T23:09:16.7998349Z     
2025-08-14T23:09:16.7998433Z         Raises:
2025-08-14T23:09:16.7998588Z             ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2025-08-14T23:09:16.7998688Z             ValueError: If only ``rtol`` or ``atol`` is specified.
2025-08-14T23:09:16.7998876Z             AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2025-08-14T23:09:16.7999084Z             AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2025-08-14T23:09:16.7999149Z                 different types.
2025-08-14T23:09:16.7999357Z             AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2025-08-14T23:09:16.7999566Z             AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2025-08-14T23:09:16.7999744Z             AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2025-08-14T23:09:16.7999940Z             AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2025-08-14T23:09:16.8002101Z                 :attr:`~torch.Tensor.layout`.
2025-08-14T23:09:16.8002291Z             AssertionError: If only one of corresponding tensors is quantized.
2025-08-14T23:09:16.8002510Z             AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2025-08-14T23:09:16.8002676Z             AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2025-08-14T23:09:16.8002749Z                 :attr:`~torch.Tensor.device`.
2025-08-14T23:09:16.8003001Z             AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2025-08-14T23:09:16.8003207Z             AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2025-08-14T23:09:16.8003413Z             AssertionError: If the values of corresponding tensors are not close according to the definition above.
2025-08-14T23:09:16.8003465Z     
2025-08-14T23:09:16.8003672Z         The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2025-08-14T23:09:16.8003766Z         ``dtype``'s, the maximum of both tolerances is used.
2025-08-14T23:09:16.8003855Z     
2025-08-14T23:09:16.8003938Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8004019Z         | ``dtype``                 | ``rtol``   | ``atol`` |
2025-08-14T23:09:16.8004085Z         +===========================+============+==========+
2025-08-14T23:09:16.8004167Z         | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2025-08-14T23:09:16.8004243Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8004326Z         | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2025-08-14T23:09:16.8004402Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8004486Z         | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8004562Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8006896Z         | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2025-08-14T23:09:16.8006980Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007064Z         | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2025-08-14T23:09:16.8007139Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007223Z         | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8007296Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007375Z         | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2025-08-14T23:09:16.8007450Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007529Z         | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8007638Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007736Z         | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8007812Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8007891Z         | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8007968Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8008044Z         | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8008117Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8008198Z         | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2025-08-14T23:09:16.8008276Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8008348Z         | other                     | ``0.0``    | ``0.0``  |
2025-08-14T23:09:16.8008423Z         +---------------------------+------------+----------+
2025-08-14T23:09:16.8008473Z     
2025-08-14T23:09:16.8010615Z         .. note::
2025-08-14T23:09:16.8010673Z     
2025-08-14T23:09:16.8010893Z             :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2025-08-14T23:09:16.8011094Z             to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2025-08-14T23:09:16.8011247Z             define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2025-08-14T23:09:16.8011296Z     
2025-08-14T23:09:16.8011361Z             >>> import functools
2025-08-14T23:09:16.8011513Z             >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2025-08-14T23:09:16.8011582Z             >>> assert_equal(1e-9, 1e-10)
2025-08-14T23:09:16.8011655Z             Traceback (most recent call last):
2025-08-14T23:09:16.8011707Z             ...
2025-08-14T23:09:16.8011784Z             AssertionError: Scalars are not equal!
2025-08-14T23:09:16.8011843Z             <BLANKLINE>
2025-08-14T23:09:16.8011917Z             Expected 1e-10 but got 1e-09.
2025-08-14T23:09:16.8011998Z             Absolute difference: 9.000000000000001e-10
2025-08-14T23:09:16.8012066Z             Relative difference: 9.0
2025-08-14T23:09:16.8012115Z     
2025-08-14T23:09:16.8012170Z         Examples:
2025-08-14T23:09:16.8012261Z             >>> # tensor to tensor comparison
2025-08-14T23:09:16.8012346Z             >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2025-08-14T23:09:16.8012470Z             >>> actual = torch.acos(torch.cos(expected))
2025-08-14T23:09:16.8012559Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8014729Z     
2025-08-14T23:09:16.8014811Z             >>> # scalar to scalar comparison
2025-08-14T23:09:16.8014872Z             >>> import math
2025-08-14T23:09:16.8014940Z             >>> expected = math.sqrt(2.0)
2025-08-14T23:09:16.8015007Z             >>> actual = 2.0 / math.sqrt(2.0)
2025-08-14T23:09:16.8015098Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8015148Z     
2025-08-14T23:09:16.8015229Z             >>> # numpy array to numpy array comparison
2025-08-14T23:09:16.8015294Z             >>> import numpy as np
2025-08-14T23:09:16.8015372Z             >>> expected = np.array([1e0, 1e-1, 1e-2])
2025-08-14T23:09:16.8015450Z             >>> actual = np.arccos(np.cos(expected))
2025-08-14T23:09:16.8015536Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8015586Z     
2025-08-14T23:09:16.8015659Z             >>> # sequence to sequence comparison
2025-08-14T23:09:16.8015728Z             >>> import numpy as np
2025-08-14T23:09:16.8015875Z             >>> # The types of the sequences do not have to match. They only have to have the same
2025-08-14T23:09:16.8015956Z             >>> # length and their elements have to match.
2025-08-14T23:09:16.8016051Z             >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2025-08-14T23:09:16.8016120Z             >>> actual = tuple(expected)
2025-08-14T23:09:16.8016262Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8016313Z     
2025-08-14T23:09:16.8016389Z             >>> # mapping to mapping comparison
2025-08-14T23:09:16.8018526Z             >>> from collections import OrderedDict
2025-08-14T23:09:16.8018596Z             >>> import numpy as np
2025-08-14T23:09:16.8018665Z             >>> foo = torch.tensor(1.0)
2025-08-14T23:09:16.8018721Z             >>> bar = 2.0
2025-08-14T23:09:16.8018785Z             >>> baz = np.array(3.0)
2025-08-14T23:09:16.8018932Z             >>> # The types and a possible ordering of mappings do not have to match. They only
2025-08-14T23:09:16.8019057Z             >>> # have to have the same set of keys and their elements have to match.
2025-08-14T23:09:16.8019174Z             >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2025-08-14T23:09:16.8019256Z             >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2025-08-14T23:09:16.8019372Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8019427Z     
2025-08-14T23:09:16.8019504Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-08-14T23:09:16.8019574Z             >>> actual = expected.clone()
2025-08-14T23:09:16.8019676Z             >>> # By default, directly related instances can be compared
2025-08-14T23:09:16.8019805Z             >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2025-08-14T23:09:16.8019919Z             >>> # This check can be made more strict with allow_subclasses=False
2025-08-14T23:09:16.8019987Z             >>> torch.testing.assert_close(
2025-08-14T23:09:16.8020106Z             ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2025-08-14T23:09:16.8020157Z             ... )
2025-08-14T23:09:16.8020231Z             Traceback (most recent call last):
2025-08-14T23:09:16.8020281Z             ...
2025-08-14T23:09:16.8022448Z             TypeError: No comparison pair was able to handle inputs of type
2025-08-14T23:09:16.8022586Z             <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2025-08-14T23:09:16.8022719Z             >>> # If the inputs are not directly related, they are never considered close
2025-08-14T23:09:16.8022821Z             >>> torch.testing.assert_close(actual.numpy(), expected)
2025-08-14T23:09:16.8022988Z             Traceback (most recent call last):
2025-08-14T23:09:16.8023039Z             ...
2025-08-14T23:09:16.8023204Z             TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2025-08-14T23:09:16.8023271Z             and <class 'torch.Tensor'>.
2025-08-14T23:09:16.8023425Z             >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2025-08-14T23:09:16.8023497Z             >>> # their type if check_dtype=False.
2025-08-14T23:09:16.8023599Z             >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2025-08-14T23:09:16.8023648Z     
2025-08-14T23:09:16.8023714Z             >>> # NaN != NaN by default.
2025-08-14T23:09:16.8023795Z             >>> expected = torch.tensor(float("Nan"))
2025-08-14T23:09:16.8023862Z             >>> actual = expected.clone()
2025-08-14T23:09:16.8023947Z             >>> torch.testing.assert_close(actual, expected)
2025-08-14T23:09:16.8024019Z             Traceback (most recent call last):
2025-08-14T23:09:16.8024071Z             ...
2025-08-14T23:09:16.8024149Z             AssertionError: Scalars are not close!
2025-08-14T23:09:16.8024205Z             <BLANKLINE>
2025-08-14T23:09:16.8024271Z             Expected nan but got nan.
2025-08-14T23:09:16.8024359Z             Absolute difference: nan (up to 1e-05 allowed)
2025-08-14T23:09:16.8026517Z             Relative difference: nan (up to 1.3e-06 allowed)
2025-08-14T23:09:16.8026642Z             >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2025-08-14T23:09:16.8026693Z     
2025-08-14T23:09:16.8026770Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2025-08-14T23:09:16.8026875Z             >>> actual = torch.tensor([1.0, 4.0, 5.0])
2025-08-14T23:09:16.8026984Z             >>> # The default error message can be overwritten.
2025-08-14T23:09:16.8027055Z             >>> torch.testing.assert_close(
2025-08-14T23:09:16.8027163Z             ...     actual, expected, msg="Argh, the tensors are not close!"
2025-08-14T23:09:16.8027216Z             ... )
2025-08-14T23:09:16.8027288Z             Traceback (most recent call last):
2025-08-14T23:09:16.8027339Z             ...
2025-08-14T23:09:16.8027430Z             AssertionError: Argh, the tensors are not close!
2025-08-14T23:09:16.8027557Z             >>> # If msg is a callable, it can be used to augment the generated message with
2025-08-14T23:09:16.8027619Z             >>> # extra information
2025-08-14T23:09:16.8027688Z             >>> torch.testing.assert_close(
2025-08-14T23:09:16.8027806Z             ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2025-08-14T23:09:16.8027857Z             ... )
2025-08-14T23:09:16.8027952Z             Traceback (most recent call last):
2025-08-14T23:09:16.8028005Z             ...
2025-08-14T23:09:16.8028071Z             AssertionError: Header
2025-08-14T23:09:16.8028126Z             <BLANKLINE>
2025-08-14T23:09:16.8030205Z             Tensor-likes are not close!
2025-08-14T23:09:16.8030269Z             <BLANKLINE>
2025-08-14T23:09:16.8030342Z             Mismatched elements: 2 / 3 (66.7%)
2025-08-14T23:09:16.8030475Z             Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2025-08-14T23:09:16.8030610Z             Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2025-08-14T23:09:16.8030662Z             <BLANKLINE>
2025-08-14T23:09:16.8030715Z             Footer
2025-08-14T23:09:16.8030767Z         
2025-08-14T23:09:16.8030912Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8030963Z     
2025-08-14T23:09:16.8031022Z       warnings.warn(msg)
2025-08-14T23:09:16.8031073Z     
2025-08-14T23:09:16.8031194Z --- Parse Warning: 72 / 146 ---
2025-08-14T23:09:16.8031785Z     /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.
2025-08-14T23:09:16.8031968Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8032018Z     
2025-08-14T23:09:16.8032145Z     Creates a RelaxedOneHotCategorical distribution parametrized by
2025-08-14T23:09:16.8032263Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2025-08-14T23:09:16.8032400Z     This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2025-08-14T23:09:16.8032500Z     its samples are on simplex, and are reparametrizable.
2025-08-14T23:09:16.8032550Z     
2025-08-14T23:09:16.8032606Z     Example::
2025-08-14T23:09:16.8034733Z     
2025-08-14T23:09:16.8034825Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.8034921Z         >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2025-08-14T23:09:16.8035003Z         ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2025-08-14T23:09:16.8035059Z         >>> m.sample()
2025-08-14T23:09:16.8035135Z         tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2025-08-14T23:09:16.8035181Z     
2025-08-14T23:09:16.8035233Z     Args:
2025-08-14T23:09:16.8035323Z         temperature (Tensor): relaxation temperature
2025-08-14T23:09:16.8035400Z         probs (Tensor): event probabilities
2025-08-14T23:09:16.8035517Z         logits (Tensor): unnormalized log probability for each event
2025-08-14T23:09:16.8035567Z     
2025-08-14T23:09:16.8035714Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8035763Z     
2025-08-14T23:09:16.8035825Z       warnings.warn(msg)
2025-08-14T23:09:16.8035874Z     
2025-08-14T23:09:16.8035990Z --- Parse Warning: 73 / 146 ---
2025-08-14T23:09:16.8036595Z     /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.
2025-08-14T23:09:16.8036749Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8036799Z     
2025-08-14T23:09:16.8036931Z     The `MixtureSameFamily` distribution implements a (batch of) mixture
2025-08-14T23:09:16.8037072Z     distribution where all component are from different parameterizations of
2025-08-14T23:09:16.8039204Z     the same distribution type. It is parameterized by a `Categorical`
2025-08-14T23:09:16.8039325Z     "selecting distribution" (over `k` component) and a component
2025-08-14T23:09:16.8039451Z     distribution, i.e., a `Distribution` with a rightmost batch shape
2025-08-14T23:09:16.8039586Z     (equal to `[k]`) which indexes each (batch of) component.
2025-08-14T23:09:16.8039639Z     
2025-08-14T23:09:16.8039696Z     Examples::
2025-08-14T23:09:16.8039747Z     
2025-08-14T23:09:16.8039821Z         >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8039945Z         >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2025-08-14T23:09:16.8040016Z         >>> # weighted normal distributions
2025-08-14T23:09:16.8040091Z         >>> mix = D.Categorical(torch.ones(5,))
2025-08-14T23:09:16.8040181Z         >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2025-08-14T23:09:16.8040259Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:16.8040310Z     
2025-08-14T23:09:16.8040430Z         >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2025-08-14T23:09:16.8040511Z         >>> # weighted bivariate normal distributions
2025-08-14T23:09:16.8040583Z         >>> mix = D.Categorical(torch.ones(5,))
2025-08-14T23:09:16.8040654Z         >>> comp = D.Independent(D.Normal(
2025-08-14T23:09:16.8040736Z         ...          torch.randn(5,2), torch.rand(5,2)), 1)
2025-08-14T23:09:16.8040810Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:16.8040860Z     
2025-08-14T23:09:16.8043101Z         >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2025-08-14T23:09:16.8043227Z         >>> # consisting of 5 random weighted bivariate normal distributions
2025-08-14T23:09:16.8043337Z         >>> mix = D.Categorical(torch.rand(3,5))
2025-08-14T23:09:16.8043407Z         >>> comp = D.Independent(D.Normal(
2025-08-14T23:09:16.8043490Z         ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2025-08-14T23:09:16.8043563Z         >>> gmm = MixtureSameFamily(mix, comp)
2025-08-14T23:09:16.8043613Z     
2025-08-14T23:09:16.8043664Z     Args:
2025-08-14T23:09:16.8043784Z         mixture_distribution: `torch.distributions.Categorical`-like
2025-08-14T23:09:16.8043935Z             instance. Manages the probability of selecting component.
2025-08-14T23:09:16.8044038Z             The number of categories must match the rightmost batch
2025-08-14T23:09:16.8044152Z             dimension of the `component_distribution`. Must have either
2025-08-14T23:09:16.8044239Z             scalar `batch_shape` or `batch_shape` matching
2025-08-14T23:09:16.8044321Z             `component_distribution.batch_shape[:-1]`
2025-08-14T23:09:16.8044451Z         component_distribution: `torch.distributions.Distribution`-like
2025-08-14T23:09:16.8044556Z             instance. Right-most batch dimension indexes component.
2025-08-14T23:09:16.8044606Z     
2025-08-14T23:09:16.8044753Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8044803Z     
2025-08-14T23:09:16.8044865Z       warnings.warn(msg)
2025-08-14T23:09:16.8044914Z     
2025-08-14T23:09:16.8045029Z --- Parse Warning: 74 / 146 ---
2025-08-14T23:09:16.8047780Z     /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.
2025-08-14T23:09:16.8047969Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8048019Z     
2025-08-14T23:09:16.8048126Z     Creates a RelaxedBernoulli distribution, parametrized by
2025-08-14T23:09:16.8048240Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2025-08-14T23:09:16.8048367Z     (but not both). This is a relaxed version of the `Bernoulli` distribution,
2025-08-14T23:09:16.8048477Z     so the values are in (0, 1), and has reparametrizable samples.
2025-08-14T23:09:16.8048528Z     
2025-08-14T23:09:16.8048583Z     Example::
2025-08-14T23:09:16.8048633Z     
2025-08-14T23:09:16.8048719Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.8048800Z         >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2025-08-14T23:09:16.8048898Z         ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2025-08-14T23:09:16.8048958Z         >>> m.sample()
2025-08-14T23:09:16.8049032Z         tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2025-08-14T23:09:16.8049083Z     
2025-08-14T23:09:16.8049134Z     Args:
2025-08-14T23:09:16.8049220Z         temperature (Tensor): relaxation temperature
2025-08-14T23:09:16.8049324Z         probs (Number, Tensor): the probability of sampling `1`
2025-08-14T23:09:16.8049423Z         logits (Number, Tensor): the log-odds of sampling `1`
2025-08-14T23:09:16.8049474Z     
2025-08-14T23:09:16.8051714Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8051767Z     
2025-08-14T23:09:16.8051832Z       warnings.warn(msg)
2025-08-14T23:09:16.8051882Z     
2025-08-14T23:09:16.8051997Z --- Parse Warning: 75 / 146 ---
2025-08-14T23:09:16.8052642Z     /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.
2025-08-14T23:09:16.8052796Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8052847Z     
2025-08-14T23:09:16.8053089Z     Creates a multivariate normal distribution with covariance matrix having a low-rank form
2025-08-14T23:09:16.8053236Z     parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
2025-08-14T23:09:16.8053286Z     
2025-08-14T23:09:16.8053389Z         covariance_matrix = cov_factor @ cov_factor.T + cov_diag
2025-08-14T23:09:16.8053438Z     
2025-08-14T23:09:16.8053492Z     Example:
2025-08-14T23:09:16.8053581Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-08-14T23:09:16.8053668Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.8053745Z         >>> m = LowRankMultivariateNormal(
2025-08-14T23:09:16.8053853Z         ...     torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2)
2025-08-14T23:09:16.8053907Z         ... )
2025-08-14T23:09:16.8054077Z         >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
2025-08-14T23:09:16.8054140Z         tensor([-0.2102, -0.5429])
2025-08-14T23:09:16.8054191Z     
2025-08-14T23:09:16.8056287Z     Args:
2025-08-14T23:09:16.8056431Z         loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
2025-08-14T23:09:16.8056581Z         cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
2025-08-14T23:09:16.8056656Z             `batch_shape + event_shape + (rank,)`
2025-08-14T23:09:16.8056809Z         cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
2025-08-14T23:09:16.8056878Z             `batch_shape + event_shape`
2025-08-14T23:09:16.8056927Z     
2025-08-14T23:09:16.8056976Z     Note:
2025-08-14T23:09:16.8057133Z         The computation for determinant and inverse of covariance matrix is avoided when
2025-08-14T23:09:16.8057322Z         `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
2025-08-14T23:09:16.8057447Z         <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
2025-08-14T23:09:16.8057615Z         `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
2025-08-14T23:09:16.8057759Z         Thanks to these formulas, we just need to compute the determinant and inverse of
2025-08-14T23:09:16.8057837Z         the small size "capacitance" matrix::
2025-08-14T23:09:16.8057889Z     
2025-08-14T23:09:16.8057996Z             capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
2025-08-14T23:09:16.8058047Z     
2025-08-14T23:09:16.8058193Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8058244Z     
2025-08-14T23:09:16.8058303Z       warnings.warn(msg)
2025-08-14T23:09:16.8058354Z     
2025-08-14T23:09:16.8058470Z --- Parse Warning: 76 / 146 ---
2025-08-14T23:09:16.8060958Z     /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.
2025-08-14T23:09:16.8061113Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8061166Z     
2025-08-14T23:09:16.8061292Z     Test if two objects are equal, and print an error message if test fails.
2025-08-14T23:09:16.8061343Z     
2025-08-14T23:09:16.8061429Z     The test is performed with ``actual == desired``.
2025-08-14T23:09:16.8061480Z     
2025-08-14T23:09:16.8061534Z     Parameters
2025-08-14T23:09:16.8061590Z     ----------
2025-08-14T23:09:16.8061648Z     test_string : str
2025-08-14T23:09:16.8061728Z         The message supplied to AssertionError.
2025-08-14T23:09:16.8061785Z     actual : object
2025-08-14T23:09:16.8061877Z         The object to test for equality against `desired`.
2025-08-14T23:09:16.8061939Z     desired : object
2025-08-14T23:09:16.8062003Z         The expected result.
2025-08-14T23:09:16.8062055Z     
2025-08-14T23:09:16.8062107Z     Examples
2025-08-14T23:09:16.8062159Z     --------
2025-08-14T23:09:16.8062231Z     >>> np.testing.print_assert_equal(
2025-08-14T23:09:16.8062305Z     ...     "Test XYZ of func xyz", [0, 1], [0, 1]
2025-08-14T23:09:16.8062383Z     ... )  # doctest: +SKIP
2025-08-14T23:09:16.8064423Z     >>> np.testing.print_assert_equal(
2025-08-14T23:09:16.8064497Z     ...     "Test XYZ of func xyz", [0, 1], [0, 2]
2025-08-14T23:09:16.8064557Z     ... )  # doctest: +SKIP
2025-08-14T23:09:16.8064625Z     Traceback (most recent call last):
2025-08-14T23:09:16.8064678Z     ...
2025-08-14T23:09:16.8064756Z     AssertionError: Test XYZ of func xyz failed
2025-08-14T23:09:16.8064810Z     ACTUAL:
2025-08-14T23:09:16.8064860Z     [0, 1]
2025-08-14T23:09:16.8064912Z     DESIRED:
2025-08-14T23:09:16.8064963Z     [0, 2]
2025-08-14T23:09:16.8065012Z     
2025-08-14T23:09:16.8065063Z     
2025-08-14T23:09:16.8065209Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8065260Z     
2025-08-14T23:09:16.8065319Z       warnings.warn(msg)
2025-08-14T23:09:16.8065369Z     
2025-08-14T23:09:16.8065483Z --- Parse Warning: 77 / 146 ---
2025-08-14T23:09:16.8065996Z     /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.
2025-08-14T23:09:16.8066150Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8066200Z     
2025-08-14T23:09:16.8066321Z     Raises an AssertionError if two items are not equal up to desired
2025-08-14T23:09:16.8066377Z     precision.
2025-08-14T23:09:16.8068338Z     
2025-08-14T23:09:16.8068443Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:16.8068553Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:16.8068729Z               instead of this function for more consistent floating point
2025-08-14T23:09:16.8068789Z               comparisons.
2025-08-14T23:09:16.8068839Z     
2025-08-14T23:09:16.8068966Z     The test verifies that the elements of `actual` and `desired` satisfy.
2025-08-14T23:09:16.8069019Z     
2025-08-14T23:09:16.8069114Z         ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2025-08-14T23:09:16.8069163Z     
2025-08-14T23:09:16.8069298Z     That is a looser test than originally documented, but agrees with what the
2025-08-14T23:09:16.8069434Z     actual implementation in `assert_array_almost_equal` did up to rounding
2025-08-14T23:09:16.8069571Z     vagaries. An exception is raised at conflicting values. For ndarrays this
2025-08-14T23:09:16.8069644Z     delegates to assert_array_almost_equal
2025-08-14T23:09:16.8069695Z     
2025-08-14T23:09:16.8069748Z     Parameters
2025-08-14T23:09:16.8069824Z     ----------
2025-08-14T23:09:16.8069883Z     actual : array_like
2025-08-14T23:09:16.8069947Z         The object to check.
2025-08-14T23:09:16.8070006Z     desired : array_like
2025-08-14T23:09:16.8070069Z         The expected object.
2025-08-14T23:09:16.8070130Z     decimal : int, optional
2025-08-14T23:09:16.8072109Z         Desired precision, default is 7.
2025-08-14T23:09:16.8072172Z     err_msg : str, optional
2025-08-14T23:09:16.8072268Z         The error message to be printed in case of failure.
2025-08-14T23:09:16.8072328Z     verbose : bool, optional
2025-08-14T23:09:16.8072449Z         If True, the conflicting values are appended to the error message.
2025-08-14T23:09:16.8072499Z     
2025-08-14T23:09:16.8072550Z     Raises
2025-08-14T23:09:16.8072604Z     ------
2025-08-14T23:09:16.8072659Z     AssertionError
2025-08-14T23:09:16.8072775Z       If actual and desired are not equal up to specified precision.
2025-08-14T23:09:16.8072824Z     
2025-08-14T23:09:16.8072876Z     See Also
2025-08-14T23:09:16.8072989Z     --------
2025-08-14T23:09:16.8073131Z     assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:16.8073210Z                      relative and/or absolute precision.
2025-08-14T23:09:16.8073335Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:16.8073420Z     
2025-08-14T23:09:16.8073474Z     Examples
2025-08-14T23:09:16.8073525Z     --------
2025-08-14T23:09:16.8073627Z     >>> from torch._numpy.testing import assert_almost_equal
2025-08-14T23:09:16.8073705Z     >>> assert_almost_equal(2.3333333333333, 2.33333334)
2025-08-14T23:09:16.8075727Z     >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2025-08-14T23:09:16.8075798Z     Traceback (most recent call last):
2025-08-14T23:09:16.8075851Z         ...
2025-08-14T23:09:16.8075908Z     AssertionError:
2025-08-14T23:09:16.8075980Z     Arrays are not almost equal to 10 decimals
2025-08-14T23:09:16.8076041Z      ACTUAL: 2.3333333333333
2025-08-14T23:09:16.8076102Z      DESIRED: 2.33333334
2025-08-14T23:09:16.8076153Z     
2025-08-14T23:09:16.8076214Z     >>> assert_almost_equal(
2025-08-14T23:09:16.8076335Z     ...     np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9
2025-08-14T23:09:16.8076385Z     ... )
2025-08-14T23:09:16.8076455Z     Traceback (most recent call last):
2025-08-14T23:09:16.8076507Z         ...
2025-08-14T23:09:16.8076565Z     AssertionError:
2025-08-14T23:09:16.8076640Z     Arrays are not almost equal to 9 decimals
2025-08-14T23:09:16.8076694Z     <BLANKLINE>
2025-08-14T23:09:16.8076760Z     Mismatched elements: 1 / 2 (50%)
2025-08-14T23:09:16.8076841Z     Max absolute difference: 6.666699636781459e-09
2025-08-14T23:09:16.8076921Z     Max relative difference: 2.8571569790287484e-09
2025-08-14T23:09:16.8077005Z      x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-08-14T23:09:16.8077088Z      y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2025-08-14T23:09:16.8077138Z     
2025-08-14T23:09:16.8079090Z     
2025-08-14T23:09:16.8079305Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8079357Z     
2025-08-14T23:09:16.8079416Z       warnings.warn(msg)
2025-08-14T23:09:16.8079467Z     
2025-08-14T23:09:16.8079581Z --- Parse Warning: 78 / 146 ---
2025-08-14T23:09:16.8080102Z     /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.
2025-08-14T23:09:16.8080255Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8080303Z     
2025-08-14T23:09:16.8080432Z     Raises an AssertionError if two items are not equal up to significant
2025-08-14T23:09:16.8080485Z     digits.
2025-08-14T23:09:16.8080534Z     
2025-08-14T23:09:16.8080637Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:16.8080768Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:16.8080884Z               instead of this function for more consistent floating point
2025-08-14T23:09:16.8080943Z               comparisons.
2025-08-14T23:09:16.8080994Z     
2025-08-14T23:09:16.8081100Z     Given two numbers, check that they are approximately equal.
2025-08-14T23:09:16.8081231Z     Approximately equal is defined as the number of significant digits
2025-08-14T23:09:16.8081284Z     that agree.
2025-08-14T23:09:16.8081335Z     
2025-08-14T23:09:16.8081389Z     Parameters
2025-08-14T23:09:16.8083419Z     ----------
2025-08-14T23:09:16.8083477Z     actual : scalar
2025-08-14T23:09:16.8083537Z         The object to check.
2025-08-14T23:09:16.8083594Z     desired : scalar
2025-08-14T23:09:16.8083656Z         The expected object.
2025-08-14T23:09:16.8083722Z     significant : int, optional
2025-08-14T23:09:16.8083797Z         Desired precision, default is 7.
2025-08-14T23:09:16.8083861Z     err_msg : str, optional
2025-08-14T23:09:16.8083960Z         The error message to be printed in case of failure.
2025-08-14T23:09:16.8084022Z     verbose : bool, optional
2025-08-14T23:09:16.8084144Z         If True, the conflicting values are appended to the error message.
2025-08-14T23:09:16.8084196Z     
2025-08-14T23:09:16.8084246Z     Raises
2025-08-14T23:09:16.8084333Z     ------
2025-08-14T23:09:16.8084388Z     AssertionError
2025-08-14T23:09:16.8084504Z       If actual and desired are not equal up to specified precision.
2025-08-14T23:09:16.8084554Z     
2025-08-14T23:09:16.8084605Z     See Also
2025-08-14T23:09:16.8084659Z     --------
2025-08-14T23:09:16.8084800Z     assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:16.8084884Z                      relative and/or absolute precision.
2025-08-14T23:09:16.8085011Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:16.8087077Z     
2025-08-14T23:09:16.8087130Z     Examples
2025-08-14T23:09:16.8087187Z     --------
2025-08-14T23:09:16.8087259Z     >>> np.testing.assert_approx_equal(
2025-08-14T23:09:16.8087328Z     ...     0.12345677777777e-20, 0.1234567e-20
2025-08-14T23:09:16.8087387Z     ... )  # doctest: +SKIP
2025-08-14T23:09:16.8087460Z     >>> np.testing.assert_approx_equal(
2025-08-14T23:09:16.8087517Z     ...     0.12345670e-20,
2025-08-14T23:09:16.8087590Z     ...     0.12345671e-20,  # doctest: +SKIP
2025-08-14T23:09:16.8087649Z     ...     significant=8,
2025-08-14T23:09:16.8087701Z     ... )
2025-08-14T23:09:16.8087769Z     >>> np.testing.assert_approx_equal(
2025-08-14T23:09:16.8087827Z     ...     0.12345670e-20,
2025-08-14T23:09:16.8087894Z     ...     0.12345672e-20,  # doctest: +SKIP
2025-08-14T23:09:16.8087952Z     ...     significant=8,
2025-08-14T23:09:16.8088004Z     ... )
2025-08-14T23:09:16.8088073Z     Traceback (most recent call last):
2025-08-14T23:09:16.8088126Z         ...
2025-08-14T23:09:16.8088182Z     AssertionError:
2025-08-14T23:09:16.8088264Z     Items are not equal to 8 significant digits:
2025-08-14T23:09:16.8088357Z      ACTUAL: 1.234567e-21
2025-08-14T23:09:16.8090350Z      DESIRED: 1.2345672e-21
2025-08-14T23:09:16.8090402Z     
2025-08-14T23:09:16.8090503Z     the evaluated condition that raises the exception is
2025-08-14T23:09:16.8090552Z     
2025-08-14T23:09:16.8090662Z     >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1)
2025-08-14T23:09:16.8090715Z     True
2025-08-14T23:09:16.8090766Z     
2025-08-14T23:09:16.8090815Z     
2025-08-14T23:09:16.8090964Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8091013Z     
2025-08-14T23:09:16.8091072Z       warnings.warn(msg)
2025-08-14T23:09:16.8091123Z     
2025-08-14T23:09:16.8091238Z --- Parse Warning: 79 / 146 ---
2025-08-14T23:09:16.8091773Z     /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.
2025-08-14T23:09:16.8091927Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8091978Z     
2025-08-14T23:09:16.8092098Z     Raises an AssertionError if two array_like objects are not equal.
2025-08-14T23:09:16.8092149Z     
2025-08-14T23:09:16.8092271Z     Given two array_like objects, check that the shape is equal and all
2025-08-14T23:09:16.8092396Z     elements of these objects are equal (but see the Notes for the special
2025-08-14T23:09:16.8092514Z     handling of a scalar). An exception is raised at shape mismatch or
2025-08-14T23:09:16.8092649Z     conflicting values. In contrast to the standard usage in numpy, NaNs
2025-08-14T23:09:16.8094784Z     are compared like numbers, no assertion is raised if both objects have
2025-08-14T23:09:16.8094854Z     NaNs in the same positions.
2025-08-14T23:09:16.8094904Z     
2025-08-14T23:09:16.8095039Z     The usual caution for verifying equality with floating point numbers is
2025-08-14T23:09:16.8095092Z     advised.
2025-08-14T23:09:16.8095145Z     
2025-08-14T23:09:16.8095199Z     Parameters
2025-08-14T23:09:16.8095253Z     ----------
2025-08-14T23:09:16.8095307Z     x : array_like
2025-08-14T23:09:16.8095374Z         The actual object to check.
2025-08-14T23:09:16.8095428Z     y : array_like
2025-08-14T23:09:16.8095529Z         The desired, expected object.
2025-08-14T23:09:16.8095591Z     err_msg : str, optional
2025-08-14T23:09:16.8095687Z         The error message to be printed in case of failure.
2025-08-14T23:09:16.8095750Z     verbose : bool, optional
2025-08-14T23:09:16.8095868Z         If True, the conflicting values are appended to the error message.
2025-08-14T23:09:16.8095929Z     strict : bool, optional
2025-08-14T23:09:16.8096044Z         If True, raise an AssertionError when either the shape or the data
2025-08-14T23:09:16.8096148Z         type of the array_like objects does not match. The special
2025-08-14T23:09:16.8096268Z         handling for scalars mentioned in the Notes section is disabled.
2025-08-14T23:09:16.8096322Z     
2025-08-14T23:09:16.8096373Z     Raises
2025-08-14T23:09:16.8098340Z     ------
2025-08-14T23:09:16.8098397Z     AssertionError
2025-08-14T23:09:16.8098486Z         If actual and desired objects are not equal.
2025-08-14T23:09:16.8098536Z     
2025-08-14T23:09:16.8098591Z     See Also
2025-08-14T23:09:16.8098642Z     --------
2025-08-14T23:09:16.8098785Z     assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:16.8098863Z                      relative and/or absolute precision.
2025-08-14T23:09:16.8098987Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:16.8099036Z     
2025-08-14T23:09:16.8099087Z     Notes
2025-08-14T23:09:16.8099139Z     -----
2025-08-14T23:09:16.8099251Z     When one of `x` and `y` is a scalar and the other is array_like, the
2025-08-14T23:09:16.8099386Z     function checks that each element of the array_like object is equal to
2025-08-14T23:09:16.8099566Z     the scalar. This behaviour can be disabled with the `strict` parameter.
2025-08-14T23:09:16.8099617Z     
2025-08-14T23:09:16.8099669Z     Examples
2025-08-14T23:09:16.8099722Z     --------
2025-08-14T23:09:16.8099804Z     The first assert does not raise an exception:
2025-08-14T23:09:16.8099855Z     
2025-08-14T23:09:16.8099922Z     >>> np.testing.assert_array_equal(
2025-08-14T23:09:16.8102459Z     ...     [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan]
2025-08-14T23:09:16.8102513Z     ... )
2025-08-14T23:09:16.8102568Z     
2025-08-14T23:09:16.8102701Z     Use `assert_allclose` or one of the nulp (number of floating point values)
2025-08-14T23:09:16.8102771Z     functions for these cases instead:
2025-08-14T23:09:16.8102821Z     
2025-08-14T23:09:16.8102894Z     >>> np.testing.assert_allclose(
2025-08-14T23:09:16.8103091Z     ...     [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0
2025-08-14T23:09:16.8103142Z     ... )
2025-08-14T23:09:16.8103235Z     
2025-08-14T23:09:16.8103359Z     As mentioned in the Notes section, `assert_array_equal` has special
2025-08-14T23:09:16.8103512Z     handling for scalars. Here the test checks that each value in `x` is 3:
2025-08-14T23:09:16.8103568Z     
2025-08-14T23:09:16.8103636Z     >>> x = np.full((2, 5), fill_value=3)
2025-08-14T23:09:16.8103711Z     >>> np.testing.assert_array_equal(x, 3)
2025-08-14T23:09:16.8103762Z     
2025-08-14T23:09:16.8103885Z     Use `strict` to raise an AssertionError when comparing a scalar with an
2025-08-14T23:09:16.8103937Z     array:
2025-08-14T23:09:16.8103987Z     
2025-08-14T23:09:16.8104078Z     >>> np.testing.assert_array_equal(x, 3, strict=True)
2025-08-14T23:09:16.8104151Z     Traceback (most recent call last):
2025-08-14T23:09:16.8106983Z         ...
2025-08-14T23:09:16.8107049Z     AssertionError:
2025-08-14T23:09:16.8107113Z     Arrays are not equal
2025-08-14T23:09:16.8107167Z     <BLANKLINE>
2025-08-14T23:09:16.8107232Z     (shapes (2, 5), () mismatch)
2025-08-14T23:09:16.8107305Z      x: torch.ndarray([[3, 3, 3, 3, 3],
2025-08-14T23:09:16.8107364Z             [3, 3, 3, 3, 3]])
2025-08-14T23:09:16.8107426Z      y: torch.ndarray(3)
2025-08-14T23:09:16.8107475Z     
2025-08-14T23:09:16.8107605Z     The `strict` parameter also ensures that the array data types match:
2025-08-14T23:09:16.8107688Z     
2025-08-14T23:09:16.8107755Z     >>> x = np.array([2, 2, 2])
2025-08-14T23:09:16.8107837Z     >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32)
2025-08-14T23:09:16.8107929Z     >>> np.testing.assert_array_equal(x, y, strict=True)
2025-08-14T23:09:16.8107998Z     Traceback (most recent call last):
2025-08-14T23:09:16.8108075Z         ...
2025-08-14T23:09:16.8108134Z     AssertionError:
2025-08-14T23:09:16.8108194Z     Arrays are not equal
2025-08-14T23:09:16.8108247Z     <BLANKLINE>
2025-08-14T23:09:16.8108346Z     (dtypes dtype("int64"), dtype("float32") mismatch)
2025-08-14T23:09:16.8108414Z      x: torch.ndarray([2, 2, 2])
2025-08-14T23:09:16.8111217Z      y: torch.ndarray([2., 2., 2.])
2025-08-14T23:09:16.8111277Z     
2025-08-14T23:09:16.8111432Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8111522Z     
2025-08-14T23:09:16.8111582Z       warnings.warn(msg)
2025-08-14T23:09:16.8111632Z     
2025-08-14T23:09:16.8111756Z --- Parse Warning: 80 / 146 ---
2025-08-14T23:09:16.8112296Z     /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.
2025-08-14T23:09:16.8112451Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8112502Z     
2025-08-14T23:09:16.8112628Z     Raises an AssertionError if two objects are not equal up to desired
2025-08-14T23:09:16.8112685Z     precision.
2025-08-14T23:09:16.8112734Z     
2025-08-14T23:09:16.8112841Z     .. note:: It is recommended to use one of `assert_allclose`,
2025-08-14T23:09:16.8113105Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2025-08-14T23:09:16.8113225Z               instead of this function for more consistent floating point
2025-08-14T23:09:16.8113285Z               comparisons.
2025-08-14T23:09:16.8113336Z     
2025-08-14T23:09:16.8113476Z     The test verifies identical shapes and that the elements of ``actual`` and
2025-08-14T23:09:16.8113536Z     ``desired`` satisfy.
2025-08-14T23:09:16.8113585Z     
2025-08-14T23:09:16.8113667Z         ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2025-08-14T23:09:16.8116313Z     
2025-08-14T23:09:16.8116487Z     That is a looser test than originally documented, but agrees with what the
2025-08-14T23:09:16.8116630Z     actual implementation did up to rounding vagaries. An exception is raised
2025-08-14T23:09:16.8116766Z     at shape mismatch or conflicting values. In contrast to the standard usage
2025-08-14T23:09:16.8116923Z     in numpy, NaNs are compared like numbers, no assertion is raised if both
2025-08-14T23:09:16.8117005Z     objects have NaNs in the same positions.
2025-08-14T23:09:16.8117055Z     
2025-08-14T23:09:16.8117109Z     Parameters
2025-08-14T23:09:16.8117165Z     ----------
2025-08-14T23:09:16.8117219Z     x : array_like
2025-08-14T23:09:16.8117289Z         The actual object to check.
2025-08-14T23:09:16.8117344Z     y : array_like
2025-08-14T23:09:16.8117414Z         The desired, expected object.
2025-08-14T23:09:16.8117476Z     decimal : int, optional
2025-08-14T23:09:16.8117550Z         Desired precision, default is 6.
2025-08-14T23:09:16.8117608Z     err_msg : str, optional
2025-08-14T23:09:16.8117704Z       The error message to be printed in case of failure.
2025-08-14T23:09:16.8117766Z     verbose : bool, optional
2025-08-14T23:09:16.8117892Z         If True, the conflicting values are appended to the error message.
2025-08-14T23:09:16.8117942Z     
2025-08-14T23:09:16.8117994Z     Raises
2025-08-14T23:09:16.8120350Z     ------
2025-08-14T23:09:16.8120414Z     AssertionError
2025-08-14T23:09:16.8120539Z         If actual and desired are not equal up to specified precision.
2025-08-14T23:09:16.8120590Z     
2025-08-14T23:09:16.8120641Z     See Also
2025-08-14T23:09:16.8120694Z     --------
2025-08-14T23:09:16.8120835Z     assert_allclose: Compare two array_like objects for equality with desired
2025-08-14T23:09:16.8120946Z                      relative and/or absolute precision.
2025-08-14T23:09:16.8121071Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2025-08-14T23:09:16.8121120Z     
2025-08-14T23:09:16.8121171Z     Examples
2025-08-14T23:09:16.8121223Z     --------
2025-08-14T23:09:16.8121305Z     the first assert does not raise an exception
2025-08-14T23:09:16.8121354Z     
2025-08-14T23:09:16.8121503Z     >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan])
2025-08-14T23:09:16.8121553Z     
2025-08-14T23:09:16.8121630Z     >>> np.testing.assert_array_almost_equal(
2025-08-14T23:09:16.8121725Z     ...     [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5
2025-08-14T23:09:16.8121778Z     ... )
2025-08-14T23:09:16.8121847Z     Traceback (most recent call last):
2025-08-14T23:09:16.8121898Z         ...
2025-08-14T23:09:16.8121955Z     AssertionError:
2025-08-14T23:09:16.8124250Z     Arrays are not almost equal to 5 decimals
2025-08-14T23:09:16.8124310Z     <BLANKLINE>
2025-08-14T23:09:16.8124377Z     Mismatched elements: 1 / 3 (33.3%)
2025-08-14T23:09:16.8124466Z     Max absolute difference: 5.999999999994898e-05
2025-08-14T23:09:16.8124545Z     Max relative difference: 2.5713661239633743e-05
2025-08-14T23:09:16.8124688Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-08-14T23:09:16.8124786Z      y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2025-08-14T23:09:16.8124876Z     
2025-08-14T23:09:16.8124951Z     >>> np.testing.assert_array_almost_equal(
2025-08-14T23:09:16.8125034Z     ...     [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5
2025-08-14T23:09:16.8125138Z     ... )
2025-08-14T23:09:16.8125228Z     Traceback (most recent call last):
2025-08-14T23:09:16.8125279Z         ...
2025-08-14T23:09:16.8125337Z     AssertionError:
2025-08-14T23:09:16.8125411Z     Arrays are not almost equal to 5 decimals
2025-08-14T23:09:16.8125468Z     <BLANKLINE>
2025-08-14T23:09:16.8125536Z     x and y nan location mismatch:
2025-08-14T23:09:16.8125630Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2025-08-14T23:09:16.8125870Z      y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2025-08-14T23:09:16.8125921Z     
2025-08-14T23:09:16.8125972Z     
2025-08-14T23:09:16.8126120Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8128328Z     
2025-08-14T23:09:16.8128395Z       warnings.warn(msg)
2025-08-14T23:09:16.8128449Z     
2025-08-14T23:09:16.8128575Z --- Parse Warning: 81 / 146 ---
2025-08-14T23:09:16.8129140Z     /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.
2025-08-14T23:09:16.8129296Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8129423Z     Context manager that resets warning registry for catching warnings
2025-08-14T23:09:16.8129475Z     
2025-08-14T23:09:16.8129618Z         Warnings can be slippery, because, whenever a warning is triggered, Python
2025-08-14T23:09:16.8129746Z         adds a ``__warningregistry__`` member to the *calling* module.  This makes
2025-08-14T23:09:16.8129884Z         it impossible to retrigger the warning in this module, whatever you put in
2025-08-14T23:09:16.8130026Z         the warnings filters.  This context manager accepts a sequence of `modules`
2025-08-14T23:09:16.8130113Z         as a keyword argument to its constructor and:
2025-08-14T23:09:16.8130163Z     
2025-08-14T23:09:16.8130306Z         * stores and removes any ``__warningregistry__`` entries in given `modules`
2025-08-14T23:09:16.8130359Z           on entry;
2025-08-14T23:09:16.8130472Z         * resets ``__warningregistry__`` to its previous state on exit.
2025-08-14T23:09:16.8130522Z     
2025-08-14T23:09:16.8130653Z         This makes it possible to trigger any warning afresh inside the context
2025-08-14T23:09:16.8130790Z         manager without disturbing the state of warnings outside.
2025-08-14T23:09:16.8130841Z     
2025-08-14T23:09:16.8133189Z         For compatibility with Python 3.0, please consider all arguments to be
2025-08-14T23:09:16.8133259Z         keyword-only.
2025-08-14T23:09:16.8133309Z     
2025-08-14T23:09:16.8133365Z         Parameters
2025-08-14T23:09:16.8133421Z         ----------
2025-08-14T23:09:16.8133485Z         record : bool, optional
2025-08-14T23:09:16.8133602Z             Specifies whether warnings should be captured by a custom
2025-08-14T23:09:16.8133740Z             implementation of ``warnings.showwarning()`` and be appended to a list
2025-08-14T23:09:16.8133866Z             returned by the context manager. Otherwise None is returned by the
2025-08-14T23:09:16.8133998Z             context manager. The objects appended to the list are arguments whose
2025-08-14T23:09:16.8134102Z             attributes mirror the arguments to ``showwarning()``.
2025-08-14T23:09:16.8134172Z         modules : sequence, optional
2025-08-14T23:09:16.8134305Z             Sequence of modules for which to reset warnings registry on entry and
2025-08-14T23:09:16.8134420Z             restore on exit. To work correctly, all 'ignore' filters should
2025-08-14T23:09:16.8134492Z             filter by one of these modules.
2025-08-14T23:09:16.8134542Z     
2025-08-14T23:09:16.8134596Z         Examples
2025-08-14T23:09:16.8134650Z         --------
2025-08-14T23:09:16.8134710Z         >>> import warnings
2025-08-14T23:09:16.8134820Z         >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2025-08-14T23:09:16.8134929Z         ...     modules=[np.core.fromnumeric]
2025-08-14T23:09:16.8134999Z         ... ):
2025-08-14T23:09:16.8137226Z         ...     warnings.simplefilter("always")
2025-08-14T23:09:16.8137366Z         ...     warnings.filterwarnings("ignore", module="np.core.fromnumeric")
2025-08-14T23:09:16.8137470Z         ...     # do something that raises a warning but ignore those in
2025-08-14T23:09:16.8137538Z         ...     # np.core.fromnumeric
2025-08-14T23:09:16.8137591Z         
2025-08-14T23:09:16.8137742Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8137792Z     
2025-08-14T23:09:16.8137854Z       warnings.warn(msg)
2025-08-14T23:09:16.8137904Z     
2025-08-14T23:09:16.8138022Z --- Parse Warning: 82 / 146 ---
2025-08-14T23:09:16.8138585Z     /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=722.
2025-08-14T23:09:16.8138733Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2025-08-14T23:09:16.8138784Z     
2025-08-14T23:09:16.8138943Z     Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2025-08-14T23:09:16.8139099Z     The submesh created consists of the dimensions and the communicators indicated by
2025-08-14T23:09:16.8139160Z     ``mesh_dim_names``
2025-08-14T23:09:16.8139210Z     
2025-08-14T23:09:16.8139262Z     Args:
2025-08-14T23:09:16.8139400Z         mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2025-08-14T23:09:16.8139512Z         mesh dimension of the DeviceMesh to create the submesh for.
2025-08-14T23:09:16.8139565Z     Returns:
2025-08-14T23:09:16.8141763Z         A :class:`DeviceMesh` object
2025-08-14T23:09:16.8141815Z     
2025-08-14T23:09:16.8141989Z     The following program runs on each process/rank in an SPMD manner in a world size of 8.
2025-08-14T23:09:16.8142054Z     In the first example:
2025-08-14T23:09:16.8142205Z         Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2025-08-14T23:09:16.8142347Z         Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2025-08-14T23:09:16.8142512Z         Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2025-08-14T23:09:16.8142639Z         Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2025-08-14T23:09:16.8142767Z         Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2025-08-14T23:09:16.8142892Z         Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2025-08-14T23:09:16.8143013Z     
2025-08-14T23:09:16.8143075Z     In the second example:
2025-08-14T23:09:16.8143234Z         Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2025-08-14T23:09:16.8143393Z         Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2025-08-14T23:09:16.8143543Z         Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2025-08-14T23:09:16.8143688Z         Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2025-08-14T23:09:16.8143741Z     
2025-08-14T23:09:16.8143797Z     Example::
2025-08-14T23:09:16.8143847Z     
2025-08-14T23:09:16.8143917Z         >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:16.8144022Z         >>> from torch.distributed.device_mesh import DeviceMesh
2025-08-14T23:09:16.8144075Z         >>>
2025-08-14T23:09:16.8146336Z         >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2025-08-14T23:09:16.8146428Z         >>> # of cross-host(dim 0), and within-host (dim 1).
2025-08-14T23:09:16.8146587Z         >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-08-14T23:09:16.8146690Z         >>> tp_mesh = mesh_2d["tp"]
2025-08-14T23:09:16.8146772Z         >>> dp_mesh = mesh_2d["dp"]
2025-08-14T23:09:16.8146824Z         >>>
2025-08-14T23:09:16.8146885Z         >>> # Initialize a 3D mesh.
2025-08-14T23:09:16.8147047Z         >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2025-08-14T23:09:16.8147224Z         >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2025-08-14T23:09:16.8147297Z         >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2025-08-14T23:09:16.8147365Z         >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2025-08-14T23:09:16.8147416Z     
2025-08-14T23:09:16.8147801Z     Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2025-08-14T23:09:16.8147853Z     
2025-08-14T23:09:16.8148020Z     mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2025-08-14T23:09:16.8148097Z                                                                                      ^
2025-08-14T23:09:16.8148158Z       warnings.warn(msg)
2025-08-14T23:09:16.8148208Z     
2025-08-14T23:09:16.8148325Z --- Parse Warning: 83 / 146 ---
2025-08-14T23:09:16.8148867Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2706.
2025-08-14T23:09:16.8149021Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8151129Z     
2025-08-14T23:09:16.8151276Z     Send or Receive a batch of tensors asynchronously and return a list of requests.
2025-08-14T23:09:16.8151330Z     
2025-08-14T23:09:16.8151475Z     Process each of the operations in ``p2p_op_list`` and return the corresponding
2025-08-14T23:09:16.8151598Z     requests. NCCL, Gloo, and UCC backend are currently supported.
2025-08-14T23:09:16.8151648Z     
2025-08-14T23:09:16.8151700Z     Args:
2025-08-14T23:09:16.8151832Z         p2p_op_list: A list of point-to-point operations(type of each operator is
2025-08-14T23:09:16.8151966Z             ``torch.distributed.P2POp``). The order of the isend/irecv in the list
2025-08-14T23:09:16.8152119Z             matters and it needs to match with corresponding isend/irecv on the
2025-08-14T23:09:16.8152178Z             remote end.
2025-08-14T23:09:16.8152227Z     
2025-08-14T23:09:16.8152279Z     Returns:
2025-08-14T23:09:16.8152423Z         A list of distributed request objects returned by calling the corresponding
2025-08-14T23:09:16.8152484Z         op in the op_list.
2025-08-14T23:09:16.8152535Z     
2025-08-14T23:09:16.8152588Z     Examples:
2025-08-14T23:09:16.8152658Z         >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:16.8152773Z         >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
2025-08-14T23:09:16.8152869Z         >>> recv_tensor = torch.randn(2, dtype=torch.float32)
2025-08-14T23:09:16.8153064Z         >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
2025-08-14T23:09:16.8153132Z         >>> recv_op = dist.P2POp(
2025-08-14T23:09:16.8155268Z         ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
2025-08-14T23:09:16.8155324Z         ... )
2025-08-14T23:09:16.8155410Z         >>> reqs = batch_isend_irecv([send_op, recv_op])
2025-08-14T23:09:16.8155472Z         >>> for req in reqs:
2025-08-14T23:09:16.8155529Z         >>>     req.wait()
2025-08-14T23:09:16.8155587Z         >>> recv_tensor
2025-08-14T23:09:16.8155648Z         tensor([2, 3])     # Rank 0
2025-08-14T23:09:16.8155708Z         tensor([0, 1])     # Rank 1
2025-08-14T23:09:16.8155758Z     
2025-08-14T23:09:16.8155901Z     .. note:: Note that when this API is used with the NCCL PG backend, users must set
2025-08-14T23:09:16.8156038Z         the current GPU device with `torch.cuda.set_device`, otherwise it will
2025-08-14T23:09:16.8156213Z         lead to unexpected hang issues.
2025-08-14T23:09:16.8156265Z     
2025-08-14T23:09:16.8156386Z         In addition, if this API is the first collective call in the ``group``
2025-08-14T23:09:16.8156518Z         passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
2025-08-14T23:09:16.8156649Z         this API call; otherwise, the behavior is undefined. If this API call is
2025-08-14T23:09:16.8156779Z         not the first collective call in the ``group``, batched P2P operations
2025-08-14T23:09:16.8156896Z         involving only a subset of ranks of the ``group`` are allowed.
2025-08-14T23:09:16.8156947Z     
2025-08-14T23:09:16.8157092Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8157143Z     
2025-08-14T23:09:16.8157203Z       warnings.warn(msg)
2025-08-14T23:09:16.8159381Z     
2025-08-14T23:09:16.8159537Z --- Parse Warning: 84 / 146 ---
2025-08-14T23:09:16.8160064Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2838.
2025-08-14T23:09:16.8160215Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8160268Z     
2025-08-14T23:09:16.8160423Z     Reduces the tensor data across all machines in a way that all get the final result.
2025-08-14T23:09:16.8160475Z     
2025-08-14T23:09:16.8160605Z     After the call ``tensor`` is going to be bitwise identical in all processes.
2025-08-14T23:09:16.8160656Z     
2025-08-14T23:09:16.8160726Z     Complex tensors are supported.
2025-08-14T23:09:16.8160777Z     
2025-08-14T23:09:16.8160828Z     Args:
2025-08-14T23:09:16.8160948Z         tensor (Tensor): Input and output of the collective. The function
2025-08-14T23:09:16.8161014Z             operates in-place.
2025-08-14T23:09:16.8161090Z         op (optional): One of the values from
2025-08-14T23:09:16.8161170Z             ``torch.distributed.ReduceOp``
2025-08-14T23:09:16.8161288Z             enum.  Specifies an operation used for element-wise reductions.
2025-08-14T23:09:16.8161424Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8161537Z             the default process group will be used.
2025-08-14T23:09:16.8161654Z         async_op (bool, optional): Whether this op should be an async op
2025-08-14T23:09:16.8161704Z     
2025-08-14T23:09:16.8163929Z     Returns:
2025-08-14T23:09:16.8164024Z         Async work handle, if async_op is set to True.
2025-08-14T23:09:16.8164120Z         None, if not async_op or if not part of the group
2025-08-14T23:09:16.8164170Z     
2025-08-14T23:09:16.8164224Z     Examples:
2025-08-14T23:09:16.8164291Z         >>> # xdoctest: +SKIP("no rank")
2025-08-14T23:09:16.8164378Z         >>> # All tensors below are of torch.int64 type.
2025-08-14T23:09:16.8164453Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8164529Z         >>> device = torch.device(f"cuda:{rank}")
2025-08-14T23:09:16.8164667Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-08-14T23:09:16.8164721Z         >>> tensor
2025-08-14T23:09:16.8164794Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8164865Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8164947Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-08-14T23:09:16.8165001Z         >>> tensor
2025-08-14T23:09:16.8165069Z         tensor([4, 6], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8165137Z         tensor([4, 6], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8165189Z     
2025-08-14T23:09:16.8165273Z         >>> # All tensors below are of torch.cfloat type.
2025-08-14T23:09:16.8165345Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8165411Z         >>> tensor = torch.tensor(
2025-08-14T23:09:16.8165556Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-08-14T23:09:16.8167827Z         ... ) + 2 * rank * (1 + 1j)
2025-08-14T23:09:16.8167885Z         >>> tensor
2025-08-14T23:09:16.8167977Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8168062Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8168146Z         >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
2025-08-14T23:09:16.8168201Z         >>> tensor
2025-08-14T23:09:16.8168282Z         tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8168363Z         tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8168413Z     
2025-08-14T23:09:16.8168463Z     
2025-08-14T23:09:16.8168612Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8168663Z     
2025-08-14T23:09:16.8168723Z       warnings.warn(msg)
2025-08-14T23:09:16.8168774Z     
2025-08-14T23:09:16.8168924Z --- Parse Warning: 85 / 146 ---
2025-08-14T23:09:16.8169460Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3198.
2025-08-14T23:09:16.8169614Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8169666Z     
2025-08-14T23:09:16.8169799Z     Gathers picklable objects from the whole group in a single process.
2025-08-14T23:09:16.8169849Z     
2025-08-14T23:09:16.8169986Z     Similar to :func:`gather`, but Python objects can be passed in. Note that the
2025-08-14T23:09:16.8172196Z     object must be picklable in order to be gathered.
2025-08-14T23:09:16.8172247Z     
2025-08-14T23:09:16.8172305Z     Args:
2025-08-14T23:09:16.8172387Z         obj (Any): Input object. Must be picklable.
2025-08-14T23:09:16.8172514Z         object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2025-08-14T23:09:16.8172630Z             should be correctly sized as the size of the group for this
2025-08-14T23:09:16.8172756Z             collective and will contain the output. Must be ``None`` on non-dst
2025-08-14T23:09:16.8172825Z             ranks. (default is ``None``)
2025-08-14T23:09:16.8173075Z         dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
2025-08-14T23:09:16.8173224Z             (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
2025-08-14T23:09:16.8173359Z         group: (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8173472Z             the default process group will be used. Default is ``None``.
2025-08-14T23:09:16.8173670Z         group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
2025-08-14T23:09:16.8173722Z     
2025-08-14T23:09:16.8173774Z     Returns:
2025-08-14T23:09:16.8173884Z         None. On the ``dst`` rank, ``object_gather_list`` will contain the
2025-08-14T23:09:16.8173953Z         output of the collective.
2025-08-14T23:09:16.8174004Z     
2025-08-14T23:09:16.8174129Z     .. note:: Note that this API differs slightly from the gather collective
2025-08-14T23:09:16.8174257Z         since it does not provide an async_op handle and thus will be a blocking
2025-08-14T23:09:16.8174310Z         call.
2025-08-14T23:09:16.8174362Z     
2025-08-14T23:09:16.8176679Z     .. note:: For NCCL-based processed groups, internal tensor representations
2025-08-14T23:09:16.8176812Z         of objects must be moved to the GPU device before communication takes
2025-08-14T23:09:16.8176900Z         place. In this case, the device used is given by
2025-08-14T23:09:16.8177030Z         ``torch.cuda.current_device()`` and it is the user's responsibility to
2025-08-14T23:09:16.8177146Z         ensure that this is set so that each rank has an individual GPU, via
2025-08-14T23:09:16.8177216Z         ``torch.cuda.set_device()``.
2025-08-14T23:09:16.8177301Z     
2025-08-14T23:09:16.8177377Z     .. warning::
2025-08-14T23:09:16.8177514Z         Object collectives have a number of serious performance and scalability
2025-08-14T23:09:16.8177620Z         limitations.  See :ref:`object_collectives` for details.
2025-08-14T23:09:16.8177672Z     
2025-08-14T23:09:16.8177728Z     .. warning::
2025-08-14T23:09:16.8177842Z         :func:`gather_object` uses ``pickle`` module implicitly, which is
2025-08-14T23:09:16.8177967Z         known to be insecure. It is possible to construct malicious pickle data
2025-08-14T23:09:16.8178095Z         which will execute arbitrary code during unpickling. Only call this
2025-08-14T23:09:16.8178162Z         function with data you trust.
2025-08-14T23:09:16.8178214Z     
2025-08-14T23:09:16.8178267Z     .. warning::
2025-08-14T23:09:16.8178392Z         Calling :func:`gather_object` with GPU tensors is not well supported
2025-08-14T23:09:16.8178540Z         and inefficient as it incurs GPU -> CPU transfer since tensors would be
2025-08-14T23:09:16.8178648Z         pickled. Please consider using :func:`gather` instead.
2025-08-14T23:09:16.8178698Z     
2025-08-14T23:09:16.8180778Z     Example::
2025-08-14T23:09:16.8180873Z         >>> # xdoctest: +SKIP("need process group init")
2025-08-14T23:09:16.8180982Z         >>> # Note: Process group initialization omitted on each rank.
2025-08-14T23:09:16.8181058Z         >>> import torch.distributed as dist
2025-08-14T23:09:16.8181126Z         >>> # Assumes world_size of 3.
2025-08-14T23:09:16.8181232Z         >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2025-08-14T23:09:16.8181308Z         >>> output = [None for _ in gather_objects]
2025-08-14T23:09:16.8181369Z         >>> dist.gather_object(
2025-08-14T23:09:16.8181444Z         ...     gather_objects[dist.get_rank()],
2025-08-14T23:09:16.8181525Z         ...     output if dist.get_rank() == 0 else None,
2025-08-14T23:09:16.8181582Z         ...     dst=0
2025-08-14T23:09:16.8181634Z         ... )
2025-08-14T23:09:16.8181691Z         >>> # On rank 0
2025-08-14T23:09:16.8181747Z         >>> output
2025-08-14T23:09:16.8181804Z         ['foo', 12, {1: 2}]
2025-08-14T23:09:16.8181855Z     
2025-08-14T23:09:16.8182003Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8182081Z     
2025-08-14T23:09:16.8182140Z       warnings.warn(msg)
2025-08-14T23:09:16.8182191Z     
2025-08-14T23:09:16.8182309Z --- Parse Warning: 86 / 146 ---
2025-08-14T23:09:16.8184957Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3794.
2025-08-14T23:09:16.8185113Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8185165Z     
2025-08-14T23:09:16.8185250Z     Gathers tensors from the whole group in a list.
2025-08-14T23:09:16.8185304Z     
2025-08-14T23:09:16.8185392Z     Complex and uneven sized tensors are supported.
2025-08-14T23:09:16.8185443Z     
2025-08-14T23:09:16.8185494Z     Args:
2025-08-14T23:09:16.8185603Z         tensor_list (list[Tensor]): Output list. It should contain
2025-08-14T23:09:16.8185730Z             correctly-sized tensors to be used for output of the collective.
2025-08-14T23:09:16.8185805Z             Uneven sized tensors are supported.
2025-08-14T23:09:16.8185918Z         tensor (Tensor): Tensor to be broadcast from current process.
2025-08-14T23:09:16.8186054Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8186130Z             the default process group will be used.
2025-08-14T23:09:16.8186244Z         async_op (bool, optional): Whether this op should be an async op
2025-08-14T23:09:16.8186296Z     
2025-08-14T23:09:16.8186348Z     Returns:
2025-08-14T23:09:16.8186438Z         Async work handle, if async_op is set to True.
2025-08-14T23:09:16.8186564Z         None, if not async_op or if not part of the group
2025-08-14T23:09:16.8186636Z     
2025-08-14T23:09:16.8186690Z     Examples:
2025-08-14T23:09:16.8186772Z         >>> # xdoctest: +SKIP("need process group init")
2025-08-14T23:09:16.8188880Z         >>> # All tensors below are of torch.int64 dtype.
2025-08-14T23:09:16.8188960Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8189034Z         >>> device = torch.device(f"cuda:{rank}")
2025-08-14T23:09:16.8189095Z         >>> tensor_list = [
2025-08-14T23:09:16.8189220Z         ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
2025-08-14T23:09:16.8189272Z         ... ]
2025-08-14T23:09:16.8189328Z         >>> tensor_list
2025-08-14T23:09:16.8189444Z         [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
2025-08-14T23:09:16.8189559Z         [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
2025-08-14T23:09:16.8189725Z         >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
2025-08-14T23:09:16.8189781Z         >>> tensor
2025-08-14T23:09:16.8189853Z         tensor([1, 2], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8189923Z         tensor([3, 4], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8189995Z         >>> dist.all_gather(tensor_list, tensor)
2025-08-14T23:09:16.8190052Z         >>> tensor_list
2025-08-14T23:09:16.8190161Z         [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
2025-08-14T23:09:16.8190274Z         [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
2025-08-14T23:09:16.8190324Z     
2025-08-14T23:09:16.8190411Z         >>> # All tensors below are of torch.cfloat dtype.
2025-08-14T23:09:16.8190482Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8190541Z         >>> tensor_list = [
2025-08-14T23:09:16.8190664Z         ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
2025-08-14T23:09:16.8192741Z         ... ]
2025-08-14T23:09:16.8192801Z         >>> tensor_list
2025-08-14T23:09:16.8193011Z         [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
2025-08-14T23:09:16.8193148Z         [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
2025-08-14T23:09:16.8193250Z         >>> tensor = torch.tensor(
2025-08-14T23:09:16.8193343Z         ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
2025-08-14T23:09:16.8193405Z         ... ) + 2 * rank * (1 + 1j)
2025-08-14T23:09:16.8193457Z         >>> tensor
2025-08-14T23:09:16.8193542Z         tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
2025-08-14T23:09:16.8193623Z         tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
2025-08-14T23:09:16.8193696Z         >>> dist.all_gather(tensor_list, tensor)
2025-08-14T23:09:16.8193754Z         >>> tensor_list
2025-08-14T23:09:16.8193934Z         [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
2025-08-14T23:09:16.8194070Z         [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
2025-08-14T23:09:16.8194122Z     
2025-08-14T23:09:16.8194171Z     
2025-08-14T23:09:16.8194321Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8194372Z     
2025-08-14T23:09:16.8194432Z       warnings.warn(msg)
2025-08-14T23:09:16.8194483Z     
2025-08-14T23:09:16.8194600Z --- Parse Warning: 87 / 146 ---
2025-08-14T23:09:16.8197274Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4500.
2025-08-14T23:09:16.8197436Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8197485Z     
2025-08-14T23:09:16.8197633Z     Split input tensor and then scatter the split list to all processes in a group.
2025-08-14T23:09:16.8197720Z     
2025-08-14T23:09:16.8197889Z     Later the received tensors are concatenated from all the processes in the group
2025-08-14T23:09:16.8197966Z     and returned as a single output tensor.
2025-08-14T23:09:16.8198015Z     
2025-08-14T23:09:16.8198085Z     Complex tensors are supported.
2025-08-14T23:09:16.8198136Z     
2025-08-14T23:09:16.8198188Z     Args:
2025-08-14T23:09:16.8198287Z         output (Tensor): Gathered concatenated output tensor.
2025-08-14T23:09:16.8198364Z         input (Tensor): Input tensor to scatter.
2025-08-14T23:09:16.8198494Z         output_split_sizes: (list[Int], optional): Output split sizes for dim 0
2025-08-14T23:09:16.8198616Z             if specified None or empty, dim 0 of ``output`` tensor must divide
2025-08-14T23:09:16.8198682Z             equally by ``world_size``.
2025-08-14T23:09:16.8198807Z         input_split_sizes: (list[Int], optional): Input split sizes for dim 0
2025-08-14T23:09:16.8198952Z             if specified None or empty, dim 0 of ``input`` tensor must divide
2025-08-14T23:09:16.8199022Z             equally by ``world_size``.
2025-08-14T23:09:16.8199154Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8199231Z             the default process group will be used.
2025-08-14T23:09:16.8199348Z         async_op (bool, optional): Whether this op should be an async op.
2025-08-14T23:09:16.8201508Z     
2025-08-14T23:09:16.8201567Z     Returns:
2025-08-14T23:09:16.8201654Z         Async work handle, if async_op is set to True.
2025-08-14T23:09:16.8201743Z         None, if not async_op or if not part of the group.
2025-08-14T23:09:16.8201794Z     
2025-08-14T23:09:16.8201849Z     .. warning::
2025-08-14T23:09:16.8201952Z         `all_to_all_single` is experimental and subject to change.
2025-08-14T23:09:16.8202003Z     
2025-08-14T23:09:16.8202056Z     Examples:
2025-08-14T23:09:16.8202131Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-08-14T23:09:16.8202205Z         >>> input = torch.arange(4) + rank * 4
2025-08-14T23:09:16.8202260Z         >>> input
2025-08-14T23:09:16.8202324Z         tensor([0, 1, 2, 3])     # Rank 0
2025-08-14T23:09:16.8202388Z         tensor([4, 5, 6, 7])     # Rank 1
2025-08-14T23:09:16.8202449Z         tensor([8, 9, 10, 11])   # Rank 2
2025-08-14T23:09:16.8202541Z         tensor([12, 13, 14, 15]) # Rank 3
2025-08-14T23:09:16.8202628Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-08-14T23:09:16.8202704Z         >>> dist.all_to_all_single(output, input)
2025-08-14T23:09:16.8202756Z         >>> output
2025-08-14T23:09:16.8202817Z         tensor([0, 4, 8, 12])    # Rank 0
2025-08-14T23:09:16.8202877Z         tensor([1, 5, 9, 13])    # Rank 1
2025-08-14T23:09:16.8205016Z         tensor([2, 6, 10, 14])   # Rank 2
2025-08-14T23:09:16.8205077Z         tensor([3, 7, 11, 15])   # Rank 3
2025-08-14T23:09:16.8205129Z     
2025-08-14T23:09:16.8205226Z         >>> # Essentially, it is similar to following operation:
2025-08-14T23:09:16.8205316Z         >>> scatter_list = list(input.chunk(world_size))
2025-08-14T23:09:16.8205401Z         >>> gather_list = list(output.chunk(world_size))
2025-08-14T23:09:16.8205467Z         >>> for i in range(world_size):
2025-08-14T23:09:16.8205603Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
2025-08-14T23:09:16.8205654Z     
2025-08-14T23:09:16.8205804Z         >>> # Another example with uneven split
2025-08-14T23:09:16.8205856Z         >>> input
2025-08-14T23:09:16.8205951Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-08-14T23:09:16.8206046Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-08-14T23:09:16.8206137Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-08-14T23:09:16.8206228Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-08-14T23:09:16.8206288Z         >>> input_splits
2025-08-14T23:09:16.8206425Z         [2, 2, 1, 1]                                                     # Rank 0
2025-08-14T23:09:16.8206500Z         [3, 2, 2, 2]                                                     # Rank 1
2025-08-14T23:09:16.8206573Z         [2, 1, 1, 1]                                                     # Rank 2
2025-08-14T23:09:16.8206646Z         [2, 2, 2, 1]                                                     # Rank 3
2025-08-14T23:09:16.8206707Z         >>> output_splits
2025-08-14T23:09:16.8206781Z         [2, 3, 2, 2]                                                     # Rank 0
2025-08-14T23:09:16.8208878Z         [2, 2, 1, 2]                                                     # Rank 1
2025-08-14T23:09:16.8208956Z         [1, 2, 1, 2]                                                     # Rank 2
2025-08-14T23:09:16.8209034Z         [1, 2, 1, 1]                                                     # Rank 3
2025-08-14T23:09:16.8209092Z         >>> output = ...
2025-08-14T23:09:16.8209242Z         >>> dist.all_to_all_single(output, input, output_splits, input_splits)
2025-08-14T23:09:16.8209299Z         >>> output
2025-08-14T23:09:16.8209390Z         tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
2025-08-14T23:09:16.8209481Z         tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
2025-08-14T23:09:16.8209573Z         tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
2025-08-14T23:09:16.8209662Z         tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
2025-08-14T23:09:16.8209711Z     
2025-08-14T23:09:16.8209762Z     
2025-08-14T23:09:16.8209858Z         >>> # Another example with tensors of torch.cfloat type.
2025-08-14T23:09:16.8209925Z         >>> input = torch.tensor(
2025-08-14T23:09:16.8210010Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-08-14T23:09:16.8210071Z         ... ) + 4 * rank * (1 + 1j)
2025-08-14T23:09:16.8210124Z         >>> input
2025-08-14T23:09:16.8210228Z         tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
2025-08-14T23:09:16.8210330Z         tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
2025-08-14T23:09:16.8210435Z         tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
2025-08-14T23:09:16.8210542Z         tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
2025-08-14T23:09:16.8210650Z         >>> output = torch.empty([4], dtype=torch.int64)
2025-08-14T23:09:16.8212804Z         >>> dist.all_to_all_single(output, input)
2025-08-14T23:09:16.8212863Z         >>> output
2025-08-14T23:09:16.8213032Z         tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
2025-08-14T23:09:16.8213133Z         tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
2025-08-14T23:09:16.8213235Z         tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
2025-08-14T23:09:16.8213336Z         tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
2025-08-14T23:09:16.8213389Z     
2025-08-14T23:09:16.8213537Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8213588Z     
2025-08-14T23:09:16.8213648Z       warnings.warn(msg)
2025-08-14T23:09:16.8213698Z     
2025-08-14T23:09:16.8213815Z --- Parse Warning: 88 / 146 ---
2025-08-14T23:09:16.8214373Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4642.
2025-08-14T23:09:16.8214525Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8214576Z     
2025-08-14T23:09:16.8214785Z     Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
2025-08-14T23:09:16.8214836Z     
2025-08-14T23:09:16.8214906Z     Complex tensors are supported.
2025-08-14T23:09:16.8214998Z     
2025-08-14T23:09:16.8215049Z     Args:
2025-08-14T23:09:16.8215197Z         output_tensor_list (list[Tensor]): List of tensors to be gathered one
2025-08-14T23:09:16.8217480Z             per rank.
2025-08-14T23:09:16.8217629Z         input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
2025-08-14T23:09:16.8217764Z         group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8217843Z             the default process group will be used.
2025-08-14T23:09:16.8217960Z         async_op (bool, optional): Whether this op should be an async op.
2025-08-14T23:09:16.8218010Z     
2025-08-14T23:09:16.8218064Z     Returns:
2025-08-14T23:09:16.8218149Z         Async work handle, if async_op is set to True.
2025-08-14T23:09:16.8218241Z         None, if not async_op or if not part of the group.
2025-08-14T23:09:16.8218290Z     
2025-08-14T23:09:16.8218346Z     .. warning::
2025-08-14T23:09:16.8218466Z         `all_to_all` is experimental and subject to change.
2025-08-14T23:09:16.8218518Z     
2025-08-14T23:09:16.8218573Z     Examples:
2025-08-14T23:09:16.8218649Z         >>> # xdoctest: +SKIP("Undefined rank")
2025-08-14T23:09:16.8218720Z         >>> input = torch.arange(4) + rank * 4
2025-08-14T23:09:16.8218789Z         >>> input = list(input.chunk(4))
2025-08-14T23:09:16.8218843Z         >>> input
2025-08-14T23:09:16.8218946Z         [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
2025-08-14T23:09:16.8219044Z         [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
2025-08-14T23:09:16.8219141Z         [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
2025-08-14T23:09:16.8219239Z         [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
2025-08-14T23:09:16.8221439Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-08-14T23:09:16.8221512Z         >>> dist.all_to_all(output, input)
2025-08-14T23:09:16.8221588Z         >>> output
2025-08-14T23:09:16.8221687Z         [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
2025-08-14T23:09:16.8221784Z         [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
2025-08-14T23:09:16.8221878Z         [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
2025-08-14T23:09:16.8221974Z         [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
2025-08-14T23:09:16.8222058Z     
2025-08-14T23:09:16.8222155Z         >>> # Essentially, it is similar to following operation:
2025-08-14T23:09:16.8222222Z         >>> scatter_list = input
2025-08-14T23:09:16.8222283Z         >>> gather_list = output
2025-08-14T23:09:16.8222351Z         >>> for i in range(world_size):
2025-08-14T23:09:16.8222485Z         >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
2025-08-14T23:09:16.8222536Z     
2025-08-14T23:09:16.8222588Z         >>> input
2025-08-14T23:09:16.8222681Z         tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
2025-08-14T23:09:16.8222776Z         tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
2025-08-14T23:09:16.8222867Z         tensor([20, 21, 22, 23, 24])                                     # Rank 2
2025-08-14T23:09:16.8223018Z         tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
2025-08-14T23:09:16.8223080Z         >>> input_splits
2025-08-14T23:09:16.8223153Z         [2, 2, 1, 1]                                                     # Rank 0
2025-08-14T23:09:16.8225358Z         [3, 2, 2, 2]                                                     # Rank 1
2025-08-14T23:09:16.8225440Z         [2, 1, 1, 1]                                                     # Rank 2
2025-08-14T23:09:16.8225519Z         [2, 2, 2, 1]                                                     # Rank 3
2025-08-14T23:09:16.8225578Z         >>> output_splits
2025-08-14T23:09:16.8225652Z         [2, 3, 2, 2]                                                     # Rank 0
2025-08-14T23:09:16.8225780Z         [2, 2, 1, 2]                                                     # Rank 1
2025-08-14T23:09:16.8225854Z         [1, 2, 1, 2]                                                     # Rank 2
2025-08-14T23:09:16.8225926Z         [1, 2, 1, 1]                                                     # Rank 3
2025-08-14T23:09:16.8226007Z         >>> input = list(input.split(input_splits))
2025-08-14T23:09:16.8226059Z         >>> input
2025-08-14T23:09:16.8226183Z         [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
2025-08-14T23:09:16.8226301Z         [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
2025-08-14T23:09:16.8226419Z         [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
2025-08-14T23:09:16.8226534Z         [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
2025-08-14T23:09:16.8226593Z         >>> output = ...
2025-08-14T23:09:16.8226687Z         >>> dist.all_to_all(output, input)
2025-08-14T23:09:16.8226742Z         >>> output
2025-08-14T23:09:16.8226857Z         [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
2025-08-14T23:09:16.8226974Z         [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
2025-08-14T23:09:16.8227092Z         [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
2025-08-14T23:09:16.8227208Z         [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
2025-08-14T23:09:16.8229398Z     
2025-08-14T23:09:16.8229507Z         >>> # Another example with tensors of torch.cfloat type.
2025-08-14T23:09:16.8229571Z         >>> input = torch.tensor(
2025-08-14T23:09:16.8229658Z         ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
2025-08-14T23:09:16.8229718Z         ... ) + 4 * rank * (1 + 1j)
2025-08-14T23:09:16.8229786Z         >>> input = list(input.chunk(4))
2025-08-14T23:09:16.8229843Z         >>> input
2025-08-14T23:09:16.8229968Z         [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
2025-08-14T23:09:16.8230089Z         [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
2025-08-14T23:09:16.8230214Z         [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
2025-08-14T23:09:16.8230372Z         [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
2025-08-14T23:09:16.8230480Z         >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
2025-08-14T23:09:16.8230551Z         >>> dist.all_to_all(output, input)
2025-08-14T23:09:16.8230603Z         >>> output
2025-08-14T23:09:16.8230724Z         [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
2025-08-14T23:09:16.8230844Z         [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
2025-08-14T23:09:16.8230965Z         [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
2025-08-14T23:09:16.8231087Z         [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
2025-08-14T23:09:16.8231138Z     
2025-08-14T23:09:16.8231188Z     
2025-08-14T23:09:16.8231338Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8231388Z     
2025-08-14T23:09:16.8233643Z       warnings.warn(msg)
2025-08-14T23:09:16.8233700Z     
2025-08-14T23:09:16.8233825Z --- Parse Warning: 89 / 146 ---
2025-08-14T23:09:16.8234299Z     /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.
2025-08-14T23:09:16.8234454Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8234504Z     
2025-08-14T23:09:16.8234581Z     Module ``torch.distributed.launch``.
2025-08-14T23:09:16.8234666Z     
2025-08-14T23:09:16.8234844Z     ``torch.distributed.launch`` is a module that spawns up multiple distributed
2025-08-14T23:09:16.8234938Z     training processes on each of the training nodes.
2025-08-14T23:09:16.8234988Z     
2025-08-14T23:09:16.8235043Z     .. warning::
2025-08-14T23:09:16.8235093Z     
2025-08-14T23:09:16.8235244Z         This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2025-08-14T23:09:16.8235293Z     
2025-08-14T23:09:16.8235433Z     The utility can be used for single-node distributed training, in which one or
2025-08-14T23:09:16.8235568Z     more processes per node will be spawned. The utility can be used for either
2025-08-14T23:09:16.8235699Z     CPU training or GPU training. If the utility is used for GPU training,
2025-08-14T23:09:16.8235842Z     each distributed process will be operating on a single GPU. This can achieve
2025-08-14T23:09:16.8236001Z     well-improved single-node training performance. It can also be used in
2025-08-14T23:09:16.8236159Z     multi-node distributed training, by spawning up multiple processes on each node
2025-08-14T23:09:16.8238421Z     for well-improved multi-node distributed training performance as well.
2025-08-14T23:09:16.8238564Z     This will especially be beneficial for systems with multiple Infiniband
2025-08-14T23:09:16.8238716Z     interfaces that have direct-GPU support, since all of them can be utilized for
2025-08-14T23:09:16.8238787Z     aggregated communication bandwidth.
2025-08-14T23:09:16.8238838Z     
2025-08-14T23:09:16.8238973Z     In both cases of single-node distributed training or multi-node distributed
2025-08-14T23:09:16.8239111Z     training, this utility will launch the given number of processes per node
2025-08-14T23:09:16.8239242Z     (``--nproc-per-node``). If used for GPU training, this number needs to be less
2025-08-14T23:09:16.8239369Z     or equal to the number of GPUs on the current system (``nproc_per_node``),
2025-08-14T23:09:16.8239490Z     and each process will be operating on a single GPU from *GPU 0 to
2025-08-14T23:09:16.8239560Z     GPU (nproc_per_node - 1)*.
2025-08-14T23:09:16.8239610Z     
2025-08-14T23:09:16.8239675Z     **How to use this module:**
2025-08-14T23:09:16.8239724Z     
2025-08-14T23:09:16.8239819Z     1. Single-Node multi-process distributed training
2025-08-14T23:09:16.8239902Z     
2025-08-14T23:09:16.8239957Z     ::
2025-08-14T23:09:16.8240007Z     
2025-08-14T23:09:16.8240145Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:16.8240260Z                    YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2025-08-14T23:09:16.8240337Z                    arguments of your training script)
2025-08-14T23:09:16.8240388Z     
2025-08-14T23:09:16.8242602Z     2. Multi-Node multi-process distributed training: (e.g. two nodes)
2025-08-14T23:09:16.8242660Z     
2025-08-14T23:09:16.8242710Z     
2025-08-14T23:09:16.8242802Z     Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2025-08-14T23:09:16.8242853Z     
2025-08-14T23:09:16.8242905Z     ::
2025-08-14T23:09:16.8243019Z     
2025-08-14T23:09:16.8243157Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:16.8243255Z                    --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2025-08-14T23:09:16.8243382Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-08-14T23:09:16.8243473Z                    and all other arguments of your training script)
2025-08-14T23:09:16.8243524Z     
2025-08-14T23:09:16.8243576Z     Node 2:
2025-08-14T23:09:16.8243627Z     
2025-08-14T23:09:16.8243678Z     ::
2025-08-14T23:09:16.8243730Z     
2025-08-14T23:09:16.8243866Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2025-08-14T23:09:16.8243959Z                    --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2025-08-14T23:09:16.8244080Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2025-08-14T23:09:16.8244229Z                    and all other arguments of your training script)
2025-08-14T23:09:16.8244281Z     
2025-08-14T23:09:16.8244380Z     3. To look up what optional arguments this module offers:
2025-08-14T23:09:16.8246627Z     
2025-08-14T23:09:16.8246679Z     ::
2025-08-14T23:09:16.8246733Z     
2025-08-14T23:09:16.8246820Z         python -m torch.distributed.launch --help
2025-08-14T23:09:16.8246871Z     
2025-08-14T23:09:16.8246920Z     
2025-08-14T23:09:16.8246984Z     **Important Notices:**
2025-08-14T23:09:16.8247034Z     
2025-08-14T23:09:16.8247150Z     1. This utility and multi-process distributed (single-node or
2025-08-14T23:09:16.8247293Z     multi-node) GPU training currently only achieves the best performance using
2025-08-14T23:09:16.8247441Z     the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2025-08-14T23:09:16.8247502Z     use for GPU training.
2025-08-14T23:09:16.8247553Z     
2025-08-14T23:09:16.8247711Z     2. In your training program, you must parse the command-line argument:
2025-08-14T23:09:16.8247851Z     ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2025-08-14T23:09:16.8247985Z     If your training program uses GPUs, you should ensure that your code only
2025-08-14T23:09:16.8248100Z     runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2025-08-14T23:09:16.8248152Z     
2025-08-14T23:09:16.8248219Z     Parsing the local_rank argument
2025-08-14T23:09:16.8248270Z     
2025-08-14T23:09:16.8248321Z     ::
2025-08-14T23:09:16.8250418Z     
2025-08-14T23:09:16.8250482Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8250544Z         >>> import argparse
2025-08-14T23:09:16.8250624Z         >>> parser = argparse.ArgumentParser()
2025-08-14T23:09:16.8250740Z         >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2025-08-14T23:09:16.8250809Z         >>> args = parser.parse_args()
2025-08-14T23:09:16.8250859Z     
2025-08-14T23:09:16.8250936Z     Set your device to local rank using either
2025-08-14T23:09:16.8250988Z     
2025-08-14T23:09:16.8251040Z     ::
2025-08-14T23:09:16.8251091Z     
2025-08-14T23:09:16.8251209Z         >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2025-08-14T23:09:16.8251258Z     
2025-08-14T23:09:16.8251309Z     or
2025-08-14T23:09:16.8251357Z     
2025-08-14T23:09:16.8251440Z     ::
2025-08-14T23:09:16.8251489Z     
2025-08-14T23:09:16.8251572Z         >>> with torch.cuda.device(args.local_rank):
2025-08-14T23:09:16.8251634Z         >>>    # your code to run
2025-08-14T23:09:16.8251687Z         >>>    ...
2025-08-14T23:09:16.8251736Z     
2025-08-14T23:09:16.8251801Z     .. versionchanged:: 2.0.0
2025-08-14T23:09:16.8254117Z     
2025-08-14T23:09:16.8254272Z         The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2025-08-14T23:09:16.8254458Z         From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2025-08-14T23:09:16.8254552Z         previously used underscored ``--local_rank``.
2025-08-14T23:09:16.8254603Z     
2025-08-14T23:09:16.8254744Z         For backward compatibility, it may be necessary for users to handle both
2025-08-14T23:09:16.8254898Z         cases in their argument parsing code. This means including both ``"--local-rank"``
2025-08-14T23:09:16.8255026Z         and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2025-08-14T23:09:16.8255172Z         provided, the launcher will trigger an error: "error: unrecognized arguments:
2025-08-14T23:09:16.8255308Z         --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2025-08-14T23:09:16.8255400Z         including ``"--local-rank"`` should be sufficient.
2025-08-14T23:09:16.8255452Z     
2025-08-14T23:09:16.8255588Z     3. In your training program, you are supposed to call the following function
2025-08-14T23:09:16.8255725Z     at the beginning to start the distributed backend. It is strongly recommended
2025-08-14T23:09:16.8255855Z     that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2025-08-14T23:09:16.8256033Z     but ``env://`` is the one that is officially supported by this module.
2025-08-14T23:09:16.8256085Z     
2025-08-14T23:09:16.8256137Z     ::
2025-08-14T23:09:16.8256188Z     
2025-08-14T23:09:16.8256309Z         >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2025-08-14T23:09:16.8256393Z         >>>                                      init_method='env://')
2025-08-14T23:09:16.8256443Z     
2025-08-14T23:09:16.8258735Z     4. In your training program, you can either use regular distributed functions
2025-08-14T23:09:16.8258881Z     or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2025-08-14T23:09:16.8259005Z     training program uses GPUs for training and you would like to use
2025-08-14T23:09:16.8259125Z     :func:`torch.nn.parallel.DistributedDataParallel` module,
2025-08-14T23:09:16.8259193Z     here is how to configure it.
2025-08-14T23:09:16.8259243Z     
2025-08-14T23:09:16.8259326Z     ::
2025-08-14T23:09:16.8259376Z     
2025-08-14T23:09:16.8259497Z         >>> model = torch.nn.parallel.DistributedDataParallel(model,
2025-08-14T23:09:16.8259584Z         >>>                                                   device_ids=[args.local_rank],
2025-08-14T23:09:16.8259669Z         >>>                                                   output_device=args.local_rank)
2025-08-14T23:09:16.8259720Z     
2025-08-14T23:09:16.8259862Z     Please ensure that ``device_ids`` argument is set to be the only GPU device id
2025-08-14T23:09:16.8259999Z     that your code will be operating on. This is generally the local rank of the
2025-08-14T23:09:16.8260137Z     process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2025-08-14T23:09:16.8260262Z     and ``output_device`` needs to be ``args.local_rank`` in order to use this
2025-08-14T23:09:16.8260314Z     utility
2025-08-14T23:09:16.8260366Z     
2025-08-14T23:09:16.8260509Z     5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2025-08-14T23:09:16.8260637Z     ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2025-08-14T23:09:16.8260764Z     ``--use-env=True``. You must adjust the subprocess example above to replace
2025-08-14T23:09:16.8262996Z     ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2025-08-14T23:09:16.8263134Z     will not pass ``--local-rank`` when you specify this flag.
2025-08-14T23:09:16.8263186Z     
2025-08-14T23:09:16.8263241Z     .. warning::
2025-08-14T23:09:16.8263292Z     
2025-08-14T23:09:16.8263410Z         ``local_rank`` is NOT globally unique: it is only unique per process
2025-08-14T23:09:16.8263523Z         on a machine.  Thus, don't use it to decide if you should, e.g.,
2025-08-14T23:09:16.8263599Z         write to a networked filesystem.  See
2025-08-14T23:09:16.8263731Z         https://github.com/pytorch/pytorch/issues/12042 for an example of
2025-08-14T23:09:16.8263831Z         how things can go wrong if you don't do this correctly.
2025-08-14T23:09:16.8263883Z     
2025-08-14T23:09:16.8263933Z     
2025-08-14T23:09:16.8263984Z     
2025-08-14T23:09:16.8264033Z     
2025-08-14T23:09:16.8264181Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8264230Z     
2025-08-14T23:09:16.8264292Z       warnings.warn(msg)
2025-08-14T23:09:16.8264343Z     
2025-08-14T23:09:16.8264464Z --- Parse Warning: 90 / 146 ---
2025-08-14T23:09:16.8265020Z     /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.
2025-08-14T23:09:16.8265172Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8265223Z     
2025-08-14T23:09:16.8267496Z     Custom reducer class that can be used to specify a custom operation that
2025-08-14T23:09:16.8267610Z     reduces losses of multiple microbatches into one value.
2025-08-14T23:09:16.8267691Z     
2025-08-14T23:09:16.8267767Z     Example:
2025-08-14T23:09:16.8267829Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8267899Z     >>> sum_reducer = _CustomReducer(
2025-08-14T23:09:16.8267963Z     >>>     torch.tensor(0.0),
2025-08-14T23:09:16.8268025Z     >>>     lambda a, b: a + b
2025-08-14T23:09:16.8268077Z     >>> )
2025-08-14T23:09:16.8268128Z     
2025-08-14T23:09:16.8268274Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8268324Z     
2025-08-14T23:09:16.8268384Z       warnings.warn(msg)
2025-08-14T23:09:16.8268434Z     
2025-08-14T23:09:16.8268549Z --- Parse Warning: 91 / 146 ---
2025-08-14T23:09:16.8269168Z     /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.
2025-08-14T23:09:16.8269323Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8269374Z     
2025-08-14T23:09:16.8269495Z     Set device mapping between each RPC caller and callee pair. This
2025-08-14T23:09:16.8269605Z     function can be called multiple times to incrementally add
2025-08-14T23:09:16.8269679Z     device placement configurations.
2025-08-14T23:09:16.8271888Z     
2025-08-14T23:09:16.8271941Z     Args:
2025-08-14T23:09:16.8272010Z         to (str): Callee name.
2025-08-14T23:09:16.8272127Z         device_map (Dict of int, str, or torch.device): Device placement
2025-08-14T23:09:16.8272235Z             mappings from this worker to the callee. This map must be
2025-08-14T23:09:16.8272293Z             invertible.
2025-08-14T23:09:16.8272343Z     
2025-08-14T23:09:16.8272397Z     Example:
2025-08-14T23:09:16.8272469Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8272528Z         >>> # both workers
2025-08-14T23:09:16.8272589Z         >>> def add(x, y):
2025-08-14T23:09:16.8272676Z         >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2025-08-14T23:09:16.8272743Z         >>>     return x + y, (x + y).to(2)
2025-08-14T23:09:16.8272796Z         >>>
2025-08-14T23:09:16.8272853Z         >>> # on worker 0
2025-08-14T23:09:16.8273017Z         >>> options = TensorPipeRpcBackendOptions(
2025-08-14T23:09:16.8273119Z         >>>     num_worker_threads=8,
2025-08-14T23:09:16.8273192Z         >>>     device_maps={"worker1": {0: 1}}
2025-08-14T23:09:16.8273273Z         >>> # maps worker0's cuda:0 to worker1's cuda:1
2025-08-14T23:09:16.8273324Z         >>> )
2025-08-14T23:09:16.8273406Z         >>> options.set_device_map("worker1", {1: 2})
2025-08-14T23:09:16.8273484Z         >>> # maps worker0's cuda:1 to worker1's cuda:2
2025-08-14T23:09:16.8275636Z         >>>
2025-08-14T23:09:16.8275697Z         >>> rpc.init_rpc(
2025-08-14T23:09:16.8275761Z         >>>     "worker0",
2025-08-14T23:09:16.8275815Z         >>>     rank=0,
2025-08-14T23:09:16.8275878Z         >>>     world_size=2,
2025-08-14T23:09:16.8275960Z         >>>     backend=rpc.BackendType.TENSORPIPE,
2025-08-14T23:09:16.8276030Z         >>>     rpc_backend_options=options
2025-08-14T23:09:16.8276081Z         >>> )
2025-08-14T23:09:16.8276133Z         >>>
2025-08-14T23:09:16.8276192Z         >>> x = torch.ones(2)
2025-08-14T23:09:16.8276294Z         >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2025-08-14T23:09:16.8276446Z         >>> # The first argument will be moved to cuda:1 on worker1. When
2025-08-14T23:09:16.8276556Z         >>> # sending the return value back, it will follow the invert of
2025-08-14T23:09:16.8276660Z         >>> # the device map, and hence will be moved back to cuda:0 and
2025-08-14T23:09:16.8276723Z         >>> # cuda:1 on worker0
2025-08-14T23:09:16.8276813Z         >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2025-08-14T23:09:16.8276902Z         >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2025-08-14T23:09:16.8277008Z     
2025-08-14T23:09:16.8277178Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8277228Z     
2025-08-14T23:09:16.8277287Z       warnings.warn(msg)
2025-08-14T23:09:16.8277338Z     
2025-08-14T23:09:16.8279558Z --- Parse Warning: 92 / 146 ---
2025-08-14T23:09:16.8280081Z     /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.
2025-08-14T23:09:16.8280234Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8280283Z     
2025-08-14T23:09:16.8280422Z     A decorator for a function indicating that the return value of the function
2025-08-14T23:09:16.8280546Z     is guaranteed to be a :class:`~torch.futures.Future` object and this
2025-08-14T23:09:16.8280718Z     function can run asynchronously on the RPC callee. More specifically, the
2025-08-14T23:09:16.8280860Z     callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2025-08-14T23:09:16.8280995Z     function and installs subsequent processing steps as a callback to that
2025-08-14T23:09:16.8281133Z     :class:`~torch.futures.Future`. The installed callback will read the value
2025-08-14T23:09:16.8281254Z     from the :class:`~torch.futures.Future` when completed and send the
2025-08-14T23:09:16.8281364Z     value back as the RPC response. That also means the returned
2025-08-14T23:09:16.8281495Z     :class:`~torch.futures.Future` only exists on the callee side and is never
2025-08-14T23:09:16.8281626Z     sent through RPC. This decorator is useful when the wrapped function's
2025-08-14T23:09:16.8281742Z     (``fn``) execution needs to pause and resume due to, e.g., containing
2025-08-14T23:09:16.8281876Z     :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2025-08-14T23:09:16.8281926Z     
2025-08-14T23:09:16.8282054Z     .. note:: To enable asynchronous execution, applications must pass the
2025-08-14T23:09:16.8282190Z         function object returned by this decorator to RPC APIs. If RPC detected
2025-08-14T23:09:16.8282318Z         attributes installed by this decorator, it knows that this function
2025-08-14T23:09:16.8282428Z         returns a ``Future`` object and will handle that accordingly.
2025-08-14T23:09:16.8284738Z         However, this does not mean this decorator has to be outmost one when
2025-08-14T23:09:16.8284878Z         defining a function. For example, when combined with ``@staticmethod``
2025-08-14T23:09:16.8285001Z         or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2025-08-14T23:09:16.8285127Z         inner decorator to allow the target function be recognized as a static
2025-08-14T23:09:16.8285262Z         or class function. This target function can still execute asynchronously
2025-08-14T23:09:16.8285393Z         because, when accessed, the static or class method preserves attributes
2025-08-14T23:09:16.8285490Z         installed by ``@rpc.functions.async_execution``.
2025-08-14T23:09:16.8285541Z     
2025-08-14T23:09:16.8285592Z     
2025-08-14T23:09:16.8285647Z     Example::
2025-08-14T23:09:16.8285821Z         The returned :class:`~torch.futures.Future` object can come from
2025-08-14T23:09:16.8285906Z         :meth:`~torch.distributed.rpc.rpc_async`,
2025-08-14T23:09:16.8286039Z         :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2025-08-14T23:09:16.8286143Z         constructor. The example below shows directly using the
2025-08-14T23:09:16.8286224Z         :class:`~torch.futures.Future` returned by
2025-08-14T23:09:16.8286302Z         :meth:`~torch.futures.Future.then`.
2025-08-14T23:09:16.8286353Z     
2025-08-14T23:09:16.8286429Z         >>> from torch.distributed import rpc
2025-08-14T23:09:16.8286480Z         >>>
2025-08-14T23:09:16.8286553Z         >>> # omitting setup and shutdown RPC
2025-08-14T23:09:16.8286604Z         >>>
2025-08-14T23:09:16.8286700Z         >>> # On all workers
2025-08-14T23:09:16.8288829Z         >>> @rpc.functions.async_execution
2025-08-14T23:09:16.8288906Z         >>> def async_add_chained(to, x, y, z):
2025-08-14T23:09:16.8289020Z         >>>     # This function runs on "worker1" and returns immediately when
2025-08-14T23:09:16.8289131Z         >>>     # the callback is installed through the `then(cb)` API. In the
2025-08-14T23:09:16.8289240Z         >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2025-08-14T23:09:16.8289337Z         >>>     # When the return value of that `rpc_async` arrives at
2025-08-14T23:09:16.8289447Z         >>>     # "worker1", "worker1" will run the lambda function accordingly
2025-08-14T23:09:16.8289557Z         >>>     # and set the value for the previously returned `Future`, which
2025-08-14T23:09:16.8289662Z         >>>     # will then trigger RPC to send the result back to "worker0".
2025-08-14T23:09:16.8289785Z         >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:16.8289859Z         >>>         lambda fut: fut.wait() + z
2025-08-14T23:09:16.8289912Z         >>>     )
2025-08-14T23:09:16.8289963Z         >>>
2025-08-14T23:09:16.8290020Z         >>> # On worker0
2025-08-14T23:09:16.8290082Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8290146Z         >>> ret = rpc.rpc_sync(
2025-08-14T23:09:16.8290203Z         >>>     "worker1",
2025-08-14T23:09:16.8290268Z         >>>     async_add_chained,
2025-08-14T23:09:16.8290344Z         >>>     args=("worker2", torch.ones(2), 1, 1)
2025-08-14T23:09:16.8290395Z         >>> )
2025-08-14T23:09:16.8290470Z         >>> print(ret)  # prints tensor([3., 3.])
2025-08-14T23:09:16.8292533Z     
2025-08-14T23:09:16.8292666Z         When combined with TorchScript decorators, this decorator must be the
2025-08-14T23:09:16.8292721Z         outmost one.
2025-08-14T23:09:16.8292773Z     
2025-08-14T23:09:16.8292839Z         >>> from torch import Tensor
2025-08-14T23:09:16.8292980Z         >>> from torch.futures import Future
2025-08-14T23:09:16.8293058Z         >>> from torch.distributed import rpc
2025-08-14T23:09:16.8293110Z         >>>
2025-08-14T23:09:16.8293181Z         >>> # omitting setup and shutdown RPC
2025-08-14T23:09:16.8293232Z         >>>
2025-08-14T23:09:16.8293291Z         >>> # On all workers
2025-08-14T23:09:16.8293386Z         >>> @torch.jit.script
2025-08-14T23:09:16.8293476Z         >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2025-08-14T23:09:16.8293535Z         >>>     return x + y
2025-08-14T23:09:16.8293585Z         >>>
2025-08-14T23:09:16.8293658Z         >>> @rpc.functions.async_execution
2025-08-14T23:09:16.8293718Z         >>> @torch.jit.script
2025-08-14T23:09:16.8293829Z         >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2025-08-14T23:09:16.8293920Z         >>>     return rpc.rpc_async(to, script_add, (x, y))
2025-08-14T23:09:16.8293970Z         >>>
2025-08-14T23:09:16.8294027Z         >>> # On worker0
2025-08-14T23:09:16.8294090Z         >>> ret = rpc.rpc_sync(
2025-08-14T23:09:16.8296227Z         >>>     "worker1",
2025-08-14T23:09:16.8296287Z         >>>     async_add,
2025-08-14T23:09:16.8296364Z         >>>     args=("worker2", torch.ones(2), 1)
2025-08-14T23:09:16.8296415Z         >>> )
2025-08-14T23:09:16.8296489Z         >>> print(ret)  # prints tensor([2., 2.])
2025-08-14T23:09:16.8296539Z     
2025-08-14T23:09:16.8296667Z         When combined with static or class method, this decorator must be the
2025-08-14T23:09:16.8296720Z         inner one.
2025-08-14T23:09:16.8296771Z     
2025-08-14T23:09:16.8296845Z         >>> from torch.distributed import rpc
2025-08-14T23:09:16.8296896Z         >>>
2025-08-14T23:09:16.8296966Z         >>> # omitting setup and shutdown RPC
2025-08-14T23:09:16.8297018Z         >>>
2025-08-14T23:09:16.8297075Z         >>> # On all workers
2025-08-14T23:09:16.8297145Z         >>> class AsyncExecutionClass:
2025-08-14T23:09:16.8297196Z         >>>
2025-08-14T23:09:16.8297255Z         >>>     @staticmethod
2025-08-14T23:09:16.8297365Z         >>>     @rpc.functions.async_execution
2025-08-14T23:09:16.8297457Z         >>>     def static_async_add(to, x, y, z):
2025-08-14T23:09:16.8297560Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:16.8297630Z         >>>             lambda fut: fut.wait() + z
2025-08-14T23:09:16.8297685Z         >>>         )
2025-08-14T23:09:16.8299801Z         >>>
2025-08-14T23:09:16.8299862Z         >>>     @classmethod
2025-08-14T23:09:16.8299935Z         >>>     @rpc.functions.async_execution
2025-08-14T23:09:16.8300013Z         >>>     def class_async_add(cls, to, x, y, z):
2025-08-14T23:09:16.8300090Z         >>>         ret_fut = torch.futures.Future()
2025-08-14T23:09:16.8300182Z         >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:16.8300272Z         >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2025-08-14T23:09:16.8300327Z         >>>         )
2025-08-14T23:09:16.8300415Z         >>>         return ret_fut
2025-08-14T23:09:16.8300469Z         >>>
2025-08-14T23:09:16.8300542Z         >>>     @rpc.functions.async_execution
2025-08-14T23:09:16.8300623Z         >>>     def bound_async_add(self, to, x, y, z):
2025-08-14T23:09:16.8300722Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2025-08-14T23:09:16.8300794Z         >>>             lambda fut: fut.wait() + z
2025-08-14T23:09:16.8300847Z         >>>         )
2025-08-14T23:09:16.8300897Z         >>>
2025-08-14T23:09:16.8300954Z         >>> # On worker0
2025-08-14T23:09:16.8301014Z         >>> ret = rpc.rpc_sync(
2025-08-14T23:09:16.8301072Z         >>>     "worker1",
2025-08-14T23:09:16.8301155Z         >>>     AsyncExecutionClass.static_async_add,
2025-08-14T23:09:16.8301230Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:16.8301280Z         >>> )
2025-08-14T23:09:16.8303422Z         >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:16.8303474Z         >>>
2025-08-14T23:09:16.8303539Z         >>> ret = rpc.rpc_sync(
2025-08-14T23:09:16.8303595Z         >>>     "worker1",
2025-08-14T23:09:16.8303680Z         >>>     AsyncExecutionClass.class_async_add,
2025-08-14T23:09:16.8303752Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:16.8303804Z         >>> )
2025-08-14T23:09:16.8303875Z         >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:16.8303990Z     
2025-08-14T23:09:16.8304088Z         This decorator also works with RRef helpers, i.e., .
2025-08-14T23:09:16.8304216Z         :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2025-08-14T23:09:16.8304313Z         :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2025-08-14T23:09:16.8304399Z         :meth:`torch.distributed.rpc.RRef.remote`.
2025-08-14T23:09:16.8304450Z     
2025-08-14T23:09:16.8304523Z         >>> from torch.distributed import rpc
2025-08-14T23:09:16.8304578Z         >>>
2025-08-14T23:09:16.8304661Z         >>> # reuse the AsyncExecutionClass class above
2025-08-14T23:09:16.8304757Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:16.8304883Z         >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2025-08-14T23:09:16.8304955Z         >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:16.8305006Z         >>>
2025-08-14T23:09:16.8307213Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:16.8307361Z         >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2025-08-14T23:09:16.8307434Z         >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:16.8307484Z         >>>
2025-08-14T23:09:16.8307575Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2025-08-14T23:09:16.8307711Z         >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2025-08-14T23:09:16.8307782Z         >>> print(ret)  # prints tensor([4., 4.])
2025-08-14T23:09:16.8307832Z     
2025-08-14T23:09:16.8307982Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8308069Z     
2025-08-14T23:09:16.8308151Z       warnings.warn(msg)
2025-08-14T23:09:16.8308201Z     
2025-08-14T23:09:16.8308322Z --- Parse Warning: 93 / 146 ---
2025-08-14T23:09:16.8308944Z     /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.
2025-08-14T23:09:16.8309099Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8309149Z     
2025-08-14T23:09:16.8309270Z     It has the same API as ``torch.autograd.profiler.profile`` class,
2025-08-14T23:09:16.8309431Z     except that it enables profiling on all threads running RPC server request callbacks.
2025-08-14T23:09:16.8309480Z     
2025-08-14T23:09:16.8309669Z     Context manager that manages autograd profiler state and holds a summary of results.
2025-08-14T23:09:16.8309808Z     Under the hood it just records events of functions being executed in C++ and
2025-08-14T23:09:16.8309942Z     exposes those events to Python. You can wrap any code into it and it will
2025-08-14T23:09:16.8312133Z     only report runtime of PyTorch functions.
2025-08-14T23:09:16.8312297Z     Note: profiler is thread local and is automatically propagated into the async tasks
2025-08-14T23:09:16.8312348Z     
2025-08-14T23:09:16.8312401Z     Args:
2025-08-14T23:09:16.8312559Z         enabled (bool, optional): Setting this to False makes this context manager a no-op.
2025-08-14T23:09:16.8312622Z             Default: ``True``.
2025-08-14T23:09:16.8312671Z     
2025-08-14T23:09:16.8312839Z         use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
2025-08-14T23:09:16.8313064Z             Adds approximately 4us of overhead to each tensor operation.
2025-08-14T23:09:16.8313126Z             Default: ``False``
2025-08-14T23:09:16.8313178Z     
2025-08-14T23:09:16.8313313Z         record_shapes (bool, optional): If shapes recording is set, information
2025-08-14T23:09:16.8313446Z             about input dimensions will be collected. This allows one to see which
2025-08-14T23:09:16.8313570Z             dimensions have been used under the hood and further group by them
2025-08-14T23:09:16.8313745Z             using prof.key_averages(group_by_input_shape=True). Please note that
2025-08-14T23:09:16.8313877Z             shape recording might skew your profiling data. It is recommended to
2025-08-14T23:09:16.8314014Z             use separate runs with and without shape recording to validate the timing.
2025-08-14T23:09:16.8314148Z             Most likely the skew will be negligible for bottom most events (in a case
2025-08-14T23:09:16.8314271Z             of nested function calls). But for higher level functions the total
2025-08-14T23:09:16.8314391Z             self cpu time might be artificially increased because of the shape
2025-08-14T23:09:16.8314451Z             collection.
2025-08-14T23:09:16.8316639Z     
2025-08-14T23:09:16.8316805Z         profile_memory (bool, optional): Whether to report memory usage, default: ``False``
2025-08-14T23:09:16.8316857Z     
2025-08-14T23:09:16.8316912Z     .. warning::
2025-08-14T23:09:16.8317033Z         Enabling memory profiling incurs additional profiler overhead
2025-08-14T23:09:16.8317085Z     
2025-08-14T23:09:16.8317140Z     .. warning::
2025-08-14T23:09:16.8317307Z         Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`),
2025-08-14T23:09:16.8317426Z         one cannot use the profiler with ``use_cuda = True`` to benchmark
2025-08-14T23:09:16.8317563Z         DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
2025-08-14T23:09:16.8317660Z         please use ``use_cuda = False`` or ``num_workers = 0``.
2025-08-14T23:09:16.8317712Z     
2025-08-14T23:09:16.8317764Z     Example:
2025-08-14T23:09:16.8317828Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8317923Z         >>> # On worker 0:
2025-08-14T23:09:16.8318001Z         >>> import torch
2025-08-14T23:09:16.8318080Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8318169Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:16.8318244Z         >>> x, y = torch.tensor(1), torch.tensor(2)
2025-08-14T23:09:16.8318319Z         >>> outer_profile_rref = rpc.remote(
2025-08-14T23:09:16.8318417Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-08-14T23:09:16.8318469Z         ... )
2025-08-14T23:09:16.8320639Z         >>> outer_profile_rref.rpc_sync().__enter__()
2025-08-14T23:09:16.8320740Z         >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
2025-08-14T23:09:16.8320812Z         >>> inner_profile_rref = rpc.remote(
2025-08-14T23:09:16.8320909Z         ...     dst_worker_name, rpc._server_process_global_profile
2025-08-14T23:09:16.8320959Z         ... )
2025-08-14T23:09:16.8321066Z         >>> inner_profile_rref.rpc_sync().__enter__()
2025-08-14T23:09:16.8321159Z         >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
2025-08-14T23:09:16.8321268Z         >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
2025-08-14T23:09:16.8321373Z         >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
2025-08-14T23:09:16.8321475Z         >>> print(inner_profile_rref.rpc_sync().key_averages())
2025-08-14T23:09:16.8321626Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8321806Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-08-14T23:09:16.8321942Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8322055Z         sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
2025-08-14T23:09:16.8322168Z         empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
2025-08-14T23:09:16.8322304Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8322373Z         Self CPU time total: 89.667us
2025-08-14T23:09:16.8322469Z         >>> print(outer_profile_rref.rpc_sync().key_averages())
2025-08-14T23:09:16.8322617Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8322793Z         Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
2025-08-14T23:09:16.8322988Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8325138Z         sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
2025-08-14T23:09:16.8325249Z         empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
2025-08-14T23:09:16.8325356Z         add        51.68%           110.550us        58.09%           124.259us        124.259us        1
2025-08-14T23:09:16.8325489Z         ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
2025-08-14T23:09:16.8325560Z         Self CPU time total: 213.926us
2025-08-14T23:09:16.8325622Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8325674Z     
2025-08-14T23:09:16.8325785Z         >>> # On worker 1:
2025-08-14T23:09:16.8325867Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8326282Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:16.8326378Z         >>> # wait for worker 0 to finish work, and then shutdown.
2025-08-14T23:09:16.8326427Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8326460Z     
2025-08-14T23:09:16.8326570Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8326708Z     
2025-08-14T23:09:16.8326774Z       warnings.warn(msg)
2025-08-14T23:09:16.8326807Z     
2025-08-14T23:09:16.8326937Z --- Parse Warning: 94 / 146 ---
2025-08-14T23:09:16.8327327Z     /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.
2025-08-14T23:09:16.8327428Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8327460Z     
2025-08-14T23:09:16.8327612Z     Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2025-08-14T23:09:16.8329926Z     
2025-08-14T23:09:16.8330037Z     The sharing is done as described by `ZeRO <https://arxiv.org/abs/1910.02054>`_.
2025-08-14T23:09:16.8330068Z     
2025-08-14T23:09:16.8330172Z     The local optimizer instance in each rank is only
2025-08-14T23:09:16.8330267Z     responsible for updating approximately ``1 / world_size`` parameters and
2025-08-14T23:09:16.8330347Z     hence only needs to keep ``1 / world_size`` optimizer states. After
2025-08-14T23:09:16.8330435Z     parameters are updated locally, each rank will broadcast its parameters to
2025-08-14T23:09:16.8330510Z     all other peers to keep all model replicas in the same state.
2025-08-14T23:09:16.8330585Z     ``ZeroRedundancyOptimizer`` can be used in conjunction with
2025-08-14T23:09:16.8330682Z     :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2025-08-14T23:09:16.8330724Z     memory consumption.
2025-08-14T23:09:16.8330757Z     
2025-08-14T23:09:16.8330850Z     ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2025-08-14T23:09:16.8330933Z     of parameters at each rank. Each parameter belongs to a single rank and is
2025-08-14T23:09:16.8331020Z     not divided among ranks. The partition is arbitrary and might not match the
2025-08-14T23:09:16.8331077Z     the parameter registration or usage order.
2025-08-14T23:09:16.8331109Z     
2025-08-14T23:09:16.8331145Z     Arguments:
2025-08-14T23:09:16.8331221Z         params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2025-08-14T23:09:16.8331292Z             or :class:`dict` s giving all parameters, which will be sharded
2025-08-14T23:09:16.8331352Z             across ranks.
2025-08-14T23:09:16.8331384Z     
2025-08-14T23:09:16.8333069Z     Keyword Args:
2025-08-14T23:09:16.8333156Z         optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2025-08-14T23:09:16.8333194Z             optimizer.
2025-08-14T23:09:16.8333273Z         process_group (``ProcessGroup``, optional): ``torch.distributed``
2025-08-14T23:09:16.8333349Z             ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2025-08-14T23:09:16.8333411Z             :meth:`torch.distributed.init_process_group`).
2025-08-14T23:09:16.8333502Z         parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2025-08-14T23:09:16.8333584Z             packed into buckets to speed up communication, and ``param.data``
2025-08-14T23:09:16.8333658Z             fields point to bucket views at different offsets; if ``False``,
2025-08-14T23:09:16.8333733Z             each individual parameter is communicated separately, and each
2025-08-14T23:09:16.8333795Z             ``params.data`` stays intact (default: ``False``).
2025-08-14T23:09:16.8333869Z         overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2025-08-14T23:09:16.8333950Z             overlapped with :class:`DistributedDataParallel` 's gradient
2025-08-14T23:09:16.8334028Z             synchronization; this requires (1) either a functional optimizer
2025-08-14T23:09:16.8334096Z             for the ``optimizer_class`` argument or one with a functional
2025-08-14T23:09:16.8334162Z             equivalent and (2) registering a DDP communication hook
2025-08-14T23:09:16.8334268Z             constructed from one of the functions in ``ddp_zero_hook.py``;
2025-08-14T23:09:16.8334347Z             parameters are packed into buckets matching those in
2025-08-14T23:09:16.8334410Z             :class:`DistributedDataParallel`, meaning that the
2025-08-14T23:09:16.8334467Z             ``parameters_as_bucket_view`` argument is ignored.
2025-08-14T23:09:16.8334542Z             If ``False``, :meth:`step` runs disjointly after the backward pass
2025-08-14T23:09:16.8335975Z             (per normal).
2025-08-14T23:09:16.8336022Z             (default: ``False``)
2025-08-14T23:09:16.8336100Z         **defaults: any trailing arguments, which are forwarded to the local
2025-08-14T23:09:16.8336138Z             optimizer.
2025-08-14T23:09:16.8336170Z     
2025-08-14T23:09:16.8336216Z     Example::
2025-08-14T23:09:16.8336248Z     
2025-08-14T23:09:16.8336290Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8336334Z         >>> import torch.nn as nn
2025-08-14T23:09:16.8336439Z         >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2025-08-14T23:09:16.8336522Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-08-14T23:09:16.8336609Z         >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2025-08-14T23:09:16.8336660Z         >>> ddp = DDP(model, device_ids=[rank])
2025-08-14T23:09:16.8336712Z         >>> opt = ZeroRedundancyOptimizer(
2025-08-14T23:09:16.8336755Z         >>>     ddp.parameters(),
2025-08-14T23:09:16.8336806Z         >>>     optimizer_class=torch.optim.Adam,
2025-08-14T23:09:16.8336845Z         >>>     lr=0.01
2025-08-14T23:09:16.8336878Z         >>> )
2025-08-14T23:09:16.8336923Z         >>> ddp(inputs).sum().backward()
2025-08-14T23:09:16.8336960Z         >>> opt.step()
2025-08-14T23:09:16.8336993Z     
2025-08-14T23:09:16.8337029Z     .. warning::
2025-08-14T23:09:16.8338511Z         Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2025-08-14T23:09:16.8338581Z         passed-in parameters are the same dense type.
2025-08-14T23:09:16.8338619Z     
2025-08-14T23:09:16.8338655Z     .. warning::
2025-08-14T23:09:16.8338737Z         If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2025-08-14T23:09:16.8338814Z         the way that overlapping :class:`DistributedDataParallel` with
2025-08-14T23:09:16.8338929Z         :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2025-08-14T23:09:16.8339007Z         two or three training iterations do not perform parameter updates in
2025-08-14T23:09:16.8339080Z         the optimizer step, depending on if ``static_graph=False`` or
2025-08-14T23:09:16.8339152Z         ``static_graph=True``, respectively. This is because it needs
2025-08-14T23:09:16.8339222Z         information about the gradient bucketing strategy used by
2025-08-14T23:09:16.8339303Z         :class:`DistributedDataParallel`, which is not finalized until the
2025-08-14T23:09:16.8339377Z         second forward pass if ``static_graph=False`` or until the third
2025-08-14T23:09:16.8339457Z         forward pass if ``static_graph=True``. To adjust for this, one option
2025-08-14T23:09:16.8339503Z         is to prepend dummy inputs.
2025-08-14T23:09:16.8339536Z     
2025-08-14T23:09:16.8339627Z     .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2025-08-14T23:09:16.8339662Z     
2025-08-14T23:09:16.8339753Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8339786Z     
2025-08-14T23:09:16.8339825Z       warnings.warn(msg)
2025-08-14T23:09:16.8341246Z     
2025-08-14T23:09:16.8341346Z --- Parse Warning: 95 / 146 ---
2025-08-14T23:09:16.8341765Z     /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.
2025-08-14T23:09:16.8341865Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8341928Z     
2025-08-14T23:09:16.8342088Z     Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-08-14T23:09:16.8342151Z     This optimizer runs local optimizer at every step.
2025-08-14T23:09:16.8342271Z     After the warm-up stage, it averages parameters periodically after the local optimizer is applied.
2025-08-14T23:09:16.8342308Z     
2025-08-14T23:09:16.8342342Z     Args:
2025-08-14T23:09:16.8342386Z         optim: The local optimizer.
2025-08-14T23:09:16.8342468Z         averager: A model averager instance to run post-localSGD algorithm.
2025-08-14T23:09:16.8342501Z     
2025-08-14T23:09:16.8342538Z     Example::
2025-08-14T23:09:16.8342571Z     
2025-08-14T23:09:16.8342625Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8342663Z         >>> import torch
2025-08-14T23:09:16.8342712Z         >>> import torch.distributed as dist
2025-08-14T23:09:16.8342846Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-08-14T23:09:16.8342893Z         >>> import torch.nn as nn
2025-08-14T23:09:16.8343044Z         >>> from torch.distributed.optim import PostLocalSGDOptimizer
2025-08-14T23:09:16.8343144Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-08-14T23:09:16.8344588Z         >>>   PostLocalSGDState,
2025-08-14T23:09:16.8344632Z         >>>   post_localSGD_hook,
2025-08-14T23:09:16.8344668Z         >>> )
2025-08-14T23:09:16.8344704Z         >>>
2025-08-14T23:09:16.8344770Z         >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:16.8344829Z         >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:16.8344862Z         >>> )
2025-08-14T23:09:16.8344895Z         >>>
2025-08-14T23:09:16.8344952Z         >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:16.8345064Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-08-14T23:09:16.8345134Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:16.8345168Z         >>>
2025-08-14T23:09:16.8345242Z         >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2025-08-14T23:09:16.8345337Z         >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2025-08-14T23:09:16.8345435Z         >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:16.8345514Z         >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2025-08-14T23:09:16.8345562Z         >>> opt = PostLocalSGDOptimizer(
2025-08-14T23:09:16.8345605Z         >>>     optim=local_optim,
2025-08-14T23:09:16.8345698Z         >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-08-14T23:09:16.8345730Z         >>> )
2025-08-14T23:09:16.8345763Z         >>>
2025-08-14T23:09:16.8347168Z         >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2025-08-14T23:09:16.8347284Z         >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2025-08-14T23:09:16.8347421Z         >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2025-08-14T23:09:16.8347466Z         >>> for step in range(0, 200):
2025-08-14T23:09:16.8347507Z         >>>    opt.zero_grad()
2025-08-14T23:09:16.8347556Z         >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:16.8347596Z         >>>    loss.backward()
2025-08-14T23:09:16.8347635Z         >>>    opt.step()
2025-08-14T23:09:16.8347666Z     
2025-08-14T23:09:16.8347759Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8347790Z     
2025-08-14T23:09:16.8347830Z       warnings.warn(msg)
2025-08-14T23:09:16.8347861Z     
2025-08-14T23:09:16.8347943Z --- Parse Warning: 96 / 146 ---
2025-08-14T23:09:16.8348311Z     /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.
2025-08-14T23:09:16.8348426Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8348462Z     
2025-08-14T23:09:16.8348549Z     DistributedOptimizer takes remote references to parameters scattered
2025-08-14T23:09:16.8348635Z     across workers and applies the given optimizer locally for each parameter.
2025-08-14T23:09:16.8348667Z     
2025-08-14T23:09:16.8348753Z     This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2025-08-14T23:09:16.8348813Z     to retrieve the gradients for specific parameters.
2025-08-14T23:09:16.8350205Z     
2025-08-14T23:09:16.8350245Z     Concurrent calls to
2025-08-14T23:09:16.8350334Z     :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2025-08-14T23:09:16.8350415Z     either from the same or different clients, will
2025-08-14T23:09:16.8350505Z     be serialized on each worker -- as each worker's optimizer can only work
2025-08-14T23:09:16.8350579Z     on one set of gradients at a time. However, there is no guarantee that
2025-08-14T23:09:16.8350669Z     the full forward-backward-optimizer sequence will execute for one client
2025-08-14T23:09:16.8350749Z     at a time. This means that the gradients being applied may not correspond
2025-08-14T23:09:16.8350828Z     to the latest forward pass executed on a given worker. Also, there is no
2025-08-14T23:09:16.8350873Z     guaranteed ordering across workers.
2025-08-14T23:09:16.8350906Z     
2025-08-14T23:09:16.8350998Z     `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2025-08-14T23:09:16.8351080Z     by default, so that optimizer updates are not blocked by the Python Global
2025-08-14T23:09:16.8351168Z     Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2025-08-14T23:09:16.8351259Z     Model Parallel). This feature is currently enabled for most optimizers. You
2025-08-14T23:09:16.8351351Z     can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2025-08-14T23:09:16.8351396Z     for your own custom optimizers.
2025-08-14T23:09:16.8351428Z     
2025-08-14T23:09:16.8351461Z     Args:
2025-08-14T23:09:16.8351555Z         optimizer_class (optim.Optimizer): the class of optimizer to
2025-08-14T23:09:16.8351599Z             instantiate on each worker.
2025-08-14T23:09:16.8351675Z         params_rref (list[RRef]): list of RRefs to local or remote parameters
2025-08-14T23:09:16.8353127Z             to optimize.
2025-08-14T23:09:16.8353206Z         args: arguments to pass to the optimizer constructor on each worker.
2025-08-14T23:09:16.8353286Z         kwargs: arguments to pass to the optimizer constructor on each worker.
2025-08-14T23:09:16.8353321Z     
2025-08-14T23:09:16.8353357Z     Example::
2025-08-14T23:09:16.8353409Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8353479Z         >>> import torch.distributed.autograd as dist_autograd
2025-08-14T23:09:16.8353529Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8353571Z         >>> from torch import optim
2025-08-14T23:09:16.8353643Z         >>> from torch.distributed.optim import DistributedOptimizer
2025-08-14T23:09:16.8353677Z         >>>
2025-08-14T23:09:16.8353731Z         >>> with dist_autograd.context() as context_id:
2025-08-14T23:09:16.8353773Z         >>>   # Forward pass.
2025-08-14T23:09:16.8353847Z         >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2025-08-14T23:09:16.8353917Z         >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2025-08-14T23:09:16.8353968Z         >>>   loss = rref1.to_here() + rref2.to_here()
2025-08-14T23:09:16.8354000Z         >>>
2025-08-14T23:09:16.8354040Z         >>>   # Backward pass.
2025-08-14T23:09:16.8354099Z         >>>   dist_autograd.backward(context_id, [loss.sum()])
2025-08-14T23:09:16.8354163Z         >>>
2025-08-14T23:09:16.8354217Z         >>>   # Optimizer.
2025-08-14T23:09:16.8355571Z         >>>   dist_optim = DistributedOptimizer(
2025-08-14T23:09:16.8355611Z         >>>      optim.SGD,
2025-08-14T23:09:16.8355652Z         >>>      [rref1, rref2],
2025-08-14T23:09:16.8355688Z         >>>      lr=0.05,
2025-08-14T23:09:16.8355724Z         >>>   )
2025-08-14T23:09:16.8355770Z         >>>   dist_optim.step(context_id)
2025-08-14T23:09:16.8355806Z     
2025-08-14T23:09:16.8355869Z     __ https://github.com/pytorch/tutorials/pull/1465
2025-08-14T23:09:16.8355901Z     
2025-08-14T23:09:16.8355992Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8356027Z     
2025-08-14T23:09:16.8356064Z       warnings.warn(msg)
2025-08-14T23:09:16.8356095Z     
2025-08-14T23:09:16.8356175Z --- Parse Warning: 97 / 146 ---
2025-08-14T23:09:16.8356560Z     /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.
2025-08-14T23:09:16.8356658Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8356690Z     
2025-08-14T23:09:16.8356795Z     StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2025-08-14T23:09:16.8356882Z     on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2025-08-14T23:09:16.8356914Z     
2025-08-14T23:09:16.8356977Z     . N.B. Intended to be used with DynamicMetaLoadPlanner
2025-08-14T23:09:16.8357009Z     
2025-08-14T23:09:16.8358360Z     .. warning::
2025-08-14T23:09:16.8358429Z         Current implementation only supports loading Tensors.
2025-08-14T23:09:16.8358466Z     
2025-08-14T23:09:16.8358512Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8358552Z     >>> sd = {"mode": model}
2025-08-14T23:09:16.8358590Z     >>> dcp.load(
2025-08-14T23:09:16.8358623Z     >>>    sd,
2025-08-14T23:09:16.8358683Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-08-14T23:09:16.8358732Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-08-14T23:09:16.8358777Z     >>>    checkpoint_id="path_to_model.pt"
2025-08-14T23:09:16.8358837Z     >>> )
2025-08-14T23:09:16.8358869Z     
2025-08-14T23:09:16.8358957Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8358987Z     
2025-08-14T23:09:16.8359025Z       warnings.warn(msg)
2025-08-14T23:09:16.8359055Z     
2025-08-14T23:09:16.8359128Z --- Parse Warning: 98 / 146 ---
2025-08-14T23:09:16.8359467Z     /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.
2025-08-14T23:09:16.8359568Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8359609Z     
2025-08-14T23:09:16.8359742Z     Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2025-08-14T23:09:16.8359858Z     avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2025-08-14T23:09:16.8363882Z     metadata file, like Torch Save files.
2025-08-14T23:09:16.8363914Z     
2025-08-14T23:09:16.8363987Z     . N.B. Intended to be used with BroadcastingTorchSaveReader
2025-08-14T23:09:16.8364019Z     
2025-08-14T23:09:16.8364053Z     .. warning::
2025-08-14T23:09:16.8364117Z         Current implementation only supports loading Tensors.
2025-08-14T23:09:16.8364148Z     
2025-08-14T23:09:16.8364191Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8364231Z     >>> sd = {"mode": model}
2025-08-14T23:09:16.8364264Z     >>> dcp.load(
2025-08-14T23:09:16.8364297Z     >>>    sd,
2025-08-14T23:09:16.8364386Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2025-08-14T23:09:16.8364450Z     >>>    planner=DynamicMetaLoadPlanner(),
2025-08-14T23:09:16.8364495Z     >>>    checkpoint_id="path_to_model.pt"
2025-08-14T23:09:16.8364526Z     >>> )
2025-08-14T23:09:16.8364557Z     
2025-08-14T23:09:16.8364644Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8364678Z     
2025-08-14T23:09:16.8364714Z       warnings.warn(msg)
2025-08-14T23:09:16.8364745Z     
2025-08-14T23:09:16.8364819Z --- Parse Warning: 99 / 146 ---
2025-08-14T23:09:16.8367194Z     /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.
2025-08-14T23:09:16.8367298Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8367335Z     
2025-08-14T23:09:16.8367452Z     Save a distributed model in SPMD style.
2025-08-14T23:09:16.8367498Z     
2025-08-14T23:09:16.8367572Z     This function is different from ``torch.save()`` as it handles
2025-08-14T23:09:16.8367665Z     ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2025-08-14T23:09:16.8367698Z     
2025-08-14T23:09:16.8367791Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-08-14T23:09:16.8367852Z     save will call ``state_dict`` before serialization.
2025-08-14T23:09:16.8367883Z     
2025-08-14T23:09:16.8367917Z     .. warning::
2025-08-14T23:09:16.8368000Z         There is no guarantees of Backwards Compatibility across PyTorch versions
2025-08-14T23:09:16.8368040Z         for saved state_dicts.
2025-08-14T23:09:16.8368071Z     
2025-08-14T23:09:16.8368104Z     .. warning::
2025-08-14T23:09:16.8368178Z         If using the `process_group` argument, make sure that only its ranks
2025-08-14T23:09:16.8368254Z         call `save_state_dict` and that all data in state_dict belong to it.
2025-08-14T23:09:16.8368287Z     
2025-08-14T23:09:16.8368321Z     .. note::
2025-08-14T23:09:16.8368413Z         When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2025-08-14T23:09:16.8369951Z         the shard_group should be calling `save_state_dict` and the corresponding process
2025-08-14T23:09:16.8370029Z         group needs to be passed in.
2025-08-14T23:09:16.8370061Z     
2025-08-14T23:09:16.8370094Z     .. note::
2025-08-14T23:09:16.8370193Z         If no process group is available, this function assumes the intention is to save the
2025-08-14T23:09:16.8370237Z          state_dict in the local process.
2025-08-14T23:09:16.8370268Z     
2025-08-14T23:09:16.8370300Z     .. note:
2025-08-14T23:09:16.8370356Z         Rank 0 is assumed to be the coordinator rank.
2025-08-14T23:09:16.8370386Z     
2025-08-14T23:09:16.8370417Z     
2025-08-14T23:09:16.8370448Z     Args:
2025-08-14T23:09:16.8370515Z         state_dict (Dict[str, Any]): The state_dict to save.
2025-08-14T23:09:16.8370577Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:16.8370653Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:16.8370727Z             depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:16.8370792Z             It can also be a key if the storage is a key-value store.
2025-08-14T23:09:16.8370831Z             (Default: ``None``)
2025-08-14T23:09:16.8370881Z         storage_writer (Optional[StorageWriter]):
2025-08-14T23:09:16.8370956Z             Instance of StorageWriter used to perform writes. If this is not
2025-08-14T23:09:16.8371028Z             specified, DCP will automatically infer the writer based on the
2025-08-14T23:09:16.8371101Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:16.8372408Z             be raised. (Default: ``None``)
2025-08-14T23:09:16.8372457Z         planner (Optional[SavePlanner]):
2025-08-14T23:09:16.8372573Z             Instance of SavePlanner. If this is not specified, the default
2025-08-14T23:09:16.8372628Z             planner will be used. (Default: ``None``)
2025-08-14T23:09:16.8372679Z         process_group (Optional[ProcessGroup]):
2025-08-14T23:09:16.8372748Z             ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:16.8372789Z             (Default: ``None``)
2025-08-14T23:09:16.8372826Z         no_dist (bool):
2025-08-14T23:09:16.8372889Z             If ``True``, this function will assume the intent is to load
2025-08-14T23:09:16.8373026Z             a checkpoint on a single rank/process.
2025-08-14T23:09:16.8373066Z             (Default: ``False``)
2025-08-14T23:09:16.8373157Z         use_collectives (bool): If ``False``, this function will assume the intent is to save
2025-08-14T23:09:16.8373222Z             a checkpoint without using cross-rank synchronization.
2025-08-14T23:09:16.8373281Z             (Default: ``True``)
2025-08-14T23:09:16.8373363Z             This configuration is experimental and should be used with caution.
2025-08-14T23:09:16.8373459Z             It will change the format of the saved checkpoint and may not be backward compatible.
2025-08-14T23:09:16.8373490Z     
2025-08-14T23:09:16.8373523Z     Returns:
2025-08-14T23:09:16.8373588Z         Metadata: Metadata object for the saved checkpoint.
2025-08-14T23:09:16.8373620Z     
2025-08-14T23:09:16.8373653Z     Example:
2025-08-14T23:09:16.8374959Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8375000Z         >>> my_model = MyModule()
2025-08-14T23:09:16.8375032Z     
2025-08-14T23:09:16.8375077Z         >>> state_dict = {"model": my_model}
2025-08-14T23:09:16.8375108Z     
2025-08-14T23:09:16.8375198Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-08-14T23:09:16.8375236Z         ...     "/checkpoint/1"
2025-08-14T23:09:16.8375269Z         ... )
2025-08-14T23:09:16.8375322Z         >>> torch.distributed.checkpoint.save(
2025-08-14T23:09:16.8375367Z         >>>     state_dict=state_dict,
2025-08-14T23:09:16.8375416Z         >>>     storage_writer=fs_storage_writer,
2025-08-14T23:09:16.8375448Z         >>> )
2025-08-14T23:09:16.8375478Z     
2025-08-14T23:09:16.8375511Z     .. note::
2025-08-14T23:09:16.8375586Z         save_state_dict uses collectives to coordinate writes across ranks.
2025-08-14T23:09:16.8375688Z         For NCCL-based process groups, internal tensor representations of
2025-08-14T23:09:16.8375769Z         objects must be moved to the GPU device before communication takes place.
2025-08-14T23:09:16.8375847Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-08-14T23:09:16.8375920Z         and it is the user's responsibility to ensure that this is set so that
2025-08-14T23:09:16.8375992Z         each rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-08-14T23:09:16.8376023Z     
2025-08-14T23:09:16.8376112Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8377431Z     
2025-08-14T23:09:16.8377474Z       warnings.warn(msg)
2025-08-14T23:09:16.8377509Z     
2025-08-14T23:09:16.8377592Z --- Parse Warning: 100 / 146 ---
2025-08-14T23:09:16.8377916Z     /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.
2025-08-14T23:09:16.8378010Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8378103Z     Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2025-08-14T23:09:16.8378205Z         staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2025-08-14T23:09:16.8378235Z     
2025-08-14T23:09:16.8378270Z         .. warning::
2025-08-14T23:09:16.8378335Z             This feature is experimental and subject to change.
2025-08-14T23:09:16.8378432Z             MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED
2025-08-14T23:09:16.8378464Z     
2025-08-14T23:09:16.8378495Z         Args:
2025-08-14T23:09:16.8378556Z             state_dict (Dict[str, Any]): The state_dict to save.
2025-08-14T23:09:16.8378611Z             checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:16.8378689Z                 The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:16.8378763Z                 depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:16.8378826Z                 It can also be a key if the storage is a key-value store.
2025-08-14T23:09:16.8378867Z                 (Default: ``None``)
2025-08-14T23:09:16.8378919Z             storage_writer (Optional[StorageWriter]):
2025-08-14T23:09:16.8378993Z                 Instance of StorageWriter used to perform 'stage' and  'save'. If
2025-08-14T23:09:16.8380403Z                 this is not specified, DCP will automatically infer the writer based on the
2025-08-14T23:09:16.8380484Z                 checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:16.8380531Z                 be raised. (Default: ``None``)
2025-08-14T23:09:16.8380577Z             planner (Optional[SavePlanner]):
2025-08-14T23:09:16.8380651Z                 Instance of SavePlanner. If this is not specified, the default
2025-08-14T23:09:16.8380703Z                 planner will be used. (Default: ``None``)
2025-08-14T23:09:16.8380755Z             process_group (Optional[ProcessGroup]):
2025-08-14T23:09:16.8380822Z                 ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:16.8380864Z                 (Default: ``None``)
2025-08-14T23:09:16.8380925Z             async_checkpointer_type (AsyncCheckpointerType):
2025-08-14T23:09:16.8380989Z                 whether to do checkpoint in separate thread or process
2025-08-14T23:09:16.8381048Z                 (Default: ``AsyncCheckpointerType.THREAD``)
2025-08-14T23:09:16.8381094Z             async_stager (AsyncStager):
2025-08-14T23:09:16.8381185Z                 provides staging implementation. If storage_writer implements AsyncStager
2025-08-14T23:09:16.8381263Z                 and async_stager is provided, async_stager will be used for staging
2025-08-14T23:09:16.8381319Z             no_dist (bool):
2025-08-14T23:09:16.8381382Z                 If ``True``, this function will assume the intent is to save
2025-08-14T23:09:16.8381433Z                 a checkpoint on a single rank/process.
2025-08-14T23:09:16.8381474Z                 (Default: ``False``)
2025-08-14T23:09:16.8381582Z             use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``)
2025-08-14T23:09:16.8381661Z                 This configuration is experimental and should be used with caution.
2025-08-14T23:09:16.8383110Z                 It will change the format of the saved checkpoint and may not be backward compatible.
2025-08-14T23:09:16.8383150Z     
2025-08-14T23:09:16.8383187Z         Returns:
2025-08-14T23:09:16.8383266Z             Future: A future holding the resultant Metadata object from `save`.
2025-08-14T23:09:16.8383298Z     
2025-08-14T23:09:16.8383331Z         Example:
2025-08-14T23:09:16.8383373Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8383415Z             >>> my_model = MyModule()
2025-08-14T23:09:16.8383447Z     
2025-08-14T23:09:16.8383492Z             >>> state_dict = {"model": my_model}
2025-08-14T23:09:16.8383524Z     
2025-08-14T23:09:16.8383611Z             >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
2025-08-14T23:09:16.8383652Z             ...     "/checkpoint/1"
2025-08-14T23:09:16.8383684Z             ... )
2025-08-14T23:09:16.8383764Z             >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2025-08-14T23:09:16.8383807Z             >>>     state_dict=state_dict,
2025-08-14T23:09:16.8383858Z             >>>     storage_writer=fs_storage_writer,
2025-08-14T23:09:16.8383918Z             >>> )
2025-08-14T23:09:16.8383950Z             >>>
2025-08-14T23:09:16.8384006Z             >>> # ... do some work ...
2025-08-14T23:09:16.8384038Z             >>>
2025-08-14T23:09:16.8384083Z             >>> checkpoint_future.result()
2025-08-14T23:09:16.8385418Z     
2025-08-14T23:09:16.8385454Z         
2025-08-14T23:09:16.8385547Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8385579Z     
2025-08-14T23:09:16.8385617Z       warnings.warn(msg)
2025-08-14T23:09:16.8385648Z     
2025-08-14T23:09:16.8385726Z --- Parse Warning: 101 / 146 ---
2025-08-14T23:09:16.8386041Z     /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.
2025-08-14T23:09:16.8386158Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8386193Z     
2025-08-14T23:09:16.8386292Z     Abstract class defining the protocol used by save_state_dict to plan the save process.
2025-08-14T23:09:16.8386324Z     
2025-08-14T23:09:16.8386425Z     SavePlanners are stateful objects that can be used to customize the whole save process.
2025-08-14T23:09:16.8386457Z     
2025-08-14T23:09:16.8386558Z     SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-08-14T23:09:16.8386604Z     will be visible to the whole process.
2025-08-14T23:09:16.8386635Z     
2025-08-14T23:09:16.8386732Z     A planner subclass can expect the following sequence of calls during save_state_dict:
2025-08-14T23:09:16.8386762Z     
2025-08-14T23:09:16.8386810Z     1) set_up_planner - called on all ranks.
2025-08-14T23:09:16.8386860Z         Signals the start of a checkpoint save.
2025-08-14T23:09:16.8388132Z     
2025-08-14T23:09:16.8388182Z     2) create_local_plan - called on all ranks.
2025-08-14T23:09:16.8388288Z         Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2025-08-14T23:09:16.8388326Z     
2025-08-14T23:09:16.8388394Z     3) create_global_plan - called on the coordinator rank only.
2025-08-14T23:09:16.8388468Z         Takes the SavePlan from all ranks and make any global decision.
2025-08-14T23:09:16.8388522Z     
2025-08-14T23:09:16.8388566Z     4) finish_plan - called on all ranks.
2025-08-14T23:09:16.8388643Z         This gives each rank a chance to adjust to global planning decisions.
2025-08-14T23:09:16.8388675Z     
2025-08-14T23:09:16.8388733Z     5) resolve_data - called multiple times on each rank
2025-08-14T23:09:16.8388807Z         Lookups a value on the `state_dict` for the storage layer to write.
2025-08-14T23:09:16.8388838Z     
2025-08-14T23:09:16.8388943Z     Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2025-08-14T23:09:16.8389012Z     most changes can be expressed by changes in a single method.
2025-08-14T23:09:16.8389047Z     
2025-08-14T23:09:16.8389094Z     There are 3 usual patterns of extension:
2025-08-14T23:09:16.8389128Z     
2025-08-14T23:09:16.8389223Z     Rewriting state_dict. This is the simplest way to extend the save process as it
2025-08-14T23:09:16.8389338Z     doesn't requite understanding the intrincacies of how SavePlan works:
2025-08-14T23:09:16.8389370Z     
2025-08-14T23:09:16.8389415Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8390792Z     >>> class RenamePlanner(DefaultSavePlanner):
2025-08-14T23:09:16.8390835Z     >>>     def set_up_planner(
2025-08-14T23:09:16.8390871Z     >>>         self,
2025-08-14T23:09:16.8390917Z     >>>         state_dict: STATE_DICT_TYPE,
2025-08-14T23:09:16.8390970Z     >>>         storage_meta: Optional[StorageMeta],
2025-08-14T23:09:16.8391012Z     >>>         is_coordinator: bool,
2025-08-14T23:09:16.8391049Z     >>>     ) -> None:
2025-08-14T23:09:16.8391091Z     >>> # prefix all keys with `foo_``
2025-08-14T23:09:16.8391234Z     >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2025-08-14T23:09:16.8391282Z     
2025-08-14T23:09:16.8391399Z     Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2025-08-14T23:09:16.8391430Z     
2025-08-14T23:09:16.8391475Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8391528Z     >>> class FP16Planner(DefaultSavePlanner):
2025-08-14T23:09:16.8391574Z     >>>     def create_local_plan(self):
2025-08-14T23:09:16.8391623Z     >>>         plan = super().create_local_plan()
2025-08-14T23:09:16.8391665Z     >>>         for p in plan:
2025-08-14T23:09:16.8391710Z     >>>             if p.tensor_data is not None:
2025-08-14T23:09:16.8391775Z     >>>                 p.tensor_data.properties.dtype = torch.float16
2025-08-14T23:09:16.8391812Z     >>>         return plan
2025-08-14T23:09:16.8391845Z     >>>
2025-08-14T23:09:16.8391891Z     >>>     def resolve_data(self, write_item):
2025-08-14T23:09:16.8393236Z     >>>         item = super().resolve_data(write_item)
2025-08-14T23:09:16.8393340Z     >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2025-08-14T23:09:16.8393372Z     
2025-08-14T23:09:16.8393490Z     Using the global planning step to make central decisions that can't be made individually by each rank
2025-08-14T23:09:16.8393524Z     
2025-08-14T23:09:16.8393569Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8393616Z     >>> from itertools import zip_longest
2025-08-14T23:09:16.8393660Z     >>> from dataclasses import replace
2025-08-14T23:09:16.8393724Z     >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2025-08-14T23:09:16.8393823Z     >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
2025-08-14T23:09:16.8393873Z     >>> # This sample doesn't handle ShardedTensors
2025-08-14T23:09:16.8393926Z     >>>     def create_global_plan(self, all_plans):
2025-08-14T23:09:16.8393985Z     >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2025-08-14T23:09:16.8394033Z     >>>         items_per_rank = [
2025-08-14T23:09:16.8394085Z     >>>             [item for item in items if item is not None]
2025-08-14T23:09:16.8394146Z     >>>             for items in zip(*zip_longest(*iters), strict=True)
2025-08-14T23:09:16.8394197Z     >>>         ]
2025-08-14T23:09:16.8394238Z     >>>         all_plans = [
2025-08-14T23:09:16.8394283Z     >>>             replace(plan, items=items)
2025-08-14T23:09:16.8394354Z     >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2025-08-14T23:09:16.8394386Z     >>>         ]
2025-08-14T23:09:16.8395620Z     >>>         return super().create_global_plan(all_plans)
2025-08-14T23:09:16.8395652Z     
2025-08-14T23:09:16.8395747Z     Finally, some planners need to save additional metadata in the checkpoint, this is
2025-08-14T23:09:16.8395846Z     accomplished by having each rank contribute their data items in the local plan and
2025-08-14T23:09:16.8395896Z     the global planner aggregate them:
2025-08-14T23:09:16.8395929Z     
2025-08-14T23:09:16.8395974Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8396034Z     >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2025-08-14T23:09:16.8396087Z     >>>     def create_local_plan(self) -> SavePlan:
2025-08-14T23:09:16.8396137Z     >>>         plan = super().create_local_plan()
2025-08-14T23:09:16.8396201Z     >>>         return replace(plan, planner_data="per-rank-data")
2025-08-14T23:09:16.8396233Z     >>>
2025-08-14T23:09:16.8396338Z     >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2025-08-14T23:09:16.8396409Z     >>>         global_plan, metadata = super().create_global_plan(all_plans)
2025-08-14T23:09:16.8396469Z     >>>         merged_data = [p.planner_data for p in global_plan]
2025-08-14T23:09:16.8396535Z     >>>         metadata = replace(metadata, planner_data=merged_data)
2025-08-14T23:09:16.8396581Z     >>>         return global_plan, metadata
2025-08-14T23:09:16.8396644Z     
2025-08-14T23:09:16.8396749Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8396782Z     
2025-08-14T23:09:16.8396819Z       warnings.warn(msg)
2025-08-14T23:09:16.8396851Z     
2025-08-14T23:09:16.8398102Z --- Parse Warning: 102 / 146 ---
2025-08-14T23:09:16.8398422Z     /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.
2025-08-14T23:09:16.8398513Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8398545Z     
2025-08-14T23:09:16.8398645Z     Abstract class defining the protocol used by load_state_dict to plan the load process.
2025-08-14T23:09:16.8398676Z     
2025-08-14T23:09:16.8398798Z     LoadPlanner are stateful objects that can be used to customize the whole load process.
2025-08-14T23:09:16.8398832Z     
2025-08-14T23:09:16.8398929Z     LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2025-08-14T23:09:16.8398975Z     will be visible to the whole process.
2025-08-14T23:09:16.8399005Z     
2025-08-14T23:09:16.8399104Z     A planner subclass can expect the following sequence of calls during load_state_dict:
2025-08-14T23:09:16.8399135Z     
2025-08-14T23:09:16.8399182Z     1) set_up_planner - called on all ranks.
2025-08-14T23:09:16.8399232Z         Signals the start of loading a checkpoint.
2025-08-14T23:09:16.8399263Z     
2025-08-14T23:09:16.8399312Z     2) create_local_plan - called on all ranks.
2025-08-14T23:09:16.8399412Z         Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2025-08-14T23:09:16.8399444Z     
2025-08-14T23:09:16.8399510Z     3) create_global_plan - called on the coordinator rank only.
2025-08-14T23:09:16.8399584Z         Takes the LoadPlan from all ranks and make any global decision.
2025-08-14T23:09:16.8399619Z     
2025-08-14T23:09:16.8400841Z     4) load_bytes - called multiple times on each rank
2025-08-14T23:09:16.8400906Z         This is called once per non-tensor value in state_dict.
2025-08-14T23:09:16.8400937Z     
2025-08-14T23:09:16.8401017Z     5) resolve_tensor and commit_tensor - called multiple times on each rank
2025-08-14T23:09:16.8401103Z         They are called in pair for each Tensor value in state_dict.
2025-08-14T23:09:16.8401134Z     
2025-08-14T23:09:16.8401240Z     Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2025-08-14T23:09:16.8401306Z     most changes can be expressed by changes in a single method.
2025-08-14T23:09:16.8401337Z     
2025-08-14T23:09:16.8401386Z     There are two usual patterns of extension:
2025-08-14T23:09:16.8401417Z     
2025-08-14T23:09:16.8401509Z     Rewriting state_dict. This is the simplest way to extend the load process as it
2025-08-14T23:09:16.8401608Z     doesn't requite understanding the intrincacies of how LoadPlan works. We need
2025-08-14T23:09:16.8401688Z     to keep a reference to the original state_dict as load happens in place so
2025-08-14T23:09:16.8401738Z     we need to be able to perform it in place
2025-08-14T23:09:16.8401769Z     
2025-08-14T23:09:16.8401815Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8401866Z     >>> class RenamePlanner(DefaultLoadPlanner):
2025-08-14T23:09:16.8401906Z     >>>     def set_up_planner(
2025-08-14T23:09:16.8401941Z     >>>         self,
2025-08-14T23:09:16.8401986Z     >>>         state_dict: STATE_DICT_TYPE,
2025-08-14T23:09:16.8403242Z     >>>         metadata: Metadata,
2025-08-14T23:09:16.8403286Z     >>>         is_coordinator: bool,
2025-08-14T23:09:16.8403322Z     >>>     ) -> None:
2025-08-14T23:09:16.8403373Z     >>>         self.original_state_dict = state_dict
2025-08-14T23:09:16.8403444Z     >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2025-08-14T23:09:16.8403506Z     >>>
2025-08-14T23:09:16.8403568Z     >>>         if self.flatten_sharded_tensors:
2025-08-14T23:09:16.8403627Z     >>>             state_dict = _flatten_sharded_tensors(state_dict)
2025-08-14T23:09:16.8403659Z     >>>
2025-08-14T23:09:16.8403702Z     >>>         if self.flatten_state_dict:
2025-08-14T23:09:16.8403772Z     >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2025-08-14T23:09:16.8403803Z     >>>
2025-08-14T23:09:16.8403848Z     >>>         self.state_dict = state_dict
2025-08-14T23:09:16.8403892Z     >>>         self.metadata = metadata
2025-08-14T23:09:16.8403942Z     >>>         self.is_coordinator = is_coordinator
2025-08-14T23:09:16.8403973Z     >>>
2025-08-14T23:09:16.8404020Z     >>>     def load_bytes(self, read_item, value):
2025-08-14T23:09:16.8404062Z     >>> # Remove the "foo_" prefix
2025-08-14T23:09:16.8404174Z     >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2025-08-14T23:09:16.8404227Z     
2025-08-14T23:09:16.8404259Z     
2025-08-14T23:09:16.8404352Z     Modifying resolve_tensor and commit_tensor to handle load time transformation.
2025-08-14T23:09:16.8405558Z     
2025-08-14T23:09:16.8405606Z     >>> # xdoctest: +SKIP("undefined vars")
2025-08-14T23:09:16.8405667Z     >>> class MetaModelMaterialize(DefaultSavePlanner):
2025-08-14T23:09:16.8405780Z     >>>     def resolve_tensor(self, read_item):
2025-08-14T23:09:16.8405832Z     >>>         tensor = super().resolve_tensor(read_item)
2025-08-14T23:09:16.8405890Z     >>>         return torch.empty_like(tensor, device="cpu")
2025-08-14T23:09:16.8405921Z     >>>
2025-08-14T23:09:16.8405973Z     >>>     def commit_tensor(self, read_item, tensor):
2025-08-14T23:09:16.8406033Z     >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2025-08-14T23:09:16.8406065Z     
2025-08-14T23:09:16.8406155Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8406187Z     
2025-08-14T23:09:16.8406228Z       warnings.warn(msg)
2025-08-14T23:09:16.8406259Z     
2025-08-14T23:09:16.8406336Z --- Parse Warning: 103 / 146 ---
2025-08-14T23:09:16.8406659Z     /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.
2025-08-14T23:09:16.8406777Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8406808Z     
2025-08-14T23:09:16.8406870Z     Return the model state_dict and optimizers state_dict.
2025-08-14T23:09:16.8406901Z     
2025-08-14T23:09:16.8406981Z     ``get_state_dict`` can process any module that is parallelized by PyTorch
2025-08-14T23:09:16.8408300Z     FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
2025-08-14T23:09:16.8408393Z     combination of these parallelisms. The main functions of ``get_state_dict``
2025-08-14T23:09:16.8408477Z     are: 1.) returning a model and optimizer state_dict that can be resharded
2025-08-14T23:09:16.8408552Z     with a different number of trainers and/or different parallelisms.
2025-08-14T23:09:16.8408644Z     2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
2025-08-14T23:09:16.8408681Z     these APIs.
2025-08-14T23:09:16.8408729Z     3.) sanity checking the result state_dict.
2025-08-14T23:09:16.8408761Z     
2025-08-14T23:09:16.8408836Z     The keys of the result state dictionary are the canonical FQNs (Fully
2025-08-14T23:09:16.8408920Z     Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
2025-08-14T23:09:16.8409003Z     position in an nn.Module hierarchy. More specifically, a canonical FQN to a
2025-08-14T23:09:16.8409078Z     parameter is the FQN returned by ``module.named_parameters()`` or
2025-08-14T23:09:16.8409153Z     ``module.named_buffers()`` when the module is not distributed by any
2025-08-14T23:09:16.8409279Z     parallelisms. Since the optimizer internally uses parameter IDs to represent
2025-08-14T23:09:16.8409356Z     a parameter, there will be a conversion from the parameter IDs to the
2025-08-14T23:09:16.8409402Z     canonical FQNs when calling this API.
2025-08-14T23:09:16.8409433Z     
2025-08-14T23:09:16.8409510Z     ``get_state_dict`` can also process a module that is not parallelized. In
2025-08-14T23:09:16.8409591Z     such a case, ``get_state_dict`` only performs one function -- converting the
2025-08-14T23:09:16.8409646Z     optimizer parameter IDs to the canonical FQNs.
2025-08-14T23:09:16.8409677Z     
2025-08-14T23:09:16.8409712Z     Example:
2025-08-14T23:09:16.8410918Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8410957Z         >>> import torch
2025-08-14T23:09:16.8411043Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:16.8411117Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2025-08-14T23:09:16.8411216Z         >>> from torch.distributed.checkpoint.state_dict import get_state_dict
2025-08-14T23:09:16.8411249Z     
2025-08-14T23:09:16.8411299Z         >>> fsdp_model = FSDP(copy.deepcopy(model))
2025-08-14T23:09:16.8411371Z         >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-08-14T23:09:16.8411420Z         >>> ddp_model = DDP(copy.deepcopy(model))
2025-08-14T23:09:16.8411487Z         >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
2025-08-14T23:09:16.8411519Z     
2025-08-14T23:09:16.8411550Z     
2025-08-14T23:09:16.8411634Z         >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
2025-08-14T23:09:16.8411696Z         >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
2025-08-14T23:09:16.8411740Z         ...     fsdp_model, fsdp_optim
2025-08-14T23:09:16.8411772Z         ... )
2025-08-14T23:09:16.8411804Z     
2025-08-14T23:09:16.8411880Z         >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
2025-08-14T23:09:16.8411924Z         >>> # the asserts will fail.
2025-08-14T23:09:16.8411975Z         >>> assert ddp_state_dict == fsdp_state_dict
2025-08-14T23:09:16.8412034Z         >>> assert ddp_optim_state == fsdp_optim_state_dict
2025-08-14T23:09:16.8412065Z     
2025-08-14T23:09:16.8413438Z     
2025-08-14T23:09:16.8413475Z     Args:
2025-08-14T23:09:16.8413561Z         model (nn.Module): the nn.Module to the model.
2025-08-14T23:09:16.8413632Z         optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
2025-08-14T23:09:16.8413694Z             The optimizers that are used to optimize ``model``.
2025-08-14T23:09:16.8413793Z         submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
2025-08-14T23:09:16.8413838Z             that belong to the submodules.
2025-08-14T23:09:16.8413903Z         options (StateDictOptions): the options to control how
2025-08-14T23:09:16.8413978Z             model state_dict and optimizer state_dict should be returned. See
2025-08-14T23:09:16.8414030Z             `StateDictOptions` for the details.
2025-08-14T23:09:16.8414061Z     
2025-08-14T23:09:16.8414098Z     Returns:
2025-08-14T23:09:16.8414170Z         ``Tuple`` that contain model state_dict and optimizer state_dict.
2025-08-14T23:09:16.8414201Z     
2025-08-14T23:09:16.8414283Z     :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
2025-08-14T23:09:16.8414316Z     
2025-08-14T23:09:16.8414407Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8414441Z     
2025-08-14T23:09:16.8414478Z       warnings.warn(msg)
2025-08-14T23:09:16.8414509Z     
2025-08-14T23:09:16.8414585Z --- Parse Warning: 104 / 146 ---
2025-08-14T23:09:16.8416480Z     /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.
2025-08-14T23:09:16.8416581Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8416658Z     
2025-08-14T23:09:16.8416734Z     Load a checkpoint into a distributed state dict in SPMD style.
2025-08-14T23:09:16.8416765Z     
2025-08-14T23:09:16.8416850Z     Each rank must have the same keys in their ``state_dict`` provided to this
2025-08-14T23:09:16.8416935Z     API. Mismatched keys may result in hangs or errors. If unsure, you can use
2025-08-14T23:09:16.8417012Z     the ``utils._assert_same_keys`` API to check (but may incur communication
2025-08-14T23:09:16.8417046Z     costs).
2025-08-14T23:09:16.8417077Z     
2025-08-14T23:09:16.8417145Z     Each rank will try to read the least amount of data necessary
2025-08-14T23:09:16.8417225Z     to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
2025-08-14T23:09:16.8417313Z     or :class:`DTensor` instances, each rank only reads data for their local shards.
2025-08-14T23:09:16.8417344Z     
2025-08-14T23:09:16.8417454Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2025-08-14T23:09:16.8417547Z     load will first call ``state_dict`` before attempting deserialization, followed by
2025-08-14T23:09:16.8417612Z     ``load_state_dict`` once the deserialization is complete.
2025-08-14T23:09:16.8417704Z     For each non-``Stateful`` object, load will deserialize the object, and then replace
2025-08-14T23:09:16.8417764Z     it in the ``state_dict`` with the deserialized object.
2025-08-14T23:09:16.8417796Z     
2025-08-14T23:09:16.8417832Z     .. warning::
2025-08-14T23:09:16.8419368Z         All tensors in ``state_dict`` must be allocated on their
2025-08-14T23:09:16.8419435Z         destination device *prior to* calling this function.
2025-08-14T23:09:16.8419467Z     
2025-08-14T23:09:16.8419546Z         All non-tensor data is loaded using `torch.load()` and modified in place
2025-08-14T23:09:16.8419583Z         on state_dict.
2025-08-14T23:09:16.8419617Z     
2025-08-14T23:09:16.8419653Z     .. warning::
2025-08-14T23:09:16.8419732Z         Users must call `load_state_dict` on the root module to ensure load
2025-08-14T23:09:16.8419801Z         pos-processing and non-tensor data properly propagates.
2025-08-14T23:09:16.8419832Z     
2025-08-14T23:09:16.8419865Z     .. note:
2025-08-14T23:09:16.8419947Z         If no process group is initialized, this function will assume the intent
2025-08-14T23:09:16.8420051Z         is to load a checkpoint into the local process. This can be useful in the
2025-08-14T23:09:16.8420139Z         case of local inference, and when using regular Tensors (as opposed to DTensor
2025-08-14T23:09:16.8420179Z          or ShardedTensor)
2025-08-14T23:09:16.8420210Z     
2025-08-14T23:09:16.8420242Z     .. note:
2025-08-14T23:09:16.8420296Z         Rank 0 is assumed to be the coordinator rank.
2025-08-14T23:09:16.8420327Z     
2025-08-14T23:09:16.8420361Z     Args:
2025-08-14T23:09:16.8420436Z         state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
2025-08-14T23:09:16.8420499Z         checkpoint_id (Union[str, os.PathLike, None]):
2025-08-14T23:09:16.8421924Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2025-08-14T23:09:16.8422000Z             depends on the storage. It can be a path to a folder or to a file.
2025-08-14T23:09:16.8422063Z             It can also be a key if the storage is a key-value store.
2025-08-14T23:09:16.8422105Z             (Default: ``None``)
2025-08-14T23:09:16.8422155Z         storage_reader (Optional[StorageReader]):
2025-08-14T23:09:16.8422230Z             Instance of StorageWriter used to perform reads. If this is not
2025-08-14T23:09:16.8422301Z             specified, DCP will automatically infer the reader based on the
2025-08-14T23:09:16.8422373Z             checkpoint_id. If checkpoint_id is also None, an exception will
2025-08-14T23:09:16.8422416Z             be raised. (Default: ``None``)
2025-08-14T23:09:16.8422461Z         planner (Optional[LoadPlanner]):
2025-08-14T23:09:16.8422532Z             Instance of LoadPlanner. If this is not specified, the default
2025-08-14T23:09:16.8422615Z             planner will be used. (Default: ``None``)
2025-08-14T23:09:16.8422666Z         process_group (Optional[ProcessGroup]):
2025-08-14T23:09:16.8422736Z             ProcessGroup to be used for cross-rank synchronization.
2025-08-14T23:09:16.8422777Z             (Default: ``None``)
2025-08-14T23:09:16.8422856Z         no_dist (bool): If ``True``, this function will assume the intent is to load
2025-08-14T23:09:16.8423036Z             a checkpoint without using cross-rank synchronization. (Default: ``False``)
2025-08-14T23:09:16.8423071Z     Returns:
2025-08-14T23:09:16.8423103Z         None.
2025-08-14T23:09:16.8423135Z     
2025-08-14T23:09:16.8423168Z     Examples
2025-08-14T23:09:16.8424668Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8424712Z         >>> my_model = MyModule()
2025-08-14T23:09:16.8424768Z         >>> optimizer = Adagrad(my_model.parameters())
2025-08-14T23:09:16.8424857Z         >>> model_state_dict = my_model.state_dict()
2025-08-14T23:09:16.8424945Z         >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
2025-08-14T23:09:16.8424985Z         ...     "/checkpoint/1"
2025-08-14T23:09:16.8425017Z         ... )
2025-08-14T23:09:16.8425048Z     
2025-08-14T23:09:16.8425110Z         >>> torch.distributed.checkpoint.load_state_dict(
2025-08-14T23:09:16.8425159Z         >>>     state_dict=model_state_dict,
2025-08-14T23:09:16.8425205Z         >>>     storage_reader=fs_storage_reader,
2025-08-14T23:09:16.8425237Z         >>> )
2025-08-14T23:09:16.8425268Z     
2025-08-14T23:09:16.8425340Z         >>> # module.load_state_dict() function might have customized steps
2025-08-14T23:09:16.8425389Z         >>> # to flush the state_dict, must call it to
2025-08-14T23:09:16.8425432Z         >>> # ensure correct behavior.
2025-08-14T23:09:16.8425481Z         >>> my_model.load_state_dict(model_state_dict)
2025-08-14T23:09:16.8425513Z     
2025-08-14T23:09:16.8425547Z     .. note::
2025-08-14T23:09:16.8425628Z         load_state_dict uses collectives to coordinate reads across ranks.
2025-08-14T23:09:16.8425706Z         For NCCL-based process groups, internal tensor representations of
2025-08-14T23:09:16.8425788Z         objects must be moved to the GPU device before communication takes place.
2025-08-14T23:09:16.8427328Z         In this case, the device used is given by ``torch.cuda.current_device()``
2025-08-14T23:09:16.8427414Z         and it is the user's responsibility to ensure that this is set so that each
2025-08-14T23:09:16.8427497Z         rank has an individual GPU, via ``torch.cuda.set_device()``.
2025-08-14T23:09:16.8427529Z     
2025-08-14T23:09:16.8427617Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8427648Z     
2025-08-14T23:09:16.8427687Z       warnings.warn(msg)
2025-08-14T23:09:16.8427717Z     
2025-08-14T23:09:16.8427795Z --- Parse Warning: 105 / 146 ---
2025-08-14T23:09:16.8428147Z     /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.
2025-08-14T23:09:16.8428243Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8428274Z     
2025-08-14T23:09:16.8428351Z     Load a state_dict in conjunction with FSDP sharded optimizer state.
2025-08-14T23:09:16.8428382Z     
2025-08-14T23:09:16.8428445Z     This is the current recommended way to checkpoint FSDP.
2025-08-14T23:09:16.8428484Z     >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8428543Z     >>> import torch.distributed.checkpoint as dist_cp
2025-08-14T23:09:16.8428576Z     >>> # Save
2025-08-14T23:09:16.8428617Z     >>> model: torch.nn.Model
2025-08-14T23:09:16.8428663Z     >>> optim_params = model.parameters()
2025-08-14T23:09:16.8428718Z     >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2025-08-14T23:09:16.8430480Z     >>> # Save
2025-08-14T23:09:16.8430596Z     >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2025-08-14T23:09:16.8430641Z     >>>     state_dict = {
2025-08-14T23:09:16.8430704Z     >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2025-08-14T23:09:16.8430748Z     >>>         "model": model.state_dict()
2025-08-14T23:09:16.8430784Z     >>>     }
2025-08-14T23:09:16.8430826Z     >>>     dist_cp.save_state_dict(
2025-08-14T23:09:16.8430868Z     >>>         state_dict=optim_state,
2025-08-14T23:09:16.8430934Z     >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2025-08-14T23:09:16.8430987Z     >>>         planner=dist_cp.DefaultSavePlanner(),
2025-08-14T23:09:16.8431020Z     >>>     )
2025-08-14T23:09:16.8431052Z     >>>
2025-08-14T23:09:16.8431085Z     >>> # Load
2025-08-14T23:09:16.8431165Z     >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2025-08-14T23:09:16.8431240Z     >>>     model_state_dict = model_tp.state_dict()
2025-08-14T23:09:16.8431281Z     >>>     checkpoint = {
2025-08-14T23:09:16.8431328Z     >>>         "model": model_state_dict
2025-08-14T23:09:16.8431360Z     >>>     }
2025-08-14T23:09:16.8431401Z     >>>     dist_cp.load_state_dict(
2025-08-14T23:09:16.8431443Z     >>>         state_dict=checkpoint,
2025-08-14T23:09:16.8431515Z     >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2025-08-14T23:09:16.8433433Z     >>>         planner=dist_cp.DefaultLoadPlanner(),
2025-08-14T23:09:16.8433474Z     >>>     )
2025-08-14T23:09:16.8433540Z     >>>     model.load_state_dict(checkpoint["model_state"])
2025-08-14T23:09:16.8433582Z     >>>
2025-08-14T23:09:16.8433647Z     >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2025-08-14T23:09:16.8433690Z     >>>         model_state_dict,
2025-08-14T23:09:16.8433736Z     >>>         optimizer_key="optimizer",
2025-08-14T23:09:16.8433804Z     >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2025-08-14T23:09:16.8433841Z     >>>     )
2025-08-14T23:09:16.8433873Z     >>>
2025-08-14T23:09:16.8433936Z     >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:16.8433989Z     >>>        model, optim, optim_state["optimizer"]
2025-08-14T23:09:16.8434021Z     >>>     )
2025-08-14T23:09:16.8434052Z     >>>
2025-08-14T23:09:16.8434128Z     >>>     optim.load_state_dict(flattened_osd)
2025-08-14T23:09:16.8434159Z     
2025-08-14T23:09:16.8434250Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8434281Z     
2025-08-14T23:09:16.8434320Z       warnings.warn(msg)
2025-08-14T23:09:16.8434351Z     
2025-08-14T23:09:16.8434428Z --- Parse Warning: 106 / 146 ---
2025-08-14T23:09:16.8436366Z     /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.
2025-08-14T23:09:16.8436473Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8436504Z     
2025-08-14T23:09:16.8436573Z     This configures FSDP-native mixed precision training.
2025-08-14T23:09:16.8436615Z     
2025-08-14T23:09:16.8436649Z     Attributes:
2025-08-14T23:09:16.8436733Z         param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2025-08-14T23:09:16.8436808Z             parameters during forward and backward and thus the dtype for
2025-08-14T23:09:16.8436885Z             forward and backward computation. Outside forward and backward, the
2025-08-14T23:09:16.8436958Z             *sharded* parameters are kept in full precision (e.g. for the
2025-08-14T23:09:16.8437031Z             optimizer step), and for model checkpointing, the parameters are
2025-08-14T23:09:16.8437092Z             always saved in full precision. (Default: ``None``)
2025-08-14T23:09:16.8437170Z         reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-08-14T23:09:16.8437300Z             gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2025-08-14T23:09:16.8437365Z             ``None`` but ``param_dtype`` is not ``None``, then this takes on
2025-08-14T23:09:16.8437436Z             the ``param_dtype`` value, still running gradient reduction in low
2025-08-14T23:09:16.8437516Z             precision. This is permitted to differ from ``param_dtype``, e.g.
2025-08-14T23:09:16.8437585Z             to force gradient reduction to run in full precision. (Default:
2025-08-14T23:09:16.8437621Z             ``None``)
2025-08-14T23:09:16.8437699Z         buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2025-08-14T23:09:16.8437774Z             buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2025-08-14T23:09:16.8439606Z             ``buffer_dtype`` in the first forward pass and keeps them in that
2025-08-14T23:09:16.8439690Z             dtype thereafter. For model checkpointing, the buffers are saved
2025-08-14T23:09:16.8439791Z             in full precision except for ``LOCAL_STATE_DICT``. (Default:
2025-08-14T23:09:16.8439840Z             ``None``)
2025-08-14T23:09:16.8439911Z         keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2025-08-14T23:09:16.8439990Z             gradients to full precision after the backward pass in preparation
2025-08-14T23:09:16.8440068Z             for the optimizer step. If ``True``, then FSDP keeps the gradients
2025-08-14T23:09:16.8440138Z             in the dtype used for gradient reduction, which can save memory if
2025-08-14T23:09:16.8440211Z             using a custom optimizer that supports running in low precision.
2025-08-14T23:09:16.8440253Z             (Default: ``False``)
2025-08-14T23:09:16.8440328Z         cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2025-08-14T23:09:16.8440400Z             its forward args and kwargs to ``param_dtype``. This is to ensure
2025-08-14T23:09:16.8440474Z             that parameter and input dtypes match for forward computation, as
2025-08-14T23:09:16.8440554Z             required by many ops. This may need to be set to ``True`` when only
2025-08-14T23:09:16.8440630Z             applying mixed precision to some but not all FSDP modules, in which
2025-08-14T23:09:16.8440704Z             case a mixed-precision FSDP submodule needs to recast its inputs.
2025-08-14T23:09:16.8440757Z             (Default: ``False``)
2025-08-14T23:09:16.8440849Z         cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2025-08-14T23:09:16.8440919Z             casts its forward args and kwargs to ``param_dtype``, overriding
2025-08-14T23:09:16.8442561Z             the value of ``cast_forward_inputs``. For non-root FSDP modules,
2025-08-14T23:09:16.8442625Z             this does not do anything. (Default: ``True``)
2025-08-14T23:09:16.8442717Z         _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2025-08-14T23:09:16.8442795Z             module classes to ignore for mixed precision when using an
2025-08-14T23:09:16.8442876Z             ``auto_wrap_policy``: Modules of these classes will have FSDP
2025-08-14T23:09:16.8443007Z             applied to them separately with mixed precision disabled (meaning
2025-08-14T23:09:16.8443079Z             that the final FSDP construction would deviate from the specified
2025-08-14T23:09:16.8443148Z             policy). If ``auto_wrap_policy`` is not specified, then this does
2025-08-14T23:09:16.8443219Z             not do anything. This API is experimental and subject to change.
2025-08-14T23:09:16.8443263Z             (Default: ``(_BatchNorm,)``)
2025-08-14T23:09:16.8443294Z     
2025-08-14T23:09:16.8443359Z     .. note:: This API is experimental and subject to change.
2025-08-14T23:09:16.8443391Z     
2025-08-14T23:09:16.8443468Z     .. note:: Only floating point tensors are cast to their specified dtypes.
2025-08-14T23:09:16.8443499Z     
2025-08-14T23:09:16.8443568Z     .. note:: In ``summon_full_params``, parameters are forced to full
2025-08-14T23:09:16.8443646Z         precision, but buffers are not.
2025-08-14T23:09:16.8443679Z     
2025-08-14T23:09:16.8443772Z     .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2025-08-14T23:09:16.8443846Z         their inputs are in a low precision like ``float16`` or ``bfloat16``.
2025-08-14T23:09:16.8445583Z         Disabling FSDP's mixed precision for those norm modules only means that
2025-08-14T23:09:16.8445673Z         the affine parameters are kept in ``float32``. However, this incurs
2025-08-14T23:09:16.8445837Z         separate all-gathers and reduce-scatters for those norm modules, which
2025-08-14T23:09:16.8445913Z         may be inefficient, so if the workload permits, the user should prefer
2025-08-14T23:09:16.8445977Z         to still apply mixed precision to those modules.
2025-08-14T23:09:16.8446013Z     
2025-08-14T23:09:16.8446090Z     .. note:: By default, if the user passes a model with any ``_BatchNorm``
2025-08-14T23:09:16.8446201Z         modules and specifies an ``auto_wrap_policy``, then the batch norm
2025-08-14T23:09:16.8446285Z         modules will have FSDP applied to them separately with mixed precision
2025-08-14T23:09:16.8446352Z         disabled. See the ``_module_classes_to_ignore`` argument.
2025-08-14T23:09:16.8446383Z     
2025-08-14T23:09:16.8446457Z     .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2025-08-14T23:09:16.8446534Z         ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2025-08-14T23:09:16.8446600Z         its ``cast_root_forward_inputs`` takes precedence over its
2025-08-14T23:09:16.8446668Z         ``cast_forward_inputs``. For non-root FSDP instances, their
2025-08-14T23:09:16.8446744Z         ``cast_root_forward_inputs`` values are ignored. The default setting is
2025-08-14T23:09:16.8446821Z         sufficient for the typical case where each FSDP instance has the same
2025-08-14T23:09:16.8446899Z         ``MixedPrecision`` configuration and only needs to cast inputs to the
2025-08-14T23:09:16.8446969Z         ``param_dtype`` at the beginning of the model's forward pass.
2025-08-14T23:09:16.8447006Z     
2025-08-14T23:09:16.8447081Z     .. note:: For nested FSDP instances with different ``MixedPrecision``
2025-08-14T23:09:16.8447164Z         configurations, we recommend setting individual ``cast_forward_inputs``
2025-08-14T23:09:16.8447256Z         values to configure casting inputs or not before each instance's
2025-08-14T23:09:16.8447328Z         forward. In such a case, since the casts happen before each FSDP
2025-08-14T23:09:16.8447405Z         instance's forward, a parent FSDP instance should have its non-FSDP
2025-08-14T23:09:16.8447487Z         submodules run before its FSDP submodules to avoid the activation dtype
2025-08-14T23:09:16.8447560Z         being changed due to a different ``MixedPrecision`` configuration.
2025-08-14T23:09:16.8448998Z     
2025-08-14T23:09:16.8449035Z         Example::
2025-08-14T23:09:16.8449067Z     
2025-08-14T23:09:16.8449123Z             >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8449191Z             >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2025-08-14T23:09:16.8449231Z             >>> model[1] = FSDP(
2025-08-14T23:09:16.8449271Z             >>>     model[1],
2025-08-14T23:09:16.8449380Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2025-08-14T23:09:16.8449414Z             >>> )
2025-08-14T23:09:16.8449452Z             >>> model = FSDP(
2025-08-14T23:09:16.8449489Z             >>>     model,
2025-08-14T23:09:16.8449598Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2025-08-14T23:09:16.8449630Z             >>> )
2025-08-14T23:09:16.8449661Z     
2025-08-14T23:09:16.8449736Z         The above shows a working example. On the other hand, if ``model[1]``
2025-08-14T23:09:16.8449811Z         were replaced with ``model[0]``, meaning that the submodule using
2025-08-14T23:09:16.8449892Z         different ``MixedPrecision`` ran its forward first, then ``model[1]``
2025-08-14T23:09:16.8450008Z         would incorrectly see ``float16`` activations instead of ``bfloat16``
2025-08-14T23:09:16.8450041Z         ones.
2025-08-14T23:09:16.8450073Z     
2025-08-14T23:09:16.8450103Z     
2025-08-14T23:09:16.8451471Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8451505Z     
2025-08-14T23:09:16.8451548Z       warnings.warn(msg)
2025-08-14T23:09:16.8451579Z     
2025-08-14T23:09:16.8451663Z --- Parse Warning: 107 / 146 ---
2025-08-14T23:09:16.8451982Z     /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.
2025-08-14T23:09:16.8452076Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8452106Z     
2025-08-14T23:09:16.8452202Z     ``FullStateDictConfig`` is a config class meant to be used with
2025-08-14T23:09:16.8452281Z     ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
2025-08-14T23:09:16.8452357Z     ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
2025-08-14T23:09:16.8452435Z     dicts to save GPU memory and CPU memory, respectively. This config class
2025-08-14T23:09:16.8452508Z     is meant to be used via the :func:`state_dict_type` context manager as
2025-08-14T23:09:16.8452541Z     follows:
2025-08-14T23:09:16.8452573Z     
2025-08-14T23:09:16.8452624Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8452710Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:16.8452760Z         >>> fsdp = FSDP(model, auto_wrap_policy=...)
2025-08-14T23:09:16.8452834Z         >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
2025-08-14T23:09:16.8453003Z         >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
2025-08-14T23:09:16.8453050Z         >>>     state = fsdp.state_dict()
2025-08-14T23:09:16.8453129Z         >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
2025-08-14T23:09:16.8454451Z         >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
2025-08-14T23:09:16.8454538Z         >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
2025-08-14T23:09:16.8454609Z         >>> if dist.get_rank() == 0:
2025-08-14T23:09:16.8454676Z         >>> # Load checkpoint only on rank 0 to avoid memory redundancy
2025-08-14T23:09:16.8454734Z         >>>     state_dict = torch.load("my_checkpoint.pt")
2025-08-14T23:09:16.8454782Z         >>>     model.load_state_dict(state_dict)
2025-08-14T23:09:16.8454898Z         >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
2025-08-14T23:09:16.8454985Z         >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
2025-08-14T23:09:16.8455025Z         >>> fsdp = FSDP(
2025-08-14T23:09:16.8455062Z         ...     model,
2025-08-14T23:09:16.8455118Z         ...     device_id=torch.cuda.current_device(),
2025-08-14T23:09:16.8455162Z         ...     auto_wrap_policy=...,
2025-08-14T23:09:16.8455205Z         ...     sync_module_states=True,
2025-08-14T23:09:16.8455237Z         ... )
2025-08-14T23:09:16.8455314Z         >>> # After this point, all ranks have FSDP model with loaded checkpoint.
2025-08-14T23:09:16.8455346Z     
2025-08-14T23:09:16.8455380Z     Attributes:
2025-08-14T23:09:16.8455452Z         rank0_only (bool): If ``True``, then only rank 0 saves the full state
2025-08-14T23:09:16.8455525Z             dict, and nonzero ranks save an empty dict. If ``False``, then all
2025-08-14T23:09:16.8455586Z             ranks save the full state dict. (Default: ``False``)
2025-08-14T23:09:16.8455617Z     
2025-08-14T23:09:16.8456950Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8456984Z     
2025-08-14T23:09:16.8457052Z       warnings.warn(msg)
2025-08-14T23:09:16.8457083Z     
2025-08-14T23:09:16.8457177Z --- Parse Warning: 108 / 146 ---
2025-08-14T23:09:16.8457565Z     /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.
2025-08-14T23:09:16.8457660Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8457748Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-08-14T23:09:16.8457779Z     
2025-08-14T23:09:16.8457873Z             Also takes (optional) configuration for the model's and optimizer's state dict.
2025-08-14T23:09:16.8457946Z             The target module does not have to be a FSDP module. If the target
2025-08-14T23:09:16.8458048Z             module is a FSDP module, its ``state_dict_type`` will also be changed.
2025-08-14T23:09:16.8458085Z     
2025-08-14T23:09:16.8458159Z             .. note:: This API should be called for only the top-level (root)
2025-08-14T23:09:16.8458195Z                 module.
2025-08-14T23:09:16.8458225Z     
2025-08-14T23:09:16.8458299Z             .. note:: This API enables users to transparently use the conventional
2025-08-14T23:09:16.8458371Z                 ``state_dict`` API to take model checkpoints in cases where the
2025-08-14T23:09:16.8458444Z                 root FSDP module is wrapped by another ``nn.Module``. For example,
2025-08-14T23:09:16.8458518Z                 the following will ensure ``state_dict`` is called on all non-FSDP
2025-08-14T23:09:16.8458601Z                 instances, while dispatching into `sharded_state_dict` implementation
2025-08-14T23:09:16.8458639Z                 for FSDP:
2025-08-14T23:09:16.8459945Z     
2025-08-14T23:09:16.8459984Z             Example::
2025-08-14T23:09:16.8460020Z     
2025-08-14T23:09:16.8460081Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8460128Z                 >>> model = DDP(FSDP(...))
2025-08-14T23:09:16.8460176Z                 >>> FSDP.set_state_dict_type(
2025-08-14T23:09:16.8460214Z                 >>>     model,
2025-08-14T23:09:16.8460268Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-08-14T23:09:16.8460367Z                 >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2025-08-14T23:09:16.8460448Z                 >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2025-08-14T23:09:16.8460481Z                 >>> )
2025-08-14T23:09:16.8460531Z                 >>> param_state_dict = model.state_dict()
2025-08-14T23:09:16.8460594Z                 >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-08-14T23:09:16.8460625Z     
2025-08-14T23:09:16.8460658Z             Args:
2025-08-14T23:09:16.8460709Z                 module (torch.nn.Module): Root module.
2025-08-14T23:09:16.8460798Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-08-14T23:09:16.8460881Z                 state_dict_config (Optional[StateDictConfig]): the configuration for the
2025-08-14T23:09:16.8460928Z                     target ``state_dict_type``.
2025-08-14T23:09:16.8461020Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2025-08-14T23:09:16.8461066Z                     for the optimizer state dict.
2025-08-14T23:09:16.8462323Z     
2025-08-14T23:09:16.8462359Z             Returns:
2025-08-14T23:09:16.8462442Z                 A StateDictSettings that include the previous state_dict type and
2025-08-14T23:09:16.8462488Z                 configuration for the module.
2025-08-14T23:09:16.8462521Z             
2025-08-14T23:09:16.8462611Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8462642Z     
2025-08-14T23:09:16.8462680Z       warnings.warn(msg)
2025-08-14T23:09:16.8462732Z     
2025-08-14T23:09:16.8462822Z --- Parse Warning: 109 / 146 ---
2025-08-14T23:09:16.8463260Z     /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.
2025-08-14T23:09:16.8463354Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8463439Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2025-08-14T23:09:16.8463471Z     
2025-08-14T23:09:16.8463581Z             This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2025-08-14T23:09:16.8463636Z             :meth:`set_state_dict_type` for the detail.
2025-08-14T23:09:16.8463667Z     
2025-08-14T23:09:16.8463703Z             Example::
2025-08-14T23:09:16.8463757Z     
2025-08-14T23:09:16.8463815Z                 >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8463859Z                 >>> model = DDP(FSDP(...))
2025-08-14T23:09:16.8463905Z                 >>> with FSDP.state_dict_type(
2025-08-14T23:09:16.8465157Z                 >>>     model,
2025-08-14T23:09:16.8465210Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2025-08-14T23:09:16.8465245Z                 >>> ):
2025-08-14T23:09:16.8465295Z                 >>>     checkpoint = model.state_dict()
2025-08-14T23:09:16.8465326Z     
2025-08-14T23:09:16.8465359Z             Args:
2025-08-14T23:09:16.8465409Z                 module (torch.nn.Module): Root module.
2025-08-14T23:09:16.8465491Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2025-08-14T23:09:16.8465573Z                 state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2025-08-14T23:09:16.8465634Z                     configuration for the target ``state_dict_type``.
2025-08-14T23:09:16.8465724Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2025-08-14T23:09:16.8465797Z                    ``state_dict`` configuration for the target ``state_dict_type``.
2025-08-14T23:09:16.8465830Z             
2025-08-14T23:09:16.8465918Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8465974Z     
2025-08-14T23:09:16.8466012Z       warnings.warn(msg)
2025-08-14T23:09:16.8466043Z     
2025-08-14T23:09:16.8466116Z --- Parse Warning: 110 / 146 ---
2025-08-14T23:09:16.8466498Z     /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.
2025-08-14T23:09:16.8466588Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8466625Z     
2025-08-14T23:09:16.8466713Z     Transform the state-dict of an optimizer corresponding to a sharded model.
2025-08-14T23:09:16.8467946Z     
2025-08-14T23:09:16.8468019Z     The given state-dict can be transformed to one of three types:
2025-08-14T23:09:16.8468126Z     1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2025-08-14T23:09:16.8468158Z     
2025-08-14T23:09:16.8468239Z     For full optimizer state_dict, all states are unflattened and not sharded.
2025-08-14T23:09:16.8468316Z     Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2025-08-14T23:09:16.8468350Z     avoid OOM.
2025-08-14T23:09:16.8468381Z     
2025-08-14T23:09:16.8468462Z     For sharded optimizer state_dict, all states are unflattened but sharded.
2025-08-14T23:09:16.8468536Z     CPU only can be specified via :meth:`state_dict_type` to further save
2025-08-14T23:09:16.8468569Z     memory.
2025-08-14T23:09:16.8468600Z     
2025-08-14T23:09:16.8468704Z     For local state_dict, no transformation will be performed. But a state
2025-08-14T23:09:16.8468809Z     will be converted from nn.Tensor to ShardedTensor to represent its sharding
2025-08-14T23:09:16.8468854Z     nature (this is not supported yet).
2025-08-14T23:09:16.8468885Z     
2025-08-14T23:09:16.8468920Z     Example::
2025-08-14T23:09:16.8468954Z     
2025-08-14T23:09:16.8469004Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8469090Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:16.8469150Z         >>> from torch.distributed.fsdp import StateDictType
2025-08-14T23:09:16.8470406Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-08-14T23:09:16.8470484Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-08-14T23:09:16.8470526Z         >>> # Save a checkpoint
2025-08-14T23:09:16.8470565Z         >>> model, optim = ...
2025-08-14T23:09:16.8470629Z         >>> FSDP.set_state_dict_type(
2025-08-14T23:09:16.8470667Z         >>>     model,
2025-08-14T23:09:16.8470716Z         >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:16.8470767Z         >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8470825Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8470856Z         >>> )
2025-08-14T23:09:16.8470904Z         >>> state_dict = model.state_dict()
2025-08-14T23:09:16.8470966Z         >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2025-08-14T23:09:16.8471022Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-08-14T23:09:16.8471061Z         >>> # Load a checkpoint
2025-08-14T23:09:16.8471100Z         >>> model, optim = ...
2025-08-14T23:09:16.8471159Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-08-14T23:09:16.8471201Z         >>> FSDP.set_state_dict_type(
2025-08-14T23:09:16.8471235Z         >>>     model,
2025-08-14T23:09:16.8471282Z         >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:16.8471336Z         >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8471393Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8471426Z         >>> )
2025-08-14T23:09:16.8472706Z         >>> model.load_state_dict(state_dict)
2025-08-14T23:09:16.8472770Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:16.8472836Z         >>>     model, optim, optim_state_dict
2025-08-14T23:09:16.8472962Z         >>> )
2025-08-14T23:09:16.8473011Z         >>> optim.load_state_dict(optim_state_dict)
2025-08-14T23:09:16.8473046Z     
2025-08-14T23:09:16.8473078Z     Args:
2025-08-14T23:09:16.8473152Z         model (torch.nn.Module): Root module (which may or may not be a
2025-08-14T23:09:16.8473224Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-08-14T23:09:16.8473278Z             were passed into the optimizer ``optim``.
2025-08-14T23:09:16.8473351Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-08-14T23:09:16.8473392Z             parameters.
2025-08-14T23:09:16.8473471Z         optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2025-08-14T23:09:16.8473547Z             transform. If the value is None, optim.state_dict() will be used. (
2025-08-14T23:09:16.8473585Z             Default: ``None``)
2025-08-14T23:09:16.8473672Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-08-14T23:09:16.8473741Z             are sharded or ``None`` if using the default process group. (
2025-08-14T23:09:16.8473780Z             Default: ``None``)
2025-08-14T23:09:16.8473810Z     
2025-08-14T23:09:16.8473843Z     Returns:
2025-08-14T23:09:16.8473913Z         Dict[str, Any]: A :class:`dict` containing the optimizer state for
2025-08-14T23:09:16.8473977Z         ``model``. The sharding of the optimizer state is based on
2025-08-14T23:09:16.8475316Z         ``state_dict_type``.
2025-08-14T23:09:16.8475352Z     
2025-08-14T23:09:16.8475445Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8475521Z     
2025-08-14T23:09:16.8475561Z       warnings.warn(msg)
2025-08-14T23:09:16.8475591Z     
2025-08-14T23:09:16.8475668Z --- Parse Warning: 111 / 146 ---
2025-08-14T23:09:16.8476061Z     /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.
2025-08-14T23:09:16.8476156Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8476186Z     
2025-08-14T23:09:16.8476310Z     Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2025-08-14T23:09:16.8476340Z     
2025-08-14T23:09:16.8476405Z     Given a ``optim_state_dict`` that is transformed through
2025-08-14T23:09:16.8476503Z     :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2025-08-14T23:09:16.8476581Z     state_dict that can be loaded to ``optim`` which is the optimizer for
2025-08-14T23:09:16.8476652Z     ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2025-08-14T23:09:16.8476683Z     
2025-08-14T23:09:16.8476737Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8476824Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2025-08-14T23:09:16.8476883Z         >>> from torch.distributed.fsdp import StateDictType
2025-08-14T23:09:16.8476950Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2025-08-14T23:09:16.8478292Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2025-08-14T23:09:16.8478340Z         >>> # Save a checkpoint
2025-08-14T23:09:16.8478378Z         >>> model, optim = ...
2025-08-14T23:09:16.8478423Z         >>> FSDP.set_state_dict_type(
2025-08-14T23:09:16.8478460Z         >>>     model,
2025-08-14T23:09:16.8478511Z         >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:16.8478566Z         >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8478621Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8478654Z         >>> )
2025-08-14T23:09:16.8478698Z         >>> state_dict = model.state_dict()
2025-08-14T23:09:16.8478767Z         >>> original_osd = optim.state_dict()
2025-08-14T23:09:16.8478818Z         >>> optim_state_dict = FSDP.optim_state_dict(
2025-08-14T23:09:16.8478853Z         >>>     model,
2025-08-14T23:09:16.8478886Z         >>>     optim,
2025-08-14T23:09:16.8478932Z         >>>     optim_state_dict=original_osd
2025-08-14T23:09:16.8478963Z         >>> )
2025-08-14T23:09:16.8479017Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2025-08-14T23:09:16.8479056Z         >>> # Load a checkpoint
2025-08-14T23:09:16.8479096Z         >>> model, optim = ...
2025-08-14T23:09:16.8479157Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2025-08-14T23:09:16.8479207Z         >>> FSDP.set_state_dict_type(
2025-08-14T23:09:16.8479241Z         >>>     model,
2025-08-14T23:09:16.8480539Z         >>>     StateDictType.FULL_STATE_DICT,
2025-08-14T23:09:16.8480594Z         >>>     FullStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8480650Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2025-08-14T23:09:16.8480682Z         >>> )
2025-08-14T23:09:16.8480729Z         >>> model.load_state_dict(state_dict)
2025-08-14T23:09:16.8480787Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2025-08-14T23:09:16.8480832Z         >>>     model, optim, optim_state_dict
2025-08-14T23:09:16.8480864Z         >>> )
2025-08-14T23:09:16.8480912Z         >>> optim.load_state_dict(optim_state_dict)
2025-08-14T23:09:16.8480944Z     
2025-08-14T23:09:16.8480976Z     Args:
2025-08-14T23:09:16.8481049Z         model (torch.nn.Module): Root module (which may or may not be a
2025-08-14T23:09:16.8481124Z             :class:`FullyShardedDataParallel` instance) whose parameters
2025-08-14T23:09:16.8481209Z             were passed into the optimizer ``optim``.
2025-08-14T23:09:16.8481278Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2025-08-14T23:09:16.8481315Z             parameters.
2025-08-14T23:09:16.8481390Z         optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2025-08-14T23:09:16.8481465Z         is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2025-08-14T23:09:16.8481534Z             KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2025-08-14T23:09:16.8481600Z             KeyedOptimizer or torch.distributed's NamedOptimizer.
2025-08-14T23:09:16.8481669Z         load_directly (bool): If this is set to True, this API will also
2025-08-14T23:09:16.8483030Z             call optim.load_state_dict(result) before returning the result.
2025-08-14T23:09:16.8483115Z             Otherwise, users are responsible to call ``optim.load_state_dict()``
2025-08-14T23:09:16.8483185Z             (Default: ``False``)
2025-08-14T23:09:16.8483274Z         group (dist.ProcessGroup): Model's process group across which parameters
2025-08-14T23:09:16.8483343Z             are sharded or ``None`` if using the default process group. (
2025-08-14T23:09:16.8483381Z             Default: ``None``)
2025-08-14T23:09:16.8483416Z     
2025-08-14T23:09:16.8483504Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8483536Z     
2025-08-14T23:09:16.8483573Z       warnings.warn(msg)
2025-08-14T23:09:16.8483604Z     
2025-08-14T23:09:16.8483678Z --- Parse Warning: 112 / 146 ---
2025-08-14T23:09:16.8484073Z     /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.
2025-08-14T23:09:16.8484166Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8484202Z     
2025-08-14T23:09:16.8484321Z     Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2025-08-14T23:09:16.8484352Z     
2025-08-14T23:09:16.8484461Z     Process groups of different sizes are organized in a hierarchy, and they average parameters
2025-08-14T23:09:16.8484554Z     by using different periods concurrently after the warm-up stage.
2025-08-14T23:09:16.8484699Z     This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2025-08-14T23:09:16.8484817Z     that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2025-08-14T23:09:16.8484925Z     a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2025-08-14T23:09:16.8486333Z     level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2025-08-14T23:09:16.8486446Z     Similarly, the process groups within this class do not have such an intra-machine process
2025-08-14T23:09:16.8486543Z     subgroup, which should be embedded by the post-local SGD communication hook instead.
2025-08-14T23:09:16.8486574Z     
2025-08-14T23:09:16.8486606Z     Args:
2025-08-14T23:09:16.8486703Z         period_group_size_dict: An ordered dict mapping keys of model averaging period to
2025-08-14T23:09:16.8486778Z                                 process group size, used for initializing process groups of
2025-08-14T23:09:16.8486859Z                                 different sizes in a hierarchy to average parameters concurrently.
2025-08-14T23:09:16.8486937Z                                 Particularly, at each iteration, there will be at most a single
2025-08-14T23:09:16.8487019Z                                 process group that runs averaging -- the period of such group should
2025-08-14T23:09:16.8487098Z                                 have the largest period which the current step can be divided by.
2025-08-14T23:09:16.8487205Z                                 For example, if the dict has three keys: 2, 4, and 8,
2025-08-14T23:09:16.8487280Z                                 then this means totally three process groups will be created to
2025-08-14T23:09:16.8487355Z                                 average parameters every 2, 4, and 8 iterations, respectively.
2025-08-14T23:09:16.8487429Z                                 At the 4th iteration, only the second process group will run
2025-08-14T23:09:16.8487495Z                                 averaging, because the first process group should be a
2025-08-14T23:09:16.8487572Z                                 subset of the second process group, and no need to execute the first
2025-08-14T23:09:16.8487623Z                                 process group redundantly.
2025-08-14T23:09:16.8487693Z                                 On the other hand, the third process group can only be triggered
2025-08-14T23:09:16.8487791Z                                 every 8 iterations, so it will not be triggered at the 4th iteration.
2025-08-14T23:09:16.8487900Z         warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2025-08-14T23:09:16.8489274Z         process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2025-08-14T23:09:16.8489343Z                                                 If ``None``, the default process group, which is created
2025-08-14T23:09:16.8489419Z                                                 by :func:`torch.distributed.init_process_group`, will be used.
2025-08-14T23:09:16.8489469Z                                                 (default: ``None``)
2025-08-14T23:09:16.8489501Z     
2025-08-14T23:09:16.8489536Z     Example::
2025-08-14T23:09:16.8489584Z         >>> # xdoctest: +SKIP('undefined rank')
2025-08-14T23:09:16.8489631Z         >>> from collections import OrderedDict
2025-08-14T23:09:16.8489671Z         >>> import torch
2025-08-14T23:09:16.8489723Z         >>> import torch.distributed as dist
2025-08-14T23:09:16.8489820Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2025-08-14T23:09:16.8489863Z         >>>     PostLocalSGDState,
2025-08-14T23:09:16.8489905Z         >>>     post_localSGD_hook,
2025-08-14T23:09:16.8489953Z         >>> )
2025-08-14T23:09:16.8490084Z         >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2025-08-14T23:09:16.8490124Z         >>> import torch.nn as nn
2025-08-14T23:09:16.8490157Z         >>>
2025-08-14T23:09:16.8490224Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-08-14T23:09:16.8490268Z         >>> torch.cuda.set_device(rank)
2025-08-14T23:09:16.8490322Z         >>> module = nn.Linear(1, 1, bias=False).to(rank)
2025-08-14T23:09:16.8490385Z         >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:16.8490442Z         >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:16.8491677Z         >>> )
2025-08-14T23:09:16.8491736Z         >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:16.8491834Z         >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2025-08-14T23:09:16.8491883Z         >>> subgroup, _ = dist.new_subgroups()
2025-08-14T23:09:16.8491993Z         >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2025-08-14T23:09:16.8492056Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:16.8492088Z         >>>
2025-08-14T23:09:16.8492188Z         >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2025-08-14T23:09:16.8492236Z         >>> # the 16 processes every 16 iterations.
2025-08-14T23:09:16.8492311Z         >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2025-08-14T23:09:16.8492398Z         >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2025-08-14T23:09:16.8492549Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:16.8492644Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-08-14T23:09:16.8492706Z         >>> # After 100 steps, run model averaging at two levels.
2025-08-14T23:09:16.8492749Z         >>> for step in range(0, 200):
2025-08-14T23:09:16.8492791Z         >>>    optimizer.zero_grad()
2025-08-14T23:09:16.8492836Z         >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:16.8492876Z         >>>    loss.backward()
2025-08-14T23:09:16.8492974Z         >>>    optimizer.step()
2025-08-14T23:09:16.8493034Z         >>>    # Average parameters after ``optimizer.step()``.
2025-08-14T23:09:16.8493134Z         >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2025-08-14T23:09:16.8494483Z         >>>    averager.average_parameters(model.parameters())
2025-08-14T23:09:16.8494518Z     
2025-08-14T23:09:16.8494559Z     .. warning ::
2025-08-14T23:09:16.8494682Z         The last group size in the dict must be the size of the provided ``process_group``,
2025-08-14T23:09:16.8494768Z         which indicates model averaging at the highest level of the hierarchy.
2025-08-14T23:09:16.8494876Z         If ``process_group`` is not provided, then the last group size should be equal to the world size.
2025-08-14T23:09:16.8494909Z     
2025-08-14T23:09:16.8494943Z     .. warning ::
2025-08-14T23:09:16.8495027Z         `HierarchicalModelAverager` is experimental and subject to change.
2025-08-14T23:09:16.8495058Z     
2025-08-14T23:09:16.8495148Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8495179Z     
2025-08-14T23:09:16.8495217Z       warnings.warn(msg)
2025-08-14T23:09:16.8495247Z     
2025-08-14T23:09:16.8495322Z --- Parse Warning: 113 / 146 ---
2025-08-14T23:09:16.8495696Z     /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.
2025-08-14T23:09:16.8495790Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8495840Z     
2025-08-14T23:09:16.8495910Z     Averages parameters periodically after the warm-up stage.
2025-08-14T23:09:16.8495941Z     
2025-08-14T23:09:16.8496034Z     This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2025-08-14T23:09:16.8496107Z     by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2025-08-14T23:09:16.8497550Z     using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2025-08-14T23:09:16.8497586Z     
2025-08-14T23:09:16.8497618Z     Args:
2025-08-14T23:09:16.8497686Z         period (int): The number of steps per model averaging.
2025-08-14T23:09:16.8497785Z                       Usually the period should be greater than ``1`` to reduce the communication cost.
2025-08-14T23:09:16.8497838Z                       Otherwise, only DDP needs to be used.
2025-08-14T23:09:16.8497914Z         warmup_steps (int): The number of warm-up steps. During this stage,
2025-08-14T23:09:16.8497966Z                             model averaging is skipped.
2025-08-14T23:09:16.8498035Z         process_group: The process group to be used for all-reduce.
2025-08-14T23:09:16.8498092Z                        If ``None``, the default process group, which
2025-08-14T23:09:16.8498162Z                        is created by :func:`torch.distributed.init_process_group`,
2025-08-14T23:09:16.8498212Z                        will be used. (default: ``None``)
2025-08-14T23:09:16.8498244Z     
2025-08-14T23:09:16.8498278Z     Example::
2025-08-14T23:09:16.8498309Z     
2025-08-14T23:09:16.8498361Z         >>> # xdoctest: +SKIP("undefined variables")
2025-08-14T23:09:16.8498426Z         >>> import torch
2025-08-14T23:09:16.8498494Z         >>> import torch.distributed as dist
2025-08-14T23:09:16.8498605Z         >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2025-08-14T23:09:16.8498701Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2025-08-14T23:09:16.8498745Z         >>> import torch.nn as nn
2025-08-14T23:09:16.8499990Z         >>>
2025-08-14T23:09:16.8500059Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2025-08-14T23:09:16.8500103Z         >>> torch.cuda.set_device(rank)
2025-08-14T23:09:16.8500154Z         >>> module = nn.Linear(1, 1, bias=False).cuda()
2025-08-14T23:09:16.8500217Z         >>> model = nn.parallel.DistributedDataParallel(
2025-08-14T23:09:16.8500272Z         >>>    module, device_ids=[rank], output_device=rank
2025-08-14T23:09:16.8500303Z         >>> )
2025-08-14T23:09:16.8500359Z         >>> # Register a post-localSGD communication hook.
2025-08-14T23:09:16.8500493Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2025-08-14T23:09:16.8500556Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:16.8500587Z         >>>
2025-08-14T23:09:16.8500682Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2025-08-14T23:09:16.8500743Z         >>> # After 100 steps, run model averaging every 4 steps.
2025-08-14T23:09:16.8500856Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2025-08-14T23:09:16.8500945Z         >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2025-08-14T23:09:16.8500987Z         >>> for step in range(0, 200):
2025-08-14T23:09:16.8501028Z         >>>    optimizer.zero_grad()
2025-08-14T23:09:16.8501075Z         >>>    loss = loss_fn(output, labels)
2025-08-14T23:09:16.8501114Z         >>>    loss.backward()
2025-08-14T23:09:16.8501158Z         >>>    optimizer.step()
2025-08-14T23:09:16.8501230Z         >>>    # Will average model parameters globally every 4 steps. Thus,
2025-08-14T23:09:16.8501304Z         >>>    # inter-node communication only occurs every 4 iterations after
2025-08-14T23:09:16.8502578Z         >>>    # the initial ``warmup_steps`` period.
2025-08-14T23:09:16.8502669Z         >>>    averager.average_parameters(model.parameters())
2025-08-14T23:09:16.8502734Z     
2025-08-14T23:09:16.8502826Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8502857Z     
2025-08-14T23:09:16.8502895Z       warnings.warn(msg)
2025-08-14T23:09:16.8502990Z     
2025-08-14T23:09:16.8503065Z --- Parse Warning: 114 / 146 ---
2025-08-14T23:09:16.8503431Z     /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.
2025-08-14T23:09:16.8503527Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8503558Z     
2025-08-14T23:09:16.8503601Z     Run post-localSGD algorithm.
2025-08-14T23:09:16.8503632Z     
2025-08-14T23:09:16.8503715Z     This DDP communication hook is used for running post-localSGD algorithm,
2025-08-14T23:09:16.8503777Z     by combining with a model averaging component (e.g.,
2025-08-14T23:09:16.8503896Z     :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2025-08-14T23:09:16.8503941Z     that runs after the optimizer step.
2025-08-14T23:09:16.8503972Z     
2025-08-14T23:09:16.8504004Z     Args:
2025-08-14T23:09:16.8504084Z         state (PostLocalSGDState): State information to run post-localSGD.
2025-08-14T23:09:16.8504181Z             Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2025-08-14T23:09:16.8504351Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-08-14T23:09:16.8505747Z             Note that since DDP comm hook only supports single process single device mode,
2025-08-14T23:09:16.8505807Z             only exactly one tensor is stored in this bucket.
2025-08-14T23:09:16.8505843Z     
2025-08-14T23:09:16.8505875Z     Returns:
2025-08-14T23:09:16.8505962Z         Future handler of the communication, which updates the gradients in place.
2025-08-14T23:09:16.8505992Z     
2025-08-14T23:09:16.8506027Z     Example::
2025-08-14T23:09:16.8506067Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8506158Z         >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2025-08-14T23:09:16.8506205Z                                   start_localSGD_iter=10)
2025-08-14T23:09:16.8506270Z         >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2025-08-14T23:09:16.8506407Z         >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2025-08-14T23:09:16.8506532Z         >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2025-08-14T23:09:16.8506563Z     
2025-08-14T23:09:16.8506652Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8506685Z     
2025-08-14T23:09:16.8506724Z       warnings.warn(msg)
2025-08-14T23:09:16.8506754Z     
2025-08-14T23:09:16.8506826Z --- Parse Warning: 115 / 146 ---
2025-08-14T23:09:16.8507172Z     /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.
2025-08-14T23:09:16.8507264Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8508581Z     
2025-08-14T23:09:16.8508628Z     Implement PowerSGD algorithm.
2025-08-14T23:09:16.8508667Z     
2025-08-14T23:09:16.8508751Z     This DDP communication hook implements PowerSGD gradient compression
2025-08-14T23:09:16.8508836Z     algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2025-08-14T23:09:16.8508920Z     Once gradient tensors are aggregated across all workers, this hook applies
2025-08-14T23:09:16.8508986Z     compression as follows:
2025-08-14T23:09:16.8509017Z     
2025-08-14T23:09:16.8509168Z     1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2025-08-14T23:09:16.8509199Z     
2025-08-14T23:09:16.8509343Z         1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2025-08-14T23:09:16.8509374Z     
2025-08-14T23:09:16.8509515Z         1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2025-08-14T23:09:16.8509550Z     
2025-08-14T23:09:16.8509594Z     2. Handles uncompressed tensors:
2025-08-14T23:09:16.8509625Z     
2025-08-14T23:09:16.8509803Z         2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2025-08-14T23:09:16.8509834Z     
2025-08-14T23:09:16.8509952Z         2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2025-08-14T23:09:16.8509982Z     
2025-08-14T23:09:16.8510066Z     3. Handles the tensors that should be compressed by PowerSGD compression:
2025-08-14T23:09:16.8510096Z     
2025-08-14T23:09:16.8511456Z         3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2025-08-14T23:09:16.8511569Z         such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2025-08-14T23:09:16.8511600Z     
2025-08-14T23:09:16.8511656Z         3.2. Computes each P in Ps, which is equal to MQ;
2025-08-14T23:09:16.8511711Z     
2025-08-14T23:09:16.8511766Z         3.3. Allreduces Ps as a batch;
2025-08-14T23:09:16.8511798Z     
2025-08-14T23:09:16.8511843Z         3.4. Orthogonalizes each P in Ps;
2025-08-14T23:09:16.8511873Z     
2025-08-14T23:09:16.8511946Z         3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2025-08-14T23:09:16.8511979Z     
2025-08-14T23:09:16.8512021Z         3.6. Allreduces Qs as a batch;
2025-08-14T23:09:16.8512051Z     
2025-08-14T23:09:16.8512156Z         3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2025-08-14T23:09:16.8512187Z     
2025-08-14T23:09:16.8512328Z     Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2025-08-14T23:09:16.8512430Z     This not only gives the user more control over the tradeoff between speedup and accuracy,
2025-08-14T23:09:16.8512591Z     but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2025-08-14T23:09:16.8512624Z     
2025-08-14T23:09:16.8512659Z     Args:
2025-08-14T23:09:16.8512805Z         state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2025-08-14T23:09:16.8514267Z             To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2025-08-14T23:09:16.8514315Z             and ``min_compression_rate``.
2025-08-14T23:09:16.8514463Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2025-08-14T23:09:16.8514551Z             Note that since DDP comm hook only supports single process single device mode,
2025-08-14T23:09:16.8514611Z             only exactly one tensor is stored in this bucket.
2025-08-14T23:09:16.8514641Z     
2025-08-14T23:09:16.8514674Z     Returns:
2025-08-14T23:09:16.8514762Z         Future handler of the communication, which updates the gradients in place.
2025-08-14T23:09:16.8514795Z     
2025-08-14T23:09:16.8514832Z     Example::
2025-08-14T23:09:16.8514873Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8514970Z         >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2025-08-14T23:09:16.8515031Z                                   start_powerSGD_iter=10, min_compression_rate=0.5)
2025-08-14T23:09:16.8515126Z         >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2025-08-14T23:09:16.8515158Z     
2025-08-14T23:09:16.8515247Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8515278Z     
2025-08-14T23:09:16.8515316Z       warnings.warn(msg)
2025-08-14T23:09:16.8515346Z     
2025-08-14T23:09:16.8515421Z --- Parse Warning: 116 / 146 ---
2025-08-14T23:09:16.8515769Z     /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.
2025-08-14T23:09:16.8515864Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8517176Z     
2025-08-14T23:09:16.8517267Z     Creates an :class:`ShardedTensor` from local shards and the global metadata.
2025-08-14T23:09:16.8517325Z     Needs to be called on all ranks in an SPMD fashion.
2025-08-14T23:09:16.8517357Z     
2025-08-14T23:09:16.8517389Z     Args:
2025-08-14T23:09:16.8517487Z         local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2025-08-14T23:09:16.8517550Z             of shards that represent the local shards on this rank.
2025-08-14T23:09:16.8517635Z         global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2025-08-14T23:09:16.8517682Z             shape of the overall sharded tensor.
2025-08-14T23:09:16.8517713Z     
2025-08-14T23:09:16.8517747Z     Keyword args:
2025-08-14T23:09:16.8517887Z         process_group (ProcessGroup, optional): The process group to work on. If None,
2025-08-14T23:09:16.8517937Z             the default process group will be used.
2025-08-14T23:09:16.8518006Z         init_rrefs (bool, optional): Whether or not to initialize
2025-08-14T23:09:16.8518081Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-08-14T23:09:16.8518153Z             Need to initialize the RPC Framework if specified as ``True``.
2025-08-14T23:09:16.8518192Z             Default: ``False``.
2025-08-14T23:09:16.8518224Z     
2025-08-14T23:09:16.8518256Z     Returns:
2025-08-14T23:09:16.8518313Z         A :class:`ShardedTensor` object handle on this rank
2025-08-14T23:09:16.8518345Z     
2025-08-14T23:09:16.8519635Z     
2025-08-14T23:09:16.8519669Z     Examples:
2025-08-14T23:09:16.8519766Z         Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2025-08-14T23:09:16.8519859Z         each shard have a (5, 5) local tensor, we can do it like below:
2025-08-14T23:09:16.8519892Z     
2025-08-14T23:09:16.8519929Z         on rank 0:
2025-08-14T23:09:16.8519975Z         >>> # xdoctest: +SKIP("not distributed")
2025-08-14T23:09:16.8520022Z         >>> local_shard_metadata = ShardMetadata(
2025-08-14T23:09:16.8520064Z         >>>     shard_offsets=[0, 0],
2025-08-14T23:09:16.8520109Z         >>>     shard_lengths=[5, 5],
2025-08-14T23:09:16.8520153Z         >>>     placement="rank:0/cuda:0"
2025-08-14T23:09:16.8520186Z         >>> )
2025-08-14T23:09:16.8520257Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-08-14T23:09:16.8520330Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-08-14T23:09:16.8520361Z     
2025-08-14T23:09:16.8520395Z         on rank 1:
2025-08-14T23:09:16.8520440Z         >>> # xdoctest: +SKIP("not distributed")
2025-08-14T23:09:16.8520488Z         >>> local_shard_metadata = ShardMetadata(
2025-08-14T23:09:16.8520527Z         >>>     shard_offsets=[5, 0],
2025-08-14T23:09:16.8520571Z         >>>     shard_lengths=[5, 5],
2025-08-14T23:09:16.8520617Z         >>>     placement="rank:1/cuda:1"
2025-08-14T23:09:16.8520648Z         >>> )
2025-08-14T23:09:16.8521987Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2025-08-14T23:09:16.8522061Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2025-08-14T23:09:16.8522113Z     
2025-08-14T23:09:16.8522202Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8522234Z     
2025-08-14T23:09:16.8522272Z       warnings.warn(msg)
2025-08-14T23:09:16.8522304Z     
2025-08-14T23:09:16.8522375Z --- Parse Warning: 117 / 146 ---
2025-08-14T23:09:16.8522698Z     /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.
2025-08-14T23:09:16.8522798Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8522830Z     
2025-08-14T23:09:16.8522970Z     Representation of a sharding plan, describes how to shard a module
2025-08-14T23:09:16.8523067Z     across hosts. `plan` is used to shard module parameters according to the spec provided,
2025-08-14T23:09:16.8523170Z     `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2025-08-14T23:09:16.8523258Z     layout of a module with a spec, and when to convert back to data parallel fashion.
2025-08-14T23:09:16.8523289Z     
2025-08-14T23:09:16.8523321Z     Args:
2025-08-14T23:09:16.8523416Z         plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2025-08-14T23:09:16.8523481Z               :class:`torch.distributed._shard.sharder.Sharder`]):
2025-08-14T23:09:16.8523577Z             a dict describes how to shard a module, there're currently two ways to shard a module:
2025-08-14T23:09:16.8523713Z                 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2025-08-14T23:09:16.8523762Z                    a parameter to a `ShardingSpec`.
2025-08-14T23:09:16.8525157Z                 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2025-08-14T23:09:16.8525208Z                    to a `Sharder` object.
2025-08-14T23:09:16.8525326Z         output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2025-08-14T23:09:16.8525418Z             a dict specifies the layout of a module's output which produces a ShardedTensor,
2025-08-14T23:09:16.8525502Z             keyed by the name of module to ShardingSpec("" in key means the root module).
2025-08-14T23:09:16.8525540Z             Default: `None`
2025-08-14T23:09:16.8525631Z         return_local_tensor (List[str], optional): a list of string, each element enables
2025-08-14T23:09:16.8525772Z             a module's sharded output to be returned as a Tensor from its local shards to
2025-08-14T23:09:16.8525867Z             ensure further processing in a data parallel fashion. ("" in list means the
2025-08-14T23:09:16.8525904Z             root module).
2025-08-14T23:09:16.8525942Z             Default: None
2025-08-14T23:09:16.8525975Z     Example:
2025-08-14T23:09:16.8526075Z       Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2025-08-14T23:09:16.8526177Z       want to convert the output of the second linear layer back to DDP, we can do it as follows:
2025-08-14T23:09:16.8526209Z     
2025-08-14T23:09:16.8526274Z         >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2025-08-14T23:09:16.8526317Z         >>> class MyModule(nn.Module):
2025-08-14T23:09:16.8526363Z         >>>     def __init__(self) -> None:
2025-08-14T23:09:16.8526402Z         >>>        super().__init__()
2025-08-14T23:09:16.8526445Z         >>>        self.fc1 = nn.Linear()
2025-08-14T23:09:16.8526487Z         >>>        self.gelu = nn.GELU()
2025-08-14T23:09:16.8527756Z         >>>        self.fc2 = nn.Linear()
2025-08-14T23:09:16.8527802Z         >>>        self.relu = nn.Linear()
2025-08-14T23:09:16.8527835Z         >>>
2025-08-14T23:09:16.8527876Z         >>>     def forward(self, input):
2025-08-14T23:09:16.8527942Z         >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2025-08-14T23:09:16.8528000Z     
2025-08-14T23:09:16.8528032Z     
2025-08-14T23:09:16.8528085Z         >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2025-08-14T23:09:16.8528129Z         >>> sharding_plan = ShardingPlan(
2025-08-14T23:09:16.8528164Z         >>>    plan={
2025-08-14T23:09:16.8528205Z         >>>        "fc1.weight": spec1,
2025-08-14T23:09:16.8528245Z         >>>        "fc2.weight": spec2
2025-08-14T23:09:16.8528278Z         >>>    },
2025-08-14T23:09:16.8528317Z         >>>    output_plan={
2025-08-14T23:09:16.8528357Z         >>>        "fc2": output_spec
2025-08-14T23:09:16.8528391Z         >>>    },
2025-08-14T23:09:16.8528441Z         >>>    return_local_tensor=["fc2"]
2025-08-14T23:09:16.8528476Z         >>> )
2025-08-14T23:09:16.8528507Z     
2025-08-14T23:09:16.8528599Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8528630Z     
2025-08-14T23:09:16.8528668Z       warnings.warn(msg)
2025-08-14T23:09:16.8529963Z     
2025-08-14T23:09:16.8530039Z --- Parse Warning: 118 / 146 ---
2025-08-14T23:09:16.8530397Z     /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.
2025-08-14T23:09:16.8530490Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8530521Z     
2025-08-14T23:09:16.8530612Z     Initialize a ShardedTensor given only one local tensor, global sharded tensor
2025-08-14T23:09:16.8530659Z     size and sharding spec on each rank.
2025-08-14T23:09:16.8530710Z     
2025-08-14T23:09:16.8530755Z     Args:
2025-08-14T23:09:16.8530838Z         local_tensor (Tensor): Single tensor of local shard stored in each rank.
2025-08-14T23:09:16.8530930Z         sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2025-08-14T23:09:16.8530997Z             The specification describing how to shard the Tensor.
2025-08-14T23:09:16.8531060Z         global_size (Sequence[int]): Size of the sharded tensor.
2025-08-14T23:09:16.8531149Z         process_group (ProcessGroup, optional): The process group to aggregate on.
2025-08-14T23:09:16.8531187Z             Default: None
2025-08-14T23:09:16.8531251Z         init_rrefs (bool, optional): Whether or not to initialize
2025-08-14T23:09:16.8531327Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2025-08-14T23:09:16.8531397Z             Need to initialize the RPC Framework if specified as ``True``.
2025-08-14T23:09:16.8531456Z             Default: ``False``.
2025-08-14T23:09:16.8531488Z     
2025-08-14T23:09:16.8531523Z     Returns:
2025-08-14T23:09:16.8531608Z         A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2025-08-14T23:09:16.8533008Z             tensor stored in the current rank.
2025-08-14T23:09:16.8533040Z     
2025-08-14T23:09:16.8533079Z     Examples:
2025-08-14T23:09:16.8533121Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8533176Z         >>> # All tensors below are of torch.int64 type.
2025-08-14T23:09:16.8533222Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8533289Z         >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2025-08-14T23:09:16.8533360Z         >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2025-08-14T23:09:16.8533398Z         >>> local_tensor
2025-08-14T23:09:16.8533438Z         tensor([[1, 2, 3, 4]]) # Rank 0
2025-08-14T23:09:16.8533477Z         tensor([[3, 4, 5, 6]]) # Rank 1
2025-08-14T23:09:16.8533523Z         >>> sharding_dim = 0
2025-08-14T23:09:16.8533575Z         >>> sharding_spec = ChunkShardingSpec(
2025-08-14T23:09:16.8533614Z                 dim=sharding_dim,
2025-08-14T23:09:16.8533653Z                 placements=[
2025-08-14T23:09:16.8533692Z                     "rank:0/cuda:0",
2025-08-14T23:09:16.8533729Z                     "rank:1/cuda:1",
2025-08-14T23:09:16.8533791Z                 ],
2025-08-14T23:09:16.8533822Z             )
2025-08-14T23:09:16.8533876Z         >>> st = ShardedTensor._init_from_local_tensor(
2025-08-14T23:09:16.8533925Z         ...     local_tensor, sharding_spec, [2, 4]
2025-08-14T23:09:16.8535230Z         ... )
2025-08-14T23:09:16.8535264Z         >>> st
2025-08-14T23:09:16.8535305Z         ShardedTensor(
2025-08-14T23:09:16.8535349Z             ShardedTensorMetadata(
2025-08-14T23:09:16.8535391Z                 shards_metadata=[
2025-08-14T23:09:16.8535488Z                     ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2025-08-14T23:09:16.8535588Z                     ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2025-08-14T23:09:16.8535621Z                 ],
2025-08-14T23:09:16.8535662Z                 size=torch.Size([2, 4])
2025-08-14T23:09:16.8535693Z         )
2025-08-14T23:09:16.8535733Z         >>> st.local_tensor()
2025-08-14T23:09:16.8535772Z         tensor([1, 2, 3, 4]) # Rank 0
2025-08-14T23:09:16.8535811Z         tensor([3, 4, 5, 6]) # Rank 1
2025-08-14T23:09:16.8535841Z     
2025-08-14T23:09:16.8535940Z     Warning: This API is experimental and subject to change. It lacks of a fully across
2025-08-14T23:09:16.8536024Z              rank validations, and we only validate the local shard on the current rank.
2025-08-14T23:09:16.8536102Z              We fully rely on the user to ensure local tensor is sharded based on the
2025-08-14T23:09:16.8536140Z              sharding spec.
2025-08-14T23:09:16.8536170Z     
2025-08-14T23:09:16.8536265Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8536323Z     
2025-08-14T23:09:16.8536377Z       warnings.warn(msg)
2025-08-14T23:09:16.8537610Z     
2025-08-14T23:09:16.8537685Z --- Parse Warning: 119 / 146 ---
2025-08-14T23:09:16.8538027Z     /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.
2025-08-14T23:09:16.8538122Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8538152Z     
2025-08-14T23:09:16.8538242Z     Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2025-08-14T23:09:16.8538279Z     single local shard.
2025-08-14T23:09:16.8538310Z     
2025-08-14T23:09:16.8538388Z     If ``resharding_spec`` is same as the original one, this becomes a no-op.
2025-08-14T23:09:16.8538502Z     If only ``resharding_spec`` shares the same sharding dim with the original one,
2025-08-14T23:09:16.8538548Z     we swap local shards directly.
2025-08-14T23:09:16.8538640Z     For more generic cases, we merge different shards across different ranks and split
2025-08-14T23:09:16.8538729Z     the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2025-08-14T23:09:16.8538761Z     
2025-08-14T23:09:16.8538792Z     Args:
2025-08-14T23:09:16.8538896Z         resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2025-08-14T23:09:16.8538958Z             specification describing how the tensor is sharded.
2025-08-14T23:09:16.8538990Z     
2025-08-14T23:09:16.8539022Z     Returns:
2025-08-14T23:09:16.8539095Z         A :class:`ShardedTensor` object whose local shards are resharded.
2025-08-14T23:09:16.8539126Z     
2025-08-14T23:09:16.8540399Z     Examples:
2025-08-14T23:09:16.8540439Z         >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8540490Z         >>> # We have 2 process groups, 2 ranks.
2025-08-14T23:09:16.8540563Z         >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2025-08-14T23:09:16.8540611Z         >>> tensor = torch.stack([tensor, tensor])
2025-08-14T23:09:16.8540646Z         >>> tensor
2025-08-14T23:09:16.8540695Z         tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2025-08-14T23:09:16.8540759Z         tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2025-08-14T23:09:16.8540807Z         tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2025-08-14T23:09:16.8540859Z         tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2025-08-14T23:09:16.8540898Z         >>> sharding_dim = 0
2025-08-14T23:09:16.8540942Z         >>> spec = ChunkShardingSpec(
2025-08-14T23:09:16.8540981Z                 dim=sharding_dim,
2025-08-14T23:09:16.8541020Z                 placements=[
2025-08-14T23:09:16.8541058Z                     "rank:0/cuda:0",
2025-08-14T23:09:16.8541096Z                     "rank:1/cuda:1",
2025-08-14T23:09:16.8541133Z                     "rank:2/cuda:2",
2025-08-14T23:09:16.8541175Z                     "rank:3/cuda:3",
2025-08-14T23:09:16.8541211Z                 ],
2025-08-14T23:09:16.8541243Z             )
2025-08-14T23:09:16.8541283Z         >>> current_offsets = [0] * 2
2025-08-14T23:09:16.8541326Z         >>> current_offsets[0] = rank * 2
2025-08-14T23:09:16.8542582Z         >>> shard_metadata = ShardMetadata(
2025-08-14T23:09:16.8542643Z                 shard_offsets=copy.deepcopy(current_offsets),
2025-08-14T23:09:16.8542686Z                 shard_sizes=tensor.size(),
2025-08-14T23:09:16.8542734Z                 placement=spec.placements[rank],
2025-08-14T23:09:16.8542766Z             )
2025-08-14T23:09:16.8542803Z         >>> local_shards = [
2025-08-14T23:09:16.8542837Z                 Shard(
2025-08-14T23:09:16.8542876Z                     tensor=tensor,
2025-08-14T23:09:16.8542980Z                     metadata=shard_metadata,
2025-08-14T23:09:16.8543012Z                 )
2025-08-14T23:09:16.8543044Z             ]
2025-08-14T23:09:16.8543129Z         >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2025-08-14T23:09:16.8543209Z         >>> sharding_dim = 1
2025-08-14T23:09:16.8543257Z         >>> resharding_spec = ChunkShardingSpec(
2025-08-14T23:09:16.8543296Z                 dim=sharding_dim,
2025-08-14T23:09:16.8543333Z                 placements=[
2025-08-14T23:09:16.8543373Z                     "rank:0/cuda:0",
2025-08-14T23:09:16.8543412Z                     "rank:1/cuda:1",
2025-08-14T23:09:16.8543451Z                     "rank:2/cuda:2",
2025-08-14T23:09:16.8543487Z                     "rank:3/cuda:3",
2025-08-14T23:09:16.8543521Z                 ],
2025-08-14T23:09:16.8543554Z             )
2025-08-14T23:09:16.8544856Z         >>> st.reshard(resharding_spec)
2025-08-14T23:09:16.8544910Z         >>> tensor = st.local_shards()[0].tensor
2025-08-14T23:09:16.8544943Z         >>> tensor
2025-08-14T23:09:16.8545033Z         tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2025-08-14T23:09:16.8545116Z         tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2025-08-14T23:09:16.8545176Z         tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2025-08-14T23:09:16.8545233Z         tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2025-08-14T23:09:16.8545265Z     
2025-08-14T23:09:16.8545357Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8545392Z     
2025-08-14T23:09:16.8545429Z       warnings.warn(msg)
2025-08-14T23:09:16.8545460Z     
2025-08-14T23:09:16.8545534Z --- Parse Warning: 120 / 146 ---
2025-08-14T23:09:16.8545887Z     /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.
2025-08-14T23:09:16.8545979Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8546012Z     
2025-08-14T23:09:16.8546116Z     :meth:`register_sharding` is an experimental API that allows users to register sharding
2025-08-14T23:09:16.8546201Z     strategies for an operator when the tensor inputs and outputs are DTensor.
2025-08-14T23:09:16.8546289Z     It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2025-08-14T23:09:16.8546396Z     e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2025-08-14T23:09:16.8546491Z     when users would like to overwrite default sharding strategies of existing operators.
2025-08-14T23:09:16.8547843Z     
2025-08-14T23:09:16.8547877Z     Args:
2025-08-14T23:09:16.8547933Z         op (Union[OpOverload, List[OpOverload]]):
2025-08-14T23:09:16.8548006Z             An op or a list of ops to register the customized sharding function.
2025-08-14T23:09:16.8548037Z     
2025-08-14T23:09:16.8548070Z     Returns:
2025-08-14T23:09:16.8548166Z         A function decorator which can be used to wrap a function that defines the sharding
2025-08-14T23:09:16.8548268Z         strategy for the operator specified in ``op``. The defined sharding strategy will be
2025-08-14T23:09:16.8548365Z         registered to DTensor and will override the default sharding strategy if DTensor has
2025-08-14T23:09:16.8548470Z         already implemented the operator. The customized sharding function takes the same inputs
2025-08-14T23:09:16.8548556Z         as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2025-08-14T23:09:16.8548651Z         replaced by a tensor-like object that DTensor uses internally). The function should
2025-08-14T23:09:16.8548749Z         return a sequence of 2-tuples, each specifying acceptable output placements and its
2025-08-14T23:09:16.8548795Z         corresponding input placements.
2025-08-14T23:09:16.8548825Z     
2025-08-14T23:09:16.8548859Z     Example:
2025-08-14T23:09:16.8548905Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8548961Z         >>> @register_sharding(aten._softmax.default)
2025-08-14T23:09:16.8549054Z         >>> def custom_softmax_sharding(x, dim, half_to_float):
2025-08-14T23:09:16.8549111Z         >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2025-08-14T23:09:16.8549155Z         >>>     acceptable_shardings = []
2025-08-14T23:09:16.8549187Z         >>>
2025-08-14T23:09:16.8550619Z         >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2025-08-14T23:09:16.8550682Z         >>>     acceptable_shardings.append(all_replicate)
2025-08-14T23:09:16.8550714Z         >>>
2025-08-14T23:09:16.8550762Z         >>>     for sharding_dim in range(x.ndim):
2025-08-14T23:09:16.8550807Z         >>>         if sharding_dim != softmax_dim:
2025-08-14T23:09:16.8550848Z         >>>             all_sharded = (
2025-08-14T23:09:16.8550894Z         >>>                 [Shard(sharding_dim)],
2025-08-14T23:09:16.8550945Z         >>>                 [Shard(sharding_dim), None, None],
2025-08-14T23:09:16.8550980Z         >>>             )
2025-08-14T23:09:16.8551068Z         >>>             acceptable_shardings.append(all_sharded)
2025-08-14T23:09:16.8551100Z         >>>
2025-08-14T23:09:16.8551145Z         >>>     return acceptable_shardings
2025-08-14T23:09:16.8551176Z     
2025-08-14T23:09:16.8551248Z     .. note:: This API is currently experimental and subject to change
2025-08-14T23:09:16.8551281Z     
2025-08-14T23:09:16.8551370Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8551402Z     
2025-08-14T23:09:16.8551439Z       warnings.warn(msg)
2025-08-14T23:09:16.8551470Z     
2025-08-14T23:09:16.8551542Z --- Parse Warning: 121 / 146 ---
2025-08-14T23:09:16.8551872Z     /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.
2025-08-14T23:09:16.8551965Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8553349Z     
2025-08-14T23:09:16.8553449Z     :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2025-08-14T23:09:16.8553549Z     to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2025-08-14T23:09:16.8553673Z     the local components of :class:`DTensor`, call the function, and wrap the outputs to
2025-08-14T23:09:16.8553737Z     :class:`DTensor` according to the ``out_placements``.
2025-08-14T23:09:16.8553768Z     
2025-08-14T23:09:16.8553801Z     Args:
2025-08-14T23:09:16.8553875Z         func (Callable): the function to be applied on each local shard of
2025-08-14T23:09:16.8553915Z             :class:`DTensor` s.
2025-08-14T23:09:16.8553995Z         out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2025-08-14T23:09:16.8554086Z             the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2025-08-14T23:09:16.8554178Z             If the flattened ``output`` is a single value, the ``out_placements`` should be
2025-08-14T23:09:16.8554265Z             of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2025-08-14T23:09:16.8554352Z             values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2025-08-14T23:09:16.8554402Z             mapping to the flattened ``output``.
2025-08-14T23:09:16.8554474Z             Besides, for :class:`Tensor` output, we use `PlacementType` as its
2025-08-14T23:09:16.8554572Z             placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2025-08-14T23:09:16.8554611Z             should be `None`.
2025-08-14T23:09:16.8554694Z             Note that the only exception is when no :class:`DTensor` argument is passed
2025-08-14T23:09:16.8554772Z             in. In this case, even if `out_placements` is not `None`, the result function
2025-08-14T23:09:16.8554862Z             should ignore the desired placements because the function is not running with
2025-08-14T23:09:16.8556173Z             :class:`DTensor` s.
2025-08-14T23:09:16.8556239Z         in_placements (Tuple[`PlacementType`, ...], optional):
2025-08-14T23:09:16.8556337Z             the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2025-08-14T23:09:16.8556423Z             If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2025-08-14T23:09:16.8556506Z             placements of each :class:`DTensor` argument is the same as the required
2025-08-14T23:09:16.8556582Z             placements or not. If the placements are not the same and
2025-08-14T23:09:16.8556675Z             ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2025-08-14T23:09:16.8556761Z             ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2025-08-14T23:09:16.8556854Z             the required sharding placements before passing its local tensor to ``func``.
2025-08-14T23:09:16.8556960Z             The only exception is when required placements are not ``None`` and the
2025-08-14T23:09:16.8557045Z             argument is a :class:`torch.Tensor`. In this case, the placements examination
2025-08-14T23:09:16.8557122Z             will be skipped and the argument will be directly passed to ``func``.
2025-08-14T23:09:16.8557202Z             If ``in_placements`` is ``None``, no placements examination will be performed.
2025-08-14T23:09:16.8557241Z             Default: None
2025-08-14T23:09:16.8557309Z         in_grad_placements (Tuple[`PlacementType`, ...], optional):
2025-08-14T23:09:16.8557384Z             the placements hint of the :class:`DTensor` s gradient corresponds
2025-08-14T23:09:16.8557458Z             to the flattened input DTensor. This argument is the hint that user
2025-08-14T23:09:16.8557527Z             can give to :meth:`to_local` in case the gradient layout of the
2025-08-14T23:09:16.8557603Z             local tensor input does not match its :class:`DTensor` input layout.
2025-08-14T23:09:16.8557679Z             If not specified, we will assume the gradient layout of the local
2025-08-14T23:09:16.8557755Z             tensor input remains the same as the original :class:`DTensor` input
2025-08-14T23:09:16.8559034Z             and use that for gradient computation. Default: None.
2025-08-14T23:09:16.8559108Z         device_mesh (:class:`DeviceMesh`, optional):
2025-08-14T23:09:16.8559189Z             the device mesh that the output :class:`DTensor` s are placed on. If not
2025-08-14T23:09:16.8559280Z             specified, this will be inferred from the first input :class:`DTensor`'s device
2025-08-14T23:09:16.8559322Z             mesh. Default: None.
2025-08-14T23:09:16.8559353Z     
2025-08-14T23:09:16.8559387Z     Keyword Args:
2025-08-14T23:09:16.8559437Z         redistribute_inputs (bool, optional):
2025-08-14T23:09:16.8559524Z             the bool value indicating whether to reshard the input :class:`DTensor` s when
2025-08-14T23:09:16.8559614Z             their placements are different from the required input placements. If this
2025-08-14T23:09:16.8559699Z             value is ``False`` and some :class:`DTensor` input has a different placement,
2025-08-14T23:09:16.8559755Z             an exception will be raised. Default: False.
2025-08-14T23:09:16.8559786Z     
2025-08-14T23:09:16.8559820Z     Returns:
2025-08-14T23:09:16.8559914Z         A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2025-08-14T23:09:16.8560000Z         and returns a :class:`DTensor` constructed from the return value of ``func``.
2025-08-14T23:09:16.8560030Z     
2025-08-14T23:09:16.8560063Z     Raises:
2025-08-14T23:09:16.8560148Z         AssertionError: For any non-DTensor output, we require its corresponding
2025-08-14T23:09:16.8560240Z             output placement in ``out_placements`` be None. An AssertionError will be raised
2025-08-14T23:09:16.8560281Z             if this is not the case.
2025-08-14T23:09:16.8560312Z     
2025-08-14T23:09:16.8561650Z         ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2025-08-14T23:09:16.8561754Z             a redistribution according to ``in_placements``.
2025-08-14T23:09:16.8561785Z     
2025-08-14T23:09:16.8561855Z     Example:
2025-08-14T23:09:16.8561903Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8561963Z         >>> def mm_allreduce_forward(device_mesh, W, X):
2025-08-14T23:09:16.8562010Z         >>>     partial_sum_tensor = torch.mm(W, X)
2025-08-14T23:09:16.8562098Z         >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2025-08-14T23:09:16.8562139Z         >>>     return reduced_tensor
2025-08-14T23:09:16.8562170Z         >>>
2025-08-14T23:09:16.8562222Z         >>> W = torch.randn(12, 8, requires_grad=False)
2025-08-14T23:09:16.8562271Z         >>> X = torch.randn(8, 16, requires_grad=False)
2025-08-14T23:09:16.8562311Z         >>> Y = torch.mm(W, X)
2025-08-14T23:09:16.8562396Z         >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2025-08-14T23:09:16.8562466Z         >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2025-08-14T23:09:16.8562498Z         >>>
2025-08-14T23:09:16.8562595Z         >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion
2025-08-14T23:09:16.8562644Z         >>> local_mm_allreduce_forward = local_map(
2025-08-14T23:09:16.8562689Z         >>>     mm_allreduce_forward,
2025-08-14T23:09:16.8562735Z         >>>     out_placements=[Replicate()],
2025-08-14T23:09:16.8562782Z         >>>     in_placements=[col_wise, row_wise],
2025-08-14T23:09:16.8562827Z         >>>     device_mesh=device_mesh,
2025-08-14T23:09:16.8564206Z         >>> )
2025-08-14T23:09:16.8564239Z         >>>
2025-08-14T23:09:16.8564285Z         >>> W_dt = distribute_tensor(
2025-08-14T23:09:16.8564330Z         ...     W, device_mesh, (col_wise)
2025-08-14T23:09:16.8564376Z         ... )  # col-wisely sharded W tensor
2025-08-14T23:09:16.8564418Z         >>> X_dt = distribute_tensor(
2025-08-14T23:09:16.8564467Z         ...     X, device_mesh, (row_wise)
2025-08-14T23:09:16.8564511Z         ... )  # row-wisely sharded X tensor
2025-08-14T23:09:16.8564557Z         >>> Y_dt = local_mm_allreduce_forward(
2025-08-14T23:09:16.8564596Z         ...     device_mesh, W_dt, X_dt
2025-08-14T23:09:16.8564683Z         ... )  # apply local_mm_allreduce_forward to DTensors
2025-08-14T23:09:16.8564715Z     
2025-08-14T23:09:16.8564787Z     .. note:: This API is currently experimental and subject to change
2025-08-14T23:09:16.8564818Z     
2025-08-14T23:09:16.8564911Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8564943Z     
2025-08-14T23:09:16.8564980Z       warnings.warn(msg)
2025-08-14T23:09:16.8565011Z     
2025-08-14T23:09:16.8565086Z --- Parse Warning: 122 / 146 ---
2025-08-14T23:09:16.8565432Z     /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.
2025-08-14T23:09:16.8565526Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8567091Z     
2025-08-14T23:09:16.8567232Z     Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2025-08-14T23:09:16.8567346Z     ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2025-08-14T23:09:16.8567376Z     
2025-08-14T23:09:16.8567411Z     Keyword Args:
2025-08-14T23:09:16.8567484Z         input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:16.8567600Z             The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-08-14T23:09:16.8567724Z             DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-08-14T23:09:16.8567797Z             as a placeholder. default: None.
2025-08-14T23:09:16.8567897Z         desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:16.8568030Z             The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:16.8568169Z             have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-08-14T23:09:16.8568224Z         input_kwarg_layouts (Dict[str, Placement]):
2025-08-14T23:09:16.8568351Z             The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-08-14T23:09:16.8568388Z             default: None
2025-08-14T23:09:16.8568450Z         desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-08-14T23:09:16.8568591Z             The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:16.8568650Z             have the desired DTensor layouts. default: None.
2025-08-14T23:09:16.8568697Z         use_local_output (bool, optional):
2025-08-14T23:09:16.8568820Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-08-14T23:09:16.8568856Z     Returns:
2025-08-14T23:09:16.8568964Z         A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2025-08-14T23:09:16.8570274Z     
2025-08-14T23:09:16.8570312Z     Example::
2025-08-14T23:09:16.8570358Z         >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:16.8570469Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2025-08-14T23:09:16.8570541Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:16.8570573Z         >>> ...
2025-08-14T23:09:16.8570683Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:16.8570740Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:16.8570772Z         >>>
2025-08-14T23:09:16.8570887Z         >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2025-08-14T23:09:16.8570945Z         >>> # and then redistributed to Replicated DTensor.
2025-08-14T23:09:16.8571005Z         >>> parallelize_module(
2025-08-14T23:09:16.8571059Z         >>>     block, # this can be a submodule or module
2025-08-14T23:09:16.8571096Z         >>>     tp_mesh,
2025-08-14T23:09:16.8571138Z         >>>     parallelize_plan={
2025-08-14T23:09:16.8571189Z         >>>         "attn": PrepareModuleInput(
2025-08-14T23:09:16.8571244Z         >>>             input_layouts=(Shard(0), None, None, ...),
2025-08-14T23:09:16.8571308Z         >>>             desired_input_layouts=(Replicate(), None, None, ...)
2025-08-14T23:09:16.8571342Z         >>>         ),
2025-08-14T23:09:16.8571377Z         >>>     }
2025-08-14T23:09:16.8571409Z         >>> )
2025-08-14T23:09:16.8572710Z     
2025-08-14T23:09:16.8572805Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8572836Z     
2025-08-14T23:09:16.8572873Z       warnings.warn(msg)
2025-08-14T23:09:16.8572971Z     
2025-08-14T23:09:16.8573046Z --- Parse Warning: 123 / 146 ---
2025-08-14T23:09:16.8573386Z     /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.
2025-08-14T23:09:16.8573478Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8573509Z     
2025-08-14T23:09:16.8573643Z     Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2025-08-14T23:09:16.8573759Z     ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2025-08-14T23:09:16.8573841Z     
2025-08-14T23:09:16.8573877Z     Keyword Args:
2025-08-14T23:09:16.8573939Z         output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:16.8574055Z             The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-08-14T23:09:16.8574192Z             DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-08-14T23:09:16.8574249Z             ``None`` need to be specified as a placeholder.
2025-08-14T23:09:16.8574322Z         desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:16.8574453Z             The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-08-14T23:09:16.8574500Z             have the desired DTensor layouts.
2025-08-14T23:09:16.8574562Z         use_local_output (bool, optional):
2025-08-14T23:09:16.8574690Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-08-14T23:09:16.8576025Z     Returns:
2025-08-14T23:09:16.8576134Z         A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2025-08-14T23:09:16.8576169Z     
2025-08-14T23:09:16.8576203Z     Example::
2025-08-14T23:09:16.8576246Z         >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:16.8576359Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2025-08-14T23:09:16.8576429Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:16.8576462Z         >>> ...
2025-08-14T23:09:16.8576568Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:16.8576620Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:16.8576651Z         >>>
2025-08-14T23:09:16.8576794Z         >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2025-08-14T23:09:16.8576847Z         >>> # and then redistributed to Sharded DTensor.
2025-08-14T23:09:16.8576889Z         >>> parallelize_module(
2025-08-14T23:09:16.8576941Z         >>>     block, # this can be a submodule or module
2025-08-14T23:09:16.8577000Z         >>>     tp_mesh,
2025-08-14T23:09:16.8577055Z         >>>     parallelize_plan = PrepareModuleOutput(
2025-08-14T23:09:16.8577101Z         >>>         output_layouts=Replicate(),
2025-08-14T23:09:16.8577147Z         >>>         desired_output_layouts=Shard(0)
2025-08-14T23:09:16.8577180Z         >>>     )
2025-08-14T23:09:16.8577211Z         >>> )
2025-08-14T23:09:16.8578517Z     
2025-08-14T23:09:16.8578614Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8578646Z     
2025-08-14T23:09:16.8578683Z       warnings.warn(msg)
2025-08-14T23:09:16.8578720Z     
2025-08-14T23:09:16.8578795Z --- Parse Warning: 124 / 146 ---
2025-08-14T23:09:16.8579145Z     /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.
2025-08-14T23:09:16.8579239Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8579271Z     
2025-08-14T23:09:16.8579419Z     Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module
2025-08-14T23:09:16.8579565Z     to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution
2025-08-14T23:09:16.8579695Z     according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of
2025-08-14T23:09:16.8579796Z     :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`.
2025-08-14T23:09:16.8579841Z     
2025-08-14T23:09:16.8579876Z     Keyword Args:
2025-08-14T23:09:16.8579949Z         input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:16.8580062Z             The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2025-08-14T23:09:16.8580188Z             DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2025-08-14T23:09:16.8580235Z             as a placeholder. default: None.
2025-08-14T23:09:16.8580316Z         desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2025-08-14T23:09:16.8580444Z             The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:16.8580593Z             have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2025-08-14T23:09:16.8581892Z         input_kwarg_layouts (Dict[str, Placement]):
2025-08-14T23:09:16.8582022Z             The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2025-08-14T23:09:16.8582065Z             default: None
2025-08-14T23:09:16.8582126Z         desired_input_kwarg_layouts: (Dict[str, Placement]):
2025-08-14T23:09:16.8582253Z             The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2025-08-14T23:09:16.8582311Z             have the desired DTensor layouts. default: None.
2025-08-14T23:09:16.8582355Z         use_local_input (bool, optional):
2025-08-14T23:09:16.8582478Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2025-08-14T23:09:16.8582538Z         output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:16.8582657Z             The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2025-08-14T23:09:16.8582793Z             DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2025-08-14T23:09:16.8582850Z             ``None`` need to be specified as a placeholder.
2025-08-14T23:09:16.8583091Z         desired_output_layouts (Union[Placement, Tuple[Placement]]):
2025-08-14T23:09:16.8583223Z             The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2025-08-14T23:09:16.8583269Z             have the desired DTensor layouts.
2025-08-14T23:09:16.8583314Z         use_local_output (bool, optional):
2025-08-14T23:09:16.8583435Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2025-08-14T23:09:16.8583469Z     Returns:
2025-08-14T23:09:16.8583596Z         A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs.
2025-08-14T23:09:16.8583629Z     
2025-08-14T23:09:16.8583666Z     Example::
2025-08-14T23:09:16.8584997Z         >>> # xdoctest: +SKIP(failing)
2025-08-14T23:09:16.8585120Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput
2025-08-14T23:09:16.8585191Z         >>> from torch.distributed.device_mesh import init_device_mesh
2025-08-14T23:09:16.8585225Z         >>> ...
2025-08-14T23:09:16.8585330Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2025-08-14T23:09:16.8585381Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2025-08-14T23:09:16.8585414Z         >>>
2025-08-14T23:09:16.8585526Z         >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor
2025-08-14T23:09:16.8585647Z         >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated
2025-08-14T23:09:16.8585780Z         >>> # as Replicated DTensor and then redistributed to Sharded DTensor.
2025-08-14T23:09:16.8585822Z         >>> parallelize_module(
2025-08-14T23:09:16.8585873Z         >>>     block, # this can be a submodule or module
2025-08-14T23:09:16.8585910Z         >>>     tp_mesh,
2025-08-14T23:09:16.8585952Z         >>>     parallelize_plan={
2025-08-14T23:09:16.8586010Z         >>>         "attn": PrepareModuleInputOutput(
2025-08-14T23:09:16.8586066Z         >>>             input_layouts=(Shard(0), None, None, ...),
2025-08-14T23:09:16.8586135Z         >>>             desired_input_layouts=(Replicate(), None, None, ...),
2025-08-14T23:09:16.8586181Z         >>>             output_layouts=Replicate(),
2025-08-14T23:09:16.8586231Z         >>>             desired_output_layouts=Shard(0),
2025-08-14T23:09:16.8586265Z         >>>         ),
2025-08-14T23:09:16.8586297Z         >>>     }
2025-08-14T23:09:16.8586329Z         >>> )
2025-08-14T23:09:16.8587588Z     
2025-08-14T23:09:16.8587703Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8587740Z     
2025-08-14T23:09:16.8587778Z       warnings.warn(msg)
2025-08-14T23:09:16.8587808Z     
2025-08-14T23:09:16.8587886Z --- Parse Warning: 125 / 146 ---
2025-08-14T23:09:16.8588231Z     /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.
2025-08-14T23:09:16.8588324Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8588355Z     
2025-08-14T23:09:16.8588430Z     Initialize rendezvous event object and record its operations.
2025-08-14T23:09:16.8588460Z     
2025-08-14T23:09:16.8588492Z     Args:
2025-08-14T23:09:16.8588541Z         run_id (str): The run id of the rendezvous.
2025-08-14T23:09:16.8588599Z         message (str): The message describing the event.
2025-08-14T23:09:16.8588695Z         node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2025-08-14T23:09:16.8588765Z         name (str): Event name. (E.g. Current action being performed).
2025-08-14T23:09:16.8588812Z         hostname (str): Hostname of the node.
2025-08-14T23:09:16.8588886Z         pid (Optional[int]): The process id of the node.
2025-08-14T23:09:16.8588974Z         master_endpoint (str): The master endpoint for the rendezvous store, if known.
2025-08-14T23:09:16.8589072Z         local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2025-08-14T23:09:16.8589135Z         rank (Optional[int]): The rank of the node, if known.
2025-08-14T23:09:16.8590500Z     Returns:
2025-08-14T23:09:16.8590536Z         None
2025-08-14T23:09:16.8590570Z     Example:
2025-08-14T23:09:16.8590620Z         >>> # See DynamicRendezvousHandler class
2025-08-14T23:09:16.8590657Z         >>> def _record(
2025-08-14T23:09:16.8590695Z         ...     self,
2025-08-14T23:09:16.8590737Z         ...     message: str,
2025-08-14T23:09:16.8590795Z         ...     node_state: NodeState = NodeState.RUNNING,
2025-08-14T23:09:16.8590839Z         ...     rank: Optional[int] = None,
2025-08-14T23:09:16.8590875Z         ... ) -> None:
2025-08-14T23:09:16.8590921Z         ...     construct_and_record_rdzv_event(
2025-08-14T23:09:16.8590986Z         ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2025-08-14T23:09:16.8591033Z         ...         run_id=self._settings.run_id,
2025-08-14T23:09:16.8591073Z         ...         message=message,
2025-08-14T23:09:16.8591115Z         ...         node_state=node_state,
2025-08-14T23:09:16.8591162Z         ...         hostname=self._this_node.addr,
2025-08-14T23:09:16.8591205Z         ...         pid=self._this_node.pid,
2025-08-14T23:09:16.8591255Z         ...         local_id=self._this_node.local_id,
2025-08-14T23:09:16.8591292Z         ...         rank=rank,
2025-08-14T23:09:16.8591329Z         ...     )
2025-08-14T23:09:16.8591382Z     
2025-08-14T23:09:16.8591484Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8592871Z     
2025-08-14T23:09:16.8592966Z       warnings.warn(msg)
2025-08-14T23:09:16.8592999Z     
2025-08-14T23:09:16.8593080Z --- Parse Warning: 126 / 146 ---
2025-08-14T23:09:16.8593445Z     /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.
2025-08-14T23:09:16.8593537Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8593568Z     
2025-08-14T23:09:16.8593650Z     RemoteModule instance can only be created after RPC initialization.
2025-08-14T23:09:16.8593681Z     
2025-08-14T23:09:16.8593753Z     It creates a user-specified module on a specified remote node.
2025-08-14T23:09:16.8593874Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-08-14T23:09:16.8593917Z     executed on the remote node.
2025-08-14T23:09:16.8593999Z     It takes care of autograd recording to ensure the backward pass propagates
2025-08-14T23:09:16.8594057Z     gradients back to the corresponding remote module.
2025-08-14T23:09:16.8594184Z     It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2025-08-14T23:09:16.8594256Z     without incurring any overheads of copying the actual module,
2025-08-14T23:09:16.8594331Z     which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2025-08-14T23:09:16.8594373Z     pointing to the remote module.
2025-08-14T23:09:16.8594405Z     
2025-08-14T23:09:16.8594477Z     The arguments of ``forward_async`` and ``forward`` are the same as
2025-08-14T23:09:16.8594551Z     the ``forward`` method of the module returned by the ``module_cls``.
2025-08-14T23:09:16.8594582Z     
2025-08-14T23:09:16.8595986Z     Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2025-08-14T23:09:16.8596018Z     
2025-08-14T23:09:16.8596111Z     Particularly, to create a hybrid model, typically the local modules should be
2025-08-14T23:09:16.8596237Z     created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2025-08-14T23:09:16.8596301Z     Hybrid Example:
2025-08-14T23:09:16.8596348Z             >>> class HybridModel(nn.Module):
2025-08-14T23:09:16.8596395Z             >>>     def __init__(self) -> None:
2025-08-14T23:09:16.8596439Z             >>>         nn.Module.__init__(self)
2025-08-14T23:09:16.8596496Z             >>>         self.remote_embedding = RemoteModule(...)
2025-08-14T23:09:16.8596545Z             >>>         self.local_linear = nn.Linear(...)
2025-08-14T23:09:16.8596577Z     
2025-08-14T23:09:16.8596649Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-08-14T23:09:16.8596747Z     that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2025-08-14T23:09:16.8596820Z     the generated ``RemoteModule`` will have 2 methods in signature of
2025-08-14T23:09:16.8596872Z     ``def forward(input: Tensor) -> Tensor:`` and
2025-08-14T23:09:16.8596937Z     ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2025-08-14T23:09:16.8596969Z     
2025-08-14T23:09:16.8597003Z     .. note::
2025-08-14T23:09:16.8597058Z         If the remote module is placed on a cuda device,
2025-08-14T23:09:16.8597144Z         any input CPU tensors will be automatically moved to the same cuda device,
2025-08-14T23:09:16.8597281Z         and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2025-08-14T23:09:16.8598584Z     
2025-08-14T23:09:16.8598618Z     Args:
2025-08-14T23:09:16.8598729Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:16.8598875Z             The device can be a local device or a remote device specified by one of the following remote
2025-08-14T23:09:16.8598911Z             formats:
2025-08-14T23:09:16.8598942Z     
2025-08-14T23:09:16.8598998Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-08-14T23:09:16.8599056Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-08-14T23:09:16.8599090Z     
2025-08-14T23:09:16.8599178Z             In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:16.8599226Z         module_cls (nn.Module): For example,
2025-08-14T23:09:16.8599270Z             >>> class MyModule(nn.Module):
2025-08-14T23:09:16.8599312Z             >>>     def forward(input):
2025-08-14T23:09:16.8599354Z             >>>         return input + 1
2025-08-14T23:09:16.8599386Z             >>>
2025-08-14T23:09:16.8599426Z             >>> module_cls = MyModule
2025-08-14T23:09:16.8599520Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-08-14T23:09:16.8599594Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-08-14T23:09:16.8599692Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-08-14T23:09:16.8599777Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-08-14T23:09:16.8599858Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-08-14T23:09:16.8599941Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-08-14T23:09:16.8601241Z     
2025-08-14T23:09:16.8601275Z     Returns:
2025-08-14T23:09:16.8601367Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:16.8601450Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-08-14T23:09:16.8601547Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:16.8601609Z         on the user-provided module on the remote side.
2025-08-14T23:09:16.8601643Z     
2025-08-14T23:09:16.8601678Z     Example::
2025-08-14T23:09:16.8601735Z         Run the following code in two different processes:
2025-08-14T23:09:16.8601766Z     
2025-08-14T23:09:16.8601812Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8601870Z         >>> # On worker 0:
2025-08-14T23:09:16.8601907Z         >>> import torch
2025-08-14T23:09:16.8601962Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8602010Z         >>> from torch import nn, Tensor
2025-08-14T23:09:16.8602091Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:16.8602123Z         >>>
2025-08-14T23:09:16.8602179Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:16.8602225Z         >>> remote_linear_module = RemoteModule(
2025-08-14T23:09:16.8602276Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:16.8602313Z         >>> )
2025-08-14T23:09:16.8603658Z         >>> input = torch.randn(128, 20)
2025-08-14T23:09:16.8603724Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-08-14T23:09:16.8603765Z         >>> ret = ret_fut.wait()
2025-08-14T23:09:16.8603803Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8603834Z     
2025-08-14T23:09:16.8603872Z         >>> # On worker 1:
2025-08-14T23:09:16.8603908Z         >>> import torch
2025-08-14T23:09:16.8603957Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8603989Z         >>>
2025-08-14T23:09:16.8604044Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:16.8604081Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8604112Z     
2025-08-14T23:09:16.8604201Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8604233Z     
2025-08-14T23:09:16.8604270Z       warnings.warn(msg)
2025-08-14T23:09:16.8604301Z     
2025-08-14T23:09:16.8604381Z --- Parse Warning: 127 / 146 ---
2025-08-14T23:09:16.8604772Z     /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.
2025-08-14T23:09:16.8604867Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8604898Z     
2025-08-14T23:09:16.8605010Z     Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2025-08-14T23:09:16.8605041Z     
2025-08-14T23:09:16.8606482Z     This alternate initialization method can be particularly useful if we want to create multiple
2025-08-14T23:09:16.8606599Z     RemoteModule instances that share the same underlying module and reduce memory consumption.
2025-08-14T23:09:16.8606630Z     
2025-08-14T23:09:16.8606753Z     Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2025-08-14T23:09:16.8606827Z     which is not supported. The recommended way is as follows:
2025-08-14T23:09:16.8606858Z     
2025-08-14T23:09:16.8606906Z         1. the sender creates a RemoteModule;
2025-08-14T23:09:16.8606962Z         2. the sender sends its ``module_rref`` over RPC;
2025-08-14T23:09:16.8607076Z         3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2025-08-14T23:09:16.8607110Z     
2025-08-14T23:09:16.8607145Z     Example::
2025-08-14T23:09:16.8607202Z         Run the following code in two different processes:
2025-08-14T23:09:16.8607233Z     
2025-08-14T23:09:16.8607278Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8607316Z         >>> # On worker 0:
2025-08-14T23:09:16.8607352Z         >>> import torch
2025-08-14T23:09:16.8607401Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8607444Z         >>> from torch import nn, Tensor
2025-08-14T23:09:16.8607527Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:16.8607560Z         >>>
2025-08-14T23:09:16.8607616Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:16.8607660Z         >>> remote_module = RemoteModule(
2025-08-14T23:09:16.8608989Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:16.8609052Z         >>> )
2025-08-14T23:09:16.8609084Z         >>>
2025-08-14T23:09:16.8609129Z         >>> remote_module1 = rpc.rpc_sync(
2025-08-14T23:09:16.8609169Z         >>>     "worker1/cpu",
2025-08-14T23:09:16.8609218Z         >>>     RemoteModule.init_from_module_rref,
2025-08-14T23:09:16.8609279Z         >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2025-08-14T23:09:16.8609310Z         >>> )
2025-08-14T23:09:16.8609348Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8609380Z     
2025-08-14T23:09:16.8609416Z         >>> # On worker 1:
2025-08-14T23:09:16.8609453Z         >>> import torch
2025-08-14T23:09:16.8609504Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8609539Z         >>>
2025-08-14T23:09:16.8609594Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:16.8609631Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8609662Z     
2025-08-14T23:09:16.8609695Z     Args:
2025-08-14T23:09:16.8609798Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:16.8609899Z             The device can be a local device or a remote device specified by one of the following remote
2025-08-14T23:09:16.8609932Z             formats:
2025-08-14T23:09:16.8611219Z     
2025-08-14T23:09:16.8611276Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2025-08-14T23:09:16.8611341Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2025-08-14T23:09:16.8611372Z     
2025-08-14T23:09:16.8611463Z             In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:16.8611555Z         module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2025-08-14T23:09:16.8611634Z             the created remote module.
2025-08-14T23:09:16.8611732Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2025-08-14T23:09:16.8611814Z             to be created. The type object should be decorated by @torch.jit.interface.
2025-08-14T23:09:16.8611896Z             If not provided, the generated RemoteModule is not torchscript-able.
2025-08-14T23:09:16.8611979Z             Warning, this is an experimental API and susceptible to frequent changes.
2025-08-14T23:09:16.8612011Z     
2025-08-14T23:09:16.8612043Z     Returns:
2025-08-14T23:09:16.8612131Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:16.8612214Z         user-provided ``module_rref``, it has a blocking ``forward`` method and an
2025-08-14T23:09:16.8612310Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:16.8612392Z         on the user-provided module on the remote side.
2025-08-14T23:09:16.8612427Z     
2025-08-14T23:09:16.8612514Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8612546Z     
2025-08-14T23:09:16.8612583Z       warnings.warn(msg)
2025-08-14T23:09:16.8612614Z     
2025-08-14T23:09:16.8614002Z --- Parse Warning: 128 / 146 ---
2025-08-14T23:09:16.8614329Z     /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.
2025-08-14T23:09:16.8614454Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8614485Z     
2025-08-14T23:09:16.8614564Z         A RemoteModule instance can only be created after RPC initialization.
2025-08-14T23:09:16.8614596Z     
2025-08-14T23:09:16.8614669Z         It creates a user-specified module on a specified remote node.
2025-08-14T23:09:16.8614758Z         It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2025-08-14T23:09:16.8614801Z         executed on the remote node.
2025-08-14T23:09:16.8614886Z         It takes care of autograd recording to ensure the backward pass propagates
2025-08-14T23:09:16.8614975Z         gradients back to the corresponding remote module.
2025-08-14T23:09:16.8615006Z     
2025-08-14T23:09:16.8615084Z         It generates two methods ``forward_async`` and ``forward`` based on the
2025-08-14T23:09:16.8615161Z         signature of the ``forward`` method of ``module_cls``. ``forward_async``
2025-08-14T23:09:16.8615247Z         runs asynchronously and returns a Future. The arguments of ``forward_async``
2025-08-14T23:09:16.8615319Z         and ``forward`` are the same as the ``forward`` method of the module
2025-08-14T23:09:16.8615361Z         returned by the ``module_cls``.
2025-08-14T23:09:16.8615392Z     
2025-08-14T23:09:16.8615471Z         For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2025-08-14T23:09:16.8615560Z         that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2025-08-14T23:09:16.8615639Z         the generated ``RemoteModule`` will have 2 methods with the signatures:
2025-08-14T23:09:16.8616914Z     
2025-08-14T23:09:16.8616965Z         | ``def forward(input: Tensor) -> Tensor:``
2025-08-14T23:09:16.8617027Z         | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2025-08-14T23:09:16.8617059Z     
2025-08-14T23:09:16.8617091Z     Args:
2025-08-14T23:09:16.8617195Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2025-08-14T23:09:16.8617315Z             The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2025-08-14T23:09:16.8617372Z             E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2025-08-14T23:09:16.8617460Z             In addition, the device field can be optional and the default value is "cpu".
2025-08-14T23:09:16.8617592Z         module_cls (nn.Module): Class for the module to be created remotely. For example,
2025-08-14T23:09:16.8617624Z     
2025-08-14T23:09:16.8617670Z             >>> class MyModule(nn.Module):
2025-08-14T23:09:16.8617711Z             >>>     def forward(input):
2025-08-14T23:09:16.8617755Z             >>>         return input + 1
2025-08-14T23:09:16.8617787Z             >>>
2025-08-14T23:09:16.8617829Z             >>> module_cls = MyModule
2025-08-14T23:09:16.8617860Z     
2025-08-14T23:09:16.8617934Z         args (Sequence, optional): args to be passed to ``module_cls``.
2025-08-14T23:09:16.8618003Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2025-08-14T23:09:16.8618036Z     
2025-08-14T23:09:16.8618068Z     Returns:
2025-08-14T23:09:16.8618157Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2025-08-14T23:09:16.8619512Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2025-08-14T23:09:16.8619618Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2025-08-14T23:09:16.8619675Z         on the user-provided module on the remote side.
2025-08-14T23:09:16.8619707Z     
2025-08-14T23:09:16.8619741Z     Example::
2025-08-14T23:09:16.8619801Z         Run the following code in two different processes:
2025-08-14T23:09:16.8619833Z     
2025-08-14T23:09:16.8619878Z         >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8619916Z         >>> # On worker 0:
2025-08-14T23:09:16.8619952Z         >>> import torch
2025-08-14T23:09:16.8620002Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8620044Z         >>> from torch import nn, Tensor
2025-08-14T23:09:16.8620125Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2025-08-14T23:09:16.8620157Z         >>>
2025-08-14T23:09:16.8620212Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2025-08-14T23:09:16.8620264Z         >>> remote_linear_module = RemoteModule(
2025-08-14T23:09:16.8620319Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2025-08-14T23:09:16.8620351Z         >>> )
2025-08-14T23:09:16.8620393Z         >>> input = torch.randn(128, 20)
2025-08-14T23:09:16.8620452Z         >>> ret_fut = remote_linear_module.forward_async(input)
2025-08-14T23:09:16.8620507Z         >>> ret = ret_fut.wait()
2025-08-14T23:09:16.8620544Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8621848Z     
2025-08-14T23:09:16.8621886Z         >>> # On worker 1:
2025-08-14T23:09:16.8621926Z         >>> import torch
2025-08-14T23:09:16.8621976Z         >>> import torch.distributed.rpc as rpc
2025-08-14T23:09:16.8622007Z         >>>
2025-08-14T23:09:16.8622061Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2025-08-14T23:09:16.8622098Z         >>> rpc.shutdown()
2025-08-14T23:09:16.8622129Z     
2025-08-14T23:09:16.8622200Z         Furthermore, a more practical example that is combined with
2025-08-14T23:09:16.8622375Z         `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2025-08-14T23:09:16.8622489Z         can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2025-08-14T23:09:16.8622521Z     
2025-08-14T23:09:16.8622610Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8622641Z     
2025-08-14T23:09:16.8622678Z       warnings.warn(msg)
2025-08-14T23:09:16.8622741Z     
2025-08-14T23:09:16.8622814Z --- Parse Warning: 129 / 146 ---
2025-08-14T23:09:16.8623167Z     /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.
2025-08-14T23:09:16.8623259Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8623345Z     Return the recommended gain value for the given nonlinearity function.
2025-08-14T23:09:16.8623422Z     
2025-08-14T23:09:16.8624787Z         The values are as follows:
2025-08-14T23:09:16.8624818Z     
2025-08-14T23:09:16.8624870Z         ================= ====================================================
2025-08-14T23:09:16.8624912Z         nonlinearity      gain
2025-08-14T23:09:16.8624956Z         ================= ====================================================
2025-08-14T23:09:16.8625000Z         Linear / Identity :math:`1`
2025-08-14T23:09:16.8625040Z         Conv{1,2,3}D      :math:`1`
2025-08-14T23:09:16.8625077Z         Sigmoid           :math:`1`
2025-08-14T23:09:16.8625123Z         Tanh              :math:`\frac{5}{3}`
2025-08-14T23:09:16.8625166Z         ReLU              :math:`\sqrt{2}`
2025-08-14T23:09:16.8625236Z         Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
2025-08-14T23:09:16.8625280Z         SELU              :math:`\frac{3}{4}`
2025-08-14T23:09:16.8625352Z         ================= ====================================================
2025-08-14T23:09:16.8625384Z     
2025-08-14T23:09:16.8625420Z         .. warning::
2025-08-14T23:09:16.8625489Z             In order to implement `Self-Normalizing Neural Networks`_ ,
2025-08-14T23:09:16.8625576Z             you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``.
2025-08-14T23:09:16.8625641Z             This gives the initial weights a variance of ``1 / N``,
2025-08-14T23:09:16.8625719Z             which is necessary to induce a stable fixed point in the forward pass.
2025-08-14T23:09:16.8625799Z             In contrast, the default gain for ``SELU`` sacrifices the normalization
2025-08-14T23:09:16.8625865Z             effect for more stable gradient flow in rectangular layers.
2025-08-14T23:09:16.8625896Z     
2025-08-14T23:09:16.8627202Z         Args:
2025-08-14T23:09:16.8627284Z             nonlinearity: the non-linear function (`nn.functional` name)
2025-08-14T23:09:16.8627354Z             param: optional parameter for the non-linear function
2025-08-14T23:09:16.8627389Z     
2025-08-14T23:09:16.8627423Z         Examples:
2025-08-14T23:09:16.8627470Z             >>> gain = nn.init.calculate_gain(
2025-08-14T23:09:16.8627511Z             ...     "leaky_relu", 0.2
2025-08-14T23:09:16.8627563Z             ... )  # leaky_relu with negative_slope=0.2
2025-08-14T23:09:16.8627616Z     
2025-08-14T23:09:16.8627784Z         .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html
2025-08-14T23:09:16.8627817Z         
2025-08-14T23:09:16.8627906Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8627939Z     
2025-08-14T23:09:16.8627976Z       warnings.warn(msg)
2025-08-14T23:09:16.8628007Z     
2025-08-14T23:09:16.8628079Z --- Parse Warning: 130 / 146 ---
2025-08-14T23:09:16.8628417Z     /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.
2025-08-14T23:09:16.8628510Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8628542Z     
2025-08-14T23:09:16.8628627Z     Context manager for training with uneven inputs across processes in DDP.
2025-08-14T23:09:16.8628658Z     
2025-08-14T23:09:16.8628738Z     This context manager will keep track of already-joined DDP processes,
2025-08-14T23:09:16.8630093Z     and "shadow" the forward and backward passes by inserting collective
2025-08-14T23:09:16.8630180Z     communication operations to match with the ones created by non-joined
2025-08-14T23:09:16.8630263Z     DDP processes. This will ensure each collective call has a corresponding
2025-08-14T23:09:16.8630340Z     call by already-joined DDP processes, preventing hangs or errors that
2025-08-14T23:09:16.8630417Z     would otherwise happen when training with uneven inputs across
2025-08-14T23:09:16.8630531Z     processes. Alternatively, if the flag ``throw_on_early_termination`` is
2025-08-14T23:09:16.8630607Z     specified to be ``True``, all trainers will throw an error once one rank
2025-08-14T23:09:16.8630678Z     runs out of inputs, allowing these errors to be caught and handled
2025-08-14T23:09:16.8630725Z     according to application logic.
2025-08-14T23:09:16.8630756Z     
2025-08-14T23:09:16.8630833Z     Once all DDP processes have joined, the context manager will broadcast
2025-08-14T23:09:16.8630913Z     the model corresponding to the last joined process to all processes to
2025-08-14T23:09:16.8630970Z     ensure the model is the same across all processes
2025-08-14T23:09:16.8631013Z     (which is guaranteed by DDP).
2025-08-14T23:09:16.8631045Z     
2025-08-14T23:09:16.8631117Z     To use this to enable training with uneven inputs across processes,
2025-08-14T23:09:16.8631212Z     simply wrap this context manager around your training loop. No further
2025-08-14T23:09:16.8631282Z     modifications to the model or data loading is required.
2025-08-14T23:09:16.8631313Z     
2025-08-14T23:09:16.8631349Z     .. warning::
2025-08-14T23:09:16.8631422Z         If the model or training loop this context manager is wrapped around
2025-08-14T23:09:16.8632726Z         has additional distributed collective operations, such as
2025-08-14T23:09:16.8632806Z         ``SyncBatchNorm`` in the model's forward pass, then the flag
2025-08-14T23:09:16.8632879Z         ``throw_on_early_termination`` must be enabled. This is because this
2025-08-14T23:09:16.8633011Z         context manager is not aware of non-DDP collective communication.
2025-08-14T23:09:16.8633076Z         This flag will cause all ranks to throw when any one rank
2025-08-14T23:09:16.8633152Z         exhausts inputs, allowing these errors to be caught and recovered
2025-08-14T23:09:16.8633193Z         from across all ranks.
2025-08-14T23:09:16.8633224Z     
2025-08-14T23:09:16.8633261Z     Args:
2025-08-14T23:09:16.8633331Z         divide_by_initial_world_size (bool): If ``True``, will divide
2025-08-14T23:09:16.8633405Z             gradients by the initial ``world_size`` DDP training was launched
2025-08-14T23:09:16.8633466Z             with. If ``False``, will compute the effective world size
2025-08-14T23:09:16.8633560Z             (number of ranks that have not depleted their inputs yet) and
2025-08-14T23:09:16.8633616Z             divide gradients by that during allreduce. Set
2025-08-14T23:09:16.8633682Z             ``divide_by_initial_world_size=True`` to ensure every input
2025-08-14T23:09:16.8633754Z             sample including the uneven inputs have equal weight in terms of
2025-08-14T23:09:16.8633817Z             how much they contribute to the global gradient. This is
2025-08-14T23:09:16.8633880Z             achieved by always dividing the gradient by the initial
2025-08-14T23:09:16.8633947Z             ``world_size`` even when we encounter uneven inputs. If you set
2025-08-14T23:09:16.8634019Z             this to ``False``, we divide the gradient by the remaining
2025-08-14T23:09:16.8634089Z             number of nodes. This ensures parity with training on a smaller
2025-08-14T23:09:16.8634154Z             ``world_size`` although it also means the uneven inputs would
2025-08-14T23:09:16.8635509Z             contribute more towards the global gradient. Typically, you
2025-08-14T23:09:16.8635582Z             would want to set this to ``True`` for cases where the last few
2025-08-14T23:09:16.8635652Z             inputs of your training job are uneven. In extreme cases, where
2025-08-14T23:09:16.8635717Z             there is a large discrepancy in the number of inputs, setting
2025-08-14T23:09:16.8635774Z             this to ``False`` might provide better results.
2025-08-14T23:09:16.8635853Z         enable (bool): Whether to enable uneven input detection or not. Pass
2025-08-14T23:09:16.8635916Z             in ``enable=False`` to disable in cases where you know that
2025-08-14T23:09:16.8636008Z             inputs are even across participating processes. Default is
2025-08-14T23:09:16.8636068Z             ``True``.
2025-08-14T23:09:16.8636135Z         throw_on_early_termination (bool): Whether to throw an error
2025-08-14T23:09:16.8636199Z             or continue training when at least one rank has exhausted
2025-08-14T23:09:16.8636270Z             inputs. If ``True``, will throw upon the first rank reaching end
2025-08-14T23:09:16.8636335Z             of data. If ``False``, will continue training with a smaller
2025-08-14T23:09:16.8636404Z             effective world size until all ranks are joined. Note that if
2025-08-14T23:09:16.8636452Z             this flag is specified, then the flag
2025-08-14T23:09:16.8636515Z             ``divide_by_initial_world_size`` would be ignored. Default
2025-08-14T23:09:16.8636552Z             is ``False``.
2025-08-14T23:09:16.8636583Z     
2025-08-14T23:09:16.8636614Z     
2025-08-14T23:09:16.8636649Z     Example::
2025-08-14T23:09:16.8636680Z     
2025-08-14T23:09:16.8638015Z         >>> # xdoctest: +SKIP("Distributed")
2025-08-14T23:09:16.8638059Z         >>> import torch
2025-08-14T23:09:16.8638106Z         >>> import torch.distributed as dist
2025-08-14T23:09:16.8638141Z         >>> import os
2025-08-14T23:09:16.8638190Z         >>> import torch.multiprocessing as mp
2025-08-14T23:09:16.8638233Z         >>> import torch.nn as nn
2025-08-14T23:09:16.8638275Z         >>> # On each spawned worker
2025-08-14T23:09:16.8638314Z         >>> def worker(rank):
2025-08-14T23:09:16.8638382Z         >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2025-08-14T23:09:16.8638427Z         >>>     torch.cuda.set_device(rank)
2025-08-14T23:09:16.8638482Z         >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2025-08-14T23:09:16.8638552Z         >>>     model = torch.nn.parallel.DistributedDataParallel(
2025-08-14T23:09:16.8638609Z         >>>         model, device_ids=[rank], output_device=rank
2025-08-14T23:09:16.8638641Z         >>>     )
2025-08-14T23:09:16.8638698Z         >>>     # Rank 1 gets one more input than rank 0.
2025-08-14T23:09:16.8638769Z         >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2025-08-14T23:09:16.8638810Z         >>>     with model.join():
2025-08-14T23:09:16.8638853Z         >>>         for _ in range(5):
2025-08-14T23:09:16.8638910Z         >>>             for inp in inputs:
2025-08-14T23:09:16.8638957Z         >>>                 loss = model(inp).sum()
2025-08-14T23:09:16.8638999Z         >>>                 loss.backward()
2025-08-14T23:09:16.8639068Z         >>>     # Without the join() API, the below synchronization will hang
2025-08-14T23:09:16.8640502Z         >>>     # blocking for rank 1's allreduce to complete.
2025-08-14T23:09:16.8640577Z         >>>     torch.cuda.synchronize(device=rank)
2025-08-14T23:09:16.8640608Z     
2025-08-14T23:09:16.8640702Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8640733Z     
2025-08-14T23:09:16.8640778Z       warnings.warn(msg)
2025-08-14T23:09:16.8640808Z     
2025-08-14T23:09:16.8640892Z --- Parse Warning: 131 / 146 ---
2025-08-14T23:09:16.8641248Z     /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.
2025-08-14T23:09:16.8641344Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8641375Z     
2025-08-14T23:09:16.8641482Z     Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2025-08-14T23:09:16.8641513Z     
2025-08-14T23:09:16.8641589Z     Registers an optimizer with DDP such that the optimization for a
2025-08-14T23:09:16.8641663Z     parameter will run immediately when that parameter's gradient is
2025-08-14T23:09:16.8641740Z     finished with reduction, instead of waiting for all parameters'
2025-08-14T23:09:16.8641857Z     gradients to finish reduction. This can result in a training speedup
2025-08-14T23:09:16.8641937Z     depending on your workload since the optimizer can run while gradient
2025-08-14T23:09:16.8642017Z     reduction for other parameters are still ongoing. In addition, this has
2025-08-14T23:09:16.8642100Z     the potential to reduce peak memory consumption during training, as it
2025-08-14T23:09:16.8642172Z     only needs to load the per-parameter optimizer states of a single
2025-08-14T23:09:16.8643578Z     parameter at a time, instead of loading all per-parameter optimizer
2025-08-14T23:09:16.8643617Z     states at once.
2025-08-14T23:09:16.8643649Z     
2025-08-14T23:09:16.8643680Z     Args:
2025-08-14T23:09:16.8643754Z         optim (Type): a ``torch.optim.Optimizer`` class to be registered
2025-08-14T23:09:16.8643796Z         as a fused optimizer.
2025-08-14T23:09:16.8643861Z         *args (Sequence[Any]): Arguments to forward to `optim`.
2025-08-14T23:09:16.8643975Z         optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2025-08-14T23:09:16.8644055Z         to optimize, similar to `params` argument of traditional `torch.optim`
2025-08-14T23:09:16.8644129Z         Optimizers. If this is omitted, all DDP model parameters will be
2025-08-14T23:09:16.8644164Z         optimized.
2025-08-14T23:09:16.8644239Z         **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2025-08-14T23:09:16.8644270Z     
2025-08-14T23:09:16.8644306Z     .. warning ::
2025-08-14T23:09:16.8644382Z         _register_fused_optim should only be called once on a DDP instance,
2025-08-14T23:09:16.8644457Z         and registering multiple fused optimizers for the same DDP model
2025-08-14T23:09:16.8644506Z         is not currently supported. Please ping
2025-08-14T23:09:16.8644589Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:16.8644627Z         for your use case.
2025-08-14T23:09:16.8644659Z     
2025-08-14T23:09:16.8644696Z     .. warning ::
2025-08-14T23:09:16.8644769Z         _register_fused_optim and register_comm_hook currently do not
2025-08-14T23:09:16.8646182Z         compose together, meaning that custom DDP communication hooks are
2025-08-14T23:09:16.8646255Z         not supported with overlapped optimizers. Please ping
2025-08-14T23:09:16.8646398Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:16.8646437Z         for your use case.
2025-08-14T23:09:16.8646471Z     
2025-08-14T23:09:16.8646506Z     .. warning ::
2025-08-14T23:09:16.8646585Z         Gradient accumulation and DDP `no_sync` are currently not supported
2025-08-14T23:09:16.8646633Z         with overlapped optimizer. Please ping
2025-08-14T23:09:16.8646711Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2025-08-14T23:09:16.8646749Z         for your use case.
2025-08-14T23:09:16.8646780Z     
2025-08-14T23:09:16.8646814Z     Example::
2025-08-14T23:09:16.8646851Z     
2025-08-14T23:09:16.8646904Z         >>> # xdoctest: +SKIP("No rendezvous handler")
2025-08-14T23:09:16.8647011Z         >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2025-08-14T23:09:16.8647086Z         >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2025-08-14T23:09:16.8647126Z         >>> lr = 1e-2
2025-08-14T23:09:16.8647164Z         >>> betas = (0.9, 0.99)
2025-08-14T23:09:16.8647200Z         >>> eps = 1e-6
2025-08-14T23:09:16.8647281Z         >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2025-08-14T23:09:16.8647330Z         >>> # Example with subset of parameters
2025-08-14T23:09:16.8647383Z         >>> params_to_opt = [list(net.parameters())[0]]
2025-08-14T23:09:16.8648705Z         >>> net._register_fused_optim(
2025-08-14T23:09:16.8648792Z         ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2025-08-14T23:09:16.8648824Z         ... )
2025-08-14T23:09:16.8648855Z     
2025-08-14T23:09:16.8648996Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8649027Z     
2025-08-14T23:09:16.8649066Z       warnings.warn(msg)
2025-08-14T23:09:16.8649101Z     
2025-08-14T23:09:16.8649180Z --- Parse Warning: 132 / 146 ---
2025-08-14T23:09:16.8649476Z     /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.
2025-08-14T23:09:16.8649572Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8649681Z     Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2025-08-14T23:09:16.8649712Z     
2025-08-14T23:09:16.8649826Z         For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2025-08-14T23:09:16.8649887Z         and with 2D inputs, this class
2025-08-14T23:09:16.8649921Z     
2025-08-14T23:09:16.8650031Z             * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2025-08-14T23:09:16.8650141Z             * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2025-08-14T23:09:16.8650246Z             * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2025-08-14T23:09:16.8650277Z     
2025-08-14T23:09:16.8650398Z         However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2025-08-14T23:09:16.8651888Z         operations.
2025-08-14T23:09:16.8651923Z     
2025-08-14T23:09:16.8652023Z         EmbeddingBag also supports per-sample weights as an argument to the forward
2025-08-14T23:09:16.8652108Z         pass. This scales the output of the Embedding before performing a weighted
2025-08-14T23:09:16.8652202Z         reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2025-08-14T23:09:16.8652292Z         only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2025-08-14T23:09:16.8652336Z         :attr:`per_sample_weights`.
2025-08-14T23:09:16.8652366Z     
2025-08-14T23:09:16.8652399Z         Args:
2025-08-14T23:09:16.8652491Z             num_embeddings (int): size of the dictionary of embeddings
2025-08-14T23:09:16.8652556Z             embedding_dim (int): the size of each embedding vector
2025-08-14T23:09:16.8652665Z             max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2025-08-14T23:09:16.8652724Z                                         is renormalized to have norm :attr:`max_norm`.
2025-08-14T23:09:16.8652844Z             norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2025-08-14T23:09:16.8653019Z             scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2025-08-14T23:09:16.8653085Z                                                     the words in the mini-batch. Default ``False``.
2025-08-14T23:09:16.8653155Z                                                     Note: this option is not supported when ``mode="max"``.
2025-08-14T23:09:16.8653243Z             mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2025-08-14T23:09:16.8653324Z                                      ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2025-08-14T23:09:16.8653404Z                                      into consideration. ``"mean"`` computes the average of the values
2025-08-14T23:09:16.8653471Z                                      in the bag, ``"max"`` computes the max value over each bag.
2025-08-14T23:09:16.8653520Z                                      Default: ``"mean"``
2025-08-14T23:09:16.8654958Z             sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2025-08-14T23:09:16.8655095Z                                      Notes for more details regarding sparse gradients. Note: this option is not
2025-08-14T23:09:16.8655148Z                                      supported when ``mode="max"``.
2025-08-14T23:09:16.8655278Z             include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2025-08-14T23:09:16.8655361Z                                           is equivalent to the size of `indices`. This matches the CSR format.
2025-08-14T23:09:16.8655475Z             padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2025-08-14T23:09:16.8655574Z                                          gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2025-08-14T23:09:16.8655678Z                                          during training, i.e. it remains as a fixed "pad". For a newly constructed
2025-08-14T23:09:16.8655778Z                                          EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2025-08-14T23:09:16.8655864Z                                          zeros, but can be updated to another value to be used as the padding vector.
2025-08-14T23:09:16.8655950Z                                          Note that the embedding vector at :attr:`padding_idx` is excluded from the
2025-08-14T23:09:16.8655996Z                                          reduction.
2025-08-14T23:09:16.8656028Z     
2025-08-14T23:09:16.8656063Z         Attributes:
2025-08-14T23:09:16.8656174Z             weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2025-08-14T23:09:16.8656228Z                              initialized from :math:`\mathcal{N}(0, 1)`.
2025-08-14T23:09:16.8656260Z     
2025-08-14T23:09:16.8656296Z         Examples::
2025-08-14T23:09:16.8656328Z     
2025-08-14T23:09:16.8656391Z             >>> # an EmbeddingBag module containing 10 tensors of size 3
2025-08-14T23:09:16.8656460Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2025-08-14T23:09:16.8657742Z             >>> # a batch of 2 samples of 4 indices each
2025-08-14T23:09:16.8657815Z             >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2025-08-14T23:09:16.8657895Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-08-14T23:09:16.8657952Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2025-08-14T23:09:16.8657998Z             >>> embedding_sum(input, offsets)
2025-08-14T23:09:16.8658046Z             tensor([[-0.8861, -5.4350, -0.0523],
2025-08-14T23:09:16.8658088Z                     [ 1.1306, -2.5798, -1.0044]])
2025-08-14T23:09:16.8658120Z     
2025-08-14T23:09:16.8658164Z             >>> # Example with padding_idx
2025-08-14T23:09:16.8658239Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2025-08-14T23:09:16.8658309Z             >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2025-08-14T23:09:16.8658370Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2025-08-14T23:09:16.8658415Z             >>> embedding_sum(input, offsets)
2025-08-14T23:09:16.8658458Z             tensor([[ 0.0000,  0.0000,  0.0000],
2025-08-14T23:09:16.8658499Z                     [-0.7082,  3.2145, -2.6251]])
2025-08-14T23:09:16.8658531Z     
2025-08-14T23:09:16.8658594Z             >>> # An EmbeddingBag can be loaded from an Embedding like so
2025-08-14T23:09:16.8658650Z             >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2025-08-14T23:09:16.8658710Z             >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2025-08-14T23:09:16.8658753Z                     embedding.weight,
2025-08-14T23:09:16.8658806Z                     padding_idx=embedding.padding_idx,
2025-08-14T23:09:16.8658844Z                     mode='sum')
2025-08-14T23:09:16.8660108Z         
2025-08-14T23:09:16.8660199Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8660257Z     
2025-08-14T23:09:16.8660312Z       warnings.warn(msg)
2025-08-14T23:09:16.8660344Z     
2025-08-14T23:09:16.8660423Z --- Parse Warning: 133 / 146 ---
2025-08-14T23:09:16.8660738Z     /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.
2025-08-14T23:09:16.8660833Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8660891Z     Take in and process masked source/target sequences.
2025-08-14T23:09:16.8660922Z     
2025-08-14T23:09:16.8660958Z             .. note::
2025-08-14T23:09:16.8660989Z     
2025-08-14T23:09:16.8661128Z                 If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are
2025-08-14T23:09:16.8661201Z                 not allowed to participate in the attention,
2025-08-14T23:09:16.8661274Z                 which is the opposite of the definition for :attr:`attn_mask`
2025-08-14T23:09:16.8661343Z                 in :func:`torch.nn.functional.scaled_dot_product_attention`.
2025-08-14T23:09:16.8661374Z     
2025-08-14T23:09:16.8661407Z             Args:
2025-08-14T23:09:16.8661462Z                 src: the sequence to the encoder (required).
2025-08-14T23:09:16.8661514Z                 tgt: the sequence to the decoder (required).
2025-08-14T23:09:16.8661580Z                 src_mask: the additive mask for the src sequence (optional).
2025-08-14T23:09:16.8662972Z                 tgt_mask: the additive mask for the tgt sequence (optional).
2025-08-14T23:09:16.8663050Z                 memory_mask: the additive mask for the encoder output (optional).
2025-08-14T23:09:16.8663133Z                 src_key_padding_mask: the Tensor mask for src keys per batch (optional).
2025-08-14T23:09:16.8663248Z                 tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional).
2025-08-14T23:09:16.8663342Z                 memory_key_padding_mask: the Tensor mask for memory keys per batch (optional).
2025-08-14T23:09:16.8663418Z                 src_is_causal: If specified, applies a causal mask as ``src_mask``.
2025-08-14T23:09:16.8663476Z                     Default: ``None``; try to detect a causal mask.
2025-08-14T23:09:16.8663552Z                     Warning:
2025-08-14T23:09:16.8663612Z                     ``src_is_causal`` provides a hint that ``src_mask`` is
2025-08-14T23:09:16.8663676Z                     the causal mask. Providing incorrect hints can result in
2025-08-14T23:09:16.8663737Z                     incorrect execution, including forward and backward
2025-08-14T23:09:16.8663778Z                     compatibility.
2025-08-14T23:09:16.8663852Z                 tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``.
2025-08-14T23:09:16.8663911Z                     Default: ``None``; try to detect a causal mask.
2025-08-14T23:09:16.8663952Z                     Warning:
2025-08-14T23:09:16.8664014Z                     ``tgt_is_causal`` provides a hint that ``tgt_mask`` is
2025-08-14T23:09:16.8664077Z                     the causal mask. Providing incorrect hints can result in
2025-08-14T23:09:16.8664136Z                     incorrect execution, including forward and backward
2025-08-14T23:09:16.8664179Z                     compatibility.
2025-08-14T23:09:16.8664242Z                 memory_is_causal: If specified, applies a causal mask as
2025-08-14T23:09:16.8664283Z                     ``memory_mask``.
2025-08-14T23:09:16.8664324Z                     Default: ``False``.
2025-08-14T23:09:16.8665615Z                     Warning:
2025-08-14T23:09:16.8665670Z                     ``memory_is_causal`` provides a hint that
2025-08-14T23:09:16.8665732Z                     ``memory_mask`` is the causal mask. Providing incorrect
2025-08-14T23:09:16.8665792Z                     hints can result in incorrect execution, including
2025-08-14T23:09:16.8665887Z                     forward and backward compatibility.
2025-08-14T23:09:16.8665922Z     
2025-08-14T23:09:16.8665957Z             Shape:
2025-08-14T23:09:16.8666047Z                 - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or
2025-08-14T23:09:16.8666097Z                   `(N, S, E)` if `batch_first=True`.
2025-08-14T23:09:16.8666187Z                 - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
2025-08-14T23:09:16.8666234Z                   `(N, T, E)` if `batch_first=True`.
2025-08-14T23:09:16.8666305Z                 - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`.
2025-08-14T23:09:16.8666373Z                 - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`.
2025-08-14T23:09:16.8666419Z                 - memory_mask: :math:`(T, S)`.
2025-08-14T23:09:16.8666528Z                 - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
2025-08-14T23:09:16.8666618Z                 - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`.
2025-08-14T23:09:16.8666708Z                 - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`.
2025-08-14T23:09:16.8666739Z     
2025-08-14T23:09:16.8666850Z                 Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked
2025-08-14T23:09:16.8666930Z                 positions. If a BoolTensor is provided, positions with ``True``
2025-08-14T23:09:16.8667022Z                 are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
2025-08-14T23:09:16.8668368Z                 is provided, it will be added to the attention weight.
2025-08-14T23:09:16.8668483Z                 [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
2025-08-14T23:09:16.8668560Z                 the attention. If a BoolTensor is provided, the positions with the
2025-08-14T23:09:16.8668676Z                 value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
2025-08-14T23:09:16.8668707Z     
2025-08-14T23:09:16.8668798Z                 - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or
2025-08-14T23:09:16.8668863Z                   `(N, T, E)` if `batch_first=True`.
2025-08-14T23:09:16.8668894Z     
2025-08-14T23:09:16.8668981Z                 Note: Due to the multi-head attention architecture in the transformer model,
2025-08-14T23:09:16.8669066Z                 the output sequence length of a transformer is same as the input sequence
2025-08-14T23:09:16.8669113Z                 (i.e. target) length of the decoder.
2025-08-14T23:09:16.8669145Z     
2025-08-14T23:09:16.8669257Z                 where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the
2025-08-14T23:09:16.8669311Z                 batch size, :math:`E` is the feature number
2025-08-14T23:09:16.8669346Z     
2025-08-14T23:09:16.8669385Z             Examples:
2025-08-14T23:09:16.8669428Z                 >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8669474Z                 >>> output = transformer_model(
2025-08-14T23:09:16.8669532Z                 ...     src, tgt, src_mask=src_mask, tgt_mask=tgt_mask
2025-08-14T23:09:16.8669566Z                 ... )
2025-08-14T23:09:16.8669600Z             
2025-08-14T23:09:16.8669689Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8670944Z     
2025-08-14T23:09:16.8670983Z       warnings.warn(msg)
2025-08-14T23:09:16.8671014Z     
2025-08-14T23:09:16.8671095Z --- Parse Warning: 134 / 146 ---
2025-08-14T23:09:16.8671393Z     /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.
2025-08-14T23:09:16.8671525Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8671589Z     Computes a partial inverse of :class:`MaxPool2d`.
2025-08-14T23:09:16.8671619Z     
2025-08-14T23:09:16.8671712Z         :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2025-08-14T23:09:16.8671746Z     
2025-08-14T23:09:16.8671826Z         :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2025-08-14T23:09:16.8671910Z         including the indices of the maximal values and computes a partial inverse
2025-08-14T23:09:16.8671968Z         in which all non-maximal values are set to zero.
2025-08-14T23:09:16.8671999Z     
2025-08-14T23:09:16.8672031Z         Note:
2025-08-14T23:09:16.8672141Z             This operation may behave nondeterministically when the input indices has repeat values.
2025-08-14T23:09:16.8672275Z             See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2025-08-14T23:09:16.8672339Z     
2025-08-14T23:09:16.8672424Z         .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2025-08-14T23:09:16.8672489Z                   sizes. Hence, the inversion process can get ambiguous.
2025-08-14T23:09:16.8672557Z                   To accommodate this, you can provide the needed output size
2025-08-14T23:09:16.8674028Z                   as an additional argument :attr:`output_size` in the forward call.
2025-08-14T23:09:16.8674079Z                   See the Inputs and Example below.
2025-08-14T23:09:16.8674115Z     
2025-08-14T23:09:16.8674147Z         Args:
2025-08-14T23:09:16.8674216Z             kernel_size (int or tuple): Size of the max pooling window.
2025-08-14T23:09:16.8674281Z             stride (int or tuple): Stride of the max pooling window.
2025-08-14T23:09:16.8674336Z                 It is set to :attr:`kernel_size` by default.
2025-08-14T23:09:16.8674402Z             padding (int or tuple): Padding that was added to the input
2025-08-14T23:09:16.8674440Z     
2025-08-14T23:09:16.8674480Z         Inputs:
2025-08-14T23:09:16.8674532Z             - `input`: the input Tensor to invert
2025-08-14T23:09:16.8674609Z             - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2025-08-14T23:09:16.8674669Z             - `output_size` (optional): the targeted output size
2025-08-14T23:09:16.8674731Z     
2025-08-14T23:09:16.8674766Z         Shape:
2025-08-14T23:09:16.8674836Z             - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2025-08-14T23:09:16.8674931Z             - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2025-08-14T23:09:16.8674962Z     
2025-08-14T23:09:16.8676785Z               .. math::
2025-08-14T23:09:16.8676892Z                 H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2025-08-14T23:09:16.8676924Z     
2025-08-14T23:09:16.8676959Z               .. math::
2025-08-14T23:09:16.8677071Z                 W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2025-08-14T23:09:16.8677104Z     
2025-08-14T23:09:16.8677167Z               or as given by :attr:`output_size` in the call operator
2025-08-14T23:09:16.8677199Z     
2025-08-14T23:09:16.8677233Z         Example::
2025-08-14T23:09:16.8677271Z     
2025-08-14T23:09:16.8677333Z             >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2025-08-14T23:09:16.8677384Z             >>> unpool = nn.MaxUnpool2d(2, stride=2)
2025-08-14T23:09:16.8677437Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2025-08-14T23:09:16.8677483Z                                         [ 5.,  6.,  7.,  8.],
2025-08-14T23:09:16.8677525Z                                         [ 9., 10., 11., 12.],
2025-08-14T23:09:16.8677604Z                                         [13., 14., 15., 16.]]]])
2025-08-14T23:09:16.8677657Z             >>> output, indices = pool(input)
2025-08-14T23:09:16.8677702Z             >>> unpool(output, indices)
2025-08-14T23:09:16.8677796Z             tensor([[[[  0.,   0.,   0.,   0.],
2025-08-14T23:09:16.8677839Z                       [  0.,   6.,   0.,   8.],
2025-08-14T23:09:16.8677878Z                       [  0.,   0.,   0.,   0.],
2025-08-14T23:09:16.8677920Z                       [  0.,  14.,   0.,  16.]]]])
2025-08-14T23:09:16.8678001Z             >>> # Now using output_size to resolve an ambiguous size for the inverse
2025-08-14T23:09:16.8678058Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2025-08-14T23:09:16.8678103Z                                         [ 6.,  7.,  8.,  9., 10.],
2025-08-14T23:09:16.8678148Z                                         [11., 12., 13., 14., 15.],
2025-08-14T23:09:16.8678193Z                                         [16., 17., 18., 19., 20.]]]])
2025-08-14T23:09:16.8678240Z             >>> output, indices = pool(input)
2025-08-14T23:09:16.8678305Z             >>> # This call will not work without specifying output_size
2025-08-14T23:09:16.8678390Z             >>> unpool(output, indices, output_size=input.size())
2025-08-14T23:09:16.8678437Z             tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2025-08-14T23:09:16.8678477Z                       [ 0.,  7.,  0.,  9.,  0.],
2025-08-14T23:09:16.8678516Z                       [ 0.,  0.,  0.,  0.,  0.],
2025-08-14T23:09:16.8678561Z                       [ 0., 17.,  0., 19.,  0.]]]])
2025-08-14T23:09:16.8678593Z     
2025-08-14T23:09:16.8678624Z     
2025-08-14T23:09:16.8678657Z         
2025-08-14T23:09:16.8678747Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8678779Z     
2025-08-14T23:09:16.8678818Z       warnings.warn(msg)
2025-08-14T23:09:16.8678850Z     
2025-08-14T23:09:16.8678924Z --- Parse Warning: 135 / 146 ---
2025-08-14T23:09:16.8679250Z     /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.
2025-08-14T23:09:16.8679347Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8679421Z     Creates a criterion that measures the triplet loss given input
2025-08-14T23:09:16.8679493Z         tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2025-08-14T23:09:16.8679596Z         positive, and negative examples, respectively), and a nonnegative,
2025-08-14T23:09:16.8679687Z         real-valued function ("distance function") used to compute the relationship
2025-08-14T23:09:16.8679766Z         between the anchor and positive example ("positive distance") and the
2025-08-14T23:09:16.8679824Z         anchor and negative example ("negative distance").
2025-08-14T23:09:16.8679857Z     
2025-08-14T23:09:16.8679933Z         The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2025-08-14T23:09:16.8679973Z         can be described as:
2025-08-14T23:09:16.8680006Z     
2025-08-14T23:09:16.8681489Z         .. math::
2025-08-14T23:09:16.8681553Z             \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2025-08-14T23:09:16.8681616Z             l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2025-08-14T23:09:16.8681653Z     
2025-08-14T23:09:16.8681740Z         where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2025-08-14T23:09:16.8681846Z         quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2025-08-14T23:09:16.8681932Z         and :math:`margin` is a nonnegative margin representing the minimum difference
2025-08-14T23:09:16.8682019Z         between the positive and negative distances that is required for the loss to
2025-08-14T23:09:16.8682101Z         be 0.  The input tensors have :math:`N` elements each and can be of any shape
2025-08-14T23:09:16.8682151Z         that the distance function can handle.
2025-08-14T23:09:16.8682183Z     
2025-08-14T23:09:16.8682230Z         If :attr:`reduction` is not ``'none'``
2025-08-14T23:09:16.8682296Z         (default ``'mean'``), then:
2025-08-14T23:09:16.8682345Z     
2025-08-14T23:09:16.8682380Z         .. math::
2025-08-14T23:09:16.8682416Z             \ell(x, y) =
2025-08-14T23:09:16.8682453Z             \begin{cases}
2025-08-14T23:09:16.8682526Z                 \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2025-08-14T23:09:16.8682597Z                 \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2025-08-14T23:09:16.8682632Z             \end{cases}
2025-08-14T23:09:16.8682664Z     
2025-08-14T23:09:16.8682747Z         See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2025-08-14T23:09:16.8682838Z         loss for input tensors using the :math:`l_p` distance as the distance function.
2025-08-14T23:09:16.8682869Z     
2025-08-14T23:09:16.8682901Z         Args:
2025-08-14T23:09:16.8683052Z             distance_function (Callable, optional): A nonnegative, real-valued function that
2025-08-14T23:09:16.8683152Z                 quantifies the closeness of two tensors. If not specified,
2025-08-14T23:09:16.8683219Z                 `nn.PairwiseDistance` will be used.  Default: ``None``
2025-08-14T23:09:16.8683313Z             margin (float, optional): A nonnegative margin representing the minimum difference
2025-08-14T23:09:16.8683408Z                 between the positive and negative distances required for the loss to be 0. Larger
2025-08-14T23:09:16.8683507Z                 margins penalize cases where the negative examples are not distant enough from the
2025-08-14T23:09:16.8683572Z                 anchors, relative to the positives. Default: :math:`1`.
2025-08-14T23:09:16.8683662Z             swap (bool, optional): Whether to use the distance swap described in the paper
2025-08-14T23:09:16.8683752Z                 `Learning shallow convolutional feature descriptors with triplet losses` by
2025-08-14T23:09:16.8683839Z                 V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2025-08-14T23:09:16.8683940Z                 negative example than the anchor is, swaps the positive example and the anchor in
2025-08-14T23:09:16.8683996Z                 the loss computation. Default: ``False``.
2025-08-14T23:09:16.8684094Z             reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2025-08-14T23:09:16.8684181Z                 ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-08-14T23:09:16.8684248Z                 ``'mean'``: the sum of the output will be divided by the number of
2025-08-14T23:09:16.8684335Z                 elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2025-08-14T23:09:16.8685652Z     
2025-08-14T23:09:16.8685685Z     
2025-08-14T23:09:16.8685772Z         Shape:
2025-08-14T23:09:16.8685861Z             - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2025-08-14T23:09:16.8685915Z               as supported by the distance function.
2025-08-14T23:09:16.8686013Z             - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2025-08-14T23:09:16.8686049Z               otherwise.
2025-08-14T23:09:16.8686081Z     
2025-08-14T23:09:16.8686114Z         Examples:
2025-08-14T23:09:16.8686145Z     
2025-08-14T23:09:16.8686186Z         >>> # Initialize embeddings
2025-08-14T23:09:16.8686234Z         >>> embedding = nn.Embedding(1000, 128)
2025-08-14T23:09:16.8686288Z         >>> anchor_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:16.8686342Z         >>> positive_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:16.8686391Z         >>> negative_ids = torch.randint(0, 1000, (1,))
2025-08-14T23:09:16.8686436Z         >>> anchor = embedding(anchor_ids)
2025-08-14T23:09:16.8686485Z         >>> positive = embedding(positive_ids)
2025-08-14T23:09:16.8686530Z         >>> negative = embedding(negative_ids)
2025-08-14T23:09:16.8686563Z         >>>
2025-08-14T23:09:16.8686608Z         >>> # Built-in Distance Function
2025-08-14T23:09:16.8686676Z         >>> triplet_loss = \
2025-08-14T23:09:16.8686803Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2025-08-14T23:09:16.8688112Z         >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:16.8688154Z         >>> output.backward()
2025-08-14T23:09:16.8688189Z         >>>
2025-08-14T23:09:16.8688231Z         >>> # Custom Distance Function
2025-08-14T23:09:16.8688272Z         >>> def l_infinity(x1, x2):
2025-08-14T23:09:16.8688333Z         >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2025-08-14T23:09:16.8688365Z         >>>
2025-08-14T23:09:16.8688436Z         >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2025-08-14T23:09:16.8688476Z         >>> triplet_loss = (
2025-08-14T23:09:16.8688576Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2025-08-14T23:09:16.8688654Z         >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:16.8688698Z         >>> output.backward()
2025-08-14T23:09:16.8688732Z         >>>
2025-08-14T23:09:16.8688779Z         >>> # Custom Distance Function (Lambda)
2025-08-14T23:09:16.8688816Z         >>> triplet_loss = (
2025-08-14T23:09:16.8688869Z         >>>     nn.TripletMarginWithDistanceLoss(
2025-08-14T23:09:16.8688950Z         >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2025-08-14T23:09:16.8689008Z         >>> output = triplet_loss(anchor, positive, negative)
2025-08-14T23:09:16.8689046Z         >>> output.backward()
2025-08-14T23:09:16.8689078Z     
2025-08-14T23:09:16.8689112Z         Reference:
2025-08-14T23:09:16.8690486Z             V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2025-08-14T23:09:16.8690570Z             https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html
2025-08-14T23:09:16.8690605Z         
2025-08-14T23:09:16.8690731Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2025-08-14T23:09:16.8690768Z     
2025-08-14T23:09:16.8690809Z       warnings.warn(msg)
2025-08-14T23:09:16.8690841Z     
2025-08-14T23:09:16.8690916Z --- Parse Warning: 136 / 146 ---
2025-08-14T23:09:16.8691201Z     /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.
2025-08-14T23:09:16.8691316Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8691370Z     The Connectionist Temporal Classification loss.
2025-08-14T23:09:16.8691401Z     
2025-08-14T23:09:16.8691534Z         Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
2025-08-14T23:09:16.8691655Z         probability of possible alignments of input to target, producing a loss value which is differentiable
2025-08-14T23:09:16.8691776Z         with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
2025-08-14T23:09:16.8691880Z         limits the length of the target sequence such that it must be :math:`\leq` the input length.
2025-08-14T23:09:16.8691912Z     
2025-08-14T23:09:16.8691944Z         Args:
2025-08-14T23:09:16.8692010Z             blank (int, optional): blank label. Default :math:`0`.
2025-08-14T23:09:16.8692096Z             reduction (str, optional): Specifies the reduction to apply to the output:
2025-08-14T23:09:16.8692166Z                 ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2025-08-14T23:09:16.8692242Z                 ``'mean'``: the output losses will be divided by the target lengths and
2025-08-14T23:09:16.8693653Z                 then the mean over the batch is taken, ``'sum'``: the output losses will be summed.
2025-08-14T23:09:16.8693695Z                 Default: ``'mean'``
2025-08-14T23:09:16.8693744Z             zero_infinity (bool, optional):
2025-08-14T23:09:16.8693870Z                 Whether to zero infinite losses and the associated gradients.
2025-08-14T23:09:16.8693912Z                 Default: ``False``
2025-08-14T23:09:16.8693980Z                 Infinite losses mainly occur when the inputs are too short
2025-08-14T23:09:16.8694026Z                 to be aligned to the targets.
2025-08-14T23:09:16.8694060Z     
2025-08-14T23:09:16.8694093Z         Shape:
2025-08-14T23:09:16.8694164Z             - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`,
2025-08-14T23:09:16.8694214Z               where :math:`T = \text{input length}`,
2025-08-14T23:09:16.8694261Z               :math:`N = \text{batch size}`, and
2025-08-14T23:09:16.8694322Z               :math:`C = \text{number of classes (including blank)}`.
2025-08-14T23:09:16.8694403Z               The logarithmized probabilities of the outputs (e.g. obtained with
2025-08-14T23:09:16.8694456Z               :func:`torch.nn.functional.log_softmax`).
2025-08-14T23:09:16.8694531Z             - Targets: Tensor of size :math:`(N, S)` or
2025-08-14T23:09:16.8694595Z               :math:`(\operatorname{sum}(\text{target\_lengths}))`,
2025-08-14T23:09:16.8694645Z               where :math:`N = \text{batch size}` and
2025-08-14T23:09:16.8694706Z               :math:`S = \text{max target length, if shape is } (N, S)`.
2025-08-14T23:09:16.8694779Z               It represents the target sequences. Each element in the target
2025-08-14T23:09:16.8694864Z               sequence is a class index. And the target index cannot be blank (default=0).
2025-08-14T23:09:16.8696203Z               In the :math:`(N, S)` form, targets are padded to the
2025-08-14T23:09:16.8696258Z               length of the longest sequence, and stacked.
2025-08-14T23:09:16.8696335Z               In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form,
2025-08-14T23:09:16.8696388Z               the targets are assumed to be un-padded and
2025-08-14T23:09:16.8696436Z               concatenated within 1 dimension.
2025-08-14T23:09:16.8696517Z             - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`,
2025-08-14T23:09:16.8696592Z               where :math:`N = \text{batch size}`. It represents the lengths of the
2025-08-14T23:09:16.8696664Z               inputs (must each be :math:`\leq T`). And the lengths are specified
2025-08-14T23:09:16.8696771Z               for each sequence to achieve masking under the assumption that sequences
2025-08-14T23:09:16.8696816Z               are padded to equal lengths.
2025-08-14T23:09:16.8696893Z             - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`,
2025-08-14T23:09:16.8696968Z               where :math:`N = \text{batch size}`. It represents lengths of the targets.
2025-08-14T23:09:16.8697047Z               Lengths are specified for each sequence to achieve masking under the
2025-08-14T23:09:16.8697132Z               assumption that sequences are padded to equal lengths. If target shape is
2025-08-14T23:09:16.8697199Z               :math:`(N,S)`, target_lengths are effectively the stop index
2025-08-14T23:09:16.8697295Z               :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for
2025-08-14T23:09:16.8697362Z               each target in a batch. Lengths must each be :math:`\leq S`
2025-08-14T23:09:16.8697448Z               If the targets are given as a 1d tensor that is the concatenation of individual
2025-08-14T23:09:16.8697532Z               targets, the target_lengths must add up to the total length of the tensor.
2025-08-14T23:09:16.8697601Z             - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or
2025-08-14T23:09:16.8697687Z               ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or
2025-08-14T23:09:16.8697757Z               :math:`()` if input is unbatched, where :math:`N = \text{batch size}`.
2025-08-14T23:09:16.8699042Z     
2025-08-14T23:09:16.8699082Z         Examples:
2025-08-14T23:09:16.8699114Z     
2025-08-14T23:09:16.8699160Z             >>> # Target are to be padded
2025-08-14T23:09:16.8699243Z             >>> T = 50  # Input sequence length
2025-08-14T23:09:16.8699300Z             >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:16.8699340Z             >>> N = 16  # Batch size
2025-08-14T23:09:16.8699422Z             >>> S = 30  # Target sequence length of longest target in batch (padding length)
2025-08-14T23:09:16.8699492Z             >>> S_min = 10  # Minimum target length, for demonstration purposes
2025-08-14T23:09:16.8699524Z             >>>
2025-08-14T23:09:16.8699596Z             >>> # Initialize random batch of input vectors, for *size = (T,N,C)
2025-08-14T23:09:16.8699673Z             >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
2025-08-14T23:09:16.8699705Z             >>>
2025-08-14T23:09:16.8699774Z             >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:16.8699877Z             >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)
2025-08-14T23:09:16.8699913Z             >>>
2025-08-14T23:09:16.8699993Z             >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
2025-08-14T23:09:16.8700041Z             >>> target_lengths = torch.randint(
2025-08-14T23:09:16.8700079Z             ...     low=S_min,
2025-08-14T23:09:16.8700120Z             ...     high=S,
2025-08-14T23:09:16.8700157Z             ...     size=(N,),
2025-08-14T23:09:16.8701464Z             ...     dtype=torch.long,
2025-08-14T23:09:16.8701497Z             ... )
2025-08-14T23:09:16.8701543Z             >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:16.8701610Z             >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:16.8701650Z             >>> loss.backward()
2025-08-14T23:09:16.8701682Z             >>>
2025-08-14T23:09:16.8701714Z             >>>
2025-08-14T23:09:16.8701760Z             >>> # Target are to be un-padded
2025-08-14T23:09:16.8701805Z             >>> T = 50  # Input sequence length
2025-08-14T23:09:16.8701866Z             >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:16.8701906Z             >>> N = 16  # Batch size
2025-08-14T23:09:16.8701937Z             >>>
2025-08-14T23:09:16.8702007Z             >>> # Initialize random batch of input vectors, for *size = (T,N,C)
2025-08-14T23:09:16.8702080Z             >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
2025-08-14T23:09:16.8702177Z             >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
2025-08-14T23:09:16.8702210Z             >>>
2025-08-14T23:09:16.8702280Z             >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:16.8702370Z             >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long)
2025-08-14T23:09:16.8702411Z             >>> target = torch.randint(
2025-08-14T23:09:16.8702448Z             ...     low=1,
2025-08-14T23:09:16.8702484Z             ...     high=C,
2025-08-14T23:09:16.8702533Z             ...     size=(sum(target_lengths),),
2025-08-14T23:09:16.8703916Z             ...     dtype=torch.long,
2025-08-14T23:09:16.8703952Z             ... )
2025-08-14T23:09:16.8703994Z             >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:16.8704062Z             >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:16.8704101Z             >>> loss.backward()
2025-08-14T23:09:16.8704133Z             >>>
2025-08-14T23:09:16.8704165Z             >>>
2025-08-14T23:09:16.8704234Z             >>> # Target are to be un-padded and unbatched (effectively N=1)
2025-08-14T23:09:16.8704279Z             >>> T = 50  # Input sequence length
2025-08-14T23:09:16.8704331Z             >>> C = 20  # Number of classes (including blank)
2025-08-14T23:09:16.8704364Z             >>>
2025-08-14T23:09:16.8704436Z             >>> # Initialize random batch of input vectors, for *size = (T,C)
2025-08-14T23:09:16.8704489Z             >>> # xdoctest: +SKIP("FIXME: error in doctest")
2025-08-14T23:09:16.8704613Z             >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_()
2025-08-14T23:09:16.8704673Z             >>> input_lengths = torch.tensor(T, dtype=torch.long)
2025-08-14T23:09:16.8704705Z             >>>
2025-08-14T23:09:16.8704774Z             >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
2025-08-14T23:09:16.8704857Z             >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long)
2025-08-14T23:09:16.8704898Z             >>> target = torch.randint(
2025-08-14T23:09:16.8704935Z             ...     low=1,
2025-08-14T23:09:16.8704971Z             ...     high=C,
2025-08-14T23:09:16.8705014Z             ...     size=(target_lengths,),
2025-08-14T23:09:16.8706327Z             ...     dtype=torch.long,
2025-08-14T23:09:16.8706364Z             ... )
2025-08-14T23:09:16.8706405Z             >>> ctc_loss = nn.CTCLoss()
2025-08-14T23:09:16.8706472Z             >>> loss = ctc_loss(input, target, input_lengths, target_lengths)
2025-08-14T23:09:16.8706539Z             >>> loss.backward()
2025-08-14T23:09:16.8706571Z     
2025-08-14T23:09:16.8706608Z         Reference:
2025-08-14T23:09:16.8706674Z             A. Graves et al.: Connectionist Temporal Classification:
2025-08-14T23:09:16.8706760Z             Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
2025-08-14T23:09:16.8706828Z             https://www.cs.toronto.edu/~graves/icml_2006.pdf
2025-08-14T23:09:16.8706860Z     
2025-08-14T23:09:16.8706892Z         Note:
2025-08-14T23:09:16.8706979Z             In order to use CuDNN, the following must be satisfied: :attr:`targets` must be
2025-08-14T23:09:16.8707069Z             in concatenated format, all :attr:`input_lengths` must be `T`.  :math:`blank=0`,
2025-08-14T23:09:16.8707149Z             :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of
2025-08-14T23:09:16.8707192Z             dtype :attr:`torch.int32`.
2025-08-14T23:09:16.8707224Z     
2025-08-14T23:09:16.8707319Z             The regular implementation uses the (more common in PyTorch) `torch.long` dtype.
2025-08-14T23:09:16.8707356Z     
2025-08-14T23:09:16.8707387Z     
2025-08-14T23:09:16.8707418Z         Note:
2025-08-14T23:09:16.8708774Z             In some circumstances when using the CUDA backend with CuDNN, this operator
2025-08-14T23:09:16.8708871Z             may select a nondeterministic algorithm to increase performance. If this is
2025-08-14T23:09:16.8708985Z             undesirable, you can try to make the operation deterministic (potentially at
2025-08-14T23:09:16.8709069Z             a performance cost) by setting ``torch.backends.cudnn.deterministic =
2025-08-14T23:09:16.8709103Z             True``.
2025-08-14T23:09:16.8709174Z             Please see the notes on :doc:`/notes/randomness` for background.
2025-08-14T23:09:16.8709207Z         
2025-08-14T23:09:16.8709295Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8709327Z     
2025-08-14T23:09:16.8709364Z       warnings.warn(msg)
2025-08-14T23:09:16.8709402Z     
2025-08-14T23:09:16.8709480Z --- Parse Warning: 137 / 146 ---
2025-08-14T23:09:16.8709781Z     /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.
2025-08-14T23:09:16.8709875Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8709943Z     Applies Batch Normalization over a N-Dimensional input.
2025-08-14T23:09:16.8709974Z     
2025-08-14T23:09:16.8710096Z         The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2025-08-14T23:09:16.8710174Z         `Batch Normalization: Accelerating Deep Network Training by Reducing
2025-08-14T23:09:16.8710252Z         Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2025-08-14T23:09:16.8710282Z     
2025-08-14T23:09:16.8710339Z         .. math::
2025-08-14T23:09:16.8710370Z     
2025-08-14T23:09:16.8711737Z             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2025-08-14T23:09:16.8711773Z     
2025-08-14T23:09:16.8711854Z         The mean and standard-deviation are calculated per-dimension over all
2025-08-14T23:09:16.8711944Z         mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2025-08-14T23:09:16.8712034Z         are learnable parameter vectors of size `C` (where `C` is the input size).
2025-08-14T23:09:16.8712100Z         By default, the elements of :math:`\gamma` are sampled from
2025-08-14T23:09:16.8712173Z         :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2025-08-14T23:09:16.8712264Z         The standard-deviation is calculated via the biased estimator, equivalent to
2025-08-14T23:09:16.8712311Z         `torch.var(input, unbiased=False)`.
2025-08-14T23:09:16.8712342Z     
2025-08-14T23:09:16.8712443Z         Also by default, during training this layer keeps running estimates of its
2025-08-14T23:09:16.8712530Z         computed mean and variance, which are then used for normalization during
2025-08-14T23:09:16.8712615Z         evaluation. The running estimates are kept with a default :attr:`momentum`
2025-08-14T23:09:16.8712648Z         of 0.1.
2025-08-14T23:09:16.8712681Z     
2025-08-14T23:09:16.8712763Z         If :attr:`track_running_stats` is set to ``False``, this layer then does not
2025-08-14T23:09:16.8712839Z         keep running estimates, and batch statistics are instead used during
2025-08-14T23:09:16.8712881Z         evaluation time as well.
2025-08-14T23:09:16.8712965Z     
2025-08-14T23:09:16.8713000Z         .. note::
2025-08-14T23:09:16.8713078Z             This :attr:`momentum` argument is different from one used in optimizer
2025-08-14T23:09:16.8714390Z             classes and the conventional notion of momentum. Mathematically, the
2025-08-14T23:09:16.8714447Z             update rule for running statistics here is
2025-08-14T23:09:16.8714554Z             :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2025-08-14T23:09:16.8714629Z             where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2025-08-14T23:09:16.8714668Z             new observed value.
2025-08-14T23:09:16.8714732Z     
2025-08-14T23:09:16.8714838Z         Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2025-08-14T23:09:16.8714927Z         statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2025-08-14T23:09:16.8714995Z         Normalization or Spatio-temporal Batch Normalization.
2025-08-14T23:09:16.8715026Z     
2025-08-14T23:09:16.8715082Z         Currently :class:`SyncBatchNorm` only supports
2025-08-14T23:09:16.8715182Z         :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2025-08-14T23:09:16.8715260Z         :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2025-08-14T23:09:16.8715338Z         :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2025-08-14T23:09:16.8715378Z         Network with DDP.
2025-08-14T23:09:16.8715409Z     
2025-08-14T23:09:16.8715441Z         Args:
2025-08-14T23:09:16.8715505Z             num_features: :math:`C` from an expected input of size
2025-08-14T23:09:16.8715546Z                 :math:`(N, C, +)`
2025-08-14T23:09:16.8715615Z             eps: a value added to the denominator for numerical stability.
2025-08-14T23:09:16.8715656Z                 Default: ``1e-5``
2025-08-14T23:09:16.8715726Z             momentum: the value used for the running_mean and running_var
2025-08-14T23:09:16.8717015Z                 computation. Can be set to ``None`` for cumulative moving average
2025-08-14T23:09:16.8717066Z                 (i.e. simple average). Default: 0.1
2025-08-14T23:09:16.8717139Z             affine: a boolean value that when set to ``True``, this module has
2025-08-14T23:09:16.8717202Z                 learnable affine parameters. Default: ``True``
2025-08-14T23:09:16.8717324Z             track_running_stats: a boolean value that when set to ``True``, this
2025-08-14T23:09:16.8717408Z                 module tracks the running mean and variance, and when set to ``False``,
2025-08-14T23:09:16.8717486Z                 this module does not track such statistics, and initializes statistics
2025-08-14T23:09:16.8717562Z                 buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2025-08-14T23:09:16.8717643Z                 When these buffers are ``None``, this module always uses batch statistics.
2025-08-14T23:09:16.8717701Z                 in both training and eval modes. Default: ``True``
2025-08-14T23:09:16.8717791Z             process_group: synchronization of stats happen within each process group
2025-08-14T23:09:16.8717870Z                 individually. Default behavior is synchronization across the whole
2025-08-14T23:09:16.8717904Z                 world
2025-08-14T23:09:16.8717936Z     
2025-08-14T23:09:16.8717991Z         Shape:
2025-08-14T23:09:16.8718038Z             - Input: :math:`(N, C, +)`
2025-08-14T23:09:16.8718096Z             - Output: :math:`(N, C, +)` (same shape as input)
2025-08-14T23:09:16.8718128Z     
2025-08-14T23:09:16.8718161Z         .. note::
2025-08-14T23:09:16.8718248Z             Synchronization of batchnorm statistics occurs only while training, i.e.
2025-08-14T23:09:16.8718324Z             synchronization is disabled when ``model.eval()`` is set or if
2025-08-14T23:09:16.8719627Z             ``self.training`` is otherwise ``False``.
2025-08-14T23:09:16.8719659Z     
2025-08-14T23:09:16.8719698Z         Examples::
2025-08-14T23:09:16.8719729Z     
2025-08-14T23:09:16.8719770Z             >>> # xdoctest: +SKIP
2025-08-14T23:09:16.8719849Z             >>> # With Learnable Parameters
2025-08-14T23:09:16.8719893Z             >>> m = nn.SyncBatchNorm(100)
2025-08-14T23:09:16.8719949Z             >>> # creating process group (optional)
2025-08-14T23:09:16.8720007Z             >>> # ranks is a list of int identifying rank ids.
2025-08-14T23:09:16.8720055Z             >>> ranks = list(range(8))
2025-08-14T23:09:16.8720101Z             >>> r1, r2 = ranks[:4], ranks[4:]
2025-08-14T23:09:16.8720163Z             >>> # Note: every rank calls into new_group for every
2025-08-14T23:09:16.8720221Z             >>> # process group created, even if that rank is not
2025-08-14T23:09:16.8720286Z             >>> # part of the group.
2025-08-14T23:09:16.8720376Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-08-14T23:09:16.8720453Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-08-14T23:09:16.8720501Z             >>> # Without Learnable Parameters
2025-08-14T23:09:16.8720578Z             >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2025-08-14T23:09:16.8720628Z             >>> input = torch.randn(20, 100, 35, 45, 10)
2025-08-14T23:09:16.8720669Z             >>> output = m(input)
2025-08-14T23:09:16.8720705Z     
2025-08-14T23:09:16.8720755Z             >>> # network is nn.BatchNorm layer
2025-08-14T23:09:16.8722136Z             >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2025-08-14T23:09:16.8722205Z             >>> # only single gpu per process is currently supported
2025-08-14T23:09:16.8722288Z             >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2025-08-14T23:09:16.8722335Z             >>>                         sync_bn_network,
2025-08-14T23:09:16.8722386Z             >>>                         device_ids=[args.local_rank],
2025-08-14T23:09:16.8722438Z             >>>                         output_device=args.local_rank)
2025-08-14T23:09:16.8722470Z         
2025-08-14T23:09:16.8722558Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8722589Z     
2025-08-14T23:09:16.8722629Z       warnings.warn(msg)
2025-08-14T23:09:16.8722660Z     
2025-08-14T23:09:16.8722759Z --- Parse Warning: 138 / 146 ---
2025-08-14T23:09:16.8723186Z     /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.
2025-08-14T23:09:16.8723281Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8723389Z     Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2025-08-14T23:09:16.8723420Z     
2025-08-14T23:09:16.8723453Z             Args:
2025-08-14T23:09:16.8723540Z                 module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2025-08-14T23:09:16.8723618Z                 process_group (optional): process group to scope synchronization,
2025-08-14T23:09:16.8723664Z                     default is the whole world
2025-08-14T23:09:16.8723696Z     
2025-08-14T23:09:16.8723752Z             Returns:
2025-08-14T23:09:16.8725133Z                 The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2025-08-14T23:09:16.8725213Z                 layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2025-08-14T23:09:16.8725293Z                 a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2025-08-14T23:09:16.8725332Z                 instead.
2025-08-14T23:09:16.8725364Z     
2025-08-14T23:09:16.8725400Z             Example::
2025-08-14T23:09:16.8725432Z     
2025-08-14T23:09:16.8725482Z                 >>> # Network with nn.BatchNorm layer
2025-08-14T23:09:16.8725537Z                 >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.8725585Z                 >>> module = torch.nn.Sequential(
2025-08-14T23:09:16.8725634Z                 >>>            torch.nn.Linear(20, 100),
2025-08-14T23:09:16.8725685Z                 >>>            torch.nn.BatchNorm1d(100),
2025-08-14T23:09:16.8725767Z                 >>>          ).cuda()
2025-08-14T23:09:16.8725826Z                 >>> # creating process group (optional)
2025-08-14T23:09:16.8725883Z                 >>> # ranks is a list of int identifying rank ids.
2025-08-14T23:09:16.8725926Z                 >>> ranks = list(range(8))
2025-08-14T23:09:16.8725971Z                 >>> r1, r2 = ranks[:4], ranks[4:]
2025-08-14T23:09:16.8726051Z                 >>> # Note: every rank calls into new_group for every
2025-08-14T23:09:16.8726107Z                 >>> # process group created, even if that rank is not
2025-08-14T23:09:16.8726151Z                 >>> # part of the group.
2025-08-14T23:09:16.8726200Z                 >>> # xdoctest: +SKIP("distributed")
2025-08-14T23:09:16.8727564Z                 >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2025-08-14T23:09:16.8727659Z                 >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2025-08-14T23:09:16.8727765Z                 >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2025-08-14T23:09:16.8727803Z     
2025-08-14T23:09:16.8727835Z             
2025-08-14T23:09:16.8727924Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8727956Z     
2025-08-14T23:09:16.8727993Z       warnings.warn(msg)
2025-08-14T23:09:16.8728026Z     
2025-08-14T23:09:16.8728099Z --- Parse Warning: 139 / 146 ---
2025-08-14T23:09:16.8728392Z     /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.
2025-08-14T23:09:16.8728483Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8728514Z     
2025-08-14T23:09:16.8728623Z     Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2025-08-14T23:09:16.8728654Z     
2025-08-14T23:09:16.8728794Z     * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2025-08-14T23:09:16.8728876Z       be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2025-08-14T23:09:16.8728907Z     
2025-08-14T23:09:16.8729017Z     * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2025-08-14T23:09:16.8729116Z       a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2025-08-14T23:09:16.8729182Z       (tuple of `(name, size)` tuples) for `NamedTensor` input.
2025-08-14T23:09:16.8729213Z     
2025-08-14T23:09:16.8730522Z     Shape:
2025-08-14T23:09:16.8730609Z         - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2025-08-14T23:09:16.8730698Z           dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2025-08-14T23:09:16.8730801Z         - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2025-08-14T23:09:16.8730861Z           :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2025-08-14T23:09:16.8730892Z     
2025-08-14T23:09:16.8730926Z     Args:
2025-08-14T23:09:16.8730983Z         dim (Union[int, str]): Dimension to be unflattened
2025-08-14T23:09:16.8731104Z         unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2025-08-14T23:09:16.8731138Z     
2025-08-14T23:09:16.8731171Z     Examples:
2025-08-14T23:09:16.8731215Z         >>> input = torch.randn(2, 50)
2025-08-14T23:09:16.8731255Z         >>> # With tuple of ints
2025-08-14T23:09:16.8731295Z         >>> m = nn.Sequential(
2025-08-14T23:09:16.8731334Z         >>>     nn.Linear(50, 50),
2025-08-14T23:09:16.8731378Z         >>>     nn.Unflatten(1, (2, 5, 5))
2025-08-14T23:09:16.8731410Z         >>> )
2025-08-14T23:09:16.8731450Z         >>> output = m(input)
2025-08-14T23:09:16.8731489Z         >>> output.size()
2025-08-14T23:09:16.8731531Z         torch.Size([2, 2, 5, 5])
2025-08-14T23:09:16.8731572Z         >>> # With torch.Size
2025-08-14T23:09:16.8732827Z         >>> m = nn.Sequential(
2025-08-14T23:09:16.8732867Z         >>>     nn.Linear(50, 50),
2025-08-14T23:09:16.8732992Z         >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2025-08-14T23:09:16.8733024Z         >>> )
2025-08-14T23:09:16.8733088Z         >>> output = m(input)
2025-08-14T23:09:16.8733125Z         >>> output.size()
2025-08-14T23:09:16.8733163Z         torch.Size([2, 2, 5, 5])
2025-08-14T23:09:16.8733212Z         >>> # With namedshape (tuple of tuples)
2025-08-14T23:09:16.8733272Z         >>> input = torch.randn(2, 50, names=("N", "features"))
2025-08-14T23:09:16.8733346Z         >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5)))
2025-08-14T23:09:16.8733390Z         >>> output = unflatten(input)
2025-08-14T23:09:16.8733428Z         >>> output.size()
2025-08-14T23:09:16.8733466Z         torch.Size([2, 2, 5, 5])
2025-08-14T23:09:16.8733497Z     
2025-08-14T23:09:16.8733589Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8733624Z     
2025-08-14T23:09:16.8733661Z       warnings.warn(msg)
2025-08-14T23:09:16.8733692Z     
2025-08-14T23:09:16.8733765Z --- Parse Warning: 140 / 146 ---
2025-08-14T23:09:16.8734087Z     /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.
2025-08-14T23:09:16.8734181Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8734242Z     Register a parametrization to a tensor in a module.
2025-08-14T23:09:16.8735501Z     
2025-08-14T23:09:16.8735601Z         Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``,
2025-08-14T23:09:16.8735700Z         the module will return the parametrized version ``parametrization(module.weight)``.
2025-08-14T23:09:16.8735841Z         If the original tensor requires a gradient, the backward pass will differentiate
2025-08-14T23:09:16.8735942Z         through :attr:`parametrization`, and the optimizer will update the tensor accordingly.
2025-08-14T23:09:16.8735974Z     
2025-08-14T23:09:16.8736080Z         The first time that a module registers a parametrization, this function will add an attribute
2025-08-14T23:09:16.8736170Z         ``parametrizations`` to the module of type :class:`~ParametrizationList`.
2025-08-14T23:09:16.8736200Z     
2025-08-14T23:09:16.8736289Z         The list of parametrizations on the tensor ``weight`` will be accessible under
2025-08-14T23:09:16.8736340Z         ``module.parametrizations.weight``.
2025-08-14T23:09:16.8736372Z     
2025-08-14T23:09:16.8736427Z         The original tensor will be accessible under
2025-08-14T23:09:16.8736489Z         ``module.parametrizations.weight.original``.
2025-08-14T23:09:16.8736519Z     
2025-08-14T23:09:16.8736631Z         Parametrizations may be concatenated by registering several parametrizations
2025-08-14T23:09:16.8736675Z         on the same attribute.
2025-08-14T23:09:16.8736705Z     
2025-08-14T23:09:16.8736791Z         The training mode of a registered parametrization is updated on registration
2025-08-14T23:09:16.8736844Z         to match the training mode of the host module
2025-08-14T23:09:16.8736878Z     
2025-08-14T23:09:16.8736983Z         Parametrized parameters and buffers have an inbuilt caching system that can be activated
2025-08-14T23:09:16.8738296Z         using the context manager :func:`cached`.
2025-08-14T23:09:16.8738329Z     
2025-08-14T23:09:16.8738420Z         A :attr:`parametrization` may optionally implement a method with signature
2025-08-14T23:09:16.8738450Z     
2025-08-14T23:09:16.8738492Z         .. code-block:: python
2025-08-14T23:09:16.8738522Z     
2025-08-14T23:09:16.8738601Z             def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]]
2025-08-14T23:09:16.8738632Z     
2025-08-14T23:09:16.8738731Z         This method is called on the unparametrized tensor when the first parametrization
2025-08-14T23:09:16.8738807Z         is registered to compute the initial value of the original tensor.
2025-08-14T23:09:16.8738912Z         If this method is not implemented, the original tensor will be just the unparametrized tensor.
2025-08-14T23:09:16.8738963Z     
2025-08-14T23:09:16.8739071Z         If all the parametrizations registered on a tensor implement `right_inverse` it is possible
2025-08-14T23:09:16.8739169Z         to initialize a parametrized tensor by assigning to it, as shown in the example below.
2025-08-14T23:09:16.8739200Z     
2025-08-14T23:09:16.8739279Z         It is possible for the first parametrization to depend on several inputs.
2025-08-14T23:09:16.8739366Z         This may be implemented returning a tuple of tensors from ``right_inverse``
2025-08-14T23:09:16.8739448Z         (see the example implementation of a ``RankOne`` parametrization below).
2025-08-14T23:09:16.8739482Z     
2025-08-14T23:09:16.8739605Z         In this case, the unconstrained tensors are also located under ``module.parametrizations.weight``
2025-08-14T23:09:16.8739655Z         with names ``original0``, ``original1``,...
2025-08-14T23:09:16.8740961Z     
2025-08-14T23:09:16.8740997Z         .. note::
2025-08-14T23:09:16.8741032Z     
2025-08-14T23:09:16.8741131Z             If unsafe=False (default) both the forward and right_inverse methods will be called
2025-08-14T23:09:16.8741191Z             once to perform a number of consistency checks.
2025-08-14T23:09:16.8741286Z             If unsafe=True, then right_inverse will be called if the tensor is not parametrized,
2025-08-14T23:09:16.8741334Z             and nothing will be called otherwise.
2025-08-14T23:09:16.8741364Z     
2025-08-14T23:09:16.8741398Z         .. note::
2025-08-14T23:09:16.8741428Z     
2025-08-14T23:09:16.8741501Z             In most situations, ``right_inverse`` will be a function such that
2025-08-14T23:09:16.8741549Z             ``forward(right_inverse(X)) == X`` (see
2025-08-14T23:09:16.8741690Z             `right inverse <https://en.wikipedia.org/wiki/Inverse_function#Right_inverses>`_).
2025-08-14T23:09:16.8741780Z             Sometimes, when the parametrization is not surjective, it may be reasonable
2025-08-14T23:09:16.8741818Z             to relax this.
2025-08-14T23:09:16.8741851Z     
2025-08-14T23:09:16.8741886Z         .. warning::
2025-08-14T23:09:16.8741917Z     
2025-08-14T23:09:16.8742014Z             If a parametrization depends on several inputs, :func:`~register_parametrization`
2025-08-14T23:09:16.8742104Z             will register a number of new parameters. If such parametrization is registered
2025-08-14T23:09:16.8742200Z             after the optimizer is created, these new parameters will need to be added manually
2025-08-14T23:09:16.8742271Z             to the optimizer. See :meth:`torch.Optimizer.add_param_group`.
2025-08-14T23:09:16.8743659Z     
2025-08-14T23:09:16.8743692Z         Args:
2025-08-14T23:09:16.8743805Z             module (nn.Module): module on which to register the parametrization
2025-08-14T23:09:16.8743886Z             tensor_name (str): name of the parameter or buffer on which to register
2025-08-14T23:09:16.8743930Z                 the parametrization
2025-08-14T23:09:16.8744004Z             parametrization (nn.Module): the parametrization to register
2025-08-14T23:09:16.8744042Z         Keyword args:
2025-08-14T23:09:16.8744120Z             unsafe (bool): a boolean flag that denotes whether the parametrization
2025-08-14T23:09:16.8744190Z                 may change the dtype and shape of the tensor. Default: `False`
2025-08-14T23:09:16.8744283Z                 Warning: the parametrization is not checked for consistency upon registration.
2025-08-14T23:09:16.8744331Z                 Enable this flag at your own risk.
2025-08-14T23:09:16.8744363Z     
2025-08-14T23:09:16.8744396Z         Raises:
2025-08-14T23:09:16.8744498Z             ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name`
2025-08-14T23:09:16.8744531Z     
2025-08-14T23:09:16.8744569Z         Examples:
2025-08-14T23:09:16.8744626Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
2025-08-14T23:09:16.8744666Z             >>> import torch
2025-08-14T23:09:16.8744707Z             >>> import torch.nn as nn
2025-08-14T23:09:16.8744786Z             >>> import torch.nn.utils.parametrize as P
2025-08-14T23:09:16.8744818Z             >>>
2025-08-14T23:09:16.8746101Z             >>> class Symmetric(nn.Module):
2025-08-14T23:09:16.8746144Z             >>>     def forward(self, X):
2025-08-14T23:09:16.8746215Z             >>>         return X.triu() + X.triu(1).T  # Return a symmetric matrix
2025-08-14T23:09:16.8746246Z             >>>
2025-08-14T23:09:16.8746293Z             >>>     def right_inverse(self, A):
2025-08-14T23:09:16.8746333Z             >>>         return A.triu()
2025-08-14T23:09:16.8746365Z             >>>
2025-08-14T23:09:16.8746404Z             >>> m = nn.Linear(5, 5)
2025-08-14T23:09:16.8746476Z             >>> P.register_parametrization(m, "weight", Symmetric())
2025-08-14T23:09:16.8746567Z             >>> print(torch.allclose(m.weight, m.weight.T))  # m.weight is now symmetric
2025-08-14T23:09:16.8746600Z             True
2025-08-14T23:09:16.8746641Z             >>> A = torch.rand(5, 5)
2025-08-14T23:09:16.8746686Z             >>> A = A + A.T  # A is now symmetric
2025-08-14T23:09:16.8746759Z             >>> m.weight = A  # Initialize the weight to be the symmetric matrix A
2025-08-14T23:09:16.8746807Z             >>> print(torch.allclose(m.weight, A))
2025-08-14T23:09:16.8746840Z             True
2025-08-14T23:09:16.8746871Z     
2025-08-14T23:09:16.8746914Z             >>> class RankOne(nn.Module):
2025-08-14T23:09:16.8746958Z             >>>     def forward(self, x, y):
2025-08-14T23:09:16.8747016Z             >>> # Form a rank 1 matrix multiplying two vectors
2025-08-14T23:09:16.8747072Z             >>>         return x.unsqueeze(-1) @ y.unsqueeze(-2)
2025-08-14T23:09:16.8747145Z             >>>
2025-08-14T23:09:16.8748421Z             >>>     def right_inverse(self, Z):
2025-08-14T23:09:16.8748474Z             >>> # Project Z onto the rank 1 matrices
2025-08-14T23:09:16.8748533Z             >>>         U, S, Vh = torch.linalg.svd(Z, full_matrices=False)
2025-08-14T23:09:16.8748580Z             >>> # Return rescaled singular vectors
2025-08-14T23:09:16.8748632Z             >>>         s0_sqrt = S[0].sqrt().unsqueeze(-1)
2025-08-14T23:09:16.8748689Z             >>>         return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt
2025-08-14T23:09:16.8748721Z             >>>
2025-08-14T23:09:16.8748779Z             >>> linear_rank_one = P.register_parametrization(
2025-08-14T23:09:16.8748828Z             ...     nn.Linear(4, 4), "weight", RankOne()
2025-08-14T23:09:16.8748861Z             ... )
2025-08-14T23:09:16.8748935Z             >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item())
2025-08-14T23:09:16.8748969Z             1
2025-08-14T23:09:16.8749000Z     
2025-08-14T23:09:16.8749087Z         
2025-08-14T23:09:16.8749179Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8749210Z     
2025-08-14T23:09:16.8749249Z       warnings.warn(msg)
2025-08-14T23:09:16.8749279Z     
2025-08-14T23:09:16.8749359Z --- Parse Warning: 141 / 146 ---
2025-08-14T23:09:16.8749698Z     /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.
2025-08-14T23:09:16.8749790Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8749870Z     Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2025-08-14T23:09:16.8751180Z     
2025-08-14T23:09:16.8751282Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-08-14T23:09:16.8751383Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-08-14T23:09:16.8751474Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-08-14T23:09:16.8751582Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-08-14T23:09:16.8751635Z     
2025-08-14T23:09:16.8751669Z         .. note::
2025-08-14T23:09:16.8751754Z             Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2025-08-14T23:09:16.8751834Z             than the utility function ``convert_conv2d_weight_memory_format``. Any
2025-08-14T23:09:16.8751908Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-08-14T23:09:16.8751987Z             necessarily benefit from conversion to specified ``memory_format``.
2025-08-14T23:09:16.8752067Z             One place we are confident in is that NHWC(channels_last) conversion for
2025-08-14T23:09:16.8752147Z             convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2025-08-14T23:09:16.8752221Z             even in cases where we have to apply permutation to input tensors.
2025-08-14T23:09:16.8752253Z     
2025-08-14T23:09:16.8752333Z             Hence our strategy here is to convert only the weight of convolution to
2025-08-14T23:09:16.8752382Z             channels_last. This ensures that;
2025-08-14T23:09:16.8752460Z             1. Fast convolution kernels will be used, the benefit of which could
2025-08-14T23:09:16.8752542Z             outweigh overhead of permutation (if input is not in the same format).
2025-08-14T23:09:16.8752624Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-08-14T23:09:16.8752671Z             from memory_format conversion.
2025-08-14T23:09:16.8754041Z     
2025-08-14T23:09:16.8754127Z             The optimal case is that, layers between convolution layers are channels
2025-08-14T23:09:16.8754213Z             last compatible. Input tensor would be permuted to channels last when it
2025-08-14T23:09:16.8754345Z             encounters the first convolution layer and stay in that memory format.
2025-08-14T23:09:16.8754430Z             Hence following convolutions will not need to permute its input tensor.
2025-08-14T23:09:16.8754463Z     
2025-08-14T23:09:16.8754540Z             In case where a channels last incompatible layer is between convolution
2025-08-14T23:09:16.8754619Z             layers, we need to permute the input tensor back to contiguous format
2025-08-14T23:09:16.8754697Z             for that layer. The input tensor will go through the remaining layers in
2025-08-14T23:09:16.8754774Z             contiguous format and be permuted to channels last when it encounters
2025-08-14T23:09:16.8754849Z             another convolution layer. There's no point in propagating that
2025-08-14T23:09:16.8754924Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-08-14T23:09:16.8754985Z             ``memory_format``.
2025-08-14T23:09:16.8755019Z     
2025-08-14T23:09:16.8755101Z             This claim might change when PyTorch supports fusion of permutation, as
2025-08-14T23:09:16.8755178Z             there might have been a better spot to fuse the permutation other than
2025-08-14T23:09:16.8755226Z             immediately before a convolution.
2025-08-14T23:09:16.8755259Z     
2025-08-14T23:09:16.8755292Z         Args:
2025-08-14T23:09:16.8755370Z             module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2025-08-14T23:09:16.8755413Z                                 ``nn.Module``
2025-08-14T23:09:16.8755471Z             memory_format: user specified ``memory_format``,
2025-08-14T23:09:16.8756815Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-08-14T23:09:16.8756851Z     
2025-08-14T23:09:16.8756887Z         Returns:
2025-08-14T23:09:16.8756942Z             The original module with updated ``nn.Conv2d``
2025-08-14T23:09:16.8756974Z     
2025-08-14T23:09:16.8757011Z         Example:
2025-08-14T23:09:16.8757068Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.8757127Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-08-14T23:09:16.8757170Z             >>> input = torch.randint(
2025-08-14T23:09:16.8757229Z             ...     1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda"
2025-08-14T23:09:16.8757291Z             ... )
2025-08-14T23:09:16.8757331Z             >>> model = nn.Sequential(
2025-08-14T23:09:16.8757378Z             >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2025-08-14T23:09:16.8757420Z             >>> # This is identical to:
2025-08-14T23:09:16.8757508Z             >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2025-08-14T23:09:16.8757571Z             >>> model = nn.utils.convert_conv2d_weight_memory_format(
2025-08-14T23:09:16.8757616Z             ...     model, torch.channels_last
2025-08-14T23:09:16.8757649Z             ... )
2025-08-14T23:09:16.8757692Z             >>> out = model(input)
2025-08-14T23:09:16.8757725Z         
2025-08-14T23:09:16.8757816Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8759065Z     
2025-08-14T23:09:16.8759103Z       warnings.warn(msg)
2025-08-14T23:09:16.8759135Z     
2025-08-14T23:09:16.8759208Z --- Parse Warning: 142 / 146 ---
2025-08-14T23:09:16.8759538Z     /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.
2025-08-14T23:09:16.8759630Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8759709Z     Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2025-08-14T23:09:16.8759804Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2025-08-14T23:09:16.8759940Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2025-08-14T23:09:16.8760031Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2025-08-14T23:09:16.8760138Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2025-08-14T23:09:16.8760173Z     
2025-08-14T23:09:16.8760207Z         .. note::
2025-08-14T23:09:16.8760293Z             Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2025-08-14T23:09:16.8760374Z             than the utility function ``convert_conv3d_weight_memory_format``. Any
2025-08-14T23:09:16.8760448Z             layer with 4d weight will be affected by ``model.to``, which does not
2025-08-14T23:09:16.8760528Z             necessarily benefit from conversion to specified ``memory_format``.
2025-08-14T23:09:16.8760612Z             One place we are confident in is that NDHWC(channels_last_3d) conversion for
2025-08-14T23:09:16.8760707Z             convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2025-08-14T23:09:16.8760779Z             even in cases where we have to apply permutation to input tensors.
2025-08-14T23:09:16.8760812Z     
2025-08-14T23:09:16.8760890Z             Hence our strategy here is to convert only the weight of convolution to
2025-08-14T23:09:16.8762157Z             channels_last_3d. This ensures that;
2025-08-14T23:09:16.8762234Z             1. Fast convolution kernels will be used, the benefit of which could
2025-08-14T23:09:16.8762323Z             outweigh overhead of permutation (if input is not in the same format).
2025-08-14T23:09:16.8762404Z             2. No unnecessary permutations are applied on layers that do not benefit
2025-08-14T23:09:16.8762449Z             from memory_format conversion.
2025-08-14T23:09:16.8762480Z     
2025-08-14T23:09:16.8762560Z             The optimal case is that, layers between convolution layers are channels
2025-08-14T23:09:16.8762643Z             last compatible. Input tensor would be permuted to channels last when it
2025-08-14T23:09:16.8762727Z             encounters the first convolution layer and stay in that memory format.
2025-08-14T23:09:16.8762810Z             Hence following convolutions will not need to permute its input tensor.
2025-08-14T23:09:16.8762841Z     
2025-08-14T23:09:16.8762998Z             In case where a channels last incompatible layer is between convolution
2025-08-14T23:09:16.8763073Z             layers, we need to permute the input tensor back to contiguous format
2025-08-14T23:09:16.8763151Z             for that layer. The input tensor will go through the remaining layers in
2025-08-14T23:09:16.8763228Z             contiguous format and be permuted to channels last when it encounters
2025-08-14T23:09:16.8763303Z             another convolution layer. There's no point in propagating that
2025-08-14T23:09:16.8763377Z             permutation to an earlier layer, as most layers are quite agnostic to
2025-08-14T23:09:16.8763421Z             ``memory_format``.
2025-08-14T23:09:16.8763455Z     
2025-08-14T23:09:16.8763537Z             This claim might change when PyTorch supports fusion of permutation, as
2025-08-14T23:09:16.8763616Z             there might have been a better spot to fuse the permutation other than
2025-08-14T23:09:16.8764946Z             immediately before a convolution.
2025-08-14T23:09:16.8764979Z     
2025-08-14T23:09:16.8765018Z         Args:
2025-08-14T23:09:16.8765095Z             module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2025-08-14T23:09:16.8765140Z                                 ``nn.Module``
2025-08-14T23:09:16.8765198Z             memory_format: user specified ``memory_format``,
2025-08-14T23:09:16.8765263Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2025-08-14T23:09:16.8765294Z     
2025-08-14T23:09:16.8765327Z         Returns:
2025-08-14T23:09:16.8765382Z             The original module with updated ``nn.Conv3d``
2025-08-14T23:09:16.8765414Z     
2025-08-14T23:09:16.8765477Z         Example:
2025-08-14T23:09:16.8765549Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2025-08-14T23:09:16.8765609Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2025-08-14T23:09:16.8765651Z             >>> input = torch.randint(
2025-08-14T23:09:16.8765753Z             ...     1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda"
2025-08-14T23:09:16.8765788Z             ... )
2025-08-14T23:09:16.8765829Z             >>> model = nn.Sequential(
2025-08-14T23:09:16.8765876Z             >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2025-08-14T23:09:16.8765918Z             >>> # This is identical to:
2025-08-14T23:09:16.8766011Z             >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2025-08-14T23:09:16.8766075Z             >>> model = nn.utils.convert_conv3d_weight_memory_format(
2025-08-14T23:09:16.8767457Z             ...     model, torch.channels_last_3d
2025-08-14T23:09:16.8767496Z             ... )
2025-08-14T23:09:16.8767562Z             >>> out = model(input)
2025-08-14T23:09:16.8767596Z         
2025-08-14T23:09:16.8767686Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8767718Z     
2025-08-14T23:09:16.8767755Z       warnings.warn(msg)
2025-08-14T23:09:16.8767787Z     
2025-08-14T23:09:16.8767864Z --- Parse Warning: 143 / 146 ---
2025-08-14T23:09:16.8768159Z     /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.
2025-08-14T23:09:16.8768250Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8768307Z     Pad a packed batch of variable length sequences.
2025-08-14T23:09:16.8768338Z     
2025-08-14T23:09:16.8768405Z         It is an inverse operation to :func:`pack_padded_sequence`.
2025-08-14T23:09:16.8768437Z     
2025-08-14T23:09:16.8768545Z         The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``)
2025-08-14T23:09:16.8768633Z         or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest
2025-08-14T23:09:16.8768680Z         sequence and ``B`` is the batch size.
2025-08-14T23:09:16.8768737Z     
2025-08-14T23:09:16.8768770Z         Example:
2025-08-14T23:09:16.8768858Z             >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
2025-08-14T23:09:16.8770957Z             >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]])
2025-08-14T23:09:16.8771000Z             >>> lens = [2, 1, 3]
2025-08-14T23:09:16.8771048Z             >>> packed = pack_padded_sequence(
2025-08-14T23:09:16.8771109Z             ...     seq, lens, batch_first=True, enforce_sorted=False
2025-08-14T23:09:16.8771150Z             ... )
2025-08-14T23:09:16.8771187Z             >>> packed
2025-08-14T23:09:16.8771280Z             PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]),
2025-08-14T23:09:16.8771367Z                            sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0]))
2025-08-14T23:09:16.8771459Z             >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True)
2025-08-14T23:09:16.8771501Z             >>> seq_unpacked
2025-08-14T23:09:16.8771539Z             tensor([[1, 2, 0],
2025-08-14T23:09:16.8771575Z                     [3, 0, 0],
2025-08-14T23:09:16.8771613Z                     [4, 5, 6]])
2025-08-14T23:09:16.8771651Z             >>> lens_unpacked
2025-08-14T23:09:16.8771724Z             tensor([2, 1, 3])
2025-08-14T23:09:16.8771755Z     
2025-08-14T23:09:16.8771789Z         .. note::
2025-08-14T23:09:16.8771849Z             :attr:`total_length` is useful to implement the
2025-08-14T23:09:16.8771935Z             ``pack sequence -> recurrent network -> unpack sequence`` pattern in a
2025-08-14T23:09:16.8774728Z             :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.
2025-08-14T23:09:16.8774908Z             See :ref:`this FAQ section <pack-rnn-unpack-with-data-parallelism>` for
2025-08-14T23:09:16.8774945Z             details.
2025-08-14T23:09:16.8774977Z     
2025-08-14T23:09:16.8775010Z         Args:
2025-08-14T23:09:16.8775063Z             sequence (PackedSequence): batch to pad
2025-08-14T23:09:16.8775153Z             batch_first (bool, optional): if ``True``, the output will be in ``B x T x *``
2025-08-14T23:09:16.8775198Z                 format, ``T x B x *`` otherwise.
2025-08-14T23:09:16.8775271Z             padding_value (float, optional): values for padded elements.
2025-08-14T23:09:16.8775354Z             total_length (int, optional): if not ``None``, the output will be padded to
2025-08-14T23:09:16.8775440Z                 have length :attr:`total_length`. This method will throw :class:`ValueError`
2025-08-14T23:09:16.8775508Z                 if :attr:`total_length` is less than the max sequence length in
2025-08-14T23:09:16.8775573Z                 :attr:`sequence`.
2025-08-14T23:09:16.8775607Z     
2025-08-14T23:09:16.8775642Z         Returns:
2025-08-14T23:09:16.8775710Z             Tuple of Tensor containing the padded sequence, and a Tensor
2025-08-14T23:09:16.8775784Z             containing the list of lengths of each sequence in the batch.
2025-08-14T23:09:16.8775867Z             Batch elements will be re-ordered as they were ordered originally when
2025-08-14T23:09:16.8775943Z             the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``.
2025-08-14T23:09:16.8775975Z         
2025-08-14T23:09:16.8778342Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8778376Z     
2025-08-14T23:09:16.8778416Z       warnings.warn(msg)
2025-08-14T23:09:16.8778447Z     
2025-08-14T23:09:16.8778533Z --- Parse Warning: 144 / 146 ---
2025-08-14T23:09:16.8778833Z     /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.
2025-08-14T23:09:16.8778928Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8779038Z     Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2025-08-14T23:09:16.8779099Z     
2025-08-14T23:09:16.8779181Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2025-08-14T23:09:16.8779258Z         by removing the specified ``amount`` of (currently unpruned) channels
2025-08-14T23:09:16.8779326Z         along the specified ``dim`` with the lowest L\ ``n``-norm.
2025-08-14T23:09:16.8779399Z         Modifies module in place (and also return the modified module)
2025-08-14T23:09:16.8779432Z         by:
2025-08-14T23:09:16.8779463Z     
2025-08-14T23:09:16.8779537Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:16.8779624Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:16.8779698Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:16.8779774Z            original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:16.8779813Z            ``name+'_orig'``.
2025-08-14T23:09:16.8782282Z     
2025-08-14T23:09:16.8782321Z         Args:
2025-08-14T23:09:16.8782392Z             module (nn.Module): module containing the tensor to prune
2025-08-14T23:09:16.8782463Z             name (str): parameter name within ``module`` on which pruning
2025-08-14T23:09:16.8782501Z                     will act.
2025-08-14T23:09:16.8782567Z             amount (int or float): quantity of parameters to prune.
2025-08-14T23:09:16.8782634Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2025-08-14T23:09:16.8782706Z                 fraction of parameters to prune. If ``int``, it represents the
2025-08-14T23:09:16.8782790Z                 absolute number of parameters to prune.
2025-08-14T23:09:16.8782879Z             n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2025-08-14T23:09:16.8783012Z                 entries for argument ``p`` in :func:`torch.norm`.
2025-08-14T23:09:16.8783086Z             dim (int): index of the dim along which we define channels to prune.
2025-08-14T23:09:16.8783173Z             importance_scores (torch.Tensor): tensor of importance scores (of same
2025-08-14T23:09:16.8783241Z                 shape as module parameter) used to compute mask for pruning.
2025-08-14T23:09:16.8783322Z                 The values in this tensor indicate the importance of the corresponding
2025-08-14T23:09:16.8783375Z                 elements in the parameter being pruned.
2025-08-14T23:09:16.8783455Z                 If unspecified or None, the module parameter will be used in its place.
2025-08-14T23:09:16.8783487Z     
2025-08-14T23:09:16.8783521Z         Returns:
2025-08-14T23:09:16.8783627Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-08-14T23:09:16.8783659Z     
2025-08-14T23:09:16.8783694Z         Examples:
2025-08-14T23:09:16.8783742Z             >>> from torch.nn.utils import prune
2025-08-14T23:09:16.8783787Z             >>> m = prune.ln_structured(
2025-08-14T23:09:16.8786566Z             ...     nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf")
2025-08-14T23:09:16.8786603Z             ... )
2025-08-14T23:09:16.8786638Z         
2025-08-14T23:09:16.8786730Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8786761Z     
2025-08-14T23:09:16.8786800Z       warnings.warn(msg)
2025-08-14T23:09:16.8786832Z     
2025-08-14T23:09:16.8786913Z --- Parse Warning: 145 / 146 ---
2025-08-14T23:09:16.8787223Z     /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.
2025-08-14T23:09:16.8787322Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8787357Z     
2025-08-14T23:09:16.8787506Z     Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2025-08-14T23:09:16.8787568Z     
2025-08-14T23:09:16.8787612Z     Modifies modules in place by:
2025-08-14T23:09:16.8787643Z     
2025-08-14T23:09:16.8787720Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:16.8787798Z        binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:16.8787875Z     2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:16.8787951Z        original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:16.8787992Z        ``name+'_orig'``.
2025-08-14T23:09:16.8788022Z     
2025-08-14T23:09:16.8788061Z     Args:
2025-08-14T23:09:16.8788138Z         parameters (Iterable of (module, name) tuples): parameters of
2025-08-14T23:09:16.8788208Z             the model to prune in a global fashion, i.e. by aggregating all
2025-08-14T23:09:16.8788284Z             weights prior to deciding which ones to prune. module must be of
2025-08-14T23:09:16.8788346Z             type :class:`nn.Module`, and name must be a string.
2025-08-14T23:09:16.8788424Z         pruning_method (function): a valid pruning function from this module,
2025-08-14T23:09:16.8788490Z             or a custom one implemented by the user that satisfies the
2025-08-14T23:09:16.8788570Z             implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2025-08-14T23:09:16.8788652Z         importance_scores (dict): a dictionary mapping (module, name) tuples to
2025-08-14T23:09:16.8788731Z             the corresponding parameter's importance scores tensor. The tensor
2025-08-14T23:09:16.8788807Z             should be the same shape as the parameter, and is used for computing
2025-08-14T23:09:16.8788894Z             mask for pruning.
2025-08-14T23:09:16.8788968Z             If unspecified or None, the parameter will be used in place of its
2025-08-14T23:09:16.8789013Z             importance scores.
2025-08-14T23:09:16.8789072Z         kwargs: other keyword arguments such as:
2025-08-14T23:09:16.8789145Z             amount (int or float): quantity of parameters to prune across the
2025-08-14T23:09:16.8789189Z             specified parameters.
2025-08-14T23:09:16.8789254Z             If ``float``, should be between 0.0 and 1.0 and represent the
2025-08-14T23:09:16.8789328Z             fraction of parameters to prune. If ``int``, it represents the
2025-08-14T23:09:16.8789381Z             absolute number of parameters to prune.
2025-08-14T23:09:16.8789413Z     
2025-08-14T23:09:16.8789447Z     Raises:
2025-08-14T23:09:16.8789507Z         TypeError: if ``PRUNING_TYPE != 'unstructured'``
2025-08-14T23:09:16.8789539Z     
2025-08-14T23:09:16.8789589Z     Note:
2025-08-14T23:09:16.8789671Z         Since global structured pruning doesn't make much sense unless the
2025-08-14T23:09:16.8789743Z         norm is normalized by the size of the parameter, we now limit the
2025-08-14T23:09:16.8789802Z         scope of global pruning to unstructured methods.
2025-08-14T23:09:16.8789833Z     
2025-08-14T23:09:16.8789871Z     Examples:
2025-08-14T23:09:16.8789918Z         >>> from torch.nn.utils import prune
2025-08-14T23:09:16.8789967Z         >>> from collections import OrderedDict
2025-08-14T23:09:16.8790007Z         >>> net = nn.Sequential(
2025-08-14T23:09:16.8790047Z         ...     OrderedDict(
2025-08-14T23:09:16.8790083Z         ...         [
2025-08-14T23:09:16.8790134Z         ...             ("first", nn.Linear(10, 4)),
2025-08-14T23:09:16.8790185Z         ...             ("second", nn.Linear(4, 1)),
2025-08-14T23:09:16.8790219Z         ...         ]
2025-08-14T23:09:16.8790251Z         ...     )
2025-08-14T23:09:16.8790283Z         ... )
2025-08-14T23:09:16.8790331Z         >>> parameters_to_prune = (
2025-08-14T23:09:16.8790374Z         ...     (net.first, "weight"),
2025-08-14T23:09:16.8790416Z         ...     (net.second, "weight"),
2025-08-14T23:09:16.8790447Z         ... )
2025-08-14T23:09:16.8790492Z         >>> prune.global_unstructured(
2025-08-14T23:09:16.8790532Z         ...     parameters_to_prune,
2025-08-14T23:09:16.8790607Z         ...     pruning_method=prune.L1Unstructured,
2025-08-14T23:09:16.8790646Z         ...     amount=10,
2025-08-14T23:09:16.8790679Z         ... )
2025-08-14T23:09:16.8790767Z         >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2025-08-14T23:09:16.8790802Z         tensor(10)
2025-08-14T23:09:16.8790838Z     
2025-08-14T23:09:16.8790870Z     
2025-08-14T23:09:16.8790963Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8790994Z     
2025-08-14T23:09:16.8791036Z       warnings.warn(msg)
2025-08-14T23:09:16.8791068Z     
2025-08-14T23:09:16.8791144Z --- Parse Warning: 146 / 146 ---
2025-08-14T23:09:16.8793460Z     /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.
2025-08-14T23:09:16.8793561Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2025-08-14T23:09:16.8793701Z     Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2025-08-14T23:09:16.8793733Z     
2025-08-14T23:09:16.8793811Z         Modifies module in place (and also return the modified module) by:
2025-08-14T23:09:16.8793842Z     
2025-08-14T23:09:16.8793917Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2025-08-14T23:09:16.8793993Z            binary mask applied to the parameter ``name`` by the pruning method.
2025-08-14T23:09:16.8794071Z         2) replacing the parameter ``name`` by its pruned version, while the
2025-08-14T23:09:16.8794201Z            original (unpruned) parameter is stored in a new parameter named
2025-08-14T23:09:16.8794240Z            ``name+'_orig'``.
2025-08-14T23:09:16.8794272Z     
2025-08-14T23:09:16.8794303Z         Args:
2025-08-14T23:09:16.8794372Z             module (nn.Module): module containing the tensor to prune
2025-08-14T23:09:16.8794442Z             name (str): parameter name within ``module`` on which pruning
2025-08-14T23:09:16.8794479Z                 will act.
2025-08-14T23:09:16.8794545Z             mask (Tensor): binary mask to be applied to the parameter.
2025-08-14T23:09:16.8794576Z     
2025-08-14T23:09:16.8794608Z         Returns:
2025-08-14T23:09:16.8794687Z             module (nn.Module): modified (i.e. pruned) version of the input module
2025-08-14T23:09:16.8794717Z     
2025-08-14T23:09:16.8796233Z         Examples:
2025-08-14T23:09:16.8796285Z             >>> from torch.nn.utils import prune
2025-08-14T23:09:16.8796367Z             >>> m = prune.custom_from_mask(
2025-08-14T23:09:16.8796439Z             ...     nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0])
2025-08-14T23:09:16.8796508Z             ... )
2025-08-14T23:09:16.8796549Z             >>> print(m.bias_mask)
2025-08-14T23:09:16.8796588Z             tensor([0., 1., 0.])
2025-08-14T23:09:16.8796622Z     
2025-08-14T23:09:16.8796653Z         
2025-08-14T23:09:16.8796744Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2025-08-14T23:09:16.8796775Z     
2025-08-14T23:09:16.8796814Z       warnings.warn(msg)
2025-08-14T23:09:16.8796845Z     
2025-08-14T23:09:16.8796893Z 
2025-08-14T23:09:16.8796961Z === Found 8 run-time warnings ===
2025-08-14T23:09:16.8797030Z --- Runtime Warning: 1 / 8 ---
2025-08-14T23:09:16.8797129Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1333) at 0x7c1dc40df890>
2025-08-14T23:09:16.8797579Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1351: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1975.)
2025-08-14T23:09:16.8797628Z       return super().refine_names(names)
2025-08-14T23:09:16.8797683Z     
2025-08-14T23:09:16.8797749Z --- Runtime Warning: 2 / 8 ---
2025-08-14T23:09:16.8797860Z example = <DocTest(torch.library Library._impl_with_aoti_compile:0 ln 247) at 0x7c1db7c3b4a0>
2025-08-14T23:09:16.8799451Z     /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.
2025-08-14T23:09:16.8799569Z       Overriding a previously registered kernel for the same operator and the same dispatch key
2025-08-14T23:09:16.8799646Z       operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor
2025-08-14T23:09:16.8799763Z         registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6
2025-08-14T23:09:16.8799801Z       dispatch key: CPU
2025-08-14T23:09:16.8799955Z       previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079
2025-08-14T23:09:16.8800145Z            new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:218.)
2025-08-14T23:09:16.8800211Z       impl_fn(self.ns, name.split("::")[-1], dispatch_key)
2025-08-14T23:09:16.8800242Z     
2025-08-14T23:09:16.8800315Z --- Runtime Warning: 3 / 8 ---
2025-08-14T23:09:16.8800400Z example = <DocTest(torch.nested as_nested_tensor:0 ln 61) at 0x7c1db7aa5850>
2025-08-14T23:09:16.8801034Z     /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.)
2025-08-14T23:09:16.8801141Z       return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
2025-08-14T23:09:16.8801176Z     
2025-08-14T23:09:16.8801243Z --- Runtime Warning: 4 / 8 ---
2025-08-14T23:09:16.8801349Z example = <DocTest(torch._functorch.eager_transforms linearize:0 ln 1704) at 0x7c1db7aa78c0>
2025-08-14T23:09:16.8801869Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:271: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
2025-08-14T23:09:16.8801952Z       new_node = root_const_gm.graph.get_attr(in_node.target)
2025-08-14T23:09:16.8801984Z     
2025-08-14T23:09:16.8802051Z --- Runtime Warning: 5 / 8 ---
2025-08-14T23:09:16.8802154Z example = <DocTest(torch.nn.modules.transformer Transformer:0 ln 90) at 0x7c1db7b2ab70>
2025-08-14T23:09:16.8803910Z     /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)
2025-08-14T23:09:16.8803952Z       warnings.warn(
2025-08-14T23:09:16.8803983Z     
2025-08-14T23:09:16.8804053Z --- Runtime Warning: 6 / 8 ---
2025-08-14T23:09:16.8804170Z example = <DocTest(torch.nn.modules.transformer TransformerEncoder:0 ln 336) at 0x7c1db7b2b6b0>
2025-08-14T23:09:16.8804525Z     /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)
2025-08-14T23:09:16.8804563Z       warnings.warn(
2025-08-14T23:09:16.8804594Z     
2025-08-14T23:09:16.8804659Z --- Runtime Warning: 7 / 8 ---
2025-08-14T23:09:16.8804786Z example = <DocTest(torch.nn.utils.weight_norm weight_norm:0 ln 134) at 0x7c1db69e6ea0>
2025-08-14T23:09:16.8805060Z     /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`.
2025-08-14T23:09:16.8805108Z       WeightNorm.apply(module, name, dim)
2025-08-14T23:09:16.8805139Z     
2025-08-14T23:09:16.8805204Z --- Runtime Warning: 8 / 8 ---
2025-08-14T23:09:16.8805313Z example = <DocTest(torch.nn.utils.weight_norm remove_weight_norm:0 ln 156) at 0x7c1db69e61e0>
2025-08-14T23:09:16.8805583Z     /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`.
2025-08-14T23:09:16.8805630Z       WeightNorm.apply(module, name, dim)
2025-08-14T23:09:16.8805661Z     
2025-08-14T23:09:16.8805863Z === 338 passed, 394 skipped, 154 warnings in 8.08 seconds ===
2025-08-14T23:09:16.8805938Z Running test_tensorboard 1/1 ... [2025-08-14 23:09:16.722224]
2025-08-14T23:09:16.8805985Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:09:16.8806280Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensorboard.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:09:16.722540]
2025-08-14T23:09:19.6428706Z 
2025-08-14T23:09:19.6429777Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_1d6a10ac16a6b36b_.log
2025-08-14T23:09:19.6442398Z Running 50 items in this shard: test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_autograd_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram_raw, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_write, test/test_tensorboard.py::TestTensorBoardUtils::test_convert_to_HWC_dtype_remains_same, test/test_tensorboard.py::TestTensorBoardUtils::test_numpy_vid_uint8, test/test_tensorboard.py::TestTensorBoardUtils::test_prepare_video, test/test_tensorboard.py::TestTensorBoardUtils::test_to_HWC, test/test_tensorboard.py::TestTensorBoardWriter::test_writer, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_pathlib, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_close, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_ctx, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding_64, test/test_tensorboard.py::TestTensorBoardSummary::test_audio, test/test_tensorboard.py::TestTensorBoardSummary::test_custom_scalars, test/test_tensorboard.py::TestTensorBoardSummary::test_empty_input, test/test_tensorboard.py::TestTensorBoardSummary::test_float32_image, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_auto, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_doane, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_fd, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_3_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_boxes, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_without_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_list_input, test/test_tensorboard.py::TestTensorBoardSummary::test_mesh, test/test_tensorboard.py::TestTensorBoardSummary::test_scalar_new_style, test/test_tensorboard.py::TestTensorBoardSummary::test_text, test/test_tensorboard.py::TestTensorBoardSummary::test_uint8_image, test/test_tensorboard.py::TestTensorBoardSummary::test_video, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_mlp_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_nested_nn_squential, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph_dict_input, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_torchvision_smoke, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_wrong_input_size, test/test_tensorboard.py::TestTensorBoardFigure::test_figure, test/test_tensorboard.py::TestTensorBoardFigure::test_figure_list, test/test_tensorboard.py::TestTensorBoardNumpy::test_pytorch_np_expect_fail, test/test_tensorboard.py::TestTensorBoardNumpy::test_scalar, test/test_tensorboard.py::TestTensorProtoSummary::test_complex_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_empty_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_float_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_bfloat16_proto_type_14, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_float16_proto_type_19, test/test_tensorboard.py::TestTensorProtoSummary::test_int_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_scalar_tensor_proto
2025-08-14T23:09:19.6447743Z 
2025-08-14T23:09:19.6447841Z Running test_transformers_privateuse1 1/1 ... [2025-08-14 23:09:19.642959]
2025-08-14T23:09:20.4897128Z -- The CXX compiler identification is GNU 11.4.0
2025-08-14T23:09:20.5624975Z -- The C compiler identification is GNU 11.4.0
2025-08-14T23:09:20.5835449Z -- Detecting CXX compiler ABI info
2025-08-14T23:09:20.7277978Z -- Detecting CXX compiler ABI info - done
2025-08-14T23:09:20.7362081Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped
2025-08-14T23:09:20.7370803Z -- Detecting CXX compile features
2025-08-14T23:09:20.7371079Z -- Detecting CXX compile features - done
2025-08-14T23:09:20.7428102Z -- Detecting C compiler ABI info
2025-08-14T23:09:20.8787607Z -- Detecting C compiler ABI info - done
2025-08-14T23:09:20.8877737Z -- Check for working C compiler: /opt/cache/bin/cc - skipped
2025-08-14T23:09:20.8880676Z -- Detecting C compile features
2025-08-14T23:09:20.8883680Z -- Detecting C compile features - done
2025-08-14T23:09:20.9257762Z Building PyTorch for GPU arch: gfx90a;gfx942
2025-08-14T23:09:21.0225982Z -- Found HIP: /opt/rocm (found suitable version "6.4.43484-123eb5128", minimum required is "1.0")
2025-08-14T23:09:21.0226508Z HIP VERSION: 6.4.43484-123eb5128
2025-08-14T23:09:21.0871694Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
2025-08-14T23:09:21.2268818Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
2025-08-14T23:09:21.2276433Z -- Found Threads: TRUE
2025-08-14T23:09:21.2530063Z hip VERSION: 6.4.43484
2025-08-14T23:09:21.2539339Z -- Reading ROCM version from: /opt/rocm/include/rocm-core/rocm_version.h
2025-08-14T23:09:21.2539579Z -- Content: 
2025-08-14T23:09:21.2539727Z 
2025-08-14T23:09:21.2539843Z ***** ROCm version from rocm_version.h ****
2025-08-14T23:09:21.2539998Z 
2025-08-14T23:09:21.2540127Z ROCM_VERSION_DEV: 6.4.2
2025-08-14T23:09:21.2540297Z ROCM_VERSION_DEV_MAJOR: 6
2025-08-14T23:09:21.2540459Z ROCM_VERSION_DEV_MINOR: 4
2025-08-14T23:09:21.2540615Z ROCM_VERSION_DEV_PATCH: 2
2025-08-14T23:09:21.2540780Z ROCM_VERSION_DEV_INT:   60402
2025-08-14T23:09:21.2540956Z HIP_VERSION_MAJOR: 6
2025-08-14T23:09:21.2541109Z HIP_VERSION_MINOR: 4
2025-08-14T23:09:21.2541267Z TORCH_HIP_VERSION: 604
2025-08-14T23:09:21.2541419Z 
2025-08-14T23:09:21.2541549Z ***** Library versions from cmake find_package *****
2025-08-14T23:09:21.2541710Z 
2025-08-14T23:09:21.2542907Z amd_comgr VERSION: 3.0.0
2025-08-14T23:09:21.2803540Z rocrand VERSION: 3.3.0
2025-08-14T23:09:21.2818359Z hiprand VERSION: 2.12.0
2025-08-14T23:09:21.2826038Z rocblas VERSION: 4.4.1
2025-08-14T23:09:21.2840933Z hipblas VERSION: 2.4.0
2025-08-14T23:09:21.2850884Z miopen VERSION: 3.4.0
2025-08-14T23:09:21.2857893Z hipfft VERSION: 1.0.18
2025-08-14T23:09:21.2865274Z hipsparse VERSION: 3.2.0
2025-08-14T23:09:21.2872335Z rocprim VERSION: 3.4.1
2025-08-14T23:09:21.2884267Z hipcub VERSION: 3.4.0
2025-08-14T23:09:21.2891616Z rocthrust VERSION: 3.3.0
2025-08-14T23:09:21.2898925Z hipsolver VERSION: 2.4.0
2025-08-14T23:09:21.2912537Z rocsolver VERSION: 3.28.2
2025-08-14T23:09:21.2913109Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:175 (message):
2025-08-14T23:09:21.2913400Z   Work around hiprtc cmake failure for cmake >= 4
2025-08-14T23:09:21.2913538Z Call Stack (most recent call first):
2025-08-14T23:09:21.2913760Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include)
2025-08-14T23:09:21.2914084Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package)
2025-08-14T23:09:21.2914309Z   CMakeLists.txt:27 (find_package)
2025-08-14T23:09:21.2914385Z 
2025-08-14T23:09:21.2914432Z 
2025-08-14T23:09:21.2917618Z CMake Deprecation Warning at /opt/rocm/lib/cmake/hiprtc/hiprtc-config.cmake:21 (cmake_minimum_required):
2025-08-14T23:09:21.2917883Z   Compatibility with CMake < 3.10 will be removed from a future version of
2025-08-14T23:09:21.2918048Z   CMake.
2025-08-14T23:09:21.2918096Z 
2025-08-14T23:09:21.2918193Z   Update the VERSION argument <min> value.  Or, use the <min>...<max> syntax
2025-08-14T23:09:21.2918552Z   to tell CMake that the project requires at least <min> but has been updated
2025-08-14T23:09:21.2918736Z   to work with policies introduced by <max> or earlier.
2025-08-14T23:09:21.2918881Z Call Stack (most recent call first):
2025-08-14T23:09:21.2919117Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:67 (find_package)
2025-08-14T23:09:21.2919493Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:177 (find_package_and_print_version)
2025-08-14T23:09:21.2919850Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include)
2025-08-14T23:09:21.2920179Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package)
2025-08-14T23:09:21.2920405Z   CMakeLists.txt:27 (find_package)
2025-08-14T23:09:21.2920486Z 
2025-08-14T23:09:21.2920528Z 
2025-08-14T23:09:21.2920693Z hiprtc VERSION: 6.4.43484
2025-08-14T23:09:21.2927894Z hipblaslt VERSION: 0.12.1
2025-08-14T23:09:21.3211957Z rccl VERSION: 2.22.3
2025-08-14T23:09:21.3215833Z hsa-runtime64 VERSION: 1.15.60402
2025-08-14T23:09:21.3223081Z hipsparselt VERSION: 0.2.3
2025-08-14T23:09:22.3110911Z hipblaslt is using scale pointer vec ext
2025-08-14T23:09:22.3548374Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message):
2025-08-14T23:09:22.3549131Z   static library kineto_LIBRARY-NOTFOUND not found.
2025-08-14T23:09:22.3549523Z Call Stack (most recent call first):
2025-08-14T23:09:22.3550212Z   /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found)
2025-08-14T23:09:22.3559027Z   CMakeLists.txt:27 (find_package)
2025-08-14T23:09:22.3559193Z 
2025-08-14T23:09:22.3559329Z 
2025-08-14T23:09:22.3559683Z -- Found Torch: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so
2025-08-14T23:09:22.3570667Z -- Configuring done (2.0s)
2025-08-14T23:09:22.3631393Z -- Generating done (0.0s)
2025-08-14T23:09:22.3633417Z -- Build files have been written to: /var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/build
2025-08-14T23:09:22.4468428Z [  5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o
2025-08-14T23:09:22.4473378Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o
2025-08-14T23:09:22.6710155Z [ 17%] Linking CXX shared library libopenreg.so
2025-08-14T23:09:22.7203928Z [ 17%] Built target openreg
2025-08-14T23:09:22.7271841Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o
2025-08-14T23:09:22.7272347Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o
2025-08-14T23:09:22.7274349Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o
2025-08-14T23:09:22.7275300Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o
2025-08-14T23:09:22.7279867Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o
2025-08-14T23:09:22.7281849Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o
2025-08-14T23:09:22.7284851Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o
2025-08-14T23:09:22.7285150Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o
2025-08-14T23:09:22.7286511Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o
2025-08-14T23:09:22.7288574Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o
2025-08-14T23:09:22.7289329Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o
2025-08-14T23:09:28.1414873Z [ 88%] Linking CXX shared library libtorch_openreg.so
2025-08-14T23:09:28.3375869Z [ 88%] Built target torch_openreg
2025-08-14T23:09:28.3463055Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o
2025-08-14T23:09:32.4375832Z [100%] Linking CXX shared library libtorch_bindings.so
2025-08-14T23:09:32.5118215Z [100%] Built target torch_bindings
2025-08-14T23:09:32.5166592Z Install the project...
2025-08-14T23:09:32.5183533Z -- Install configuration: ""
2025-08-14T23:09:32.5408830Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
2025-08-14T23:09:32.5409741Z !!
2025-08-14T23:09:32.5409879Z 
2025-08-14T23:09:32.5410489Z         ********************************************************************************
2025-08-14T23:09:32.5411346Z         Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).
2025-08-14T23:09:32.5412014Z 
2025-08-14T23:09:32.5412254Z         By 2026-Feb-18, you need to update your project and remove deprecated calls
2025-08-14T23:09:32.5412743Z         or your builds will no longer be supported.
2025-08-14T23:09:32.5413115Z 
2025-08-14T23:09:32.5413508Z         See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
2025-08-14T23:09:32.5414093Z         ********************************************************************************
2025-08-14T23:09:32.5414334Z 
2025-08-14T23:09:32.5414430Z !!
2025-08-14T23:09:32.5414679Z   corresp(dist, value, root_dir)
2025-08-14T23:09:32.5534639Z running install
2025-08-14T23:09:32.5535427Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2025-08-14T23:09:32.5535951Z !!
2025-08-14T23:09:32.5536033Z 
2025-08-14T23:09:32.5536145Z         ********************************************************************************
2025-08-14T23:09:32.5536422Z         Please avoid running ``setup.py`` directly.
2025-08-14T23:09:32.5537101Z         Instead, use pypa/build, pypa/installer or other
2025-08-14T23:09:32.5537356Z         standards-based tools.
2025-08-14T23:09:32.5537492Z 
2025-08-14T23:09:32.5537654Z         By 2025-Oct-31, you need to update your project and remove deprecated calls
2025-08-14T23:09:32.5537978Z         or your builds will no longer be supported.
2025-08-14T23:09:32.5538139Z 
2025-08-14T23:09:32.5538369Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2025-08-14T23:09:32.5538719Z         ********************************************************************************
2025-08-14T23:09:32.5538878Z 
2025-08-14T23:09:32.5538939Z !!
2025-08-14T23:09:32.5539104Z   self.initialize_options()
2025-08-14T23:09:32.5585046Z running build
2025-08-14T23:09:32.5585236Z running build_py
2025-08-14T23:09:32.5614122Z creating build/lib.linux-x86_64-cpython-312/torch_openreg
2025-08-14T23:09:32.5614462Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg
2025-08-14T23:09:32.5616877Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/openreg
2025-08-14T23:09:32.5617267Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg
2025-08-14T23:09:32.5617689Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg
2025-08-14T23:09:32.5620488Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/lib
2025-08-14T23:09:32.5620844Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib
2025-08-14T23:09:32.5622381Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib
2025-08-14T23:09:32.5634370Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib
2025-08-14T23:09:32.5638269Z running build_ext
2025-08-14T23:09:32.5672275Z building 'torch_openreg._C' extension
2025-08-14T23:09:32.5673217Z creating build/temp.linux-x86_64-cpython-312/torch_openreg/csrc
2025-08-14T23:09:32.5675093Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -g -Wall -Werror
2025-08-14T23:09:32.6824512Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -L/var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib
2025-08-14T23:09:32.7088334Z running install_lib
2025-08-14T23:09:32.7121543Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages
2025-08-14T23:09:32.7123779Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg
2025-08-14T23:09:32.7124334Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg
2025-08-14T23:09:32.7125074Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg
2025-08-14T23:09:32.7125572Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg
2025-08-14T23:09:32.7126213Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/openreg/random.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg
2025-08-14T23:09:32.7127640Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/openreg/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg
2025-08-14T23:09:32.7128631Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib
2025-08-14T23:09:32.7129577Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib
2025-08-14T23:09:32.7130806Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib
2025-08-14T23:09:32.7142684Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib
2025-08-14T23:09:32.7149673Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/__init__.py to __init__.cpython-312.pyc
2025-08-14T23:09:32.7151907Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg/random.py to random.cpython-312.pyc
2025-08-14T23:09:32.7156276Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg/__init__.py to __init__.cpython-312.pyc
2025-08-14T23:09:32.7158859Z running install_egg_info
2025-08-14T23:09:32.7217398Z running egg_info
2025-08-14T23:09:32.7246916Z creating torch_openreg.egg-info
2025-08-14T23:09:32.7247114Z writing torch_openreg.egg-info/PKG-INFO
2025-08-14T23:09:32.7250107Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt
2025-08-14T23:09:32.7250594Z writing requirements to torch_openreg.egg-info/requires.txt
2025-08-14T23:09:32.7250849Z writing top-level names to torch_openreg.egg-info/top_level.txt
2025-08-14T23:09:32.7251993Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt'
2025-08-14T23:09:32.7290988Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt'
2025-08-14T23:09:32.7294873Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt'
2025-08-14T23:09:32.7296027Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info
2025-08-14T23:09:32.7299184Z running install_scripts
2025-08-14T23:09:32.9277992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:09:32.9280772Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_transformers_privateuse1.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:09:32.927791]
2025-08-14T23:09:35.2979140Z 
2025-08-14T23:09:35.2980032Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_a8c3de1d7e497bd1_.log
2025-08-14T23:09:35.2980874Z Running 3 items in this shard: test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_fused_sdp_choice_privateuseone, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable_backward
2025-08-14T23:09:35.2981452Z 
2025-08-14T23:09:35.2981548Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-08-14 23:09:35.297279]
2025-08-14T23:09:35.2987471Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:09:35.2987887Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:09:35.297726]
2025-08-14T23:09:37.3670267Z 
2025-08-14T23:09:37.3671134Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_4a4e23c824c82712_.log
2025-08-14T23:09:37.3673177Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device
2025-08-14T23:09:37.3674403Z 
2025-08-14T23:09:37.3674555Z Running test_autograd 1/1 ... [2025-08-14 23:09:37.366969]
2025-08-14T23:09:37.3674834Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:09:37.3675482Z 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'] ... [2025-08-14 23:09:37.367273]
2025-08-14T23:10:10.2133165Z 
2025-08-14T23:10:10.2133618Z test_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_1.1_5bf2fd31a86a5e53_.log
2025-08-14T23:10:10.2208913Z Running 650 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, 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test/test_autograd.py::TestAutograd::test_setting_default_saved_variable_hooks_twice_should_use_inner, test/test_autograd.py::TestAutograd::test_setup_context_when_forward_has_default_args, test/test_autograd.py::TestAutograd::test_shape, test/test_autograd.py::TestAutograd::test_sharded_grad, test/test_autograd.py::TestAutograd::test_simple_reentrant, test/test_autograd.py::TestAutograd::test_slice_expanded_v, 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, 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test/test_autograd.py::TestAutogradFunctional::test_jacobian_output_vectorized_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_scalar_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_scalar_vectorized_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_scalar_vectorized_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_devices_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_devices_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_dtype_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_different_dtype_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_multi_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_multi_input_multi_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_simple_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_simple_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_unrelated_outputs_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_unrelated_outputs_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_zero_dim_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_correctness_zero_dim_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_raises_no_warnings_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jacobian_vectorize_raises_no_warnings_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_jvp_scalar_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vhp_scalar_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_create_graph_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_create_graph_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_strict_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_err_check_strict_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_no_grad_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_no_grad_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_output_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_output_logging_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_scalar_base_tensor, test/test_autograd.py::TestAutogradFunctional::test_vjp_scalar_logging_tensor, test/test_autograd.py::TestAutogradLogging::test_logging, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_advanced_indexing_backwards_large_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_advanced_indexing_backwards_memory_format_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_backward_device_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_complex_scalar_backward_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_copy__cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_copy_forward_ad_broadcasting_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_copy_forward_ad_same_layout_copies_grad_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_copy_r_to_c_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_cross_device_reentrant_autograd_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_free_unneeded_tensor_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_grad_assignment_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_gradcheck_input_output_different_device_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_multiple_output_view_of_view_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_backprop_base_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_backprop_view_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_backprop_view_of_view_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_gradcheck_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_makes_base_require_grad_cuda, test/test_autograd.py::TestAutogradDeviceTypeCUDA::test_inplace_on_view_modify_base_cuda, 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
2025-08-14T23:10:10.2276635Z 
2025-08-14T23:10:10.2276731Z Running profiler/test_memory_profiler 1/1 ... [2025-08-14 23:10:10.213889]
2025-08-14T23:10:10.2276908Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:10:10.2277301Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_memory_profiler.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:10.214112]
2025-08-14T23:10:15.0425100Z 
2025-08-14T23:10:15.0426013Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_aa80420cc6d35e93_.log
2025-08-14T23:10:15.0435845Z Running 33 items in this shard: test/profiler/test_memory_profiler.py::TestMemoryProfiler::test_config_check, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module_and_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer_set_to_none, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_low_level, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_complicated, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_non_op_allocations, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_inplace, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_stacked, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_with_annotations, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_tensorlist, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_lazy, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_lazily_initialized, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_manual_optimizer_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients_set_to_none, test/profiler/test_memory_profiler.py::TestMemoryProfilerTimelineCUDA::test_memory_timeline_no_id_cuda
2025-08-14T23:10:15.0441547Z 
2025-08-14T23:10:15.0446172Z Running functorch/test_ops 1/4 ... [2025-08-14 23:10:15.042471]
2025-08-14T23:10:15.0446359Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:10:15.0446754Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_ops.py', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:15.042709]
2025-08-14T23:19:52.5401621Z 
2025-08-14T23:19:52.5402681Z functorch/test_ops 1/4 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_1.4_f1b0ed1ee031bbda_.log
2025-08-14T23:19:52.5742145Z Running 2543 items in this shard: test/functorch/test_ops.py::TestOperatorsCUDA::test_data_write_errors_under_transform_cuda, test/functorch/test_ops.py::TestOperatorsCUDA::test_extremal_numerics_cross_entropy_cuda, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_NumpyCubeNotComposableAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad___rdiv___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad___rmod___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad__unsafe_masked_index_put_accumulate_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_addcdiv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_aminmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_atleast_1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_atleast_2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_baddbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_bernoulli_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_broadcast_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_ceil_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_char_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_cholesky_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_constant_pad_nd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_corrcoef_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_cross_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_diag_embed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_div_no_rounding_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_div_trunc_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_einsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_empty_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_eq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_equal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_erfinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_exp2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fft_hfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fft_hfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fft_rfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fft_rfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_flipud_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_float_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_float_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_floor_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_full_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_full_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_gradient_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_grid_sampler_2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_heaviside_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_i0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_index_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_index_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_isinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_isnan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_jiterator_4inputs_with_extra_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_kron_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_ldexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_lgamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_cholesky_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_cond_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_det_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_householder_product_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_inv_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_ldl_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_ldl_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_lstsq_grad_oriented_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_lu_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_multi_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_slogdet_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_svdvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_linspace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_log_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_long_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_fill_functorch_Scalar_only_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_logsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_masked_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_matmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_max_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_maximum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_min_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_msort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_native_batch_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nextafter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_conv_transpose2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_cosine_similarity_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_dropout3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_dropout_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_elu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_embedding_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_gaussian_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_hardswish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_hardtanh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_interpolate_nearest-exact_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_kl_div_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_max_unpool1d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_max_unpool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_pad_replicate_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_pixel_shuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_selu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_nn_functional_softsign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_norm_nuc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_polygamma_polygamma_n_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_positive_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_pow_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_randint_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_randn_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_reciprocal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_repeat_interleave_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_resize__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_roll_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_scatter_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_scatter_reduce_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_scatter_reduce_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_signal_windows_bartlett_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_sinc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_softmax_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_special_laguerre_polynomial_l_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_special_modified_bessel_k1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_special_ndtri_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_special_polygamma_special_polygamma_n_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_special_spherical_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_square_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_squeeze_multiple_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_t_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_tan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_topk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_transpose_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_triu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_trunc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_unfold_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_unsqueeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_var_mean_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_view_as_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_view_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_view_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_vstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_grad_zeros_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp___getitem___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp___rmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp___rsub___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp__native_batch_norm_legit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp__segment_reduce_lengths_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_abs_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_addcmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_addmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_addmv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_allclose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_angle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_arange_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_as_strided_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_as_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_atleast_3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_bmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_broadcast_shapes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_bucketize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_cartesian_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_chalf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_clamp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_clamp_max_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_clone_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_conj_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_count_nonzero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_cumsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_deg2rad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_diag_embed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_diagflat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_diagonal_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_div_no_rounding_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_double_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_double_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_empty_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_eq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_erf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_erfinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_expand_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_fft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_fft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_hfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_hfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_ifft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fft_irfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_flipud_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_float_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_hypot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_i0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_igamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_index_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_index_reduce_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_index_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_item_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_kthvalue_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_le_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_cond_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_inv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_lstsq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_matrix_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_matrix_rank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_multi_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_pinv_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_pinv_singular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_slogdet_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_linalg_vander_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_log10_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_logical_xor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_logspace_tensor_overload_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_lt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_lu_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_argmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_median_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_masked_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_max_binary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_max_pool2d_with_indices_backward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_max_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_max_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_minimum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_movedim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_msort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_multinomial_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nan_to_num_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_narrow_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_conv2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_conv3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_ctc_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_dropout2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_fractional_max_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_gaussian_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_hardswish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_instance_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_kl_div_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_logsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_max_unpool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_max_unpool1d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_max_unpool2d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_mse_loss_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_multi_head_attention_forward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_multilabel_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_pad_circular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_pad_replicate_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_pixel_unshuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_poisson_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_smooth_l1_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_softmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_softmin_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nn_functional_unfold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_nonzero_static_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_norm_fro_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_ones_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_pca_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_polygamma_polygamma_n_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_positive_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_qr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_randint_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_randn_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_ravel_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_reciprocal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_remainder_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_renorm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_resize__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_resolve_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_round_decimals_neg_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_scatter_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_signal_windows_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_signal_windows_nuttall_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_slice_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_airy_ai_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_bessel_y0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_i1e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_shifted_chebyshev_polynomial_u_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_spherical_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_special_zeta_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_stft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_sub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_sum_to_size_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_take_along_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_tanh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_tensordot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_topk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_trace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_transpose_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_transpose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_triangular_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_triu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_unbind_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_unfold_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_view_as_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvp_vsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpjvpvmap_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpjvpvmap_NumpyCubeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpjvpvmap_NumpyMulAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpjvpvmap_ScaleGradGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpjvpvmap_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_NumpyCubeNotComposableAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_NumpyMulAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_SelectAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp___rmatmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp___rmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp___rpow___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp__chunk_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp__segment_reduce_offsets_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp__softmax_backward_data_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp__upsample_bilinear2d_aa_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_abs_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_acos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_allclose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_angle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_as_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_asinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_atan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_atleast_2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_block_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_bool_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_byte_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_cdouble_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_cfloat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_chalf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_clamp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_cumulative_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_digamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_div_floor_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_div_no_rounding_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_div_trunc_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_empty_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_eq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_equal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_expand_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_eye_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_fft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_hfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_ifft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_ifftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_ihfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_ihfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fft_irfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_flatten_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fliplr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_flipud_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_floor_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_frac_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_geometric_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_gradient_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_half_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_hsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_i0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_index_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_index_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_index_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_int_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_isfinite_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_isneginf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_item_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_kthvalue_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_lgamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_cholesky_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_eigvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_inv_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_ldl_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_lu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_lu_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_matrix_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_slogdet_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_solve_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_solve_triangular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_tensorsolve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linalg_vector_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_linspace_tensor_overload_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_logcumsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_logical_or_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_masked_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_masked_softmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_masked_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_meshgrid_list_of_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_movedim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nan_to_num_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nanmean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_avg_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_grid_sample_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_group_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_instance_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_interpolate_area_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_local_response_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_max_pool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_max_unpool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_multi_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_pad_constant_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_pad_replicate_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_pad_replicate_negative_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_pairwise_distance_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_relu6_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_rms_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_silu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_softsign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_threshold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_nn_functional_upsample_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_norm_nuc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_normal_in_place_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_ops_aten_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_polygamma_polygamma_n_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_randn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_reshape_as_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_resolve_conj_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_resolve_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_rot90_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_rsub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_scatter_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_scatter_reduce_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_searchsorted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_select_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_signal_windows_blackman_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_sparse_mm_reduce_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_bessel_j1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_bessel_y0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_bessel_y1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_chebyshev_polynomial_v_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_erfcx_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_i0e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_log_ndtr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_ndtri_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_special_shifted_chebyshev_polynomial_v_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_split_with_sizes_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_t_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_take_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_torch_ops_aten__safe_softmax_default_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_trace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_triangular_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_unbind_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_unfold_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_unsafe_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_unsqueeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_var_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_view_as_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_vsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjp_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjpvmap_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjpvmap_NumpySortAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjpvmap_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvjpvmap_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmap_ForwardHasDefaultArgsAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmap_NumpyExpMarkDirtyAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmap_NumpySortAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmap_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmapvmap_CubeGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmapvmap_NumpyExpMarkDirtyAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmapvmap_NumpySortAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_jvpvmapvmap_ScaleGradGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_bool_raises_topk_cuda_bool, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_argmax_cuda_complex64, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_ceil_cuda_complex32, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_gt_cuda_complex64, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_maximum_cuda_complex128, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_sort_cuda_complex32, test/functorch/test_ops.py::TestOperatorsCUDA::test_ordered_complex_raises_sort_cuda_complex64, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_contiguous_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_expand_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_flatten_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_flatten_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_list_return_dsplit_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_list_return_split_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_list_return_unbind_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_mH_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_mT_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_movedim_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_permute_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_positive_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_resolve_neg_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_special_grad_op_jvp_cuda, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_special_grad_op_vjp_cuda, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_squeeze_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_squeeze_multiple_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_unfold_grad_op_jvp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_unsqueeze_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_view_then_inplace_view_grad_op_vjp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_H_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_SelectAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp___getitem___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp__chunk_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp__segment_reduce_lengths_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_addbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_addmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_addmm_decomposed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_addr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_argmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_argmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_as_strided_partial_views_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_atan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_bfloat16_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_bmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_broadcast_shapes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_broadcast_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_bucketize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_byte_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cauchy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_ceil_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cfloat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_char_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_clamp_max_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_clone_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_column_stack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cov_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cross_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cummin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_cumulative_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_diag_embed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_diff_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_div_trunc_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_dstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_empty_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_empty_permuted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_fft_irfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_flip_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_float_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_floor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_geometric_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_index_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_inner_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_isclose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_isfinite_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_isin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_isinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_isposinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_item_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_le_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_cholesky_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_eigvalsh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_inv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_ldl_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_lstsq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_lstsq_grad_oriented_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_matrix_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_pinv_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_pinv_singular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_slogdet_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_solve_triangular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_svdvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_linalg_tensorsolve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_log1p_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_logaddexp2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_logcumsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_logical_xor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_lu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_lu_unpack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_mH_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_masked_argmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_masked_argmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_masked_logsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_masked_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_matmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_matrix_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_max_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_meshgrid_variadic_tensors_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_nn_functional_threshold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_nn_functional_triplet_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_nn_functional_unfold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_norm_inf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_ormqr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_permute_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_pinverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_polar_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_polygamma_polygamma_n_0_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_i1e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_log_ndtr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_modified_bessel_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_scaled_modified_bessel_k1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_spherical_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_special_xlog1py_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_split_list_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_split_with_sizes_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_split_with_sizes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_squeeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_squeeze_multiple_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_std_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_sub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_take_along_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_torch_ops_aten__efficient_attention_forward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_unflatten_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_unfold_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_unsafe_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_unsqueeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_vsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_vstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_zeros_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjp_zeros_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_CubeGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_H_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp__native_batch_norm_legit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_abs_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_acosh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_allclose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_aminmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_arange_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_argsort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_as_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_as_strided_partial_views_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_asinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_atleast_1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_bfloat16_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_bool_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_cdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_cholesky_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_clamp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_clamp_max_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_column_stack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_count_nonzero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_cummax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_diagflat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_digamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_div_trunc_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_erfinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_fft_fftshift_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_fft_ihfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_fmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_full_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_ge_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_grid_sampler_2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_half_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_i0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_igammac_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_index_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_index_reduce_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_isinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_isposinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_isreal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_jiterator_2inputs_2outputs_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_lgamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_linalg_eig_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_linalg_inv_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_linalg_ldl_factor_ex_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_logsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_masked_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_max_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_median_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nanmedian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_narrow_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_ne_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_new_full_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_new_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_adaptive_avg_pool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_alpha_dropout_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_avg_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_batch_norm_without_cudnn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_channel_shuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv2d_stride_padding_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv2d_stride_padding_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_ctc_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_embedding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_fractional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_group_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_interpolate_linear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_kl_div_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_linear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_local_response_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_logsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_margin_ranking_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_max_unpool1d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_mish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_rms_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_scaled_dot_product_attention_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_selu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_soft_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_upsample_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_nn_functional_upsample_nearest_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_normal_in_place_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_polygamma_polygamma_n_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_polygamma_polygamma_n_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_polygamma_polygamma_n_4_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_quantile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_randn_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_real_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_reciprocal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_resize__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_round_decimals_neg_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_scatter_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_scatter_reduce_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_sgn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_signal_windows_gaussian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_signal_windows_general_cosine_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_signal_windows_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_signal_windows_nuttall_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_slice_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_softmax_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_special_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_special_modified_bessel_i0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_special_modified_bessel_k1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_special_scaled_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_special_shifted_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_split_list_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_split_with_sizes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_stft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_tanh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_tensordot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_topk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_tril_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_unbind_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_unfold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_var_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjp_view_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_ForwardHasDefaultArgsAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_NumpyCubeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_NumpyCubeNotComposableAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_NumpySortAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_ScaleGradGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvjpvmap_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_CubeGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_NumpyCubeNotComposableAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_SelectAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_SortGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_T_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap___getitem___functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap___rmod___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap__segment_reduce_offsets_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap__unsafe_masked_index_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_acos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_addcmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_addmm_decomposed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_alias_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_angle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_arange_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_as_strided_partial_views_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_asinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_bool_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_broadcast_shapes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_broadcast_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_byte_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_cdouble_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_clone_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_constant_pad_nd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_copysign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_cos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_cosh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_double_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_empty_permuted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_equal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_expand_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_fftshift_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_ifft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_ifftshift_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_ihfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_ihfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_irfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_fft_rfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_float_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_float_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_floor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_gather_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_gt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_hsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_int_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_isfinite_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_isnan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_isposinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_item_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_jiterator_4inputs_with_extra_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_jiterator_binary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_jiterator_binary_return_by_ref_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_le_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_lerp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_cholesky_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_cond_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_eig_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_ldl_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_lstsq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_matrix_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_matrix_rank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_solve_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_svd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linalg_vander_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_linspace_tensor_overload_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_log1p_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_log2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_log_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_logcumsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_logical_or_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_logsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_lu_unpack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_mH_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_fill_functorch_Scalar_only_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_normalize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_masked_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_max_binary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_max_pool2d_with_indices_backward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_min_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nansum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_narrow_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_native_batch_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_native_dropout_backward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_native_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_new_empty_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_alpha_dropout_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_avg_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_batch_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_binary_cross_entropy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv2d_strided_padding_dilation_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_conv_transpose3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_cosine_embedding_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_cross_entropy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_hardswish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_interpolate_bicubic_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_interpolate_nearest-exact_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_interpolate_nearest_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_interpolate_trilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_leaky_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_max_unpool2d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_max_unpool3d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_mse_loss_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_multilabel_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_normalize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_pad_reflect_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_pdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_pixel_unshuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_relu6_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_scaled_dot_product_attention_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_selu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_smooth_l1_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_soft_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_softplus_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_norm_fro_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_pca_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_permute_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_polygamma_polygamma_n_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_pow_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_rad2deg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_randint_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_randint_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_randn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_renorm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_scalar_tensor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_searchsorted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_signal_windows_bartlett_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_signal_windows_general_cosine_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_signal_windows_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_signal_windows_hann_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_slice_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_softmax_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_sort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_airy_ai_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_bessel_j1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_hermite_polynomial_h_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_legendre_polynomial_p_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_ndtri_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_shifted_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_shifted_chebyshev_polynomial_u_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_special_xlog1py_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_squeeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_squeeze_multiple_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_std_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_stft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_t_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_take_along_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_tan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_trunc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_unbind_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_unflatten_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_unique_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_unsafe_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_var_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmap_vstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vjpvmapvmap_CubeGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_H_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_NumpyMulAutogradFunction_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_NumpySortAutogradFunction_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_SelectAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___getitem___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___radd___cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___rmatmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___rmatmul___cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___rmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad___rsub___cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad__chunk_cat_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad__segment_reduce_lengths_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad__softmax_backward_data_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad__upsample_bilinear2d_aa_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_acos_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_addbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_addcmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_addmm_decomposed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_alias_copy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_all_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_allclose_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_any_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_arange_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_argsort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_argwhere_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_as_strided_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_as_strided_partial_views_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_atan_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_atanh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_bernoulli_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_bfloat16_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_bmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_bucketize_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_byte_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_byte_functorch_no_channels_last_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cat_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cdist_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cdouble_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cfloat_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cholesky_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_column_stack_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_combinations_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_contiguous_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_corrcoef_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_corrcoef_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cov_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cummin_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cumsum_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_cumulative_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_deg2rad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diag_embed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diagonal_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diagonal_copy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_diff_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_dist_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_div_floor_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_div_no_rounding_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_einsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_empty_like_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_empty_permuted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_empty_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_erfinv_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_expand_as_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_expand_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_expm1_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_fft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_fft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_fftshift_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_hfft2_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_ihfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_irfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_irfft2_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_irfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_rfft2_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fft_rfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fill_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_flip_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_fliplr_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_flipud_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_float_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_float_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_floor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_frac_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_frexp_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_ge_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_hash_tensor_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_hypot_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_igamma_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_fill_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_reduce_amax_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_reduce_amin_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_index_reduce_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_inner_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_int_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_isfinite_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_jiterator_4inputs_with_extra_args_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_jiterator_binary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_kron_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_kthvalue_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_cholesky_ex_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_cond_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_cross_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_eigh_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_eigvals_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_eigvalsh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_inv_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_inv_ex_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_ldl_factor_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_lstsq_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_lu_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_lu_factor_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_lu_factor_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_lu_factor_ex_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_matrix_rank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_pinv_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_pinv_singular_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_qr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_qr_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_slogdet_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_solve_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_solve_triangular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_svdvals_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linalg_vector_norm_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linspace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_linspace_tensor_overload_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_log1p_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_log_normal_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_logaddexp_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_logcumsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_logit_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_logspace_tensor_overload_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_logsumexp_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_long_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_lu_unpack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_lu_unpack_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_amin_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_argmin_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_log_softmax_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_prod_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_scatter_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_softmax_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_softmin_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_masked_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_matrix_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_max_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_max_reduction_no_dim_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_min_reduction_with_dim_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_movedim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_mul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_mv_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_5_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nanmean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nanquantile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_narrow_copy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_native_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_neg_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_new_empty_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_new_empty_strided_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_new_ones_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nextafter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_adaptive_max_pool3d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_celu_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_channel_shuffle_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv1d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_no_bias_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_stride_groups_with_bias_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv2d_with_bias_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_conv_transpose3d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_embedding_functorch_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_group_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_hardsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_hardswish_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_hinge_embedding_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_huber_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_huber_loss_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_instance_norm_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_area_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_area_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_bicubic_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_bilinear_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_linear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_interpolate_trilinear_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_kl_div_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_l1_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_linear_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_local_response_norm_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_margin_ranking_loss_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_pool1d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_unpool1d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_unpool1d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_unpool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_unpool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_max_unpool3d_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_mish_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_multi_head_attention_forward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_multi_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_multilabel_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pad_constant_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pad_constant_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pad_reflect_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pad_reflect_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pad_replicate_negative_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pairwise_distance_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_pdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_poisson_nll_loss_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_relu6_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_relu6_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_relu_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_rrelu_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_silu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_smooth_l1_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_softmin_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_softshrink_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_softshrink_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_tanhshrink_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_tanhshrink_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_threshold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_upsample_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nn_functional_upsample_bilinear_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_nonzero_static_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_norm_fro_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_normal_number_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_ones_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_ops_aten_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_polygamma_polygamma_n_3_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_polygamma_polygamma_n_4_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_qr_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_randint_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_randint_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_randint_like_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_ravel_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_renorm_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_repeat_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_reshape_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_resize__cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_resolve_conj_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_resolve_conj_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_round_decimals_3_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_round_decimals_neg_3_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_scatter_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_scatter_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_searchsorted_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_select_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_select_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sigmoid_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_bartlett_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_bartlett_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_blackman_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_exponential_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_general_cosine_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signal_windows_kaiser_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_signbit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sinc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_slice_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sort_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_bessel_y1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_chebyshev_polynomial_t_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_chebyshev_polynomial_u_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_chebyshev_polynomial_w_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_chebyshev_polynomial_w_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_entr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_erfcx_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_hermite_polynomial_h_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_hermite_polynomial_he_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_hermite_polynomial_he_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_i0e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_i1e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_i1e_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_log_ndtr_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_modified_bessel_i0_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_ndtr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_scaled_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_t_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_special_xlog1py_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_split_with_sizes_copy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_squeeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_squeeze_multiple_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_std_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_std_unbiased_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_stft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sub_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_sum_to_size_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_svd_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_svd_lowrank_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_t_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_take_along_dim_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_take_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_tensor_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_to_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_torch_ops_aten__safe_softmax_default_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_transpose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_trapz_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_triu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_true_divide_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_unfold_copy_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_unsafe_chunk_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_unsqueeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_unsqueeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_unsqueeze_cuda_float64, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_view_as_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmap_autograd_grad_zero__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_NumpyTakeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall___radd___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall__chunk_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall__native_batch_norm_legit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall__segment_reduce_offsets_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall__upsample_bilinear2d_aa_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_acos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_addbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_atan2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_baddbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_bool_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_bucketize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_byte_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_byte_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_cdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_cholesky_inverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_clamp_max_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_conj_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_count_nonzero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_deg2rad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_dsplit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_eq_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_erfinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_expm1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_eye_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_fft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_hfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_ifft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_ihfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_irfftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_rfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fft_rfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fliplr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_flipud_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_floor_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_frexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_ge_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_gt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_half_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ForwardHasDefaultArgsAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_H_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule___rdiv___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule___rpow___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule__segment_reduce_offsets_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule__upsample_bilinear2d_aa_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_abs_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_acos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_addbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_addmm_decomposed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_angle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_any_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_as_strided_partial_views_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_asinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_atan2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_atanh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_atleast_2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_bfloat16_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_broadcast_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_chalf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_cholesky_inverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_cholesky_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_clone_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_constant_pad_nd_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_digamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_double_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_erf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_erfc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_exp2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fft_fftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fft_fftshift_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fft_ifftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fft_irfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fft_rfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_flatten_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_float_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ge_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_gt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_half_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_heaviside_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_index_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_index_reduce_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_index_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_int_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_isinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_isnan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_isreal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_item_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_jiterator_unary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_kron_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_eigh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_inv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_ldl_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_matrix_rank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_pinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_qr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_solve_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_vecdot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_linalg_vector_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_logical_and_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_logical_or_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_long_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_long_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_lu_unpack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_argmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_normalize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_masked_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_matrix_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_median_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_min_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_mm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_movedim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_msort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nanmedian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nanquantile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_native_batch_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_native_dropout_backward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ne_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nextafter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_batch_norm_without_cudnn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_padding_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_embedding_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_area_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_leaky_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_max_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_mse_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_normalize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_pad_constant_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_relu6_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_soft_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_softplus_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_softshrink_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_threshold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_nn_functional_triplet_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_normal_in_place_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_outer_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_permute_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_positive_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_randint_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_randn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_randn_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_renorm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_repeat_interleave_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_reshape_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_resize__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_roll_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_round_decimals_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_round_decimals_neg_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_rsqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_scatter_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_select_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_short_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_airy_ai_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_bessel_y0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_i0e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_modified_bessel_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_ndtri_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_special_xlog1py_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_squeeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_sum_to_size_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_take_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_tan_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_tensor_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_tensordot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_tile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_to_sparse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_topk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_view_as_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_view_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_has_batch_rule_zero__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_heaviside_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_igammac_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_index_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_index_put_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_index_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_jiterator_4inputs_with_extra_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_jiterator_unary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_ldexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_cholesky_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_cond_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_det_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_diagonal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_eigvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_householder_product_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_inv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_lu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_lu_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_lu_factor_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_lu_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_matrix_rank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_multi_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_linalg_vector_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_long_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_lt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_lu_unpack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_mH_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_masked_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_masked_cumsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_masked_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_matmul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_max_binary_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_max_pool2d_with_indices_backward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_mul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_multinomial_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_neg_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_new_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_adaptive_avg_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_alpha_dropout_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_avg_pool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_celu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv2d_stride_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv2d_strided_padding_dilation_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv2d_with_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_conv_transpose1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_cross_entropy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_hardsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_hardswish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_hinge_embedding_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_huber_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_layer_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_logsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_max_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_max_unpool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_max_unpool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_mse_loss_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_multilabel_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_pad_circular_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_smooth_l1_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_softmin_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_softplus_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_softsign_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_norm_fro_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_norm_inf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_pca_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_permute_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_polar_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_polygamma_polygamma_n_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_polygamma_polygamma_n_3_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_put_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_quantile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_real_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_reciprocal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_repeat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_resize_as__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_rsub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_scalar_tensor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_scatter_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_signal_windows_exponential_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_signal_windows_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_signal_windows_kaiser_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_sinc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_sparse_sampled_addmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_bessel_y0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_chebyshev_polynomial_v_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_chebyshev_polynomial_w_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_i0e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_legendre_polynomial_p_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_modified_bessel_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_ndtr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_shifted_chebyshev_polynomial_v_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_special_spherical_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_take_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_tile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_torch_ops_aten__safe_softmax_default_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_trace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_trapz_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_tril_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_true_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_trunc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_unsafe_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_view_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_vstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_where_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpall_zero__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_ForwardHasDefaultArgsAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp___rmatmul___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp___rmod___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp__chunk_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp__native_batch_norm_legit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp__segment_reduce_lengths_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp__unsafe_masked_index_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp__unsafe_masked_index_put_accumulate_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_add_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_addcdiv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_addr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_aminmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_arange_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_argmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_argwhere_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_as_strided_scatter_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_asinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_atleast_1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_atleast_3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_block_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_broadcast_to_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_cdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_ceil_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_chalf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_cholesky_inverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_cholesky_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_clamp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_clone_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_conj_physical_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_cosh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_count_nonzero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_cumulative_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_dist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_div_floor_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_empty_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fft_fftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fft_fftshift_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fft_ifftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fft_irfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_flatten_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_float_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_float_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_fmod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_frexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_full_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_full_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_hash_tensor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_hstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_igammac_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_fill_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_put_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_put_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_reduce_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_index_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_int_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_isin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_isposinf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_jiterator_binary_return_by_ref_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_kthvalue_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_eig_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_eigvalsh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_inv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_inv_ex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_ldl_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_lstsq_grad_oriented_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_lu_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_pinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_pinv_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_svdvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linalg_tensorinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_linspace_tensor_overload_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_log10_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_log1p_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_log_softmax_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_logical_and_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_logical_or_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_long_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_lt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_mT_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_cumsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_logsumexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_masked_softmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_min_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_mul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_mvlgamma_mvlgamma_p_1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_mvlgamma_mvlgamma_p_5_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nanmedian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_new_ones_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_adaptive_avg_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_avg_pool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_avg_pool2d_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_hardshrink_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_hardsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_interpolate_bicubic_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_interpolate_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_interpolate_linear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_kl_div_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_local_response_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_max_pool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_max_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_max_unpool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_max_unpool3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_max_unpool3d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_mse_loss_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_multi_head_attention_forward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_normalize_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_pad_replicate_negative_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_pairwise_distance_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_poisson_nll_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_prelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_triplet_margin_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_unfold_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nn_functional_upsample_nearest_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_nonzero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_norm_fro_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_normal_in_place_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_pca_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_pinverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_positive_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_qr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_remainder_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_reshape_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_roll_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_round_decimals_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_scatter_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_scatter_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_scatter_reduce_prod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_short_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_signal_windows_blackman_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_signal_windows_cosine_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_signal_windows_general_cosine_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_signal_windows_kaiser_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_sparse_mm_reduce_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_special_bessel_j0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_special_bessel_y1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_special_chebyshev_polynomial_u_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_special_erfcx_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_sub_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_svd_lowrank_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_take_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_tensordot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_tile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_trace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_tril_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_triu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_unbind_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_unbind_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_unsqueeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_var_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_var_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_view_as_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_view_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_vstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvjp_zeros_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvmap_CubeGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvmap_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapjvpvmap_SelectGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_MulGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_NumpyCubeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_NumpyExpMarkDirtyAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_NumpySortAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_SelectGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp___getitem___functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp__chunk_cat_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_linalg_vector_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_linspace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_log10_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_log_normal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_long_functorch_no_channels_last_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_mH_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_logaddexp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_softmin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_masked_sum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_matrix_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_max_reduction_with_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_median_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_meshgrid_variadic_tensors_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_min_reduction_no_dim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_mode_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_movedim_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_mul_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_mv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_ne_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_new_empty_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_new_empty_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_new_full_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_new_zeros_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_adaptive_max_pool1d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_adaptive_max_pool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_bilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_channel_shuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_conv2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_conv2d_no_bias_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_cosine_similarity_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_dropout3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_elu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_embedding_functorch_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_grid_sample_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_hardsigmoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_hardswish_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_instance_norm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_interpolate_area_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_interpolate_bicubic_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_interpolate_nearest_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_interpolate_trilinear_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_leaky_relu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_max_unpool2d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_max_unpool2d_grad_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_multi_head_attention_forward_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_pad_constant_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_pad_reflect_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_pad_replicate_negative_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_pixel_unshuffle_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_rrelu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_scaled_dot_product_attention_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_softmin_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nn_functional_softshrink_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_nonzero_static_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_permute_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_pinverse_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_positive_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_qr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_rand_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_randint_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_reciprocal_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_resize_as__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_round_decimals_0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_rsqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_short_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signal_windows_bartlett_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signal_windows_blackman_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signal_windows_gaussian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signal_windows_general_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signal_windows_hamming_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_signbit_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_sin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_sinc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_sinh_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_softmax_with_dtype_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_airy_ai_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_chebyshev_polynomial_u_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_chebyshev_polynomial_w_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_erfcx_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_hermite_polynomial_he_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_i0e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_i1_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_i1e_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_log_ndtr_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_ndtri_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_scaled_modified_bessel_k0_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_special_shifted_chebyshev_polynomial_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_split_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_split_list_args_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_sqrt_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_squeeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_squeeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_squeeze_multiple_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_std_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_std_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_std_mean_unbiased_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_t_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_torch_ops_aten__safe_softmax_default_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_transpose_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_trapz_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_tril_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_zero__cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjp_zeros_like_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_NumpyCubeNotComposableAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_SelectAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_ZeroGradientsGenVmapAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp___rdiv___cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp__batch_norm_with_update_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp__segment_reduce_lengths_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp__unsafe_masked_index_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_addbmm_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_addcdiv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_argsort_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_as_strided_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_as_strided_partial_views_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_asin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_atleast_3d_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_block_diag_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_broadcast_shapes_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cat_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cdist_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_chalf_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_clamp_min_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_complex_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cos_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cross_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cummax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cumprod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_cumulative_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_diag_embed_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_digamma_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_div_floor_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_div_trunc_rounding_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_dot_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_double_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_einsum_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_empty_permuted_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_empty_strided_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_exp_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_expand_as_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_expand_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_eye_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fft_fft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fft_hfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fft_ifftn_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fft_ihfft2_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fft_irfft_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_floor_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_fmod_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_hash_tensor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_histc_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_hstack_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_index_reduce_amax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_index_reduce_amin_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_index_select_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_inner_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_ldl_factor_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_lu_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_matrix_power_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_matrix_rank_hermitian_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_norm_subgradients_at_zero_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_pinv_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_linalg_svdvals_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_log_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_log_softmax_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_logcumsumexp_cuda_float32, 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test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_tile_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_trace_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_transpose_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_trapezoid_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_triangular_solve_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_triu_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_true_divide_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_unsafe_chunk_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_unsqueeze_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_unsqueeze_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_var_mean_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_view_copy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvjp_xlogy_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvmap_NumpyCubeAutogradFunction_cuda_float32, test/functorch/test_ops.py::TestOperatorsCUDA::test_vmapvjpvmap_SelectAutogradFunction_cuda_float32
2025-08-14T23:19:52.6049710Z 
2025-08-14T23:19:52.6049812Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T23:19:52.6049991Z Uploading artifacts took 0.00 seconds
2025-08-14T23:19:52.6050135Z Running test_ops_jit 1/1 ... [2025-08-14 23:19:52.542642]
2025-08-14T23:19:52.6050319Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:19:52.6055434Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ops_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'] ... [2025-08-14 23:19:52.542863]
2025-08-14T23:28:11.6758771Z 
2025-08-14T23:28:11.6759969Z test_ops_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_1.1_5724070be4adae9f_.log
2025-08-14T23:28:11.6896602Z Running 1138 items in this shard: test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_abs_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_acos_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_acosh_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_asin_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_asinh_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_atan2_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_atan_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_atanh_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_cat_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_clamp_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_digamma_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_div_floor_rounding_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_div_no_rounding_mode_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_div_trunc_rounding_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_erf_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_erfc_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_erfinv_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_exp2_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_expm1_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_ge_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_gt_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_i0_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_igamma_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_igammac_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_le_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_lgamma_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_linalg_det_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_linalg_householder_product_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_linalg_inv_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_linalg_matrix_power_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_log1p_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_log_softmax_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_log_softmax_with_dtype_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_logit_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_logsumexp_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_lt_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_mH_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_matmul_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_matrix_exp_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_max_binary_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_min_binary_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_movedim_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_mul_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_ne_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_neg_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv1d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv2d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv3d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv_transpose1d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv_transpose2d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_conv_transpose3d_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_jit_alias_remapping_nn_functional_group_norm_cuda_float32, 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test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_shifted_chebyshev_polynomial_w_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_spherical_bessel_j0_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_xlog1py_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_special_zeta_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_list_args_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_list_args_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_with_sizes_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_with_sizes_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_with_sizes_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_split_with_sizes_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sqrt_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sqrt_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_square_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_square_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_multiple_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_squeeze_multiple_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_stack_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_stack_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_mean_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_mean_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_mean_unbiased_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_mean_unbiased_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_unbiased_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_std_unbiased_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_stft_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_stft_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sub_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sub_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sum_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sum_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sum_to_size_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_sum_to_size_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_svd_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_svd_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_svd_lowrank_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_svd_lowrank_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_t_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_t_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_t_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_t_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_take_along_dim_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_take_along_dim_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_take_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_take_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tan_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tan_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tanh_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tanh_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tensor_split_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tensor_split_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tensordot_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tensordot_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tile_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tile_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_to_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_to_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_to_sparse_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_to_sparse_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_topk_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_torch_ops_aten__safe_softmax_default_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trace_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trace_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_transpose_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_transpose_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_transpose_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_transpose_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trapezoid_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trapezoid_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trapz_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trapz_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_triangular_solve_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_triangular_solve_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tril_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_tril_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_triu_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_triu_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_true_divide_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_true_divide_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_trunc_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unbind_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unbind_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unbind_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unbind_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unflatten_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unflatten_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unfold_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unfold_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unfold_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unfold_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_uniform_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_uniform_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unique_consecutive_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unique_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsafe_chunk_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsafe_chunk_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsafe_split_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsafe_split_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsqueeze_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsqueeze_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsqueeze_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_unsqueeze_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_mean_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_mean_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_mean_unbiased_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_mean_unbiased_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_unbiased_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_var_unbiased_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vdot_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vdot_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_as_complex_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_as_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_as_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_as_real_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_copy_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_copy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_view_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vsplit_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vsplit_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vstack_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_vstack_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_where_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_where_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_xlogy_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zero__cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zero__cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zeros_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zeros_cuda_float32, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zeros_like_cuda_complex64, test/test_ops_jit.py::TestJitCUDA::test_variant_consistency_jit_zeros_like_cuda_float32
2025-08-14T23:28:11.7024083Z 
2025-08-14T23:28:11.7024172Z Running optim/test_swa_utils 1/1 ... [2025-08-14 23:28:11.676773]
2025-08-14T23:28:11.7024340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:11.7030015Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_swa_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'] ... [2025-08-14 23:28:11.676992]
2025-08-14T23:28:13.6894914Z 
2025-08-14T23:28:13.6895673Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_837f9cb20fdfeec8_.log
2025-08-14T23:28:13.6896297Z 
2025-08-14T23:28:13.6896514Z Running test_mkldnn_verbose 1/1 ... [2025-08-14 23:28:13.689276]
2025-08-14T23:28:13.6896948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:13.6898860Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_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'] ... [2025-08-14 23:28:13.689557]
2025-08-14T23:28:17.8124626Z 
2025-08-14T23:28:17.8126411Z test_mkldnn_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_verbose_1.1_d280351410f2cb00_.log
2025-08-14T23:28:17.8127327Z Running 2 items in this shard: test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_off, test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_on
2025-08-14T23:28:17.8135614Z 
2025-08-14T23:28:17.8136119Z Running test_mobile_optimizer 1/1 ... [2025-08-14 23:28:17.812455]
2025-08-14T23:28:17.8136448Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:17.8137111Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:28:17.812745]
2025-08-14T23:28:21.6040997Z 
2025-08-14T23:28:21.6042374Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_6faea1496e523162_.log
2025-08-14T23:28:21.6045390Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures
2025-08-14T23:28:21.6047573Z 
2025-08-14T23:28:21.6047699Z Running test_module_tracker 1/1 ... [2025-08-14 23:28:21.603927]
2025-08-14T23:28:21.6047896Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:21.6054049Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_module_tracker.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:28:21.604161]
2025-08-14T23:28:23.9226861Z 
2025-08-14T23:28:23.9228448Z test_module_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_module_tracker_1.1_d8beca47a4487da9_.log
2025-08-14T23:28:23.9230043Z Running 3 items in this shard: test/test_module_tracker.py::TestModuleTracker::test_bw_detection, test/test_module_tracker.py::TestModuleTracker::test_confused_hierarchy, test/test_module_tracker.py::TestModuleTracker::test_module_hierarchy
2025-08-14T23:28:23.9230996Z 
2025-08-14T23:28:23.9231907Z Running test_monitor 1/1 ... [2025-08-14 23:28:23.922846]
2025-08-14T23:28:23.9232248Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:23.9234192Z 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'] ... [2025-08-14 23:28:23.923098]
2025-08-14T23:28:26.6918994Z 
2025-08-14T23:28:26.6920350Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_3d1d379fd6fd2bb4_.log
2025-08-14T23:28:26.6921263Z 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
2025-08-14T23:28:26.6928084Z 
2025-08-14T23:28:26.6928576Z Running test_multiprocessing 1/1 ... [2025-08-14 23:28:26.691693]
2025-08-14T23:28:26.6928780Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:28:26.6929335Z 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'] ... [2025-08-14 23:28:26.691925]
2025-08-14T23:29:26.4750327Z 
2025-08-14T23:29:26.4752073Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_702bf2c4e26ec71a_.log
2025-08-14T23:29:26.4770544Z 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
2025-08-14T23:29:26.4777539Z 
2025-08-14T23:29:26.4777680Z Running test_multiprocessing_spawn 1/1 ... [2025-08-14 23:29:26.474915]
2025-08-14T23:29:26.4777924Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:29:26.4778542Z 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'] ... [2025-08-14 23:29:26.475247]
2025-08-14T23:31:21.8169767Z 
2025-08-14T23:31:21.8170743Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_a10354de487b47c8_.log
2025-08-14T23:31:21.8185773Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable
2025-08-14T23:31:21.8191298Z 
2025-08-14T23:31:21.8191430Z Running test_segment_reductions 1/1 ... [2025-08-14 23:31:21.816779]
2025-08-14T23:31:21.8191691Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:31:21.8192245Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_segment_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'] ... [2025-08-14 23:31:21.817128]
2025-08-14T23:31:25.5398307Z 
2025-08-14T23:31:25.5399492Z test_segment_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_segment_reductions_1.1_46b91a47a110546e_.log
2025-08-14T23:31:25.5423651Z Running 74 items in this shard: test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_multi_d_simple_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_max_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_mean_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_min_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_prod_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_pytorch_scatter_test_cases_reduce_sum_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_1d_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_simple_zero_length_cuda_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_unsafe_flag_cuda_int32, test/test_segment_reductions.py::TestSegmentReductionsCUDA::test_unsafe_flag_cuda_int64
2025-08-14T23:31:25.5440746Z 
2025-08-14T23:31:25.5440835Z Running test_serialization 1/1 ... [2025-08-14 23:31:25.539789]
2025-08-14T23:31:25.5441031Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:31:25.5441446Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_serialization.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:31:25.540025]
2025-08-14T23:31:48.6585069Z 
2025-08-14T23:31:48.6586732Z test_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_serialization_1.1_27989277d21c2cc8_.log
2025-08-14T23:31:48.6616418Z Running 205 items in this shard: test/test_serialization.py::TestOldSerialization::test_debug_set_in_ci, test/test_serialization.py::TestOldSerialization::test_load_error_msg, test/test_serialization.py::TestOldSerialization::test_load_nonexistent_device, test/test_serialization.py::TestOldSerialization::test_load_python2_unicode_module, test/test_serialization.py::TestOldSerialization::test_load_unicode_error_msg, test/test_serialization.py::TestOldSerialization::test_pickle_module, test/test_serialization.py::TestOldSerialization::test_safe_load_basic_types, test/test_serialization.py::TestOldSerialization::test_save_different_dtype_error, test/test_serialization.py::TestOldSerialization::test_save_different_dtype_unallocated, test/test_serialization.py::TestOldSerialization::test_serialization, test/test_serialization.py::TestOldSerialization::test_serialization_backwards_compat, test/test_serialization.py::TestOldSerialization::test_serialization_backwards_compat_safe, test/test_serialization.py::TestOldSerialization::test_serialization_container, test/test_serialization.py::TestOldSerialization::test_serialization_container_filelike, test/test_serialization.py::TestOldSerialization::test_serialization_dill, test/test_serialization.py::TestOldSerialization::test_serialization_dill_version_not_supported, test/test_serialization.py::TestOldSerialization::test_serialization_fake_zip, test/test_serialization.py::TestOldSerialization::test_serialization_filelike, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_api_requirements, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_exceptions, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_missing_attrs, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_stress, test/test_serialization.py::TestOldSerialization::test_serialization_filelike_uses_readinto, test/test_serialization.py::TestOldSerialization::test_serialization_gzip, test/test_serialization.py::TestOldSerialization::test_serialization_map_location, test/test_serialization.py::TestOldSerialization::test_serialization_offset, test/test_serialization.py::TestOldSerialization::test_serialization_offset_filelike_weights_only_False, test/test_serialization.py::TestOldSerialization::test_serialization_offset_filelike_weights_only_True, test/test_serialization.py::TestOldSerialization::test_serialization_offset_gzip, test/test_serialization.py::TestOldSerialization::test_serialization_safe, test/test_serialization.py::TestOldSerialization::test_serialization_save_warnings, test/test_serialization.py::TestOldSerialization::test_serialization_sparse, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_bsc_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_bsr_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_csc_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_csr_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_invalid, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_invalid_legacy_ctor, test/test_serialization.py::TestOldSerialization::test_serialization_sparse_safe, test/test_serialization.py::TestOldSerialization::test_serialization_storage_slice, test/test_serialization.py::TestOldSerialization::test_serialization_zipfile_utils, test/test_serialization.py::TestOldSerialization::test_serialize_device, test/test_serialization.py::TestOldSerialization::test_skip_data_load, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_False_filename_False, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_False_filename_True, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_True_filename_False, test/test_serialization.py::TestSerialization::test_crc32_options_compute_crc32_True_filename_True, test/test_serialization.py::TestSerialization::test_debug_set_in_ci, test/test_serialization.py::TestSerialization::test_filewriter_metadata_writing_filename_False, test/test_serialization.py::TestSerialization::test_filewriter_metadata_writing_filename_True, test/test_serialization.py::TestSerialization::test_get_unsafe_globals_in_checkpoint, test/test_serialization.py::TestSerialization::test_has_format_version, test/test_serialization.py::TestSerialization::test_load_error_msg, test/test_serialization.py::TestSerialization::test_load_njt_weights_only_should_import_False, test/test_serialization.py::TestSerialization::test_load_njt_weights_only_should_import_True, test/test_serialization.py::TestSerialization::test_load_nonexistent_device, test/test_serialization.py::TestSerialization::test_load_python2_unicode_module, test/test_serialization.py::TestSerialization::test_load_unicode_error_msg, test/test_serialization.py::TestSerialization::test_lr_scheduler_serialization, test/test_serialization.py::TestSerialization::test_meta_serialization_weights_only_False, test/test_serialization.py::TestSerialization::test_meta_serialization_weights_only_True, test/test_serialization.py::TestSerialization::test_mmap_load_offset_calculation_path_type0, test/test_serialization.py::TestSerialization::test_mmap_load_offset_calculation_path_type1, test/test_serialization.py::TestSerialization::test_pathlike_serialization_weights_only_False, test/test_serialization.py::TestSerialization::test_pathlike_serialization_weights_only_True, test/test_serialization.py::TestSerialization::test_pickle_module, test/test_serialization.py::TestSerialization::test_safe_load_basic_types, test/test_serialization.py::TestSerialization::test_save_different_dtype_error, test/test_serialization.py::TestSerialization::test_save_different_dtype_unallocated, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bfloat16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bfloat16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bool_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_bool_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex128_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex128_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_complex64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float32_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float32_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_float64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int16_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int16_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int32_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int32_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int64_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int64_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int8_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_int8_weights_only_True, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_uint8_weights_only_False, test/test_serialization.py::TestSerialization::test_save_load_preserves_dtype_uint8_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization, test/test_serialization.py::TestSerialization::test_serialization_2gb_file, test/test_serialization.py::TestSerialization::test_serialization_4gb_file, test/test_serialization.py::TestSerialization::test_serialization_backwards_compat, test/test_serialization.py::TestSerialization::test_serialization_backwards_compat_safe, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals0_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals0_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals1_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_byte_literal_byte_literals1_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_byteorder_mark, test/test_serialization.py::TestSerialization::test_serialization_dill, test/test_serialization.py::TestSerialization::test_serialization_dill_version_not_supported, test/test_serialization.py::TestSerialization::test_serialization_dtype_complex32_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_complex32_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e4m3fn_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e4m3fn_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e5m2_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_float8_e5m2_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint16_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint16_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint32_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint32_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint64_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_dtype_uint64_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_efficient_zerotensor_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_efficient_zerotensor_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_fake_zip, test/test_serialization.py::TestSerialization::test_serialization_filelike, test/test_serialization.py::TestSerialization::test_serialization_filelike_api_requirements, test/test_serialization.py::TestSerialization::test_serialization_filelike_exceptions, test/test_serialization.py::TestSerialization::test_serialization_filelike_missing_attrs, test/test_serialization.py::TestSerialization::test_serialization_filelike_stress, test/test_serialization.py::TestSerialization::test_serialization_filelike_uses_readinto, test/test_serialization.py::TestSerialization::test_serialization_gzip, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_bfloat16, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_bool, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_cdouble, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_cfloat, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_double, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_float, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_half, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int16, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_int8, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_long, test/test_serialization.py::TestSerialization::test_serialization_load_bom_data_uint8, test/test_serialization.py::TestSerialization::test_serialization_map_location, test/test_serialization.py::TestSerialization::test_serialization_math_bits_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_math_bits_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_ctx, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type0_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type0_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type1_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_options_path_type1_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_mmap_loading_with_map_location, test/test_serialization.py::TestSerialization::test_serialization_nested_class, test/test_serialization.py::TestSerialization::test_serialization_offset_gzip, test/test_serialization.py::TestSerialization::test_serialization_python_attr_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_python_attr_weights_only_True, test/test_serialization.py::TestSerialization::test_serialization_safe, test/test_serialization.py::TestSerialization::test_serialization_save_warnings, test/test_serialization.py::TestSerialization::test_serialization_sparse, test/test_serialization.py::TestSerialization::test_serialization_sparse_bsc_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_bsr_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_csc_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_csr_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_invalid, test/test_serialization.py::TestSerialization::test_serialization_sparse_invalid_legacy_ctor, test/test_serialization.py::TestSerialization::test_serialization_sparse_safe, test/test_serialization.py::TestSerialization::test_serialization_storage_slice, test/test_serialization.py::TestSerialization::test_serialization_uintx_intx, test/test_serialization.py::TestSerialization::test_serialization_warning_s390x, test/test_serialization.py::TestSerialization::test_serialization_with_header, test/test_serialization.py::TestSerialization::test_serialization_zipfile_actually_jit, test/test_serialization.py::TestSerialization::test_serialization_zipfile_utils, test/test_serialization.py::TestSerialization::test_serialization_zipfile_weights_only_False, test/test_serialization.py::TestSerialization::test_serialization_zipfile_weights_only_True, test/test_serialization.py::TestSerialization::test_serialize_device, test/test_serialization.py::TestSerialization::test_skip_data_load, test/test_serialization.py::TestSerialization::test_skip_data_serialization_error_cases, test/test_serialization.py::TestSerialization::test_skip_data_serialization_materialize_fake_False, test/test_serialization.py::TestSerialization::test_skip_data_serialization_materialize_fake_True, test/test_serialization.py::TestSerialization::test_skip_data_serialization_preserves_views_materialize_fake_False, test/test_serialization.py::TestSerialization::test_skip_data_serialization_preserves_views_materialize_fake_True, test/test_serialization.py::TestSerialization::test_storage_alignment, test/test_serialization.py::TestSerialization::test_use_pinned_memory_for_d2h, test/test_serialization.py::TestSerialization::test_weights_only_assert, test/test_serialization.py::TestSerialization::test_weights_only_blocked_func_error_msg, test/test_serialization.py::TestSerialization::test_weights_only_env_variables_force_weights_only_False, test/test_serialization.py::TestSerialization::test_weights_only_env_variables_force_weights_only_True, test/test_serialization.py::TestSerialization::test_weights_only_error_unsafe_global_False, test/test_serialization.py::TestSerialization::test_weights_only_error_unsafe_global_True, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_blocklist, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build_with_slots_slots_all, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_build_with_slots_slots_some, test/test_serialization.py::TestSerialization::test_weights_only_safe_globals_newobj, test/test_serialization.py::TestSerialization::test_weights_only_with_zoneinfo_unpickle_registration_success, test/test_serialization.py::TestSubclassSerialization::test_cloned_deepcopy_requires_grad_False, test/test_serialization.py::TestSubclassSerialization::test_cloned_deepcopy_requires_grad_True, test/test_serialization.py::TestSubclassSerialization::test_empty_class_serialization, test/test_serialization.py::TestSubclassSerialization::test_safe_globals_context_manager_weights_only, test/test_serialization.py::TestSubclassSerialization::test_safe_globals_for_weights_only, test/test_serialization.py::TestSubclassSerialization::test_sets_are_loadable_with_weights_only, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_deepcopy, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_getstate_overwrite, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_map_location, test/test_serialization.py::TestSubclassSerialization::test_tensor_subclass_wrapper_serialization, test/test_serialization.py::TestBothSerializationCUDA::test_serialization_new_format_old_format_compat_weights_only_False_cuda, test/test_serialization.py::TestBothSerializationCUDA::test_serialization_new_format_old_format_compat_weights_only_True_cuda
2025-08-14T23:31:48.6647336Z 
2025-08-14T23:31:48.6647440Z Running test_set_default_mobile_cpu_allocator 1/1 ... [2025-08-14 23:31:48.658646]
2025-08-14T23:31:48.6647625Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:31:48.6648020Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:31:48.658872]
2025-08-14T23:31:50.9786819Z 
2025-08-14T23:31:50.9797299Z test_set_default_mobile_cpu_allocator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_set_default_mobile_cpu_allocator_1.1_9d25eb9c510afce1_.log
2025-08-14T23:31:50.9799008Z Running 2 items in this shard: test/test_set_default_mobile_cpu_allocator.py::TestSetDefaultMobileCPUAllocator::test_exception, test/test_set_default_mobile_cpu_allocator.py::TestSetDefaultMobileCPUAllocator::test_no_exception
2025-08-14T23:31:50.9799996Z 
2025-08-14T23:31:50.9800208Z Running test_shape_ops 1/1 ... [2025-08-14 23:31:50.978705]
2025-08-14T23:31:50.9800933Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:31:50.9801657Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_shape_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'] ... [2025-08-14 23:31:50.979037]
2025-08-14T23:32:04.9220152Z 
2025-08-14T23:32:04.9226752Z test_shape_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_shape_ops_1.1_14df06f205bc2112_.log
2025-08-14T23:32:04.9236129Z Running 98 items in this shard: test/test_shape_ops.py::TestShapeOpsCUDA::test_clamp_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_clamp_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_clamp_propagates_nans_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_clamp_raises_arg_errors_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_complex_rot90_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_complex_rot90_cuda_complex64, test/test_shape_ops.py::TestShapeOpsCUDA::test_diag_cuda_bool, test/test_shape_ops.py::TestShapeOpsCUDA::test_diag_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_diagonal_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_diagonal_multidim_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_bfloat16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_bool, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_complex64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_float16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_int16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_int32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_int8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_cuda_uint8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_bfloat16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_bool, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_complex64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_float16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_int16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_int32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_int8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_errors_cuda_uint8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_large_tensor_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_bfloat16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_bool, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_complex64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_float16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_int16, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_int32, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_int8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_numpy_cuda_uint8, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_unsupported_dtype_cuda_quint2x4, test/test_shape_ops.py::TestShapeOpsCUDA::test_flip_unsupported_dtype_cuda_quint4x2, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_invalid_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_invalid_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_fliplr_invalid_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_invalid_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_invalid_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_flipud_invalid_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_invalid_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_invalid_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_movedim_invalid_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_astuple_out_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_bfloat16, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_bool, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_float16, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_int16, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_int32, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_int8, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_cuda_uint8, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_discontiguous_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_no_warning_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_nonzero_non_diff_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_rot90_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_sparse_dense_dim_cuda_complex128, test/test_shape_ops.py::TestShapeOpsCUDA::test_sparse_dense_dim_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_sparse_dense_dim_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_tolist_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_float16, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_float32, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_float64, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_int16, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_int32, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_int64, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_int8, test/test_shape_ops.py::TestShapeOpsCUDA::test_trace_cuda_uint8, test/test_shape_ops.py::TestShapeOpsCUDA::test_unbind_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_unfold_all_devices_and_dtypes_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_unfold_errors_cuda, test/test_shape_ops.py::TestShapeOpsCUDA::test_unfold_scalars_cuda
2025-08-14T23:32:04.9244924Z 
2025-08-14T23:32:04.9244999Z Running test_show_pickle 1/1 ... [2025-08-14 23:32:04.922059]
2025-08-14T23:32:04.9245153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:32:04.9245578Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:32:04.922306]
2025-08-14T23:32:07.2909522Z 
2025-08-14T23:32:07.2910156Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_24d4a7da178002fe_.log
2025-08-14T23:32:07.2917507Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model
2025-08-14T23:32:07.2917762Z 
2025-08-14T23:32:07.2917885Z Running test_sort_and_select 1/1 ... [2025-08-14 23:32:07.290918]
2025-08-14T23:32:07.2918118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:32:07.2918693Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:32:07.291154]
2025-08-14T23:32:11.9672465Z 
2025-08-14T23:32:11.9673101Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_5cd02fc1a09a1f7b_.log
2025-08-14T23:32:11.9685667Z Running 111 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCUDA::test_complex_unsupported_cpu_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_dtypes_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_kthvalue_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_kthvalue_scalar_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_output_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_discontiguous_slow_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_expanded_tensor_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_large_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_large_slice_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_restride_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_stable_none_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_1d_output_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_4d_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_arguments_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_lower_precision_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_lower_precision_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_noncontiguous_gpu_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_quantized_scalar_input_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_dim_cuda
2025-08-14T23:32:11.9702796Z 
2025-08-14T23:32:11.9702874Z Running test_sparse_csr 1/2 ... [2025-08-14 23:32:11.967257]
2025-08-14T23:32:11.9703094Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:32:11.9703460Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_csr.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:32:11.967491]
2025-08-14T23:40:03.2491175Z 
2025-08-14T23:40:03.2491865Z PRINTING LOG FILE of test_sparse_csr 1/2 (test/test-reports/test_sparse_csr_1.2_172afec2faee053e_.log)
2025-08-14T23:40:03.2502437Z /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.
2025-08-14T23:40:03.2503572Z   import pkg_resources
2025-08-14T23:40:03.2503966Z Test results will be stored in test-reports/python-pytest/test_sparse_csr/test_sparse_csr-9d801a66632ae82f.xml
2025-08-14T23:40:03.2504398Z ============================= test session starts ==============================
2025-08-14T23:40:03.2505258Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python
2025-08-14T23:40:03.2505607Z cachedir: .pytest_cache
2025-08-14T23:40:03.2506032Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2025-08-14T23:40:03.2506486Z rootdir: /var/lib/jenkins/pytorch
2025-08-14T23:40:03.2506705Z configfile: pytest.ini
2025-08-14T23:40:03.2511475Z 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
2025-08-14T23:40:03.2511861Z collecting ... collected 4958 items
2025-08-14T23:40:03.2512190Z stepcurrent: Cannot find last run test, not skipping
2025-08-14T23:40:03.2852087Z Running 2541 items in this shard: test/test_sparse_csr.py::TestSparseCSRSampler::test_make_crow_indices, test/test_sparse_csr.py::TestSparseCSRCUDA::test_add_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_add_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cuda_float64, 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test/test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tan_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tanh_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tanh_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_baddbmm_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_float16, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_True_cuda_float32, 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test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_bmm_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_bmm_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSC_SparseBSR_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSC_SparseCSC_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSR_SparseBSC_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSR_SparseCSR_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseBSC_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseBSR_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseCSR_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSR_SparseBSR_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_bool, test/test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_float16, test/test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_uint8, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_bool, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_complex64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_float32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_int32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_int64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_uint8, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_double_to_sparse_csr_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_is_contiguous_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_matvec_cuda_complex128, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_matvec_cuda_float16, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_nnz_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_blocksize_2_cuda_float64_int64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_blocksize_4_cuda_float64_int32, test/test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_errors_cuda_float64, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSC_Batched_Hybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSC_Batched_NonHybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSR_Batched_Hybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_Batched_Hybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_Batched_NonHybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_NonBatched_Hybrid_cuda, test/test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSR_Batched_Hybrid_cuda, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_int16, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_float16, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_float16, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_float32, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_frac_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_frac_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_log1p_cuda_bool, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_neg_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_neg_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_nn_functional_relu_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_abs_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_abs_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_expm1_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_expm1_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_frac_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_frac_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isneginf_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isneginf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_int16, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_int16, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int8, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSR_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_errors_SparseCSC_cuda_uint8, 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test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_int64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_complex64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_int32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_bool, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_complex128, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_float64, test/test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_uint8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_TensorAsKey_cuda, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_addmm_meta_cuda, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_32_int64_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int64_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int64_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_error_messages_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2x3_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2x3_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_int32_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_16_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_16_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_64_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scatter_mm_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scatter_mm_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_bfloat16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_int8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_int32_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_int32_cuda_float32, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_unspecified_cuda_float16, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_unspecified_cuda_float32
2025-08-14T23:40:03.3196243Z 
2025-08-14T23:40:03.3196380Z test_sparse_csr.py::TestSparseCSRSampler::test_make_crow_indices PASSED [0.0911s] [  0%]
2025-08-14T23:40:03.3196641Z test_sparse_csr.py::TestSparseCSRCUDA::test_add_SparseCSR_cuda_float32 PASSED [0.2887s] [  0%]
2025-08-14T23:40:03.3196893Z test_sparse_csr.py::TestSparseCSRCUDA::test_add_SparseCSR_cuda_float64 PASSED [0.0865s] [  0%]
2025-08-14T23:40:03.3197168Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_complex128 PASSED [0.5426s] [  0%]
2025-08-14T23:40:03.3197464Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_complex64 PASSED [0.0407s] [  0%]
2025-08-14T23:40:03.3197754Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSC_cuda_float32 PASSED [0.0387s] [  0%]
2025-08-14T23:40:03.3198046Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_complex128 PASSED [0.0346s] [  0%]
2025-08-14T23:40:03.3198337Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_complex64 PASSED [0.0345s] [  0%]
2025-08-14T23:40:03.3198625Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_all_sparse_csr_SparseCSR_cuda_float32 PASSED [0.0334s] [  0%]
2025-08-14T23:40:03.3198941Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_complex128 SKIPPED [0.0005s] (Only runs on cpu) [  0%]
2025-08-14T23:40:03.3199348Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_complex64 SKIPPED [0.0004s] (Only runs on cpu) [  0%]
2025-08-14T23:40:03.3199682Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_dense_result_SparseCSR_cuda_float64 SKIPPED [0.0004s] (Only runs on cpu) [  0%]
2025-08-14T23:40:03.3200000Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_complex128 PASSED [0.0025s] [  0%]
2025-08-14T23:40:03.3200306Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_complex64 PASSED [0.0022s] [  0%]
2025-08-14T23:40:03.3200607Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cuda_float32 PASSED [0.0021s] [  0%]
2025-08-14T23:40:03.3200905Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_complex64 PASSED [0.0023s] [  0%]
2025-08-14T23:40:03.3201204Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_float32 PASSED [0.0021s] [  0%]
2025-08-14T23:40:03.3201505Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cuda_float64 PASSED [0.0021s] [  0%]
2025-08-14T23:40:03.3201818Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cuda_complex128 PASSED [0.0022s] [  0%]
2025-08-14T23:40:03.3202127Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_10_m_25_cuda_float64 PASSED [0.0029s] [  0%]
2025-08-14T23:40:03.3202437Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cuda_complex128 PASSED [0.0029s] [  0%]
2025-08-14T23:40:03.3202767Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_0_n_1_m_1_cuda_float64 PASSED [0.0027s] [  0%]
2025-08-14T23:40:03.3203134Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_0_cuda_complex128 PASSED [0.0022s] [  0%]
2025-08-14T23:40:03.3203442Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_1_cuda_float32 PASSED [0.0023s] [  0%]
2025-08-14T23:40:03.3203746Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_0_m_25_cuda_float64 PASSED [0.0023s] [  0%]
2025-08-14T23:40:03.3204075Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cuda_complex128 PASSED [0.0025s] [  1%]
2025-08-14T23:40:03.3204384Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_0_cuda_float64 PASSED [0.0024s] [  1%]
2025-08-14T23:40:03.3204710Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cuda_complex128 PASSED [0.0034s] [  1%]
2025-08-14T23:40:03.3205020Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_1_cuda_float64 PASSED [0.0033s] [  1%]
2025-08-14T23:40:03.3205332Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_complex128 PASSED [0.0034s] [  1%]
2025-08-14T23:40:03.3205693Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_float32 PASSED [0.0033s] [  1%]
2025-08-14T23:40:03.3205998Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_10_m_25_cuda_float64 PASSED [0.0033s] [  1%]
2025-08-14T23:40:03.3206305Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cuda_complex128 PASSED [0.0024s] [  1%]
2025-08-14T23:40:03.3206608Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_0_cuda_complex64 PASSED [0.0019s] [  1%]
2025-08-14T23:40:03.3206912Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cuda_complex64 PASSED [0.0034s] [  1%]
2025-08-14T23:40:03.3207218Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_1_cuda_float64 PASSED [0.0027s] [  1%]
2025-08-14T23:40:03.3207527Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_complex128 PASSED [0.0029s] [  1%]
2025-08-14T23:40:03.3207865Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_complex64 PASSED [0.0028s] [  1%]
2025-08-14T23:40:03.3208173Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_float32 PASSED [0.0028s] [  1%]
2025-08-14T23:40:03.3208476Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_1_n_1_m_25_cuda_float64 PASSED [0.0028s] [  1%]
2025-08-14T23:40:03.3208779Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_0_cuda_float64 PASSED [0.0016s] [  1%]
2025-08-14T23:40:03.3209087Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cuda_complex128 PASSED [0.0024s] [  1%]
2025-08-14T23:40:03.3209396Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_1_cuda_float32 PASSED [0.0022s] [  1%]
2025-08-14T23:40:03.3209700Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_0_m_25_cuda_float64 PASSED [0.0023s] [  1%]
2025-08-14T23:40:03.3210009Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_complex128 PASSED [0.0020s] [  1%]
2025-08-14T23:40:03.3210317Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_float32 PASSED [0.0019s] [  1%]
2025-08-14T23:40:03.3210613Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_0_cuda_float64 PASSED [0.0019s] [  1%]
2025-08-14T23:40:03.3210914Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_complex64 PASSED [0.0029s] [  1%]
2025-08-14T23:40:03.3211232Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_float32 PASSED [0.0028s] [  1%]
2025-08-14T23:40:03.3211530Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_1_cuda_float64 PASSED [0.0028s] [  1%]
2025-08-14T23:40:03.3211835Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_complex128 PASSED [0.0031s] [  2%]
2025-08-14T23:40:03.3212147Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_complex64 PASSED [0.0029s] [  2%]
2025-08-14T23:40:03.3212567Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_10_m_25_cuda_float64 PASSED [0.0029s] [  2%]
2025-08-14T23:40:03.3212872Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cuda_complex64 PASSED [0.0019s] [  2%]
2025-08-14T23:40:03.3213311Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_0_cuda_float64 PASSED [0.0020s] [  2%]
2025-08-14T23:40:03.3213618Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cuda_float32 PASSED [0.0027s] [  2%]
2025-08-14T23:40:03.3213918Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_1_cuda_float64 PASSED [0.0028s] [  2%]
2025-08-14T23:40:03.3214226Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cuda_complex128 PASSED [0.0029s] [  2%]
2025-08-14T23:40:03.3214535Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cuda_complex64 PASSED [0.0029s] [  2%]
2025-08-14T23:40:03.3214844Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cuda_float32 PASSED [0.0028s] [  2%]
2025-08-14T23:40:03.3215146Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_sizes_all_sparse_csr_k_8_n_1_m_25_cuda_float64 PASSED [0.0029s] [  2%]
2025-08-14T23:40:03.3215447Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_11x9_cuda_float64 SKIPPED [0.0004s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3215752Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_3x3_cuda_complex128 SKIPPED [0.0004s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3216061Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_3x3_cuda_complex64 SKIPPED [0.0004s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3216379Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_3x3_cuda_float32 SKIPPED [0.0005s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3216684Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_5x7_cuda_complex128 SKIPPED [0.0004s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3216988Z test_sparse_csr.py::TestSparseCSRCUDA::test_addmv_shape_5x7_cuda_float64 SKIPPED [0.0004s] (Only runs on cpu) [  2%]
2025-08-14T23:40:03.3217283Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_dense_output_addmm_cuda_float64 PASSED [0.3541s] [  2%]
2025-08-14T23:40:03.3217572Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_dense_output_addmv_cuda_float64 PASSED [0.0769s] [  2%]
2025-08-14T23:40:03.3217852Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_dense_output_mm_cuda_float64 PASSED [0.0418s] [  2%]
2025-08-14T23:40:03.3218127Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_dense_output_mv_cuda_float64 PASSED [0.0065s] [  2%]
2025-08-14T23:40:03.3218416Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_abs_cuda_complex128 PASSED [0.0155s] [  2%]
2025-08-14T23:40:03.3218800Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_asin_cuda_complex128 SKIPPED [0.0010s] (Skipped! Unary op asin not supported with CSR input and autograd) [  2%]
2025-08-14T23:40:03.3219271Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_asinh_cuda_complex128 SKIPPED [0.0009s] (Skipped! Unary op asinh not supported with CSR input and autograd) [  2%]
2025-08-14T23:40:03.3219734Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_atan_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op atan not supported with CSR input and autograd) [  2%]
2025-08-14T23:40:03.3220210Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_atanh_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op atanh not supported with CSR input and autograd) [  2%]
2025-08-14T23:40:03.3220604Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_conj_physical_cuda_complex128 PASSED [0.0070s] [  3%]
2025-08-14T23:40:03.3220927Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_conj_physical_cuda_float64 PASSED [0.0034s] [  3%]
2025-08-14T23:40:03.3221248Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_deg2rad_cuda_float64 PASSED [0.0036s] [  3%]
2025-08-14T23:40:03.3221627Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_expm1_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op expm1 not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3225821Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_floor_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op floor not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3226199Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_frac_cuda_float64 PASSED [0.0122s] [  3%]
2025-08-14T23:40:03.3226586Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_isinf_cuda_complex128 SKIPPED [0.0017s] (Skipped! Unary op isinf not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3227044Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_isinf_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op isinf not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3227498Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_isnan_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op isnan not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3227968Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_isposinf_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op isposinf not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3228360Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_log1p_cuda_complex128 PASSED [0.0121s] [  3%]
2025-08-14T23:40:03.3228659Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_log1p_cuda_float64 PASSED [0.0045s] [  3%]
2025-08-14T23:40:03.3228972Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_neg_cuda_complex128 PASSED [0.0047s] [  3%]
2025-08-14T23:40:03.3229283Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_nn_functional_relu_cuda_float64 PASSED [0.0148s] [  3%]
2025-08-14T23:40:03.3229674Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_round_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op round not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3230125Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_sign_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op sign not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3230581Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_sinh_cuda_complex128 SKIPPED [0.0009s] (Skipped! Unary op sinh not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3231038Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_sqrt_cuda_complex128 SKIPPED [0.0009s] (Skipped! Unary op sqrt not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3231493Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_sqrt_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op sqrt not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3231940Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tan_cuda_complex128 SKIPPED [0.0009s] (Skipped! Unary op tan not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3232384Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tan_cuda_float64 SKIPPED [0.0010s] (Skipped! Unary op tan not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3232851Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tanh_cuda_complex128 SKIPPED [0.0009s] (Skipped! Unary op tanh not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3233370Z test_sparse_csr.py::TestSparseCSRCUDA::test_autograd_sparse_csr_unary_tanh_cuda_float64 SKIPPED [0.0009s] (Skipped! Unary op tanh not supported with CSR input and autograd) [  3%]
2025-08-14T23:40:03.3233716Z test_sparse_csr.py::TestSparseCSRCUDA::test_baddbmm_cuda_complex64 PASSED [0.0179s] [  3%]
2025-08-14T23:40:03.3234025Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_bfloat16 PASSED [0.5400s] [  3%]
2025-08-14T23:40:03.3234366Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_complex128 PASSED [0.0618s] [  4%]
2025-08-14T23:40:03.3234734Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_complex64 PASSED [0.0485s] [  4%]
2025-08-14T23:40:03.3235072Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_False_cuda_float16 PASSED [0.0560s] [  4%]
2025-08-14T23:40:03.3235402Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_True_cuda_float32 PASSED [0.0548s] [  4%]
2025-08-14T23:40:03.3235733Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int32_noncontiguous_True_cuda_float64 PASSED [0.0457s] [  4%]
2025-08-14T23:40:03.3236066Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int64_noncontiguous_False_cuda_complex128 PASSED [0.0500s] [  4%]
2025-08-14T23:40:03.3236403Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int64_noncontiguous_False_cuda_complex64 PASSED [0.0502s] [  4%]
2025-08-14T23:40:03.3236737Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int64_noncontiguous_False_cuda_float16 PASSED [0.0555s] [  4%]
2025-08-14T23:40:03.3237071Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int64_noncontiguous_True_cuda_complex64 PASSED [0.0515s] [  4%]
2025-08-14T23:40:03.3237404Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_2_int64_noncontiguous_True_cuda_float64 PASSED [0.0476s] [  4%]
2025-08-14T23:40:03.3237758Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_False_cuda_complex64 PASSED [0.0560s] [  4%]
2025-08-14T23:40:03.3238097Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_False_cuda_float32 PASSED [0.0505s] [  4%]
2025-08-14T23:40:03.3238432Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_True_cuda_complex128 PASSED [0.0450s] [  4%]
2025-08-14T23:40:03.3238768Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_True_cuda_complex64 PASSED [0.0447s] [  4%]
2025-08-14T23:40:03.3239097Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_True_cuda_float16 PASSED [0.0501s] [  4%]
2025-08-14T23:40:03.3239422Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_True_cuda_float32 PASSED [0.0515s] [  4%]
2025-08-14T23:40:03.3239749Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int32_noncontiguous_True_cuda_float64 PASSED [0.0417s] [  4%]
2025-08-14T23:40:03.3240075Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int64_noncontiguous_False_cuda_bfloat16 PASSED [0.0488s] [  4%]
2025-08-14T23:40:03.3240404Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int64_noncontiguous_False_cuda_float32 PASSED [0.0500s] [  4%]
2025-08-14T23:40:03.3240731Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int64_noncontiguous_False_cuda_float64 PASSED [0.0422s] [  4%]
2025-08-14T23:40:03.3241059Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmm_block_size_3_int64_noncontiguous_True_cuda_bfloat16 PASSED [0.0491s] [  4%]
2025-08-14T23:40:03.3241404Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int32_noncontiguous_False_cuda_float64 PASSED [0.0700s] [  4%]
2025-08-14T23:40:03.3241734Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int32_noncontiguous_True_cuda_complex128 PASSED [0.0054s] [  4%]
2025-08-14T23:40:03.3242066Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int32_noncontiguous_True_cuda_complex64 PASSED [0.0051s] [  4%]
2025-08-14T23:40:03.3242408Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int32_noncontiguous_True_cuda_float64 PASSED [0.0048s] [  4%]
2025-08-14T23:40:03.3242736Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int64_noncontiguous_False_cuda_complex64 PASSED [0.0067s] [  4%]
2025-08-14T23:40:03.3243163Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int64_noncontiguous_False_cuda_float32 PASSED [0.0067s] [  5%]
2025-08-14T23:40:03.3243495Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int64_noncontiguous_True_cuda_complex128 PASSED [0.0050s] [  5%]
2025-08-14T23:40:03.3243822Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int64_noncontiguous_True_cuda_float32 PASSED [0.0047s] [  5%]
2025-08-14T23:40:03.3244147Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_2_int64_noncontiguous_True_cuda_float64 PASSED [0.0049s] [  5%]
2025-08-14T23:40:03.3244477Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int32_noncontiguous_False_cuda_complex64 PASSED [0.0356s] [  5%]
2025-08-14T23:40:03.3244805Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int32_noncontiguous_False_cuda_float64 PASSED [0.0078s] [  5%]
2025-08-14T23:40:03.3245133Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int32_noncontiguous_True_cuda_complex64 PASSED [0.0049s] [  5%]
2025-08-14T23:40:03.3245518Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int32_noncontiguous_True_cuda_float32 PASSED [0.0046s] [  5%]
2025-08-14T23:40:03.3245842Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int32_noncontiguous_True_cuda_float64 PASSED [0.0047s] [  5%]
2025-08-14T23:40:03.3246187Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int64_noncontiguous_False_cuda_complex64 PASSED [0.0078s] [  5%]
2025-08-14T23:40:03.3246518Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int64_noncontiguous_False_cuda_float64 PASSED [0.0078s] [  5%]
2025-08-14T23:40:03.3246848Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int64_noncontiguous_True_cuda_complex128 PASSED [0.0049s] [  5%]
2025-08-14T23:40:03.3247179Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_addmv_block_size_3_int64_noncontiguous_True_cuda_float64 PASSED [0.0047s] [  5%]
2025-08-14T23:40:03.3247520Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int32_noncontiguous_False_cuda_float64 PASSED [0.1406s] [  5%]
2025-08-14T23:40:03.3247880Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cuda_complex128 PASSED [0.0263s] [  5%]
2025-08-14T23:40:03.3248238Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cuda_float32 PASSED [0.0243s] [  5%]
2025-08-14T23:40:03.3248591Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int32_noncontiguous_True_cuda_float64 PASSED [0.0245s] [  5%]
2025-08-14T23:40:03.3248946Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cuda_float32 PASSED [0.0249s] [  5%]
2025-08-14T23:40:03.3249297Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_2_int64_noncontiguous_True_cuda_float64 PASSED [0.0248s] [  5%]
2025-08-14T23:40:03.3249652Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cuda_float32 PASSED [0.0260s] [  5%]
2025-08-14T23:40:03.3250024Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int32_noncontiguous_False_cuda_float64 PASSED [0.0263s] [  5%]
2025-08-14T23:40:03.3250383Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cuda_complex128 PASSED [0.0253s] [  5%]
2025-08-14T23:40:03.3250742Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cuda_float32 PASSED [0.0244s] [  5%]
2025-08-14T23:40:03.3251112Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int32_noncontiguous_True_cuda_float64 PASSED [0.0242s] [  5%]
2025-08-14T23:40:03.3251472Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cuda_complex128 PASSED [0.0319s] [  5%]
2025-08-14T23:40:03.3251849Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cuda_complex64 PASSED [0.0338s] [  6%]
2025-08-14T23:40:03.3252207Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_False_cuda_float64 PASSED [0.0320s] [  6%]
2025-08-14T23:40:03.3252565Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cuda_complex64 PASSED [0.0305s] [  6%]
2025-08-14T23:40:03.3252970Z test_sparse_csr.py::TestSparseCSRCUDA::test_block_triangular_solve_block_size_3_int64_noncontiguous_True_cuda_float32 PASSED [0.0294s] [  6%]
2025-08-14T23:40:03.3253263Z test_sparse_csr.py::TestSparseCSRCUDA::test_bmm_cuda_complex128 PASSED [0.0097s] [  6%]
2025-08-14T23:40:03.3253493Z test_sparse_csr.py::TestSparseCSRCUDA::test_bmm_cuda_float64 PASSED [0.0089s] [  6%]
2025-08-14T23:40:03.3253780Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSC_SparseBSR_cuda PASSED [0.0047s] [  6%]
2025-08-14T23:40:03.3254125Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSC_SparseCSC_cuda PASSED [0.0024s] [  6%]
2025-08-14T23:40:03.3254465Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSR_SparseBSC_cuda PASSED [0.0043s] [  6%]
2025-08-14T23:40:03.3254825Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseBSR_SparseCSR_cuda PASSED [0.0022s] [  6%]
2025-08-14T23:40:03.3255163Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseBSC_cuda PASSED [0.0019s] [  6%]
2025-08-14T23:40:03.3255501Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseBSR_cuda PASSED [0.0021s] [  6%]
2025-08-14T23:40:03.3255840Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSC_SparseCSR_cuda PASSED [0.0037s] [  6%]
2025-08-14T23:40:03.3256180Z test_sparse_csr.py::TestSparseCSRCUDA::test_compressed_layout_conversions_coverage_SparseCSR_SparseBSR_cuda PASSED [0.0017s] [  6%]
2025-08-14T23:40:03.3256484Z test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_bfloat16 PASSED [0.0132s] [  6%]
2025-08-14T23:40:03.3256739Z test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_bool PASSED [0.0030s] [  6%]
2025-08-14T23:40:03.3256992Z test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_float16 PASSED [0.0035s] [  6%]
2025-08-14T23:40:03.3257245Z test_sparse_csr.py::TestSparseCSRCUDA::test_coo_csr_conversion_cuda_uint8 PASSED [0.0029s] [  6%]
2025-08-14T23:40:03.3257496Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_bool PASSED [0.0058s] [  6%]
2025-08-14T23:40:03.3257747Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_complex128 PASSED [0.0035s] [  6%]
2025-08-14T23:40:03.3258008Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_complex64 PASSED [0.0035s] [  6%]
2025-08-14T23:40:03.3258283Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_float32 PASSED [0.0033s] [  6%]
2025-08-14T23:40:03.3258535Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_int32 PASSED [0.0027s] [  6%]
2025-08-14T23:40:03.3258785Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_int64 PASSED [0.0026s] [  6%]
2025-08-14T23:40:03.3259034Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_coo_conversion_cuda_uint8 PASSED [0.0028s] [  6%]
2025-08-14T23:40:03.3259284Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_double_to_sparse_csr_cuda PASSED [0.0011s] [  7%]
2025-08-14T23:40:03.3259541Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_is_contiguous_cuda PASSED [0.0012s] [  7%]
2025-08-14T23:40:03.3259781Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_matvec_cuda_complex128 PASSED [0.0245s] [  7%]
2025-08-14T23:40:03.3260099Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_matvec_cuda_float16 SKIPPED [0.0005s] (ROCm doesn't work with half dtypes correctly.) [  7%]
2025-08-14T23:40:03.3260407Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_nnz_cuda SKIPPED [0.0005s] (Only runs on cpu) [  7%]
2025-08-14T23:40:03.3260680Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_blocksize_2_cuda_float64_int64 PASSED [0.0112s] [  7%]
2025-08-14T23:40:03.3260971Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_blocksize_4_cuda_float64_int32 PASSED [0.0175s] [  7%]
2025-08-14T23:40:03.3261247Z test_sparse_csr.py::TestSparseCSRCUDA::test_csr_to_block_csr_errors_cuda_float64 PASSED [0.0047s] [  7%]
2025-08-14T23:40:03.3261542Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSC_Batched_Hybrid_cuda PASSED [0.2222s] [  7%]
2025-08-14T23:40:03.3261873Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSC_Batched_NonHybrid_cuda PASSED [0.1215s] [  7%]
2025-08-14T23:40:03.3262206Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseBSR_Batched_Hybrid_cuda PASSED [0.2174s] [  7%]
2025-08-14T23:40:03.3262533Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_Batched_Hybrid_cuda PASSED [0.0735s] [  7%]
2025-08-14T23:40:03.3262863Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_Batched_NonHybrid_cuda PASSED [0.0395s] [  7%]
2025-08-14T23:40:03.3263267Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSC_NonBatched_Hybrid_cuda PASSED [0.0051s] [  7%]
2025-08-14T23:40:03.3263596Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSR_Batched_Hybrid_cuda PASSED [0.0717s] [  7%]
2025-08-14T23:40:03.3263925Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSR_Batched_NonHybrid_cuda PASSED [0.0386s] [  7%]
2025-08-14T23:40:03.3264260Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSR_NonBatched_Hybrid_cuda PASSED [0.0049s] [  7%]
2025-08-14T23:40:03.3264601Z test_sparse_csr.py::TestSparseCSRCUDA::test_dense_to_from_sparse_compressed_SparseCSR_NonBatched_NonHybrid_cuda PASSED [0.0029s] [  7%]
2025-08-14T23:40:03.3264919Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_complex64 PASSED [0.0037s] [  7%]
2025-08-14T23:40:03.3265202Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_float32 PASSED [0.0035s] [  7%]
2025-08-14T23:40:03.3265483Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_float64 PASSED [0.0035s] [  7%]
2025-08-14T23:40:03.3265758Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_int32 PASSED [0.0028s] [  7%]
2025-08-14T23:40:03.3266035Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_int64 PASSED [0.0029s] [  7%]
2025-08-14T23:40:03.3266308Z test_sparse_csr.py::TestSparseCSRCUDA::test_direct_coo_csr_conversion_cuda_int8 PASSED [0.0028s] [  7%]
2025-08-14T23:40:03.3266577Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_bfloat16 PASSED [0.0022s] [  7%]
2025-08-14T23:40:03.3266853Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_bool PASSED [0.0019s] [  7%]
2025-08-14T23:40:03.3267111Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_complex128 PASSED [0.0022s] [  8%]
2025-08-14T23:40:03.3267374Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_complex64 PASSED [0.0022s] [  8%]
2025-08-14T23:40:03.3267632Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_float32 PASSED [0.0022s] [  8%]
2025-08-14T23:40:03.3267888Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_float64 PASSED [0.0021s] [  8%]
2025-08-14T23:40:03.3268158Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_int64 PASSED [0.0019s] [  8%]
2025-08-14T23:40:03.3268411Z test_sparse_csr.py::TestSparseCSRCUDA::test_exercise_detach_cuda_uint8 PASSED [0.0019s] [  8%]
2025-08-14T23:40:03.3268734Z test_sparse_csr.py::TestSparseCSRCUDA::test_linalg_solve_sparse_csr_cusolver_cuda_float32 SKIPPED [0.0001s] (The test requires cudss) [  8%]
2025-08-14T23:40:03.3269100Z test_sparse_csr.py::TestSparseCSRCUDA::test_linalg_solve_sparse_csr_cusolver_cuda_float64 SKIPPED [0.0001s] (The test requires cudss) [  8%]
2025-08-14T23:40:03.3269397Z test_sparse_csr.py::TestSparseCSRCUDA::test_mm_cuda_float64 PASSED [0.6964s] [  8%]
2025-08-14T23:40:03.3269628Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_cuda_float64 PASSED [0.1966s] [  8%]
2025-08-14T23:40:03.3269904Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSC_cuda_bfloat16 PASSED [0.6606s] [  8%]
2025-08-14T23:40:03.3270224Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSC_cuda_int16 PASSED [0.4757s] [  8%]
2025-08-14T23:40:03.3270539Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_bfloat16 PASSED [0.6921s] [  8%]
2025-08-14T23:40:03.3270856Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_bool PASSED [0.3760s] [  8%]
2025-08-14T23:40:03.3271171Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_float16 PASSED [0.6222s] [  8%]
2025-08-14T23:40:03.3271480Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_int16 PASSED [0.4738s] [  8%]
2025-08-14T23:40:03.3271805Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_int32 PASSED [0.4745s] [  8%]
2025-08-14T23:40:03.3272111Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_int64 PASSED [0.4761s] [  8%]
2025-08-14T23:40:03.3272422Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseBSR_cuda_uint8 PASSED [0.4787s] [  8%]
2025-08-14T23:40:03.3272733Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSC_cuda_float32 PASSED [0.6882s] [  8%]
2025-08-14T23:40:03.3273111Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSC_cuda_int32 PASSED [0.4758s] [  8%]
2025-08-14T23:40:03.3273423Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSC_cuda_uint8 PASSED [0.4781s] [  8%]
2025-08-14T23:40:03.3273738Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_complex128 PASSED [0.7824s] [  8%]
2025-08-14T23:40:03.3274058Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_float16 PASSED [0.6193s] [  8%]
2025-08-14T23:40:03.3274372Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_float32 PASSED [0.6857s] [  8%]
2025-08-14T23:40:03.3274687Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_float64 PASSED [0.6824s] [  9%]
2025-08-14T23:40:03.3274995Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_int32 PASSED [0.4774s] [  9%]
2025-08-14T23:40:03.3275325Z test_sparse_csr.py::TestSparseCSRCUDA::test_mul_scalar_enable_hybrid_False_SparseCSR_cuda_uint8 PASSED [0.4801s] [  9%]
2025-08-14T23:40:03.3275632Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_as_sparse_compressed_SparseCSC_cuda_bool PASSED [0.0288s] [  9%]
2025-08-14T23:40:03.3275939Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_as_sparse_compressed_SparseCSC_cuda_float32 PASSED [0.0276s] [  9%]
2025-08-14T23:40:03.3276213Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_complex128 PASSED [0.0022s] [  9%]
2025-08-14T23:40:03.3276455Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_complex64 PASSED [0.0022s] [  9%]
2025-08-14T23:40:03.3276714Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_float32 PASSED [0.0021s] [  9%]
2025-08-14T23:40:03.3276946Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_float64 PASSED [0.0021s] [  9%]
2025-08-14T23:40:03.3277178Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_int16 PASSED [0.0021s] [  9%]
2025-08-14T23:40:03.3277424Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_int32 PASSED [0.0021s] [  9%]
2025-08-14T23:40:03.3277654Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_cuda_int64 PASSED [0.0021s] [  9%]
2025-08-14T23:40:03.3277892Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_errors_cuda_bool PASSED [0.0017s] [  9%]
2025-08-14T23:40:03.3278145Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_errors_cuda_complex128 PASSED [0.0016s] [  9%]
2025-08-14T23:40:03.3278399Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_errors_cuda_int64 PASSED [0.0015s] [  9%]
2025-08-14T23:40:03.3278644Z test_sparse_csr.py::TestSparseCSRCUDA::test_resize_errors_cuda_uint8 PASSED [0.0016s] [  9%]
2025-08-14T23:40:03.3278906Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_autograd_cuda_complex64 PASSED [0.0046s] [  9%]
2025-08-14T23:40:03.3279179Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_autograd_cuda_float32 PASSED [0.0033s] [  9%]
2025-08-14T23:40:03.3279441Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_cuda_float32 PASSED [1.2427s] [  9%]
2025-08-14T23:40:03.3279702Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_errors_cuda_complex64 PASSED [0.0019s] [  9%]
2025-08-14T23:40:03.3279970Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_errors_cuda_float32 PASSED [0.0015s] [  9%]
2025-08-14T23:40:03.3280264Z test_sparse_csr.py::TestSparseCSRCUDA::test_sampled_addmm_zero_sized_cuda_complex64 PASSED [0.0011s] [  9%]
2025-08-14T23:40:03.3280757Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_bfloat16 :0:rocdevice.cpp            :2992: 291391613668 us:  Callback: Queue 0x77e53b400000 aborting with error : HSA_STATUS_ERROR_EXCEPTION: An HSAIL operation resulted in a hardware exception. code: 0x1016
2025-08-14T23:40:03.3281257Z Fatal Python error: Aborted
2025-08-14T23:40:03.3281361Z 
2025-08-14T23:40:03.3281430Z Thread 0x000077e5051ff6c0 (most recent call first):
2025-08-14T23:40:03.3281603Z   <no Python frame>
2025-08-14T23:40:03.3281692Z 
2025-08-14T23:40:03.3281761Z Thread 0x000077f0413f0600 (most recent call first):
2025-08-14T23:40:03.3282072Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3614 in random_sparse_compressed
2025-08-14T23:40:03.3282488Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3629 in genSparseCompressedTensor
2025-08-14T23:40:03.3282849Z   File "/var/lib/jenkins/pytorch/test/test_sparse_csr.py", line 1104 in test_select
2025-08-14T23:40:03.3283232Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 698 in test_wrapper
2025-08-14T23:40:03.3283632Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_device_type.py", line 426 in instantiated_test
2025-08-14T23:40:03.3284032Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3224 in wrapper
2025-08-14T23:40:03.3284427Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3224 in wrapper
2025-08-14T23:40:03.3284760Z   File "/opt/conda/envs/py_3.12/lib/python3.12/unittest/case.py", line 589 in _callTestMethod
2025-08-14T23:40:03.3285029Z   File "/opt/conda/envs/py_3.12/lib/python3.12/unittest/case.py", line 634 in run
2025-08-14T23:40:03.3285337Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3376 in _run_custom
2025-08-14T23:40:03.3285752Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 3406 in run
2025-08-14T23:40:03.3286128Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_device_type.py", line 517 in run
2025-08-14T23:40:03.3286446Z   File "/opt/conda/envs/py_3.12/lib/python3.12/unittest/case.py", line 690 in __call__
2025-08-14T23:40:03.3286750Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/unittest.py", line 333 in runtest
2025-08-14T23:40:03.3287066Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 169 in pytest_runtest_call
2025-08-14T23:40:03.3287394Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_callers.py", line 121 in _multicall
2025-08-14T23:40:03.3287695Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_manager.py", line 120 in _hookexec
2025-08-14T23:40:03.3287985Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_hooks.py", line 512 in __call__
2025-08-14T23:40:03.3288302Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 262 in <lambda>
2025-08-14T23:40:03.3288600Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 341 in from_call
2025-08-14T23:40:03.3288920Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 261 in call_runtest_hook
2025-08-14T23:40:03.3289235Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 222 in call_and_report
2025-08-14T23:40:03.3289551Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/runner.py", line 133 in runtestprotocol
2025-08-14T23:40:03.3289899Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pytest_rerunfailures.py", line 549 in pytest_runtest_protocol
2025-08-14T23:40:03.3290251Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_callers.py", line 121 in _multicall
2025-08-14T23:40:03.3290559Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_manager.py", line 120 in _hookexec
2025-08-14T23:40:03.3290865Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_hooks.py", line 512 in __call__
2025-08-14T23:40:03.3291175Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/main.py", line 348 in pytest_runtestloop
2025-08-14T23:40:03.3291487Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_callers.py", line 121 in _multicall
2025-08-14T23:40:03.3291803Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_manager.py", line 120 in _hookexec
2025-08-14T23:40:03.3292101Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_hooks.py", line 512 in __call__
2025-08-14T23:40:03.3292390Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/main.py", line 323 in _main
2025-08-14T23:40:03.3292688Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/main.py", line 269 in wrap_session
2025-08-14T23:40:03.3293065Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/main.py", line 316 in pytest_cmdline_main
2025-08-14T23:40:03.3293391Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_callers.py", line 121 in _multicall
2025-08-14T23:40:03.3293694Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_manager.py", line 120 in _hookexec
2025-08-14T23:40:03.3293984Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/pluggy/_hooks.py", line 512 in __call__
2025-08-14T23:40:03.3294322Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/config/__init__.py", line 166 in main
2025-08-14T23:40:03.3294661Z   File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 1299 in run_tests
2025-08-14T23:40:03.3294981Z   File "/var/lib/jenkins/pytorch/test/test_sparse_csr.py", line 4334 in <module>
2025-08-14T23:40:03.3295126Z 
2025-08-14T23:40:03.3300387Z Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, thriftpy2.transport.cybase, thriftpy2.transport.buffered.cybuffered, thriftpy2.transport.framed.cyframed, thriftpy2.transport.memory.cymemory, thriftpy2.transport.sasl.cysasl, thriftpy2.protocol.cybin.cybin, scipy._lib._ccallback_c, yaml._yaml, numba.core.typeconv._typeconv, numba._helperlib, numba._dynfunc, numba._dispatcher, numba.core.runtime._nrt_python, numba.np.ufunc._internal, numba.experimental.jitclass._box, numba.mviewbuf, numba.types.itertools, psutil._psutil_linux, psutil._psutil_posix, scipy._lib._uarray._uarray, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.special._ellip_harm_2, scipy.signal._sigtools, scipy.signal._max_len_seq_inner, scipy.signal._upfirdn_apply, scipy.signal._spline, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, scipy.signal._sosfilt, scipy.signal._spectral, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy.stats._ansari_swilk_statistics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.signal._peak_finding_utils (total: 144)
2025-08-14T23:40:03.3305888Z Got exit code -6 (SIGIOT)
2025-08-14T23:40:03.3306031Z Retrying single test...
2025-08-14T23:40:03.3306586Z /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.
2025-08-14T23:40:03.3307099Z   import pkg_resources
2025-08-14T23:40:03.3307362Z Test results will be stored in test-reports/python-pytest/test_sparse_csr/test_sparse_csr-2743f97f047fda68.xml
2025-08-14T23:40:03.3307618Z ============================= test session starts ==============================
2025-08-14T23:40:03.3307864Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python
2025-08-14T23:40:03.3308089Z cachedir: .pytest_cache
2025-08-14T23:40:03.3308345Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2025-08-14T23:40:03.3308617Z rootdir: /var/lib/jenkins/pytorch
2025-08-14T23:40:03.3308777Z configfile: pytest.ini
2025-08-14T23:40:03.3309039Z 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
2025-08-14T23:40:03.3309336Z collecting ... collected 4958 items / 2540 deselected / 2418 selected
2025-08-14T23:40:03.3309670Z stepcurrent: skipping 250 already run items. Running only test/test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_bfloat16
2025-08-14T23:40:03.3309957Z Running 1 items in this shard
2025-08-14T23:40:03.3310047Z 
2025-08-14T23:40:03.3310331Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_bfloat16 [W814 23:32:35.821163230 Module.cpp:192] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1...
2025-08-14T23:40:03.3310670Z 
2025-08-14T23:40:03.3310827Z [W814 23:32:36.068636004 Module.cpp:192] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1...
2025-08-14T23:40:03.3311032Z 
2025-08-14T23:40:03.3311189Z [W814 23:32:36.113109226 Module.cpp:192] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1...
2025-08-14T23:40:03.3311398Z 
2025-08-14T23:40:03.3311455Z PASSED [0.5256s] [100%]
2025-08-14T23:40:03.3311543Z 
2025-08-14T23:40:03.3311734Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/test_sparse_csr/test_sparse_csr-2743f97f047fda68.xml -
2025-08-14T23:40:03.3312037Z ====================== 1 passed, 2540 deselected in 0.63s ======================
2025-08-14T23:40:03.3312227Z Got exit code 0
2025-08-14T23:40:03.3312398Z Test succeeeded in new process, continuing with the rest of the tests
2025-08-14T23:40:03.3313021Z /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.
2025-08-14T23:40:03.3313539Z   import pkg_resources
2025-08-14T23:40:03.3313766Z Test results will be stored in test-reports/python-pytest/test_sparse_csr/test_sparse_csr-41127c0396c7a2d0.xml
2025-08-14T23:40:03.3314045Z ============================= test session starts ==============================
2025-08-14T23:40:03.3314285Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python
2025-08-14T23:40:03.3314519Z cachedir: .pytest_cache
2025-08-14T23:40:03.3314789Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow]
2025-08-14T23:40:03.3315055Z rootdir: /var/lib/jenkins/pytorch
2025-08-14T23:40:03.3315215Z configfile: pytest.ini
2025-08-14T23:40:03.3315470Z 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
2025-08-14T23:40:03.3315775Z collecting ... collected 4958 items / 251 deselected / 4707 selected
2025-08-14T23:40:03.3315982Z stepcurrent: skipping 251 already run items.
2025-08-14T23:40:03.3316148Z Running 2290 items in this shard
2025-08-14T23:40:03.3316247Z 
2025-08-14T23:40:03.3316374Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_bool PASSED [0.2091s] [  0%]
2025-08-14T23:40:03.3316715Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_complex128 PASSED [0.0421s] [  0%]
2025-08-14T23:40:03.3317030Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_complex64 PASSED [0.0140s] [  0%]
2025-08-14T23:40:03.3321157Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_uint8 PASSED [0.0066s] [  0%]
2025-08-14T23:40:03.3321444Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_float16 PASSED [0.0153s] [  0%]
2025-08-14T23:40:03.3321708Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_int16 PASSED [0.0066s] [  0%]
2025-08-14T23:40:03.3321967Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_int32 PASSED [0.0065s] [  0%]
2025-08-14T23:40:03.3322226Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_int64 PASSED [0.0064s] [  0%]
2025-08-14T23:40:03.3322483Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_int8 PASSED [0.0064s] [  0%]
2025-08-14T23:40:03.3322742Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int64_cuda_uint8 PASSED [0.0064s] [  0%]
2025-08-14T23:40:03.3323097Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int32_cuda_complex128 PASSED [0.0060s] [  0%]
2025-08-14T23:40:03.3323362Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int32_cuda_int16 PASSED [0.0051s] [  0%]
2025-08-14T23:40:03.3323664Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int32_cuda_int32 PASSED [0.0050s] [  0%]
2025-08-14T23:40:03.3323921Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int32_cuda_int64 PASSED [0.0053s] [  0%]
2025-08-14T23:40:03.3325498Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int64_cuda_float16 PASSED [0.0057s] [  0%]
2025-08-14T23:40:03.3325771Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int64_cuda_float64 PASSED [0.0055s] [  0%]
2025-08-14T23:40:03.3326032Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int64_cuda_int32 PASSED [0.0050s] [  0%]
2025-08-14T23:40:03.3326289Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int64_cuda_int64 PASSED [0.0051s] [  0%]
2025-08-14T23:40:03.3326548Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSR_int64_cuda_int8 PASSED [0.0051s] [  0%]
2025-08-14T23:40:03.3326810Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_bfloat16 PASSED [0.0290s] [  0%]
2025-08-14T23:40:03.3327074Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_bool PASSED [0.0102s] [  0%]
2025-08-14T23:40:03.3327338Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_complex128 PASSED [0.0107s] [  0%]
2025-08-14T23:40:03.3327608Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_complex64 PASSED [0.0104s] [  1%]
2025-08-14T23:40:03.3327875Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_float16 PASSED [0.0104s] [  1%]
2025-08-14T23:40:03.3328137Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_float32 PASSED [0.0104s] [  1%]
2025-08-14T23:40:03.3329470Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_float64 PASSED [0.0104s] [  1%]
2025-08-14T23:40:03.3329731Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int32_cuda_uint8 PASSED [0.0099s] [  1%]
2025-08-14T23:40:03.3329990Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int64_cuda_float16 PASSED [0.0107s] [  1%]
2025-08-14T23:40:03.3330250Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int64_cuda_int64 PASSED [0.0103s] [  1%]
2025-08-14T23:40:03.3330506Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int64_cuda_int8 PASSED [0.0103s] [  1%]
2025-08-14T23:40:03.3330797Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSC_int64_cuda_uint8 PASSED [0.0101s] [  1%]
2025-08-14T23:40:03.3331057Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int32_cuda_float64 PASSED [0.0080s] [  1%]
2025-08-14T23:40:03.3331332Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int32_cuda_int64 PASSED [0.0068s] [  1%]
2025-08-14T23:40:03.3331592Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int64_cuda_bool PASSED [0.0067s] [  1%]
2025-08-14T23:40:03.3331850Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int64_cuda_float32 PASSED [0.0072s] [  1%]
2025-08-14T23:40:03.3332110Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int64_cuda_float64 PASSED [0.0071s] [  1%]
2025-08-14T23:40:03.3333417Z test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseCSR_int64_cuda_int16 PASSED [0.0068s] [  1%]
2025-08-14T23:40:03.3333670Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_add_cuda_float64 PASSED [0.2145s] [  1%]
2025-08-14T23:40:03.3333922Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_add_errors_cuda_complex128 PASSED [0.0025s] [  1%]
2025-08-14T23:40:03.3334180Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_add_errors_cuda_complex64 PASSED [0.0024s] [  1%]
2025-08-14T23:40:03.3334438Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_add_errors_cuda_float32 PASSED [0.0024s] [  1%]
2025-08-14T23:40:03.3334692Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_add_errors_cuda_float64 PASSED [0.0022s] [  1%]
2025-08-14T23:40:03.3334941Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_addmm_cuda_complex128 PASSED [0.4821s] [  1%]
2025-08-14T23:40:03.3335190Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_addmm_cuda_complex64 PASSED [0.1010s] [  1%]
2025-08-14T23:40:03.3335459Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_addmm_cuda_float32 PASSED [0.2436s] [  1%]
2025-08-14T23:40:03.3335700Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_addmm_cuda_float64 PASSED [0.0824s] [  2%]
2025-08-14T23:40:03.3335950Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_bfloat16 PASSED [0.0179s] [  2%]
2025-08-14T23:40:03.3337174Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_bool PASSED [0.0037s] [  2%]
2025-08-14T23:40:03.3337430Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_float16 PASSED [0.0042s] [  2%]
2025-08-14T23:40:03.3337686Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_float32 PASSED [0.0040s] [  2%]
2025-08-14T23:40:03.3337938Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_int32 PASSED [0.0033s] [  2%]
2025-08-14T23:40:03.3338189Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_int64 PASSED [0.0033s] [  2%]
2025-08-14T23:40:03.3338442Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csc_to_dense_cuda_int8 PASSED [0.0032s] [  2%]
2025-08-14T23:40:03.3338697Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_bfloat16 PASSED [0.0017s] [  2%]
2025-08-14T23:40:03.3338957Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_bool PASSED [0.0015s] [  2%]
2025-08-14T23:40:03.3339219Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_complex128 PASSED [0.0017s] [  2%]
2025-08-14T23:40:03.3339489Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_complex64 PASSED [0.0017s] [  2%]
2025-08-14T23:40:03.3340723Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_float32 PASSED [0.0016s] [  2%]
2025-08-14T23:40:03.3340982Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_int64 PASSED [0.0015s] [  2%]
2025-08-14T23:40:03.3341237Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_from_dense_cuda_int8 PASSED [0.0015s] [  2%]
2025-08-14T23:40:03.3341490Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_bool PASSED [0.0032s] [  2%]
2025-08-14T23:40:03.3341742Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_float32 PASSED [0.0039s] [  2%]
2025-08-14T23:40:03.3342016Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_float64 PASSED [0.0039s] [  2%]
2025-08-14T23:40:03.3342269Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_int32 PASSED [0.0031s] [  2%]
2025-08-14T23:40:03.3342533Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_int8 PASSED [0.0032s] [  2%]
2025-08-14T23:40:03.3342785Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_to_dense_cuda_uint8 PASSED [0.0032s] [  2%]
2025-08-14T23:40:03.3343104Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_bool PASSED [0.0318s] [  2%]
2025-08-14T23:40:03.3343382Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_float16 PASSED [0.0030s] [  2%]
2025-08-14T23:40:03.3344613Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_float32 PASSED [0.0029s] [  3%]
2025-08-14T23:40:03.3344894Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_float64 PASSED [0.0029s] [  3%]
2025-08-14T23:40:03.3345168Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_int32 PASSED [0.0027s] [  3%]
2025-08-14T23:40:03.3345440Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_abs_cuda_int64 PASSED [0.0027s] [  3%]
2025-08-14T23:40:03.3345779Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_complex64 SKIPPED [0.0021s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3346175Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_float64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3346582Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_int16 SKIPPED [0.0024s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3346964Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_int32 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3347342Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_int64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3347722Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_angle_cuda_int8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  3%]
2025-08-14T23:40:03.3348050Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_bool PASSED [0.0028s] [  3%]
2025-08-14T23:40:03.3349281Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_float16 PASSED [0.0081s] [  3%]
2025-08-14T23:40:03.3349563Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_int32 PASSED [0.0026s] [  3%]
2025-08-14T23:40:03.3349837Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_int64 PASSED [0.0026s] [  3%]
2025-08-14T23:40:03.3350110Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_int8 PASSED [0.0026s] [  3%]
2025-08-14T23:40:03.3350382Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asin_cuda_uint8 PASSED [0.0026s] [  3%]
2025-08-14T23:40:03.3350660Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_bfloat16 PASSED [0.0086s] [  3%]
2025-08-14T23:40:03.3350976Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_float16 PASSED [0.0029s] [  3%]
2025-08-14T23:40:03.3351257Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_float32 PASSED [0.0029s] [  3%]
2025-08-14T23:40:03.3351536Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_float64 PASSED [0.0029s] [  3%]
2025-08-14T23:40:03.3351815Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_int32 PASSED [0.0026s] [  3%]
2025-08-14T23:40:03.3352105Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_asinh_cuda_int8 PASSED [0.0025s] [  3%]
2025-08-14T23:40:03.3353398Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atan_cuda_bfloat16 PASSED [0.0059s] [  3%]
2025-08-14T23:40:03.3353682Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atan_cuda_complex32 PASSED [0.0054s] [  4%]
2025-08-14T23:40:03.3353980Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atan_cuda_int32 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3354253Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atan_cuda_int8 PASSED [0.0025s] [  4%]
2025-08-14T23:40:03.3354524Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atan_cuda_uint8 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3354802Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atanh_cuda_bfloat16 PASSED [0.0067s] [  4%]
2025-08-14T23:40:03.3355082Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atanh_cuda_int16 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3355359Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atanh_cuda_int32 PASSED [0.0025s] [  4%]
2025-08-14T23:40:03.3355633Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_atanh_cuda_uint8 PASSED [0.0025s] [  4%]
2025-08-14T23:40:03.3355912Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_ceil_cuda_float32 PASSED [0.0113s] [  4%]
2025-08-14T23:40:03.3357138Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_ceil_cuda_float64 PASSED [0.0031s] [  4%]
2025-08-14T23:40:03.3357415Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_ceil_cuda_int16 PASSED [0.0028s] [  4%]
2025-08-14T23:40:03.3357708Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_ceil_cuda_int32 PASSED [0.0027s] [  4%]
2025-08-14T23:40:03.3357997Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_bfloat16 PASSED [0.0029s] [  4%]
2025-08-14T23:40:03.3358307Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_complex32 PASSED [0.0040s] [  4%]
2025-08-14T23:40:03.3358614Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_float32 PASSED [0.0031s] [  4%]
2025-08-14T23:40:03.3358918Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_int16 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3359216Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_int64 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3359512Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_conj_physical_cuda_int8 PASSED [0.0027s] [  4%]
2025-08-14T23:40:03.3359809Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_bfloat16 PASSED [0.0029s] [  4%]
2025-08-14T23:40:03.3360098Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_bool PASSED [0.0027s] [  4%]
2025-08-14T23:40:03.3361319Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_float16 PASSED [0.0028s] [  4%]
2025-08-14T23:40:03.3361609Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_float64 PASSED [0.0029s] [  4%]
2025-08-14T23:40:03.3361894Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_int16 PASSED [0.0026s] [  4%]
2025-08-14T23:40:03.3362201Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_deg2rad_cuda_int8 PASSED [0.0026s] [  5%]
2025-08-14T23:40:03.3362480Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_bfloat16 PASSED [0.0093s] [  5%]
2025-08-14T23:40:03.3362754Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_bool PASSED [0.0027s] [  5%]
2025-08-14T23:40:03.3363093Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_float32 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3363382Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_int16 PASSED [0.0026s] [  5%]
2025-08-14T23:40:03.3363653Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_int32 PASSED [0.0026s] [  5%]
2025-08-14T23:40:03.3363923Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erf_cuda_uint8 PASSED [0.0025s] [  5%]
2025-08-14T23:40:03.3364215Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_float16 PASSED [0.0039s] [  5%]
2025-08-14T23:40:03.3365511Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_float64 PASSED [0.0035s] [  5%]
2025-08-14T23:40:03.3365793Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_int16 PASSED [0.0034s] [  5%]
2025-08-14T23:40:03.3366074Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_int32 PASSED [0.0025s] [  5%]
2025-08-14T23:40:03.3366352Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_int64 PASSED [0.0025s] [  5%]
2025-08-14T23:40:03.3366632Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_erfinv_cuda_int8 PASSED [0.0026s] [  5%]
2025-08-14T23:40:03.3366910Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_bfloat16 PASSED [0.0209s] [  5%]
2025-08-14T23:40:03.3367195Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_complex64 PASSED [0.0034s] [  5%]
2025-08-14T23:40:03.3367479Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_float32 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3367757Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_float64 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3368060Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_int16 PASSED [0.0028s] [  5%]
2025-08-14T23:40:03.3368336Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_expm1_cuda_int64 PASSED [0.0026s] [  5%]
2025-08-14T23:40:03.3369575Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_floor_cuda_bfloat16 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3369857Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_floor_cuda_float16 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3370137Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_floor_cuda_float32 PASSED [0.0029s] [  5%]
2025-08-14T23:40:03.3370415Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_floor_cuda_float64 PASSED [0.0033s] [  5%]
2025-08-14T23:40:03.3370692Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_floor_cuda_int8 PASSED [0.0028s] [  6%]
2025-08-14T23:40:03.3370969Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_frac_cuda_bfloat16 PASSED [0.0028s] [  6%]
2025-08-14T23:40:03.3371303Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isinf_cuda_bool SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3371696Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isinf_cuda_complex128 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3372088Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isinf_cuda_int16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3372496Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isinf_cuda_int8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3373897Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isinf_cuda_uint8 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3374291Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_complex64 SKIPPED [0.0021s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3374706Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_float16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3375092Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_float64 SKIPPED [0.0030s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3375491Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_int32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3375872Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_int8 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3376250Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isnan_cuda_uint8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3376635Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isneginf_cuda_int16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3377029Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isneginf_cuda_int32 SKIPPED [0.0022s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3377420Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isneginf_cuda_int64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3377812Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isneginf_cuda_int8 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3379168Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isneginf_cuda_uint8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3379579Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_bool SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3379968Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_float32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3380364Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_float64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3380756Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_int16 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3381145Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_int64 SKIPPED [0.0025s] (Skipped! Inplace variant not supported!) [  6%]
2025-08-14T23:40:03.3381532Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_isposinf_cuda_int8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3381871Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_complex64 PASSED [0.0106s] [  7%]
2025-08-14T23:40:03.3382155Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_float16 PASSED [0.0030s] [  7%]
2025-08-14T23:40:03.3382435Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_float32 PASSED [0.0028s] [  7%]
2025-08-14T23:40:03.3382714Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_int32 PASSED [0.0027s] [  7%]
2025-08-14T23:40:03.3384060Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_int8 PASSED [0.0026s] [  7%]
2025-08-14T23:40:03.3384362Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_log1p_cuda_uint8 PASSED [0.0029s] [  7%]
2025-08-14T23:40:03.3384639Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_complex32 PASSED [0.0039s] [  7%]
2025-08-14T23:40:03.3384921Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_complex64 PASSED [0.0037s] [  7%]
2025-08-14T23:40:03.3385197Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_int16 PASSED [0.0150s] [  7%]
2025-08-14T23:40:03.3385484Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_int64 PASSED [0.0031s] [  7%]
2025-08-14T23:40:03.3385752Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_int8 PASSED [0.0027s] [  7%]
2025-08-14T23:40:03.3386037Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_neg_cuda_uint8 PASSED [0.0027s] [  7%]
2025-08-14T23:40:03.3386386Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_float16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3386807Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_float32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3388178Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_float64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3388599Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_int16 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3389010Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_int32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3389424Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_int64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3389833Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_int8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3390271Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_nn_functional_relu_cuda_uint8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3390677Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_bfloat16 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3391082Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_complex32 SKIPPED [0.0022s] (Skipped! Inplace variant not supported!) [  7%]
2025-08-14T23:40:03.3391483Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_float16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  8%]
2025-08-14T23:40:03.3391878Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_float32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  8%]
2025-08-14T23:40:03.3392271Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_float64 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  8%]
2025-08-14T23:40:03.3393706Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_positive_cuda_int64 SKIPPED [0.0019s] (Skipped! Inplace variant not supported!) [  8%]
2025-08-14T23:40:03.3394048Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_bool PASSED [0.0028s] [  8%]
2025-08-14T23:40:03.3394333Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_float64 PASSED [0.0030s] [  8%]
2025-08-14T23:40:03.3394618Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_int16 PASSED [0.0026s] [  8%]
2025-08-14T23:40:03.3394924Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_int64 PASSED [0.0025s] [  8%]
2025-08-14T23:40:03.3395204Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_int8 PASSED [0.0025s] [  8%]
2025-08-14T23:40:03.3395485Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_rad2deg_cuda_uint8 PASSED [0.0026s] [  8%]
2025-08-14T23:40:03.3395765Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_round_cuda_bfloat16 PASSED [0.0030s] [  8%]
2025-08-14T23:40:03.3396060Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_round_cuda_float64 PASSED [0.0029s] [  8%]
2025-08-14T23:40:03.3396337Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_round_cuda_int64 PASSED [0.0027s] [  8%]
2025-08-14T23:40:03.3396628Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_round_cuda_int8 PASSED [0.0027s] [  8%]
2025-08-14T23:40:03.3397866Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sgn_cuda_bfloat16 PASSED [0.0031s] [  8%]
2025-08-14T23:40:03.3398149Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sgn_cuda_complex128 PASSED [0.0041s] [  8%]
2025-08-14T23:40:03.3398428Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sgn_cuda_complex32 PASSED [0.0037s] [  8%]
2025-08-14T23:40:03.3398708Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sgn_cuda_float64 PASSED [0.0028s] [  8%]
2025-08-14T23:40:03.3398982Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sgn_cuda_uint8 PASSED [0.0028s] [  8%]
2025-08-14T23:40:03.3399257Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_bfloat16 PASSED [0.0029s] [  8%]
2025-08-14T23:40:03.3399532Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_bool PASSED [0.0028s] [  8%]
2025-08-14T23:40:03.3399811Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_float32 PASSED [0.0029s] [  8%]
2025-08-14T23:40:03.3400088Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_float64 PASSED [0.0029s] [  8%]
2025-08-14T23:40:03.3400362Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_int16 PASSED [0.0028s] [  9%]
2025-08-14T23:40:03.3400656Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_int32 PASSED [0.0028s] [  9%]
2025-08-14T23:40:03.3401893Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_int64 PASSED [0.0027s] [  9%]
2025-08-14T23:40:03.3402166Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sign_cuda_uint8 PASSED [0.0027s] [  9%]
2025-08-14T23:40:03.3402501Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_signbit_cuda_bfloat16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  9%]
2025-08-14T23:40:03.3403093Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_signbit_cuda_float16 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  9%]
2025-08-14T23:40:03.3403488Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_signbit_cuda_float64 SKIPPED [0.0022s] (Skipped! Inplace variant not supported!) [  9%]
2025-08-14T23:40:03.3403880Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_signbit_cuda_int32 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  9%]
2025-08-14T23:40:03.3404265Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_signbit_cuda_int8 SKIPPED [0.0020s] (Skipped! Inplace variant not supported!) [  9%]
2025-08-14T23:40:03.3404597Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_bfloat16 PASSED [0.0077s] [  9%]
2025-08-14T23:40:03.3404872Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_bool PASSED [0.0027s] [  9%]
2025-08-14T23:40:03.3405151Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_complex128 PASSED [0.0038s] [  9%]
2025-08-14T23:40:03.3406515Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_complex32 PASSED [0.2238s] [  9%]
2025-08-14T23:40:03.3406797Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_float32 PASSED [0.0035s] [  9%]
2025-08-14T23:40:03.3407074Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_float64 PASSED [0.0030s] [  9%]
2025-08-14T23:40:03.3407346Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_int16 PASSED [0.0028s] [  9%]
2025-08-14T23:40:03.3407638Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_int64 PASSED [0.0026s] [  9%]
2025-08-14T23:40:03.3407907Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_int8 PASSED [0.0026s] [  9%]
2025-08-14T23:40:03.3408192Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sin_cuda_uint8 PASSED [0.0027s] [  9%]
2025-08-14T23:40:03.3408470Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_bfloat16 PASSED [0.0077s] [  9%]
2025-08-14T23:40:03.3408753Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_complex128 PASSED [0.0038s] [  9%]
2025-08-14T23:40:03.3409038Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_complex32 PASSED [0.0038s] [  9%]
2025-08-14T23:40:03.3409318Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_complex64 PASSED [0.0043s] [ 10%]
2025-08-14T23:40:03.3410572Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_float16 PASSED [0.0029s] [ 10%]
2025-08-14T23:40:03.3410851Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_float32 PASSED [0.0029s] [ 10%]
2025-08-14T23:40:03.3411130Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_float64 PASSED [0.0028s] [ 10%]
2025-08-14T23:40:03.3411406Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_int64 PASSED [0.0027s] [ 10%]
2025-08-14T23:40:03.3411678Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sinh_cuda_uint8 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3411953Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sqrt_cuda_bfloat16 PASSED [0.0029s] [ 10%]
2025-08-14T23:40:03.3412255Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sqrt_cuda_float16 PASSED [0.0028s] [ 10%]
2025-08-14T23:40:03.3412532Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sqrt_cuda_int64 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3412802Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sqrt_cuda_int8 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3413163Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_sqrt_cuda_uint8 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3413439Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_bfloat16 PASSED [0.0073s] [ 10%]
2025-08-14T23:40:03.3414694Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_bool PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3414970Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_complex128 PASSED [0.0038s] [ 10%]
2025-08-14T23:40:03.3415253Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_complex32 PASSED [0.0038s] [ 10%]
2025-08-14T23:40:03.3415532Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_complex64 PASSED [0.0040s] [ 10%]
2025-08-14T23:40:03.3415811Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_float32 PASSED [0.0028s] [ 10%]
2025-08-14T23:40:03.3416085Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_float64 PASSED [0.0028s] [ 10%]
2025-08-14T23:40:03.3416358Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_int16 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3416650Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tan_cuda_int8 PASSED [0.0026s] [ 10%]
2025-08-14T23:40:03.3416919Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tanh_cuda_bool PASSED [0.0065s] [ 10%]
2025-08-14T23:40:03.3417198Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tanh_cuda_complex128 PASSED [0.0037s] [ 10%]
2025-08-14T23:40:03.3417479Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tanh_cuda_float16 PASSED [0.0029s] [ 10%]
2025-08-14T23:40:03.3418749Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tanh_cuda_float64 PASSED [0.0029s] [ 11%]
2025-08-14T23:40:03.3419024Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_tanh_cuda_uint8 PASSED [0.0026s] [ 11%]
2025-08-14T23:40:03.3419299Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_trunc_cuda_float16 PASSED [0.0029s] [ 11%]
2025-08-14T23:40:03.3419597Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_trunc_cuda_float32 PASSED [0.0028s] [ 11%]
2025-08-14T23:40:03.3419875Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_trunc_cuda_int16 PASSED [0.0028s] [ 11%]
2025-08-14T23:40:03.3420149Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_trunc_cuda_int32 PASSED [0.0028s] [ 11%]
2025-08-14T23:40:03.3420424Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_inplace_trunc_cuda_int8 PASSED [0.0027s] [ 11%]
2025-08-14T23:40:03.3420693Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_bfloat16 PASSED [0.0059s] [ 11%]
2025-08-14T23:40:03.3420960Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_bool PASSED [0.0058s] [ 11%]
2025-08-14T23:40:03.3421226Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_complex64 PASSED [0.0057s] [ 11%]
2025-08-14T23:40:03.3422465Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_float16 PASSED [0.0057s] [ 11%]
2025-08-14T23:40:03.3422735Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_float32 PASSED [0.0056s] [ 11%]
2025-08-14T23:40:03.3423063Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_float64 PASSED [0.0056s] [ 11%]
2025-08-14T23:40:03.3423348Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_int16 PASSED [0.0057s] [ 11%]
2025-08-14T23:40:03.3423608Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_int32 PASSED [0.0057s] [ 11%]
2025-08-14T23:40:03.3423868Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_abs_cuda_uint8 PASSED [0.0056s] [ 11%]
2025-08-14T23:40:03.3424136Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_complex128 PASSED [0.0065s] [ 11%]
2025-08-14T23:40:03.3424412Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_complex64 PASSED [0.0065s] [ 11%]
2025-08-14T23:40:03.3424685Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_float64 PASSED [0.0083s] [ 11%]
2025-08-14T23:40:03.3424952Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_int16 PASSED [0.0058s] [ 11%]
2025-08-14T23:40:03.3425214Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_int32 PASSED [0.0056s] [ 11%]
2025-08-14T23:40:03.3426461Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_int8 PASSED [0.0057s] [ 11%]
2025-08-14T23:40:03.3426726Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_angle_cuda_uint8 PASSED [0.0058s] [ 11%]
2025-08-14T23:40:03.3426991Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asin_cuda_float16 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3427255Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asin_cuda_float32 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3427518Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asin_cuda_int16 PASSED [0.0056s] [ 12%]
2025-08-14T23:40:03.3427803Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asin_cuda_int64 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3428063Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asin_cuda_uint8 PASSED [0.0050s] [ 12%]
2025-08-14T23:40:03.3428329Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_bfloat16 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3428604Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_complex128 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3428898Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_complex32 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3429170Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_float32 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3430432Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_int16 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3430735Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_int32 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3430996Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_int8 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3431257Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_asinh_cuda_uint8 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3431521Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_bool PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3431785Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_complex64 PASSED [0.0066s] [ 12%]
2025-08-14T23:40:03.3432054Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_float16 PASSED [0.0056s] [ 12%]
2025-08-14T23:40:03.3432317Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_float32 PASSED [0.0055s] [ 12%]
2025-08-14T23:40:03.3432582Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_float64 PASSED [0.0056s] [ 12%]
2025-08-14T23:40:03.3432845Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_int32 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3433176Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_int64 PASSED [0.0058s] [ 12%]
2025-08-14T23:40:03.3434394Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atan_cuda_int8 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3434688Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_bfloat16 PASSED [0.0057s] [ 12%]
2025-08-14T23:40:03.3434965Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_complex128 PASSED [0.0067s] [ 12%]
2025-08-14T23:40:03.3435242Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_complex32 PASSED [0.0066s] [ 13%]
2025-08-14T23:40:03.3435516Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_complex64 PASSED [0.0065s] [ 13%]
2025-08-14T23:40:03.3435790Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_float16 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3436058Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_float32 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3436327Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_float64 PASSED [0.0058s] [ 13%]
2025-08-14T23:40:03.3436595Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_atanh_cuda_uint8 PASSED [0.0049s] [ 13%]
2025-08-14T23:40:03.3436859Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_float16 PASSED [0.0058s] [ 13%]
2025-08-14T23:40:03.3437125Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_float32 PASSED [0.0059s] [ 13%]
2025-08-14T23:40:03.3438634Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_float64 PASSED [0.0060s] [ 13%]
2025-08-14T23:40:03.3438901Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_int16 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3439200Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_int32 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3439462Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_ceil_cuda_uint8 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3439745Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_complex128 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3440047Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_complex32 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3440358Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_complex64 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3440649Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_float16 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3440958Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_float32 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3441248Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_int16 PASSED [0.0055s] [ 13%]
2025-08-14T23:40:03.3441529Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_int64 PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3442750Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_conj_physical_cuda_int8 PASSED [0.0055s] [ 13%]
2025-08-14T23:40:03.3443100Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_bool PASSED [0.0056s] [ 13%]
2025-08-14T23:40:03.3443365Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_float16 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3443629Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_float64 PASSED [0.0057s] [ 13%]
2025-08-14T23:40:03.3443892Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_int16 PASSED [0.0061s] [ 14%]
2025-08-14T23:40:03.3444152Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_int8 PASSED [0.0057s] [ 14%]
2025-08-14T23:40:03.3444412Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erf_cuda_uint8 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3444677Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erfinv_cuda_float16 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3444973Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erfinv_cuda_float64 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3445241Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_erfinv_cuda_int8 PASSED [0.0055s] [ 14%]
2025-08-14T23:40:03.3445541Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_bfloat16 PASSED [0.0055s] [ 14%]
2025-08-14T23:40:03.3446728Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_bool PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3447003Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_complex128 PASSED [0.0058s] [ 14%]
2025-08-14T23:40:03.3447280Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_complex64 PASSED [0.0058s] [ 14%]
2025-08-14T23:40:03.3447550Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_float16 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3447818Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_float32 PASSED [0.0058s] [ 14%]
2025-08-14T23:40:03.3448085Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_float64 PASSED [0.0057s] [ 14%]
2025-08-14T23:40:03.3448352Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_int16 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3448615Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_int32 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3448876Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_expm1_cuda_int64 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3449144Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_floor_cuda_bfloat16 PASSED [0.0059s] [ 14%]
2025-08-14T23:40:03.3450338Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_floor_cuda_float64 PASSED [0.0056s] [ 14%]
2025-08-14T23:40:03.3450604Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_floor_cuda_int16 PASSED [0.0055s] [ 14%]
2025-08-14T23:40:03.3450869Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_floor_cuda_int32 PASSED [0.0055s] [ 14%]
2025-08-14T23:40:03.3451177Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isinf_cuda_bfloat16 SKIPPED [0.0021s] (Skipped! Out not supported) [ 14%]
2025-08-14T23:40:03.3451563Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isinf_cuda_complex32 SKIPPED [0.0020s] (Skipped! Out not supported) [ 14%]
2025-08-14T23:40:03.3451909Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isinf_cuda_int16 SKIPPED [0.0020s] (Skipped! Out not supported) [ 14%]
2025-08-14T23:40:03.3452265Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isinf_cuda_int8 SKIPPED [0.0020s] (Skipped! Out not supported) [ 15%]
2025-08-14T23:40:03.3452603Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isinf_cuda_uint8 SKIPPED [0.0019s] (Skipped! Out not supported) [ 15%]
2025-08-14T23:40:03.3453008Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isnan_cuda_complex64 SKIPPED [0.0020s] (Skipped! Out not supported) [ 15%]
2025-08-14T23:40:03.3453357Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isnan_cuda_int8 SKIPPED [0.0020s] (Skipped! Out not supported) [ 15%]
2025-08-14T23:40:03.3453664Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_bool PASSED [0.0057s] [ 15%]
2025-08-14T23:40:03.3454856Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_float16 PASSED [0.0264s] [ 15%]
2025-08-14T23:40:03.3455140Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_float32 PASSED [0.0059s] [ 15%]
2025-08-14T23:40:03.3455418Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_float64 PASSED [0.0057s] [ 15%]
2025-08-14T23:40:03.3455691Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_int16 PASSED [0.0063s] [ 15%]
2025-08-14T23:40:03.3455960Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_int32 PASSED [0.0058s] [ 15%]
2025-08-14T23:40:03.3456253Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isneginf_cuda_int64 PASSED [0.0055s] [ 15%]
2025-08-14T23:40:03.3456523Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isposinf_cuda_bool PASSED [0.0055s] [ 15%]
2025-08-14T23:40:03.3456792Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isposinf_cuda_int16 PASSED [0.0056s] [ 15%]
2025-08-14T23:40:03.3457063Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isposinf_cuda_int32 PASSED [0.0056s] [ 15%]
2025-08-14T23:40:03.3457335Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_isposinf_cuda_uint8 PASSED [0.0056s] [ 15%]
2025-08-14T23:40:03.3457604Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_log1p_cuda_bfloat16 PASSED [0.0057s] [ 15%]
2025-08-14T23:40:03.3458773Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_log1p_cuda_float32 PASSED [0.0056s] [ 15%]
2025-08-14T23:40:03.3459044Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_log1p_cuda_float64 PASSED [0.0058s] [ 15%]
2025-08-14T23:40:03.3459310Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_log1p_cuda_int16 PASSED [0.0059s] [ 15%]
2025-08-14T23:40:03.3459578Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_neg_cuda_complex128 PASSED [0.0070s] [ 15%]
2025-08-14T23:40:03.3459847Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_neg_cuda_complex32 PASSED [0.0056s] [ 15%]
2025-08-14T23:40:03.3460117Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_neg_cuda_complex64 PASSED [0.0057s] [ 15%]
2025-08-14T23:40:03.3460405Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_neg_cuda_float64 PASSED [0.0058s] [ 15%]
2025-08-14T23:40:03.3460669Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_neg_cuda_int32 PASSED [0.0059s] [ 16%]
2025-08-14T23:40:03.3460997Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_bfloat16 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3461381Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_float32 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3461776Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_int16 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3463136Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_int32 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3463514Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_int64 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3463889Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_nn_functional_relu_cuda_int8 SKIPPED [0.0019s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3464257Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_complex64 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3464617Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_float16 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3464972Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_float32 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3465325Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_int16 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3465673Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_int64 SKIPPED [0.0019s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3466023Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_int8 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3466393Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_positive_cuda_uint8 SKIPPED [0.0020s] (Skipped! Out not supported) [ 16%]
2025-08-14T23:40:03.3472352Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_rad2deg_cuda_bool PASSED [0.0059s] [ 16%]
2025-08-14T23:40:03.3472640Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_rad2deg_cuda_float64 PASSED [0.0060s] [ 16%]
2025-08-14T23:40:03.3473007Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_rad2deg_cuda_int32 PASSED [0.0056s] [ 16%]
2025-08-14T23:40:03.3473296Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_rad2deg_cuda_int8 PASSED [0.0058s] [ 16%]
2025-08-14T23:40:03.3473571Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_rad2deg_cuda_uint8 PASSED [0.0057s] [ 16%]
2025-08-14T23:40:03.3475710Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_round_cuda_float64 PASSED [0.0058s] [ 16%]
2025-08-14T23:40:03.3476002Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_round_cuda_int32 PASSED [0.0055s] [ 16%]
2025-08-14T23:40:03.3476273Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_round_cuda_uint8 PASSED [0.0056s] [ 16%]
2025-08-14T23:40:03.3476546Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sgn_cuda_bfloat16 PASSED [0.0057s] [ 16%]
2025-08-14T23:40:03.3476820Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sgn_cuda_complex128 PASSED [0.0057s] [ 17%]
2025-08-14T23:40:03.3477094Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sgn_cuda_float16 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3477402Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sgn_cuda_float64 PASSED [0.0057s] [ 17%]
2025-08-14T23:40:03.3477668Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sgn_cuda_int8 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3477941Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sign_cuda_bfloat16 PASSED [0.0058s] [ 17%]
2025-08-14T23:40:03.3478233Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sign_cuda_bool PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3478496Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sign_cuda_int64 PASSED [0.0055s] [ 17%]
2025-08-14T23:40:03.3478796Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sign_cuda_int8 PASSED [0.0055s] [ 17%]
2025-08-14T23:40:03.3479061Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sign_cuda_uint8 PASSED [0.0059s] [ 17%]
2025-08-14T23:40:03.3479362Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_signbit_cuda_float32 PASSED [0.0057s] [ 17%]
2025-08-14T23:40:03.3479645Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_signbit_cuda_float64 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3481416Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_signbit_cuda_int16 PASSED [0.0055s] [ 17%]
2025-08-14T23:40:03.3481698Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_signbit_cuda_int32 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3481971Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_signbit_cuda_int64 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3482300Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sin_cuda_complex64 PASSED [0.0069s] [ 17%]
2025-08-14T23:40:03.3482604Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sin_cuda_int16 PASSED [0.0056s] [ 17%]
2025-08-14T23:40:03.3482867Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sin_cuda_int8 PASSED [0.0057s] [ 17%]
2025-08-14T23:40:03.3483209Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sin_cuda_uint8 PASSED [0.0058s] [ 17%]
2025-08-14T23:40:03.3483485Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_complex32 PASSED [0.0059s] [ 17%]
2025-08-14T23:40:03.3483767Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_float32 PASSED [0.0059s] [ 17%]
2025-08-14T23:40:03.3484089Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_float64 PASSED [0.0058s] [ 17%]
2025-08-14T23:40:03.3484360Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_int32 PASSED [0.0058s] [ 17%]
2025-08-14T23:40:03.3484633Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_int64 PASSED [0.0059s] [ 17%]
2025-08-14T23:40:03.3484893Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_int8 PASSED [0.0061s] [ 18%]
2025-08-14T23:40:03.3485164Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sinh_cuda_uint8 PASSED [0.0057s] [ 18%]
2025-08-14T23:40:03.3485524Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_bfloat16 PASSED [0.0057s] [ 18%]
2025-08-14T23:40:03.3485798Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_bool PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3486071Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_float32 PASSED [0.0059s] [ 18%]
2025-08-14T23:40:03.3486340Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_int16 PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3486600Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_int64 PASSED [0.0056s] [ 18%]
2025-08-14T23:40:03.3486869Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_sqrt_cuda_int8 PASSED [0.0057s] [ 18%]
2025-08-14T23:40:03.3487140Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_bfloat16 PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3487418Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_float16 PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3487725Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_float64 PASSED [0.0057s] [ 18%]
2025-08-14T23:40:03.3487991Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_int16 PASSED [0.0056s] [ 18%]
2025-08-14T23:40:03.3488254Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_int32 PASSED [0.0059s] [ 18%]
2025-08-14T23:40:03.3488521Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tan_cuda_int8 PASSED [0.0059s] [ 18%]
2025-08-14T23:40:03.3488812Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_bfloat16 PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3489085Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_complex32 PASSED [0.0068s] [ 18%]
2025-08-14T23:40:03.3489354Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_float32 PASSED [0.0060s] [ 18%]
2025-08-14T23:40:03.3489654Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_int16 PASSED [0.0059s] [ 18%]
2025-08-14T23:40:03.3489921Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_int32 PASSED [0.0058s] [ 18%]
2025-08-14T23:40:03.3490181Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_tanh_cuda_int8 PASSED [0.0057s] [ 18%]
2025-08-14T23:40:03.3490447Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_trunc_cuda_float16 PASSED [0.0056s] [ 18%]
2025-08-14T23:40:03.3490714Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_trunc_cuda_int32 PASSED [0.0056s] [ 18%]
2025-08-14T23:40:03.3490982Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_csr_unary_out_trunc_cuda_uint8 PASSED [0.0056s] [ 18%]
2025-08-14T23:40:03.3491237Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_cuda_complex64 PASSED [0.0810s] [ 19%]
2025-08-14T23:40:03.3491480Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_cuda_float32 PASSED [0.1566s] [ 19%]
2025-08-14T23:40:03.3491715Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_cuda_float64 PASSED [0.0563s] [ 19%]
2025-08-14T23:40:03.3491983Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_cuda_bfloat16 SKIPPED [0.0008s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3492284Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_cuda_float16 SKIPPED [0.0005s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3492599Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_cuda_float32 SKIPPED [0.0004s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3493078Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_sum_cuda_bfloat16 SKIPPED [0.0004s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3493387Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_sum_cuda_float16 SKIPPED [0.0004s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3493690Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_mm_reduce_sum_cuda_float32 SKIPPED [0.0004s] (Only runs on cpu) [ 19%]
2025-08-14T23:40:03.3493995Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_to_sparse_compressed_SparseBSR_cuda_float64 PASSED [0.0816s] [ 19%]
2025-08-14T23:40:03.3494299Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_to_sparse_compressed_SparseCSC_cuda_float64 PASSED [0.0093s] [ 19%]
2025-08-14T23:40:03.3494649Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_triangular_solve_cuda_complex128 SKIPPED [0.0006s] (cuSparse Generic API SpSV is not available) [ 19%]
2025-08-14T23:40:03.3495043Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_triangular_solve_cuda_complex64 SKIPPED [0.0004s] (cuSparse Generic API SpSV is not available) [ 19%]
2025-08-14T23:40:03.3495429Z test_sparse_csr.py::TestSparseCSRCUDA::test_sparse_triangular_solve_cuda_float64 SKIPPED [0.0004s] (cuSparse Generic API SpSV is not available) [ 19%]
2025-08-14T23:40:03.3495736Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_bfloat16 PASSED [0.0280s] [ 19%]
2025-08-14T23:40:03.3495964Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_complex128 PASSED [0.0190s] [ 19%]
2025-08-14T23:40:03.3496219Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_complex64 PASSED [0.0187s] [ 19%]
2025-08-14T23:40:03.3496442Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_float16 PASSED [0.0188s] [ 19%]
2025-08-14T23:40:03.3499674Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_float64 PASSED [0.0235s] [ 19%]
2025-08-14T23:40:03.3499994Z test_sparse_csr.py::TestSparseCSRCUDA::test_sum_cuda_int32 PASSED [0.0172s] [ 19%]
2025-08-14T23:40:03.3500332Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_bfloat16 PASSED [0.7549s] [ 19%]
2025-08-14T23:40:03.3500641Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_bool PASSED [0.6937s] [ 19%]
2025-08-14T23:40:03.3500903Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_float32 PASSED [0.7561s] [ 20%]
2025-08-14T23:40:03.3501163Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_float64 PASSED [0.7525s] [ 20%]
2025-08-14T23:40:03.3501437Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_int16 PASSED [0.6901s] [ 20%]
2025-08-14T23:40:03.3501692Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_int32 PASSED [0.6919s] [ 20%]
2025-08-14T23:40:03.3501942Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_int8 PASSED [0.6898s] [ 20%]
2025-08-14T23:40:03.3502194Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSC_cuda_uint8 PASSED [0.6907s] [ 20%]
2025-08-14T23:40:03.3502444Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSR_cuda_bool PASSED [0.7652s] [ 20%]
2025-08-14T23:40:03.3504063Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSR_cuda_complex128 PASSED [0.8252s] [ 20%]
2025-08-14T23:40:03.3504328Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSR_cuda_float32 PASSED [0.8151s] [ 20%]
2025-08-14T23:40:03.3504588Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSR_cuda_float64 PASSED [0.8100s] [ 20%]
2025-08-14T23:40:03.3504842Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseBSR_cuda_int8 PASSED [0.7441s] [ 20%]
2025-08-14T23:40:03.3505092Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSC_cuda_int32 PASSED [0.4434s] [ 20%]
2025-08-14T23:40:03.3505342Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSC_cuda_int8 PASSED [0.4410s] [ 20%]
2025-08-14T23:40:03.3505630Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_bfloat16 PASSED [0.5138s] [ 20%]
2025-08-14T23:40:03.3505884Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_bool PASSED [0.4778s] [ 20%]
2025-08-14T23:40:03.3506142Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_complex64 PASSED [0.5209s] [ 20%]
2025-08-14T23:40:03.3506402Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_float16 PASSED [0.5090s] [ 20%]
2025-08-14T23:40:03.3507667Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_int32 PASSED [0.4755s] [ 20%]
2025-08-14T23:40:03.3507923Z test_sparse_csr.py::TestSparseCSRCUDA::test_transpose_SparseCSR_cuda_int64 PASSED [0.4786s] [ 20%]
2025-08-14T23:40:03.3508204Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_abs_cuda_bfloat16 PASSED [0.0023s] [ 20%]
2025-08-14T23:40:03.3508508Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_abs_cuda_int16 PASSED [0.0019s] [ 20%]
2025-08-14T23:40:03.3508805Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_abs_cuda_int8 PASSED [0.0018s] [ 20%]
2025-08-14T23:40:03.3509101Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_abs_cuda_uint8 PASSED [0.0018s] [ 20%]
2025-08-14T23:40:03.3509397Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_angle_cuda_bool PASSED [0.0026s] [ 21%]
2025-08-14T23:40:03.3509700Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_angle_cuda_float32 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3510031Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_angle_cuda_int16 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3510330Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_angle_cuda_int32 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3510630Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_angle_cuda_uint8 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3511916Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_bfloat16 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3512239Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_bool PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3512543Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_complex128 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3512866Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_float16 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3513268Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_int16 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3513563Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_int64 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3513859Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asin_cuda_uint8 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3514157Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_bool PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3514465Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_complex128 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3514772Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_float16 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3515074Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_int16 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3516369Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_int32 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3516668Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_int64 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3516987Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_asinh_cuda_uint8 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3517290Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_bfloat16 PASSED [0.0021s] [ 21%]
2025-08-14T23:40:03.3517599Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_complex128 PASSED [0.0036s] [ 21%]
2025-08-14T23:40:03.3517910Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_complex64 PASSED [0.0022s] [ 21%]
2025-08-14T23:40:03.3518218Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_float16 PASSED [0.0020s] [ 21%]
2025-08-14T23:40:03.3518524Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_float64 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3518825Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atan_cuda_uint8 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3519129Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_bfloat16 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3519438Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_complex128 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3520715Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_float16 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3521016Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_int16 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3521314Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_int8 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3521633Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_atanh_cuda_uint8 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3521929Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_ceil_cuda_int16 PASSED [0.0018s] [ 22%]
2025-08-14T23:40:03.3522225Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_ceil_cuda_int32 PASSED [0.0018s] [ 22%]
2025-08-14T23:40:03.3522519Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_ceil_cuda_int64 PASSED [0.0019s] [ 22%]
2025-08-14T23:40:03.3522832Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_ceil_cuda_int8 PASSED [0.0019s] [ 22%]
2025-08-14T23:40:03.3523253Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_conj_physical_cuda_bfloat16 PASSED [0.0019s] [ 22%]
2025-08-14T23:40:03.3523598Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_conj_physical_cuda_float32 PASSED [0.0022s] [ 22%]
2025-08-14T23:40:03.3524899Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_conj_physical_cuda_int64 PASSED [0.0017s] [ 22%]
2025-08-14T23:40:03.3525222Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_conj_physical_cuda_int8 PASSED [0.0017s] [ 22%]
2025-08-14T23:40:03.3525588Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_bfloat16 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3525901Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_bool PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3526209Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_float64 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3526514Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_int16 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3526819Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_int32 PASSED [0.0020s] [ 22%]
2025-08-14T23:40:03.3527120Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_deg2rad_cuda_int64 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3527420Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erf_cuda_float16 PASSED [0.0021s] [ 22%]
2025-08-14T23:40:03.3527745Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erf_cuda_float32 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3528048Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erf_cuda_int32 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3529323Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erf_cuda_int64 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3529628Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erfinv_cuda_float32 PASSED [0.0028s] [ 23%]
2025-08-14T23:40:03.3529936Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erfinv_cuda_float64 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3530241Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erfinv_cuda_int16 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3530544Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_erfinv_cuda_int64 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3530847Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_bool PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3531151Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_float16 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3531453Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_int16 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3531752Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_int32 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3532073Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_int8 PASSED [0.0021s] [ 23%]
2025-08-14T23:40:03.3532369Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_expm1_cuda_uint8 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3533726Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_floor_cuda_int16 PASSED [0.0019s] [ 23%]
2025-08-14T23:40:03.3534026Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_floor_cuda_int32 PASSED [0.0019s] [ 23%]
2025-08-14T23:40:03.3534346Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_floor_cuda_uint8 PASSED [0.0018s] [ 23%]
2025-08-14T23:40:03.3534647Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_frac_cuda_float32 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3534967Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_bfloat16 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3535269Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_bool PASSED [0.0018s] [ 23%]
2025-08-14T23:40:03.3535574Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_complex128 PASSED [0.0019s] [ 23%]
2025-08-14T23:40:03.3535888Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_complex32 PASSED [0.0020s] [ 23%]
2025-08-14T23:40:03.3536196Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_complex64 PASSED [0.0019s] [ 23%]
2025-08-14T23:40:03.3536506Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_float16 PASSED [0.0019s] [ 23%]
2025-08-14T23:40:03.3536811Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_float64 PASSED [0.0020s] [ 24%]
2025-08-14T23:40:03.3538118Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_int16 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3538419Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_int32 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3538717Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isinf_cuda_uint8 PASSED [0.0022s] [ 24%]
2025-08-14T23:40:03.3539054Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_complex128 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3539364Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_complex64 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3539671Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_float32 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3539973Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_float64 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3540273Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_int32 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3540570Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isnan_cuda_int8 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3540875Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_bfloat16 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3542162Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_bool PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3542474Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_float16 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3542787Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_float32 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3543202Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_float64 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3543541Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_int64 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3543846Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isneginf_cuda_int8 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3544160Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_bfloat16 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3544473Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_bool PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3544798Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_float16 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3545111Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_float32 PASSED [0.0019s] [ 24%]
2025-08-14T23:40:03.3545437Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_float64 PASSED [0.0020s] [ 24%]
2025-08-14T23:40:03.3546744Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_int64 PASSED [0.0018s] [ 24%]
2025-08-14T23:40:03.3547052Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_isposinf_cuda_uint8 PASSED [0.0019s] [ 25%]
2025-08-14T23:40:03.3547362Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_bfloat16 PASSED [0.0022s] [ 25%]
2025-08-14T23:40:03.3547668Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_complex64 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3547976Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_float16 PASSED [0.0020s] [ 25%]
2025-08-14T23:40:03.3548279Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_float32 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3548585Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_float64 PASSED [0.0020s] [ 25%]
2025-08-14T23:40:03.3548888Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_int16 PASSED [0.0043s] [ 25%]
2025-08-14T23:40:03.3549189Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_log1p_cuda_uint8 PASSED [0.0033s] [ 25%]
2025-08-14T23:40:03.3549525Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_neg_cuda_bfloat16 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3549831Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_neg_cuda_complex128 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3551115Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_neg_cuda_float32 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3551414Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_neg_cuda_int64 PASSED [0.0018s] [ 25%]
2025-08-14T23:40:03.3551707Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_neg_cuda_int8 PASSED [0.0018s] [ 25%]
2025-08-14T23:40:03.3552026Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_bfloat16 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3552363Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_float16 PASSED [0.0020s] [ 25%]
2025-08-14T23:40:03.3552701Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_float32 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3553115Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_float64 PASSED [0.0027s] [ 25%]
2025-08-14T23:40:03.3553446Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_int32 PASSED [0.0018s] [ 25%]
2025-08-14T23:40:03.3553777Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_int64 PASSED [0.0018s] [ 25%]
2025-08-14T23:40:03.3554132Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_int8 PASSED [0.0019s] [ 25%]
2025-08-14T23:40:03.3555453Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_nn_functional_relu_cuda_uint8 PASSED [0.0018s] [ 25%]
2025-08-14T23:40:03.3555782Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_bfloat16 PASSED [0.0019s] [ 25%]
2025-08-14T23:40:03.3556101Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_complex32 PASSED [0.0021s] [ 25%]
2025-08-14T23:40:03.3556441Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_complex64 PASSED [0.0020s] [ 26%]
2025-08-14T23:40:03.3556757Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_float64 PASSED [0.0019s] [ 26%]
2025-08-14T23:40:03.3557082Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_int32 PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3557390Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_int64 PASSED [0.0017s] [ 26%]
2025-08-14T23:40:03.3557694Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_int8 PASSED [0.0017s] [ 26%]
2025-08-14T23:40:03.3557999Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_positive_cuda_uint8 PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3558305Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_rad2deg_cuda_bool PASSED [0.0021s] [ 26%]
2025-08-14T23:40:03.3558612Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_rad2deg_cuda_int16 PASSED [0.0021s] [ 26%]
2025-08-14T23:40:03.3559899Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_bfloat16 PASSED [0.0021s] [ 26%]
2025-08-14T23:40:03.3560207Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_float32 PASSED [0.0020s] [ 26%]
2025-08-14T23:40:03.3560509Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_float64 PASSED [0.0020s] [ 26%]
2025-08-14T23:40:03.3560810Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_int32 PASSED [0.0019s] [ 26%]
2025-08-14T23:40:03.3561130Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_int8 PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3561428Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_round_cuda_uint8 PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3561735Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_complex128 PASSED [0.0022s] [ 26%]
2025-08-14T23:40:03.3562039Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_complex32 PASSED [0.0021s] [ 26%]
2025-08-14T23:40:03.3562346Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_complex64 PASSED [0.0036s] [ 26%]
2025-08-14T23:40:03.3562649Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_float16 PASSED [0.0021s] [ 26%]
2025-08-14T23:40:03.3563010Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_float32 PASSED [0.0020s] [ 26%]
2025-08-14T23:40:03.3564303Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_int32 PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3564598Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sgn_cuda_int8 PASSED [0.0019s] [ 26%]
2025-08-14T23:40:03.3564897Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_bfloat16 PASSED [0.0020s] [ 26%]
2025-08-14T23:40:03.3565196Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_bool PASSED [0.0018s] [ 26%]
2025-08-14T23:40:03.3565603Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_float16 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3565904Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_int32 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3566199Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_int64 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3566496Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_int8 PASSED [0.0019s] [ 27%]
2025-08-14T23:40:03.3566807Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sign_cuda_uint8 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3567114Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_bfloat16 PASSED [0.0019s] [ 27%]
2025-08-14T23:40:03.3567425Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_float16 PASSED [0.0022s] [ 27%]
2025-08-14T23:40:03.3568748Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_float32 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3569057Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_float64 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3569362Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_int32 PASSED [0.0019s] [ 27%]
2025-08-14T23:40:03.3569666Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_int64 PASSED [0.0018s] [ 27%]
2025-08-14T23:40:03.3569970Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_signbit_cuda_int8 PASSED [0.0019s] [ 27%]
2025-08-14T23:40:03.3570274Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sin_cuda_complex32 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3570578Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sin_cuda_complex64 PASSED [0.0020s] [ 27%]
2025-08-14T23:40:03.3570882Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sin_cuda_float64 PASSED [0.0020s] [ 27%]
2025-08-14T23:40:03.3571178Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sin_cuda_int32 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3571486Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sin_cuda_int8 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3572771Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_complex32 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3573142Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_float16 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3573445Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_float64 PASSED [0.0020s] [ 27%]
2025-08-14T23:40:03.3573747Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_int16 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3574044Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_int32 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3574339Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_int64 PASSED [0.0021s] [ 27%]
2025-08-14T23:40:03.3574635Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sinh_cuda_int8 PASSED [0.0020s] [ 28%]
2025-08-14T23:40:03.3574934Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_bfloat16 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3575232Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_bool PASSED [0.0020s] [ 28%]
2025-08-14T23:40:03.3575531Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_complex128 PASSED [0.0030s] [ 28%]
2025-08-14T23:40:03.3575840Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_complex32 PASSED [0.2219s] [ 28%]
2025-08-14T23:40:03.3577167Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_float16 PASSED [0.0025s] [ 28%]
2025-08-14T23:40:03.3577472Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_float32 PASSED [0.0022s] [ 28%]
2025-08-14T23:40:03.3577775Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_float64 PASSED [0.0022s] [ 28%]
2025-08-14T23:40:03.3578074Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_int64 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3578393Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_sqrt_cuda_uint8 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3578694Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_bfloat16 PASSED [0.0022s] [ 28%]
2025-08-14T23:40:03.3579013Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_bool PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3579314Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_complex32 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3579620Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_complex64 PASSED [0.0022s] [ 28%]
2025-08-14T23:40:03.3579922Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_float16 PASSED [0.0020s] [ 28%]
2025-08-14T23:40:03.3580220Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_float32 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3581512Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_float64 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3581809Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_int16 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3582103Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tan_cuda_int64 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3582403Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_bfloat16 PASSED [0.0026s] [ 28%]
2025-08-14T23:40:03.3582708Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_complex128 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3583101Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_complex64 PASSED [0.0033s] [ 28%]
2025-08-14T23:40:03.3583402Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_int16 PASSED [0.0021s] [ 28%]
2025-08-14T23:40:03.3583698Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_int64 PASSED [0.0020s] [ 29%]
2025-08-14T23:40:03.3583993Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_tanh_cuda_int8 PASSED [0.0022s] [ 29%]
2025-08-14T23:40:03.3584292Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_float16 PASSED [0.0021s] [ 29%]
2025-08-14T23:40:03.3584598Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_float32 PASSED [0.0020s] [ 29%]
2025-08-14T23:40:03.3585895Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_int16 PASSED [0.0018s] [ 29%]
2025-08-14T23:40:03.3586196Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_int32 PASSED [0.0019s] [ 29%]
2025-08-14T23:40:03.3586493Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_int64 PASSED [0.0018s] [ 29%]
2025-08-14T23:40:03.3586790Z test_sparse_csr.py::TestSparseCSRCUDA::test_zero_to_zero_correspondence_unary_trunc_cuda_int8 PASSED [0.0018s] [ 29%]
2025-08-14T23:40:03.3587078Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_bfloat16 PASSED [0.0245s] [ 29%]
2025-08-14T23:40:03.3587356Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_complex128 PASSED [0.0248s] [ 29%]
2025-08-14T23:40:03.3587660Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_complex64 PASSED [0.0245s] [ 29%]
2025-08-14T23:40:03.3587934Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_float16 PASSED [0.0233s] [ 29%]
2025-08-14T23:40:03.3588206Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_float64 PASSED [0.0235s] [ 29%]
2025-08-14T23:40:03.3588475Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_int16 PASSED [0.0160s] [ 29%]
2025-08-14T23:40:03.3589765Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_int32 PASSED [0.0160s] [ 29%]
2025-08-14T23:40:03.3590031Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSC_cuda_int64 PASSED [0.0161s] [ 29%]
2025-08-14T23:40:03.3590300Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_bfloat16 PASSED [0.0233s] [ 29%]
2025-08-14T23:40:03.3590594Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_complex128 PASSED [0.0245s] [ 29%]
2025-08-14T23:40:03.3590872Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_complex64 PASSED [0.0245s] [ 29%]
2025-08-14T23:40:03.3591145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_float16 PASSED [0.0232s] [ 29%]
2025-08-14T23:40:03.3591412Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_int8 PASSED [0.0160s] [ 29%]
2025-08-14T23:40:03.3591673Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseBSR_cuda_uint8 PASSED [0.0160s] [ 29%]
2025-08-14T23:40:03.3591939Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_bool PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3592209Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_complex128 PASSED [0.0238s] [ 30%]
2025-08-14T23:40:03.3592486Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_complex64 PASSED [0.0240s] [ 30%]
2025-08-14T23:40:03.3593825Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_float16 PASSED [0.0227s] [ 30%]
2025-08-14T23:40:03.3594095Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_float32 PASSED [0.0225s] [ 30%]
2025-08-14T23:40:03.3594359Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_int32 PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3594644Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_int64 PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3594904Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_int8 PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3595166Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSC_cuda_uint8 PASSED [0.0155s] [ 30%]
2025-08-14T23:40:03.3595433Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_bfloat16 PASSED [0.0228s] [ 30%]
2025-08-14T23:40:03.3595708Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_complex128 PASSED [0.0238s] [ 30%]
2025-08-14T23:40:03.3595985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_complex64 PASSED [0.0238s] [ 30%]
2025-08-14T23:40:03.3596257Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_float32 PASSED [0.0225s] [ 30%]
2025-08-14T23:40:03.3596523Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_int32 PASSED [0.0153s] [ 30%]
2025-08-14T23:40:03.3597787Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_int8 PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3598050Z test_sparse_csr.py::TestSparseCompressedCUDA::test_clone_SparseCSR_cuda_uint8 PASSED [0.0154s] [ 30%]
2025-08-14T23:40:03.3598336Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_abs_cuda_complex32 PASSED [0.0028s] [ 30%]
2025-08-14T23:40:03.3598643Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_abs_cuda_complex64 PASSED [0.0026s] [ 30%]
2025-08-14T23:40:03.3598971Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_abs_cuda_float16 PASSED [0.0026s] [ 30%]
2025-08-14T23:40:03.3599266Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_abs_cuda_int8 PASSED [0.0026s] [ 30%]
2025-08-14T23:40:03.3599556Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_abs_cuda_uint8 PASSED [0.0027s] [ 30%]
2025-08-14T23:40:03.3599851Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_angle_cuda_bool PASSED [0.0026s] [ 30%]
2025-08-14T23:40:03.3600164Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_angle_cuda_float32 PASSED [0.0025s] [ 30%]
2025-08-14T23:40:03.3600464Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_angle_cuda_int16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3600760Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_angle_cuda_int64 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3602072Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_angle_cuda_int8 PASSED [0.0027s] [ 31%]
2025-08-14T23:40:03.3602374Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asin_cuda_bfloat16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3602679Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asin_cuda_complex128 PASSED [0.0025s] [ 31%]
2025-08-14T23:40:03.3603085Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asin_cuda_complex32 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3603386Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asin_cuda_uint8 PASSED [0.0027s] [ 31%]
2025-08-14T23:40:03.3603687Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_bfloat16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3603987Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_bool PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3604294Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_complex128 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3604603Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_complex64 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3604907Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_float16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3606433Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_int64 PASSED [0.0028s] [ 31%]
2025-08-14T23:40:03.3606733Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_asinh_cuda_int8 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3607033Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_bfloat16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3607332Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_bool PASSED [0.0025s] [ 31%]
2025-08-14T23:40:03.3607633Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_complex32 PASSED [0.0027s] [ 31%]
2025-08-14T23:40:03.3607937Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_float16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3608236Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_float64 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3608536Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_int16 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3608830Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_int32 PASSED [0.0026s] [ 31%]
2025-08-14T23:40:03.3609124Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atan_cuda_int8 PASSED [0.0025s] [ 31%]
2025-08-14T23:40:03.3609426Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_complex128 PASSED [0.0027s] [ 31%]
2025-08-14T23:40:03.3610749Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_complex32 PASSED [0.0027s] [ 32%]
2025-08-14T23:40:03.3611086Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_complex64 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3611391Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_float16 PASSED [0.0027s] [ 32%]
2025-08-14T23:40:03.3611695Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_float32 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3611997Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_int16 PASSED [0.0028s] [ 32%]
2025-08-14T23:40:03.3612308Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_int32 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3612602Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_atanh_cuda_uint8 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3612979Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_ceil_cuda_bfloat16 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3613281Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_ceil_cuda_float32 PASSED [0.0025s] [ 32%]
2025-08-14T23:40:03.3613577Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_ceil_cuda_int32 PASSED [0.0024s] [ 32%]
2025-08-14T23:40:03.3613888Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_bfloat16 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3615256Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_complex128 PASSED [0.0027s] [ 32%]
2025-08-14T23:40:03.3615595Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_complex32 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3615923Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_float32 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3616250Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_float64 PASSED [0.0025s] [ 32%]
2025-08-14T23:40:03.3616574Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_int8 PASSED [0.0025s] [ 32%]
2025-08-14T23:40:03.3616892Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_conj_physical_cuda_uint8 PASSED [0.0025s] [ 32%]
2025-08-14T23:40:03.3617230Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_deg2rad_cuda_bfloat16 PASSED [0.0026s] [ 32%]
2025-08-14T23:40:03.3617540Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_deg2rad_cuda_bool PASSED [0.0028s] [ 32%]
2025-08-14T23:40:03.3617846Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_deg2rad_cuda_float32 PASSED [0.2307s] [ 32%]
2025-08-14T23:40:03.3618152Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_deg2rad_cuda_int64 PASSED [0.0036s] [ 32%]
2025-08-14T23:40:03.3618453Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_bool PASSED [0.0027s] [ 32%]
2025-08-14T23:40:03.3618751Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_float32 PASSED [0.0027s] [ 32%]
2025-08-14T23:40:03.3620021Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_float64 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3620321Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_int16 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3620612Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_int32 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3620905Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erf_cuda_int8 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3621202Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erfinv_cuda_bfloat16 PASSED [0.0041s] [ 33%]
2025-08-14T23:40:03.3621507Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erfinv_cuda_float16 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3621834Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_erfinv_cuda_float64 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3622135Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_expm1_cuda_bfloat16 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3622438Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_expm1_cuda_float64 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3622740Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_expm1_cuda_int8 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3623131Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_expm1_cuda_uint8 PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3625057Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_floor_cuda_bfloat16 PASSED [0.0028s] [ 33%]
2025-08-14T23:40:03.3625401Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_floor_cuda_float32 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3625708Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_floor_cuda_float64 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3626009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_floor_cuda_int64 PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3626307Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_floor_cuda_uint8 PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3626608Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_frac_cuda_bfloat16 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3626912Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_frac_cuda_float64 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3627214Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_bfloat16 PASSED [0.0027s] [ 33%]
2025-08-14T23:40:03.3627516Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_bool PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3627820Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_complex32 PASSED [0.0026s] [ 33%]
2025-08-14T23:40:03.3629092Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_complex64 PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3629422Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_int16 PASSED [0.0025s] [ 33%]
2025-08-14T23:40:03.3629719Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_int64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3630017Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_int8 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3630312Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isinf_cuda_uint8 PASSED [0.0028s] [ 34%]
2025-08-14T23:40:03.3630617Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_bfloat16 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3630923Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_float16 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3631226Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_float64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3631527Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_int16 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3631824Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_int64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3632120Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_int8 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3633435Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isnan_cuda_uint8 PASSED [0.0024s] [ 34%]
2025-08-14T23:40:03.3633747Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isneginf_cuda_float32 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3634084Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isneginf_cuda_float64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3634393Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isneginf_cuda_int32 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3634698Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isneginf_cuda_int64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3635005Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isneginf_cuda_uint8 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3635347Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isposinf_cuda_bfloat16 PASSED [0.0026s] [ 34%]
2025-08-14T23:40:03.3635662Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isposinf_cuda_float64 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3635985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isposinf_cuda_int64 PASSED [0.0027s] [ 34%]
2025-08-14T23:40:03.3636292Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_isposinf_cuda_int8 PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3636596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_bfloat16 PASSED [0.0026s] [ 34%]
2025-08-14T23:40:03.3637872Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_bool PASSED [0.0025s] [ 34%]
2025-08-14T23:40:03.3638179Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_complex64 PASSED [0.0027s] [ 34%]
2025-08-14T23:40:03.3638489Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_float64 PASSED [0.0026s] [ 34%]
2025-08-14T23:40:03.3638790Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_int16 PASSED [0.0026s] [ 35%]
2025-08-14T23:40:03.3639088Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_int32 PASSED [0.0026s] [ 35%]
2025-08-14T23:40:03.3639387Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_int64 PASSED [0.0026s] [ 35%]
2025-08-14T23:40:03.3639682Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_int8 PASSED [0.0026s] [ 35%]
2025-08-14T23:40:03.3639978Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_log1p_cuda_uint8 PASSED [0.0026s] [ 35%]
2025-08-14T23:40:03.3640398Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amax_cuda_float16 SKIPPED [0.0011s] (masked.amax does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3640883Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amax_cuda_int8 SKIPPED [0.0009s] (masked.amax does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3641365Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amin_cuda_bfloat16 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3642752Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amin_cuda_int16 SKIPPED [0.0011s] (masked.amin does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3643320Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amin_cuda_int32 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3643801Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_amin_cuda_int64 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3644283Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_mean_cuda_bfloat16 SKIPPED [0.0009s] (masked.mean does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3644762Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_bool SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3645268Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_complex64 SKIPPED [0.0012s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3645802Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_float16 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3646303Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_float64 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3646782Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_int16 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3647273Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_int32 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3648687Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_prod_cuda_uint8 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3649174Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_bfloat16 SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3649664Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_complex64 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3650150Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_float16 SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3650628Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_float32 SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_bsc layout) [ 35%]
2025-08-14T23:40:03.3651102Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_int16 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsc layout) [ 36%]
2025-08-14T23:40:03.3651594Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_int32 SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_bsc layout) [ 36%]
2025-08-14T23:40:03.3652067Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_masked_sum_cuda_int8 SKIPPED [0.0011s] (masked.sum does not support input with torch.sparse_bsc layout) [ 36%]
2025-08-14T23:40:03.3652457Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_bfloat16 PASSED [0.0049s] [ 36%]
2025-08-14T23:40:03.3652758Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_bool PASSED [0.0041s] [ 36%]
2025-08-14T23:40:03.3653136Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_float32 PASSED [0.0047s] [ 36%]
2025-08-14T23:40:03.3654364Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_float64 PASSED [0.0049s] [ 36%]
2025-08-14T23:40:03.3654666Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_mul_cuda_int64 PASSED [0.0041s] [ 36%]
2025-08-14T23:40:03.3654964Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_bfloat16 PASSED [0.0026s] [ 36%]
2025-08-14T23:40:03.3655262Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_int64 PASSED [0.0025s] [ 36%]
2025-08-14T23:40:03.3655557Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_int8 PASSED [0.0027s] [ 36%]
2025-08-14T23:40:03.3655855Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_neg_cuda_uint8 PASSED [0.0025s] [ 36%]
2025-08-14T23:40:03.3656202Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_bfloat16 PASSED [0.0032s] [ 36%]
2025-08-14T23:40:03.3656543Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_float32 PASSED [0.0035s] [ 36%]
2025-08-14T23:40:03.3656880Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int16 PASSED [0.0029s] [ 36%]
2025-08-14T23:40:03.3657227Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int64 PASSED [0.0030s] [ 36%]
2025-08-14T23:40:03.3657558Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_int8 PASSED [0.0030s] [ 36%]
2025-08-14T23:40:03.3658848Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_nn_functional_relu_cuda_uint8 PASSED [0.0030s] [ 36%]
2025-08-14T23:40:03.3659177Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_complex64 PASSED [0.0027s] [ 36%]
2025-08-14T23:40:03.3659495Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float16 PASSED [0.0030s] [ 36%]
2025-08-14T23:40:03.3659809Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float32 PASSED [0.0025s] [ 36%]
2025-08-14T23:40:03.3660120Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_float64 PASSED [0.0026s] [ 36%]
2025-08-14T23:40:03.3660430Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_positive_cuda_int64 PASSED [0.0025s] [ 36%]
2025-08-14T23:40:03.3660741Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_bfloat16 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3661051Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int32 PASSED [0.0028s] [ 37%]
2025-08-14T23:40:03.3661359Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int64 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3661661Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_rad2deg_cuda_int8 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3662977Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_complex128 PASSED [0.0057s] [ 37%]
2025-08-14T23:40:03.3663306Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_complex64 PASSED [0.0042s] [ 37%]
2025-08-14T23:40:03.3663628Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_randn_like_cuda_float32 PASSED [0.0040s] [ 37%]
2025-08-14T23:40:03.3663939Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_float32 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3664246Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_int16 PASSED [0.0024s] [ 37%]
2025-08-14T23:40:03.3664547Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_round_cuda_uint8 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3664843Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_bool PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3665142Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_complex64 PASSED [0.0029s] [ 37%]
2025-08-14T23:40:03.3665443Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_float64 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3665738Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int16 PASSED [0.0024s] [ 37%]
2025-08-14T23:40:03.3666034Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int64 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3667258Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_int8 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3667586Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sgn_cuda_uint8 PASSED [0.0028s] [ 37%]
2025-08-14T23:40:03.3667882Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_bfloat16 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3668185Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float16 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3668487Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float32 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3668801Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_float64 PASSED [0.0026s] [ 37%]
2025-08-14T23:40:03.3669098Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_int16 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3669393Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_int64 PASSED [0.0025s] [ 37%]
2025-08-14T23:40:03.3669706Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sign_cuda_uint8 PASSED [0.0025s] [ 38%]
2025-08-14T23:40:03.3670009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_signbit_cuda_float32 PASSED [0.0024s] [ 38%]
2025-08-14T23:40:03.3670318Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_signbit_cuda_float64 PASSED [0.0025s] [ 38%]
2025-08-14T23:40:03.3671553Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_signbit_cuda_int16 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3671861Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_signbit_cuda_int64 PASSED [0.0025s] [ 38%]
2025-08-14T23:40:03.3672163Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_bfloat16 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3672459Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_bool PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3672763Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_complex128 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3673141Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_float64 PASSED [0.0028s] [ 38%]
2025-08-14T23:40:03.3673437Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sin_cuda_int32 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3673760Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_complex128 PASSED [0.0027s] [ 38%]
2025-08-14T23:40:03.3674068Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_complex32 PASSED [0.0027s] [ 38%]
2025-08-14T23:40:03.3674374Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_float16 PASSED [0.0025s] [ 38%]
2025-08-14T23:40:03.3674674Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_float64 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3675921Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sinh_cuda_int64 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3676223Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_bfloat16 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3676522Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_bool PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3676826Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_complex128 PASSED [0.0027s] [ 38%]
2025-08-14T23:40:03.3677131Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_float16 PASSED [0.0025s] [ 38%]
2025-08-14T23:40:03.3677433Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_float32 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3677732Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_int16 PASSED [0.0027s] [ 38%]
2025-08-14T23:40:03.3678029Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sqrt_cuda_int64 PASSED [0.0026s] [ 38%]
2025-08-14T23:40:03.3678353Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_bfloat16 PASSED [0.0069s] [ 38%]
2025-08-14T23:40:03.3678655Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_complex128 PASSED [0.0071s] [ 39%]
2025-08-14T23:40:03.3679886Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_complex32 PASSED [0.0095s] [ 39%]
2025-08-14T23:40:03.3680193Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_complex64 PASSED [0.0068s] [ 39%]
2025-08-14T23:40:03.3680515Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_float64 PASSED [0.0065s] [ 39%]
2025-08-14T23:40:03.3680810Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_int32 PASSED [0.0058s] [ 39%]
2025-08-14T23:40:03.3681118Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_int64 PASSED [0.0054s] [ 39%]
2025-08-14T23:40:03.3681412Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_int8 PASSED [0.0056s] [ 39%]
2025-08-14T23:40:03.3681704Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_sum_cuda_uint8 PASSED [0.0055s] [ 39%]
2025-08-14T23:40:03.3682004Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_complex128 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3682310Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_complex64 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3682614Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_float16 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3682995Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_float32 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3684221Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_float64 PASSED [0.0027s] [ 39%]
2025-08-14T23:40:03.3684519Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tan_cuda_int64 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3684816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_bfloat16 PASSED [0.0025s] [ 39%]
2025-08-14T23:40:03.3685146Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_complex128 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3685505Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_complex32 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3685813Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_complex64 PASSED [0.0028s] [ 39%]
2025-08-14T23:40:03.3686117Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_float16 PASSED [0.0029s] [ 39%]
2025-08-14T23:40:03.3686417Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_float64 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3686716Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_int32 PASSED [0.0025s] [ 39%]
2025-08-14T23:40:03.3687012Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_int64 PASSED [0.0026s] [ 39%]
2025-08-14T23:40:03.3687306Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_tanh_cuda_int8 PASSED [0.0026s] [ 40%]
2025-08-14T23:40:03.3688618Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_to_sparse_cuda_bool SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsc layout) [ 40%]
2025-08-14T23:40:03.3689087Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_to_sparse_cuda_float16 SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsc layout) [ 40%]
2025-08-14T23:40:03.3689555Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_to_sparse_cuda_int32 SKIPPED [0.0009s] (to_sparse does not support input with torch.sparse_bsc layout) [ 40%]
2025-08-14T23:40:03.3690043Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_to_sparse_cuda_int64 SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsc layout) [ 40%]
2025-08-14T23:40:03.3690502Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_to_sparse_cuda_int8 SKIPPED [0.0016s] (to_sparse does not support input with torch.sparse_bsc layout) [ 40%]
2025-08-14T23:40:03.3690887Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_trunc_cuda_float16 PASSED [0.0027s] [ 40%]
2025-08-14T23:40:03.3691209Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_trunc_cuda_float64 PASSED [0.0029s] [ 40%]
2025-08-14T23:40:03.3691511Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_trunc_cuda_int32 PASSED [0.0024s] [ 40%]
2025-08-14T23:40:03.3691823Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_trunc_cuda_int8 PASSED [0.0025s] [ 40%]
2025-08-14T23:40:03.3692135Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_bfloat16 PASSED [0.0036s] [ 40%]
2025-08-14T23:40:03.3693450Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_bool PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3693773Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_complex128 PASSED [0.0036s] [ 40%]
2025-08-14T23:40:03.3694100Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_complex64 PASSED [0.0036s] [ 40%]
2025-08-14T23:40:03.3694423Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_float16 PASSED [0.0035s] [ 40%]
2025-08-14T23:40:03.3694737Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int16 PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3695051Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int32 PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3695363Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int64 PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3695672Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_int8 PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3696007Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSC_zeros_like_cuda_uint8 PASSED [0.0034s] [ 40%]
2025-08-14T23:40:03.3696309Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_bool PASSED [0.0024s] [ 40%]
2025-08-14T23:40:03.3696610Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_complex32 PASSED [0.0027s] [ 40%]
2025-08-14T23:40:03.3697834Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_complex64 PASSED [0.0028s] [ 40%]
2025-08-14T23:40:03.3698139Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_float16 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3698439Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_float32 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3698736Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_float64 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3699032Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_int16 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3699326Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_int32 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3699618Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_int64 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3699912Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_int8 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3700203Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_abs_cuda_uint8 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3700519Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_bool PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3700823Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_complex128 PASSED [0.0027s] [ 41%]
2025-08-14T23:40:03.3702058Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_float32 PASSED [0.0027s] [ 41%]
2025-08-14T23:40:03.3702359Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int16 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3702674Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int32 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3703034Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_int8 PASSED [0.0028s] [ 41%]
2025-08-14T23:40:03.3703346Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_angle_cuda_uint8 PASSED [0.0025s] [ 41%]
2025-08-14T23:40:03.3703647Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_bfloat16 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3703954Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_complex128 PASSED [0.0027s] [ 41%]
2025-08-14T23:40:03.3704262Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_complex32 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3704568Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float16 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3704871Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float32 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3705172Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_float64 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3706401Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asin_cuda_int8 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3706706Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_bfloat16 PASSED [0.0026s] [ 41%]
2025-08-14T23:40:03.3707007Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_bool PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3707306Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_float64 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3707635Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_int64 PASSED [0.0025s] [ 42%]
2025-08-14T23:40:03.3707932Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_asinh_cuda_int8 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3708225Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_bool PASSED [0.0027s] [ 42%]
2025-08-14T23:40:03.3708523Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_complex64 PASSED [0.0029s] [ 42%]
2025-08-14T23:40:03.3708827Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_float32 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3709123Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atan_cuda_int16 PASSED [0.0025s] [ 42%]
2025-08-14T23:40:03.3710347Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_bfloat16 PASSED [0.0027s] [ 42%]
2025-08-14T23:40:03.3710653Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_bool PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3710956Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_complex128 PASSED [0.0027s] [ 42%]
2025-08-14T23:40:03.3711264Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_float32 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3711566Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_float64 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3711866Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_int16 PASSED [0.0025s] [ 42%]
2025-08-14T23:40:03.3712186Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_atanh_cuda_int64 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3712485Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_bfloat16 PASSED [0.0027s] [ 42%]
2025-08-14T23:40:03.3712786Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_float16 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3713152Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_float32 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3713471Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int32 PASSED [0.0025s] [ 42%]
2025-08-14T23:40:03.3714706Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int64 PASSED [0.0024s] [ 42%]
2025-08-14T23:40:03.3715019Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_int8 PASSED [0.0027s] [ 42%]
2025-08-14T23:40:03.3715314Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_ceil_cuda_uint8 PASSED [0.0025s] [ 42%]
2025-08-14T23:40:03.3715626Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_bfloat16 PASSED [0.0026s] [ 42%]
2025-08-14T23:40:03.3715957Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_complex64 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3716284Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_float32 PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3716609Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_int16 PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3716928Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_conj_physical_cuda_uint8 PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3717250Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_bfloat16 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3717558Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_bool PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3717863Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_float16 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3719116Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_float32 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3719423Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_int32 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3719727Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_int64 PASSED [0.0027s] [ 43%]
2025-08-14T23:40:03.3720029Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_deg2rad_cuda_uint8 PASSED [0.0027s] [ 43%]
2025-08-14T23:40:03.3720331Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_bfloat16 PASSED [0.0029s] [ 43%]
2025-08-14T23:40:03.3720632Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_bool PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3720927Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_float32 PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3721231Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_float64 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3721526Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_int16 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3721820Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_int32 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3722113Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_int64 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3723407Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erf_cuda_int8 PASSED [0.0025s] [ 43%]
2025-08-14T23:40:03.3723726Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erfinv_cuda_bool PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3724027Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erfinv_cuda_float16 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3724335Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erfinv_cuda_float32 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3724637Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erfinv_cuda_int64 PASSED [0.0026s] [ 43%]
2025-08-14T23:40:03.3724951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_erfinv_cuda_uint8 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3725257Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_complex128 PASSED [0.0028s] [ 44%]
2025-08-14T23:40:03.3725985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_complex64 PASSED [0.0027s] [ 44%]
2025-08-14T23:40:03.3726293Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_float16 PASSED [0.0028s] [ 44%]
2025-08-14T23:40:03.3726596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_float64 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3727870Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int16 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3728169Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int32 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3728468Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_expm1_cuda_int64 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3728768Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_float16 PASSED [0.0027s] [ 44%]
2025-08-14T23:40:03.3729070Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int16 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3729367Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int32 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3729663Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_floor_cuda_int64 PASSED [0.0028s] [ 44%]
2025-08-14T23:40:03.3729985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_frac_cuda_bfloat16 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3730286Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_frac_cuda_float32 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3730591Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_bfloat16 PASSED [0.0027s] [ 44%]
2025-08-14T23:40:03.3730899Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_complex32 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3732149Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_complex64 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3732455Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int16 PASSED [0.0027s] [ 44%]
2025-08-14T23:40:03.3732751Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int32 PASSED [0.0024s] [ 44%]
2025-08-14T23:40:03.3733144Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isinf_cuda_int8 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3733445Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_bfloat16 PASSED [0.0026s] [ 44%]
2025-08-14T23:40:03.3733756Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_complex128 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3734064Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_float32 PASSED [0.0025s] [ 44%]
2025-08-14T23:40:03.3734368Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_float64 PASSED [0.0024s] [ 45%]
2025-08-14T23:40:03.3734693Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int16 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3734990Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int64 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3735288Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_int8 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3736532Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isnan_cuda_uint8 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3736858Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_bool PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3737168Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_float16 PASSED [0.0026s] [ 45%]
2025-08-14T23:40:03.3737495Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_float64 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3737805Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_int64 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3738112Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_int8 PASSED [0.0027s] [ 45%]
2025-08-14T23:40:03.3738417Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isneginf_cuda_uint8 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3738723Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_bool PASSED [0.0024s] [ 45%]
2025-08-14T23:40:03.3739036Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_float16 PASSED [0.0027s] [ 45%]
2025-08-14T23:40:03.3739349Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_float64 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3739660Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_int16 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3740908Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_int8 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3741215Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_isposinf_cuda_uint8 PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3741515Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_log1p_cuda_bool PASSED [0.0025s] [ 45%]
2025-08-14T23:40:03.3741835Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_log1p_cuda_float16 PASSED [0.0026s] [ 45%]
2025-08-14T23:40:03.3742234Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amax_cuda_bfloat16 SKIPPED [0.0011s] (masked.amax does not support input with torch.sparse_bsr layout) [ 45%]
2025-08-14T23:40:03.3742721Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amax_cuda_float16 SKIPPED [0.0011s] (masked.amax does not support input with torch.sparse_bsr layout) [ 45%]
2025-08-14T23:40:03.3743264Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amax_cuda_int16 SKIPPED [0.0010s] (masked.amax does not support input with torch.sparse_bsr layout) [ 45%]
2025-08-14T23:40:03.3743738Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amax_cuda_int8 SKIPPED [0.0011s] (masked.amax does not support input with torch.sparse_bsr layout) [ 45%]
2025-08-14T23:40:03.3744216Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amin_cuda_float16 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3744699Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amin_cuda_float64 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3746128Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amin_cuda_int16 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3746630Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amin_cuda_int32 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3747103Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_amin_cuda_int8 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3747601Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_mean_cuda_complex64 SKIPPED [0.0009s] (masked.mean does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3748087Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_mean_cuda_float16 SKIPPED [0.0012s] (masked.mean does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3748590Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_mean_cuda_float32 SKIPPED [0.0010s] (masked.mean does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3749074Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_mean_cuda_float64 SKIPPED [0.0009s] (masked.mean does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3749561Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_bfloat16 SKIPPED [0.0011s] (masked.prod does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3750042Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_bool SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3750522Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_complex64 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3751008Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_int64 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3752435Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_prod_cuda_int8 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3752991Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_bfloat16 SKIPPED [0.0011s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3753469Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_bool SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3753951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_complex128 SKIPPED [0.0011s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3754437Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_float32 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3754916Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_float64 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3755396Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int32 SKIPPED [0.0011s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3755866Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int64 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3756335Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_masked_sum_cuda_int8 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_bsr layout) [ 46%]
2025-08-14T23:40:03.3756738Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_bool PASSED [0.0042s] [ 46%]
2025-08-14T23:40:03.3758015Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_complex128 PASSED [0.0049s] [ 47%]
2025-08-14T23:40:03.3758328Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_complex64 PASSED [0.0049s] [ 47%]
2025-08-14T23:40:03.3758627Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_int32 PASSED [0.0041s] [ 47%]
2025-08-14T23:40:03.3758942Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_int64 PASSED [0.0041s] [ 47%]
2025-08-14T23:40:03.3759237Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_mul_cuda_uint8 PASSED [0.0044s] [ 47%]
2025-08-14T23:40:03.3759552Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_complex64 PASSED [0.0026s] [ 47%]
2025-08-14T23:40:03.3759853Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_int16 PASSED [0.0025s] [ 47%]
2025-08-14T23:40:03.3760145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_neg_cuda_int32 PASSED [0.0025s] [ 47%]
2025-08-14T23:40:03.3760463Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_float32 PASSED [0.0032s] [ 47%]
2025-08-14T23:40:03.3760801Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int16 PASSED [0.0030s] [ 47%]
2025-08-14T23:40:03.3761136Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int32 PASSED [0.0030s] [ 47%]
2025-08-14T23:40:03.3762421Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_nn_functional_relu_cuda_int64 PASSED [0.0029s] [ 47%]
2025-08-14T23:40:03.3762750Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_float64 PASSED [0.0026s] [ 47%]
2025-08-14T23:40:03.3763119Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_int32 PASSED [0.0025s] [ 47%]
2025-08-14T23:40:03.3763425Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_positive_cuda_int8 PASSED [0.0025s] [ 47%]
2025-08-14T23:40:03.3763751Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_bool PASSED [0.0026s] [ 47%]
2025-08-14T23:40:03.3764056Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_float16 PASSED [0.0026s] [ 47%]
2025-08-14T23:40:03.3764365Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_float64 PASSED [0.0027s] [ 47%]
2025-08-14T23:40:03.3764670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_int64 PASSED [0.0026s] [ 47%]
2025-08-14T23:40:03.3764973Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_rad2deg_cuda_uint8 PASSED [0.0028s] [ 47%]
2025-08-14T23:40:03.3765292Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_complex128 PASSED [0.0041s] [ 47%]
2025-08-14T23:40:03.3765736Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_complex32 PASSED [0.0041s] [ 47%]
2025-08-14T23:40:03.3767031Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float16 PASSED [0.0040s] [ 47%]
2025-08-14T23:40:03.3767351Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float32 PASSED [0.0041s] [ 48%]
2025-08-14T23:40:03.3767670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_randn_like_cuda_float64 PASSED [0.0040s] [ 48%]
2025-08-14T23:40:03.3767981Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_float64 PASSED [0.0026s] [ 48%]
2025-08-14T23:40:03.3768286Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_int16 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3768613Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_int64 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3768911Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_round_cuda_uint8 PASSED [0.0024s] [ 48%]
2025-08-14T23:40:03.3769209Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_bool PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3769511Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_complex128 PASSED [0.0027s] [ 48%]
2025-08-14T23:40:03.3769844Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_complex64 PASSED [0.0027s] [ 48%]
2025-08-14T23:40:03.3770143Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int16 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3771414Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int32 PASSED [0.0024s] [ 48%]
2025-08-14T23:40:03.3771711Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sgn_cuda_int8 PASSED [0.0027s] [ 48%]
2025-08-14T23:40:03.3772008Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_bfloat16 PASSED [0.0026s] [ 48%]
2025-08-14T23:40:03.3772314Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_float16 PASSED [0.0026s] [ 48%]
2025-08-14T23:40:03.3772615Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_float64 PASSED [0.0027s] [ 48%]
2025-08-14T23:40:03.3772969Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_int32 PASSED [0.0024s] [ 48%]
2025-08-14T23:40:03.3773264Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_int8 PASSED [0.0024s] [ 48%]
2025-08-14T23:40:03.3773562Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sign_cuda_uint8 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3773864Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_bool PASSED [0.0026s] [ 48%]
2025-08-14T23:40:03.3774171Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_float16 PASSED [0.0027s] [ 48%]
2025-08-14T23:40:03.3775466Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_float32 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3775776Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_int16 PASSED [0.0024s] [ 48%]
2025-08-14T23:40:03.3776081Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_signbit_cuda_int64 PASSED [0.0025s] [ 48%]
2025-08-14T23:40:03.3776380Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_bool PASSED [0.0027s] [ 49%]
2025-08-14T23:40:03.3776679Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_complex128 PASSED [0.0027s] [ 49%]
2025-08-14T23:40:03.3776983Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_float16 PASSED [0.0028s] [ 49%]
2025-08-14T23:40:03.3777280Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_float64 PASSED [0.0025s] [ 49%]
2025-08-14T23:40:03.3777578Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int32 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3777870Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int64 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3778163Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_int8 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3778453Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sin_cuda_uint8 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3779700Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_bool PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3780026Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_complex128 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3780333Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_float16 PASSED [0.0027s] [ 49%]
2025-08-14T23:40:03.3780630Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_int16 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3780924Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sinh_cuda_int8 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3781237Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_bfloat16 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3781544Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_complex128 PASSED [0.0027s] [ 49%]
2025-08-14T23:40:03.3781852Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_complex64 PASSED [0.0038s] [ 49%]
2025-08-14T23:40:03.3782172Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_float16 PASSED [0.0027s] [ 49%]
2025-08-14T23:40:03.3782472Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_float32 PASSED [0.0028s] [ 49%]
2025-08-14T23:40:03.3782770Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_int32 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3784089Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_int8 PASSED [0.0026s] [ 49%]
2025-08-14T23:40:03.3784388Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sqrt_cuda_uint8 PASSED [0.0025s] [ 49%]
2025-08-14T23:40:03.3784681Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_bool PASSED [0.0055s] [ 49%]
2025-08-14T23:40:03.3784980Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex128 PASSED [0.0066s] [ 50%]
2025-08-14T23:40:03.3785289Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex32 PASSED [0.0067s] [ 50%]
2025-08-14T23:40:03.3785593Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_complex64 PASSED [0.0068s] [ 50%]
2025-08-14T23:40:03.3785894Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_float16 PASSED [0.0066s] [ 50%]
2025-08-14T23:40:03.3786215Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_float32 PASSED [0.0065s] [ 50%]
2025-08-14T23:40:03.3786510Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int16 PASSED [0.0056s] [ 50%]
2025-08-14T23:40:03.3786804Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int32 PASSED [0.0055s] [ 50%]
2025-08-14T23:40:03.3787096Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_sum_cuda_int8 PASSED [0.0057s] [ 50%]
2025-08-14T23:40:03.3788353Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_bool PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3788650Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_float32 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3788943Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_int8 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3789238Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tan_cuda_uint8 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3789535Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_bfloat16 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3789834Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_bool PASSED [0.0027s] [ 50%]
2025-08-14T23:40:03.3790134Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_complex32 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3790440Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_complex64 PASSED [0.0027s] [ 50%]
2025-08-14T23:40:03.3790765Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_float32 PASSED [0.0027s] [ 50%]
2025-08-14T23:40:03.3791064Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_float64 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3792325Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int16 PASSED [0.0027s] [ 50%]
2025-08-14T23:40:03.3792622Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int32 PASSED [0.0026s] [ 50%]
2025-08-14T23:40:03.3792999Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_tanh_cuda_int64 PASSED [0.0025s] [ 50%]
2025-08-14T23:40:03.3793385Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_bfloat16 SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsr layout) [ 50%]
2025-08-14T23:40:03.3793875Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_complex64 SKIPPED [0.0009s] (to_sparse does not support input with torch.sparse_bsr layout) [ 50%]
2025-08-14T23:40:03.3794347Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_float16 SKIPPED [0.0012s] (to_sparse does not support input with torch.sparse_bsr layout) [ 51%]
2025-08-14T23:40:03.3794816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_float32 SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsr layout) [ 51%]
2025-08-14T23:40:03.3795287Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_int64 SKIPPED [0.0009s] (to_sparse does not support input with torch.sparse_bsr layout) [ 51%]
2025-08-14T23:40:03.3795751Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_int8 SKIPPED [0.0010s] (to_sparse does not support input with torch.sparse_bsr layout) [ 51%]
2025-08-14T23:40:03.3796215Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_to_sparse_cuda_uint8 SKIPPED [0.0009s] (to_sparse does not support input with torch.sparse_bsr layout) [ 51%]
2025-08-14T23:40:03.3796599Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_bfloat16 PASSED [0.0027s] [ 51%]
2025-08-14T23:40:03.3797893Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_float16 PASSED [0.0026s] [ 51%]
2025-08-14T23:40:03.3798201Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_float64 PASSED [0.0026s] [ 51%]
2025-08-14T23:40:03.3798504Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int16 PASSED [0.0024s] [ 51%]
2025-08-14T23:40:03.3798800Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int32 PASSED [0.0025s] [ 51%]
2025-08-14T23:40:03.3799097Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_int8 PASSED [0.0025s] [ 51%]
2025-08-14T23:40:03.3799392Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_trunc_cuda_uint8 PASSED [0.0025s] [ 51%]
2025-08-14T23:40:03.3799696Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_bool PASSED [0.0034s] [ 51%]
2025-08-14T23:40:03.3799842Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_float16 PASSED [0.0036s] [ 51%]
2025-08-14T23:40:03.3799982Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_float32 PASSED [0.0035s] [ 51%]
2025-08-14T23:40:03.3800119Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_int8 PASSED [0.0034s] [ 51%]
2025-08-14T23:40:03.3800257Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseBSR_zeros_like_cuda_uint8 PASSED [0.0034s] [ 51%]
2025-08-14T23:40:03.3800392Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_bfloat16 PASSED [0.0035s] [ 51%]
2025-08-14T23:40:03.3800556Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_complex32 PASSED [0.0031s] [ 51%]
2025-08-14T23:40:03.3800691Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_complex64 PASSED [0.0031s] [ 51%]
2025-08-14T23:40:03.3800822Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float16 PASSED [0.0030s] [ 51%]
2025-08-14T23:40:03.3801922Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float32 PASSED [0.0031s] [ 51%]
2025-08-14T23:40:03.3802069Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_float64 PASSED [0.0031s] [ 51%]
2025-08-14T23:40:03.3802198Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_int64 PASSED [0.0029s] [ 52%]
2025-08-14T23:40:03.3802337Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_abs_cuda_int8 PASSED [0.0029s] [ 52%]
2025-08-14T23:40:03.3802478Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex128 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3802614Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex32 PASSED [0.0041s] [ 52%]
2025-08-14T23:40:03.3802750Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_complex64 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3802884Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_float32 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3803071Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int16 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3803202Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int64 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3803333Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_angle_cuda_int8 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3803467Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_bfloat16 PASSED [0.0032s] [ 52%]
2025-08-14T23:40:03.3803596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_bool PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3803750Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_complex64 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3803882Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_float16 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3804015Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_float64 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3804144Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_int16 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3804275Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asin_cuda_uint8 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3804405Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_bool PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3804539Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_float32 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3804672Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_float64 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3806016Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int16 PASSED [0.0030s] [ 52%]
2025-08-14T23:40:03.3806153Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int32 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3806283Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_asinh_cuda_int8 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3806417Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_bfloat16 PASSED [0.0031s] [ 52%]
2025-08-14T23:40:03.3806581Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_complex128 PASSED [0.0034s] [ 53%]
2025-08-14T23:40:03.3806715Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_complex64 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3806848Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_float32 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3806979Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int32 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3807125Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int64 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3807253Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_int8 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3807383Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atan_cuda_uint8 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3807531Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_bool PASSED [0.0029s] [ 53%]
2025-08-14T23:40:03.3807670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_complex128 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3807805Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_complex32 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3807940Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_float16 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3808074Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_float64 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3808205Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_atanh_cuda_uint8 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3808339Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_bfloat16 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3808470Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_int16 PASSED [0.0029s] [ 53%]
2025-08-14T23:40:03.3808599Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_int64 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3808727Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_ceil_cuda_uint8 PASSED [0.0029s] [ 53%]
2025-08-14T23:40:03.3809824Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_bool PASSED [0.0029s] [ 53%]
2025-08-14T23:40:03.3809975Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_complex32 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3810124Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_conj_physical_cuda_float64 PASSED [0.0030s] [ 53%]
2025-08-14T23:40:03.3810257Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_bool PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3810398Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_float16 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3810533Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_float32 PASSED [0.0031s] [ 53%]
2025-08-14T23:40:03.3810668Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_int32 PASSED [0.0030s] [ 54%]
2025-08-14T23:40:03.3810802Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_int64 PASSED [0.0030s] [ 54%]
2025-08-14T23:40:03.3810937Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_deg2rad_cuda_uint8 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3811070Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_bfloat16 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3811200Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_bool PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3811331Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_float32 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3811480Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_float64 PASSED [0.0034s] [ 54%]
2025-08-14T23:40:03.3811607Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_int16 PASSED [0.0030s] [ 54%]
2025-08-14T23:40:03.3811737Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_int8 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3811864Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erf_cuda_uint8 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3812013Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_bfloat16 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3812143Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_bool PASSED [0.0030s] [ 54%]
2025-08-14T23:40:03.3812295Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_float16 PASSED [0.0030s] [ 54%]
2025-08-14T23:40:03.3812432Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_float64 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3812564Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int16 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3812697Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int32 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3814194Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int64 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3814382Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_int8 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3814569Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_erfinv_cuda_uint8 PASSED [0.0029s] [ 54%]
2025-08-14T23:40:03.3814760Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_bfloat16 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3814947Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_bool PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3815140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_complex128 PASSED [0.0035s] [ 54%]
2025-08-14T23:40:03.3815366Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_complex64 PASSED [0.0031s] [ 54%]
2025-08-14T23:40:03.3815553Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_float16 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3815741Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_float32 PASSED [0.0029s] [ 55%]
2025-08-14T23:40:03.3815925Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3816109Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int32 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3816294Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_expm1_cuda_int64 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3816481Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3816670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float32 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3816856Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_floor_cuda_float64 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3817045Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_bfloat16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3817230Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_float16 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3817418Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_frac_cuda_float64 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3817636Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_bfloat16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3817820Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_bool PASSED [0.0029s] [ 55%]
2025-08-14T23:40:03.3818015Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_complex128 PASSED [0.0032s] [ 55%]
2025-08-14T23:40:03.3819468Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_float32 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3819677Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int16 PASSED [0.0029s] [ 55%]
2025-08-14T23:40:03.3819863Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int64 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3820064Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isinf_cuda_int8 PASSED [0.0028s] [ 55%]
2025-08-14T23:40:03.3820258Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_bfloat16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3820450Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_complex64 PASSED [0.0030s] [ 55%]
2025-08-14T23:40:03.3820640Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float16 PASSED [0.0031s] [ 55%]
2025-08-14T23:40:03.3820826Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float32 PASSED [0.0029s] [ 55%]
2025-08-14T23:40:03.3821014Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_float64 PASSED [0.0029s] [ 55%]
2025-08-14T23:40:03.3821198Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_int32 PASSED [0.0028s] [ 56%]
2025-08-14T23:40:03.3821383Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_int8 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3821569Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isnan_cuda_uint8 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3821757Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_bool PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3821951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_float64 PASSED [0.0032s] [ 56%]
2025-08-14T23:40:03.3822161Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_int32 PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3822349Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isneginf_cuda_int8 PASSED [0.0028s] [ 56%]
2025-08-14T23:40:03.3822544Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_float64 PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3822735Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int16 PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3822985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int32 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3823175Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int64 PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3823364Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_int8 PASSED [0.0029s] [ 56%]
2025-08-14T23:40:03.3824833Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_isposinf_cuda_uint8 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3825022Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_bool PASSED [0.0031s] [ 56%]
2025-08-14T23:40:03.3825216Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_complex128 PASSED [0.0031s] [ 56%]
2025-08-14T23:40:03.3825407Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_complex64 PASSED [0.0031s] [ 56%]
2025-08-14T23:40:03.3825626Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_float16 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3825813Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_float64 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3825996Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_int32 PASSED [0.0030s] [ 56%]
2025-08-14T23:40:03.3826181Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_log1p_cuda_int64 PASSED [0.0033s] [ 56%]
2025-08-14T23:40:03.3826517Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_float32 SKIPPED [0.0010s] (masked.amax does not support input with torch.sparse_csc layout) [ 56%]
2025-08-14T23:40:03.3826829Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_int16 SKIPPED [0.0010s] (masked.amax does not support input with torch.sparse_csc layout) [ 56%]
2025-08-14T23:40:03.3827158Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_int64 SKIPPED [0.0009s] (masked.amax does not support input with torch.sparse_csc layout) [ 56%]
2025-08-14T23:40:03.3827469Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amax_cuda_uint8 SKIPPED [0.0010s] (masked.amax does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3827786Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_bfloat16 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3828100Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_float16 SKIPPED [0.0009s] (masked.amin does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3828410Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_int64 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3828715Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_int8 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3829030Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_amin_cuda_uint8 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3829366Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_bfloat16 SKIPPED [0.0010s] (masked.mean does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3829690Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_complex128 SKIPPED [0.0009s] (masked.mean does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3830004Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_mean_cuda_float64 SKIPPED [0.0011s] (masked.mean does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3830321Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_bfloat16 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3831895Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_complex128 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3832212Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_float32 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3832524Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_float64 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3832855Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_int32 SKIPPED [0.0010s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3833235Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_prod_cuda_int8 SKIPPED [0.0009s] (masked.prod does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3833543Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_bool SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3833902Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_complex128 SKIPPED [0.0009s] (masked.sum does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3834145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_complex64 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3834367Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_float16 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3834585Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_masked_sum_cuda_int64 SKIPPED [0.0010s] (masked.sum does not support input with torch.sparse_csc layout) [ 57%]
2025-08-14T23:40:03.3834720Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_bfloat16 PASSED [0.0062s] [ 57%]
2025-08-14T23:40:03.3834851Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_bool PASSED [0.0060s] [ 57%]
2025-08-14T23:40:03.3834985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_complex64 PASSED [0.0065s] [ 57%]
2025-08-14T23:40:03.3835119Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_float16 PASSED [0.0062s] [ 58%]
2025-08-14T23:40:03.3835250Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_float64 PASSED [0.0062s] [ 58%]
2025-08-14T23:40:03.3835380Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_int16 PASSED [0.0060s] [ 58%]
2025-08-14T23:40:03.3835508Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_int32 PASSED [0.0060s] [ 58%]
2025-08-14T23:40:03.3835652Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_mul_cuda_uint8 PASSED [0.0058s] [ 58%]
2025-08-14T23:40:03.3835785Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_neg_cuda_bfloat16 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3835917Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_neg_cuda_float64 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3836067Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_nn_functional_relu_cuda_int32 PASSED [0.0039s] [ 58%]
2025-08-14T23:40:03.3837176Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_bfloat16 PASSED [0.0030s] [ 58%]
2025-08-14T23:40:03.3837320Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_complex64 PASSED [0.0030s] [ 58%]
2025-08-14T23:40:03.3837458Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_float16 PASSED [0.0029s] [ 58%]
2025-08-14T23:40:03.3837597Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_float64 PASSED [0.0030s] [ 58%]
2025-08-14T23:40:03.3837733Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_int16 PASSED [0.0029s] [ 58%]
2025-08-14T23:40:03.3837867Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_positive_cuda_int32 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3838005Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_bfloat16 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3838161Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_bool PASSED [0.0030s] [ 58%]
2025-08-14T23:40:03.3838298Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_float64 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3838432Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_int32 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3838566Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_rad2deg_cuda_int64 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3838726Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_complex128 PASSED [0.0049s] [ 58%]
2025-08-14T23:40:03.3838869Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_float16 PASSED [0.0047s] [ 58%]
2025-08-14T23:40:03.3839021Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_randn_like_cuda_float64 PASSED [0.0047s] [ 58%]
2025-08-14T23:40:03.3839158Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_bfloat16 PASSED [0.0031s] [ 58%]
2025-08-14T23:40:03.3839289Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_float16 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3839422Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_float64 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3839555Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int16 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3839687Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int32 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3839817Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_round_cuda_int8 PASSED [0.0028s] [ 59%]
2025-08-14T23:40:03.3839947Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_bfloat16 PASSED [0.0033s] [ 59%]
2025-08-14T23:40:03.3841013Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_complex32 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3841145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_float64 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3841274Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int16 PASSED [0.0029s] [ 59%]
2025-08-14T23:40:03.3841430Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int32 PASSED [0.0028s] [ 59%]
2025-08-14T23:40:03.3841559Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sgn_cuda_int8 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3841686Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_bool PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3841818Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_float32 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3841951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_float64 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3842081Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_int16 PASSED [0.0029s] [ 59%]
2025-08-14T23:40:03.3842208Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_int8 PASSED [0.0029s] [ 59%]
2025-08-14T23:40:03.3842339Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sign_cuda_uint8 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3842476Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_bfloat16 PASSED [0.0031s] [ 59%]
2025-08-14T23:40:03.3842609Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_bool PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3842746Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_float16 PASSED [0.0033s] [ 59%]
2025-08-14T23:40:03.3842883Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_float32 PASSED [0.0028s] [ 59%]
2025-08-14T23:40:03.3843111Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_int32 PASSED [0.0030s] [ 59%]
2025-08-14T23:40:03.3843245Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_int64 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3843379Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_signbit_cuda_uint8 PASSED [0.0029s] [ 60%]
2025-08-14T23:40:03.3843513Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_complex32 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3843664Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_float32 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3843792Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_int8 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3844880Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sin_cuda_uint8 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3845017Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_bfloat16 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3845145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_bool PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3845284Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_complex32 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3849911Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_complex64 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3850052Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_float16 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3850181Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int16 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3850312Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int32 PASSED [0.0033s] [ 60%]
2025-08-14T23:40:03.3850441Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_int8 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3850570Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sinh_cuda_uint8 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3850729Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_bfloat16 PASSED [0.0029s] [ 60%]
2025-08-14T23:40:03.3850858Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_bool PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3850995Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex128 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3851131Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex32 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3851267Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_complex64 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3851399Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_float64 PASSED [0.0030s] [ 60%]
2025-08-14T23:40:03.3851527Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_int16 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3851657Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sqrt_cuda_int32 PASSED [0.0031s] [ 60%]
2025-08-14T23:40:03.3851788Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_bfloat16 PASSED [0.0077s] [ 61%]
2025-08-14T23:40:03.3851923Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_complex32 PASSED [0.0076s] [ 61%]
2025-08-14T23:40:03.3852057Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_complex64 PASSED [0.0078s] [ 61%]
2025-08-14T23:40:03.3853217Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_float16 PASSED [0.0079s] [ 61%]
2025-08-14T23:40:03.3853374Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_float32 PASSED [0.0078s] [ 61%]
2025-08-14T23:40:03.3853501Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int16 PASSED [0.0076s] [ 61%]
2025-08-14T23:40:03.3853632Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int64 PASSED [0.0075s] [ 61%]
2025-08-14T23:40:03.3853760Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_int8 PASSED [0.0075s] [ 61%]
2025-08-14T23:40:03.3853905Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_sum_cuda_uint8 PASSED [0.0074s] [ 61%]
2025-08-14T23:40:03.3854040Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_complex128 PASSED [0.0030s] [ 61%]
2025-08-14T23:40:03.3854188Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_complex32 PASSED [0.0030s] [ 61%]
2025-08-14T23:40:03.3854318Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_float16 PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3854449Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_float64 PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3854576Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_int16 PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3854707Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tan_cuda_uint8 PASSED [0.0030s] [ 61%]
2025-08-14T23:40:03.3854841Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_bfloat16 PASSED [0.0030s] [ 61%]
2025-08-14T23:40:03.3854970Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_bool PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3855106Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_complex128 PASSED [0.0032s] [ 61%]
2025-08-14T23:40:03.3855241Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_float64 PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3855371Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_tanh_cuda_uint8 PASSED [0.0031s] [ 61%]
2025-08-14T23:40:03.3855512Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_bfloat16 PASSED [0.0144s] [ 61%]
2025-08-14T23:40:03.3855663Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_bool PASSED [0.0038s] [ 61%]
2025-08-14T23:40:03.3855810Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_complex128 PASSED [0.0038s] [ 61%]
2025-08-14T23:40:03.3855953Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_complex64 PASSED [0.0037s] [ 62%]
2025-08-14T23:40:03.3857028Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_float16 PASSED [0.0037s] [ 62%]
2025-08-14T23:40:03.3857169Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_int16 PASSED [0.0036s] [ 62%]
2025-08-14T23:40:03.3857306Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_int32 PASSED [0.0035s] [ 62%]
2025-08-14T23:40:03.3857441Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_to_sparse_cuda_uint8 PASSED [0.0034s] [ 62%]
2025-08-14T23:40:03.3857577Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_float16 PASSED [0.0032s] [ 62%]
2025-08-14T23:40:03.3857711Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_float64 PASSED [0.0031s] [ 62%]
2025-08-14T23:40:03.3857842Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_trunc_cuda_int64 PASSED [0.0030s] [ 62%]
2025-08-14T23:40:03.3857984Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_bfloat16 PASSED [0.0045s] [ 62%]
2025-08-14T23:40:03.3858145Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_complex32 PASSED [0.0042s] [ 62%]
2025-08-14T23:40:03.3858290Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_complex64 PASSED [0.0041s] [ 62%]
2025-08-14T23:40:03.3858432Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_float16 PASSED [0.0041s] [ 62%]
2025-08-14T23:40:03.3858574Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_float32 PASSED [0.0041s] [ 62%]
2025-08-14T23:40:03.3858724Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_int64 PASSED [0.0040s] [ 62%]
2025-08-14T23:40:03.3858860Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_int8 PASSED [0.0040s] [ 62%]
2025-08-14T23:40:03.3859010Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSC_zeros_like_cuda_uint8 PASSED [0.0040s] [ 62%]
2025-08-14T23:40:03.3859140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_abs_cuda_int64 PASSED [0.0029s] [ 62%]
2025-08-14T23:40:03.3859267Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_abs_cuda_int8 PASSED [0.0029s] [ 62%]
2025-08-14T23:40:03.3859407Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex128 PASSED [0.0031s] [ 62%]
2025-08-14T23:40:03.3859542Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex32 PASSED [0.0031s] [ 62%]
2025-08-14T23:40:03.3859680Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_complex64 PASSED [0.0031s] [ 62%]
2025-08-14T23:40:03.3859811Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_angle_cuda_int64 PASSED [0.0029s] [ 62%]
2025-08-14T23:40:03.3860874Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_complex64 PASSED [0.0031s] [ 62%]
2025-08-14T23:40:03.3861009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_float16 PASSED [0.0033s] [ 63%]
2025-08-14T23:40:03.3861140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int16 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3861269Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int32 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3861415Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int64 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3861545Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_int8 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3861674Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asin_cuda_uint8 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3861809Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_bfloat16 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3861941Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_bool PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3862077Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_complex32 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3862214Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_complex64 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3862350Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float16 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3862485Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float32 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3862617Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_float64 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3862747Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int16 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3862877Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int32 PASSED [0.0033s] [ 63%]
2025-08-14T23:40:03.3863075Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int64 PASSED [0.0029s] [ 63%]
2025-08-14T23:40:03.3863204Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_asinh_cuda_int8 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3863340Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_bfloat16 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3863467Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_bool PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3863622Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_complex128 PASSED [0.0031s] [ 63%]
2025-08-14T23:40:03.3863756Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_float16 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3864862Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_float64 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3864994Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_int16 PASSED [0.0030s] [ 63%]
2025-08-14T23:40:03.3865122Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atan_cuda_int64 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3865260Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_bfloat16 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3865396Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_complex32 PASSED [0.0031s] [ 64%]
2025-08-14T23:40:03.3865528Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_atanh_cuda_int16 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3865661Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_bfloat16 PASSED [0.0031s] [ 64%]
2025-08-14T23:40:03.3865792Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_float16 PASSED [0.0031s] [ 64%]
2025-08-14T23:40:03.3865925Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_float32 PASSED [0.0033s] [ 64%]
2025-08-14T23:40:03.3866054Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_int32 PASSED [0.0029s] [ 64%]
2025-08-14T23:40:03.3866201Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_ceil_cuda_int8 PASSED [0.0029s] [ 64%]
2025-08-14T23:40:03.3866343Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_bool PASSED [0.0028s] [ 64%]
2025-08-14T23:40:03.3866494Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_complex128 PASSED [0.0031s] [ 64%]
2025-08-14T23:40:03.3866641Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float16 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3866788Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float32 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3866934Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_float64 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3867076Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int16 PASSED [0.0028s] [ 64%]
2025-08-14T23:40:03.3867220Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int64 PASSED [0.0029s] [ 64%]
2025-08-14T23:40:03.3867361Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_int8 PASSED [0.0028s] [ 64%]
2025-08-14T23:40:03.3867504Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_conj_physical_cuda_uint8 PASSED [0.0029s] [ 64%]
2025-08-14T23:40:03.3867638Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_bool PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3868713Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float16 PASSED [0.0031s] [ 64%]
2025-08-14T23:40:03.3868871Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float32 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3869007Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_float64 PASSED [0.0032s] [ 64%]
2025-08-14T23:40:03.3869144Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_int32 PASSED [0.0030s] [ 64%]
2025-08-14T23:40:03.3869278Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_int64 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3869423Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_deg2rad_cuda_uint8 PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3869555Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_bfloat16 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3869698Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_float32 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3869830Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_float64 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3869959Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erf_cuda_int8 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3870097Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_bfloat16 PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3870229Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_int32 PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3870364Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_erfinv_cuda_uint8 PASSED [0.0033s] [ 65%]
2025-08-14T23:40:03.3870495Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_expm1_cuda_bool PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3870628Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_expm1_cuda_int16 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3870762Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_float64 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3870893Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int16 PASSED [0.0032s] [ 65%]
2025-08-14T23:40:03.3871036Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int32 PASSED [0.0029s] [ 65%]
2025-08-14T23:40:03.3871166Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_floor_cuda_int64 PASSED [0.0028s] [ 65%]
2025-08-14T23:40:03.3871299Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_frac_cuda_float16 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3871430Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_frac_cuda_float32 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3872497Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_bfloat16 PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3872637Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex128 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3872773Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex32 PASSED [0.0029s] [ 65%]
2025-08-14T23:40:03.3872968Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_complex64 PASSED [0.0030s] [ 65%]
2025-08-14T23:40:03.3873103Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float16 PASSED [0.0031s] [ 65%]
2025-08-14T23:40:03.3873238Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float32 PASSED [0.0029s] [ 65%]
2025-08-14T23:40:03.3873371Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_float64 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3873501Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_int16 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3873656Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isinf_cuda_int64 PASSED [0.0028s] [ 66%]
2025-08-14T23:40:03.3873795Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_complex128 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3873932Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_complex64 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3874064Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float16 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3874213Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float32 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3874345Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_float64 PASSED [0.0028s] [ 66%]
2025-08-14T23:40:03.3874492Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int16 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3874623Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int32 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3874753Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isnan_cuda_int8 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3874891Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isneginf_cuda_float16 PASSED [0.0030s] [ 66%]
2025-08-14T23:40:03.3875026Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isneginf_cuda_int16 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3875168Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_bfloat16 PASSED [0.0030s] [ 66%]
2025-08-14T23:40:03.3875307Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float16 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3876383Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float32 PASSED [0.0030s] [ 66%]
2025-08-14T23:40:03.3876524Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_float64 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3876657Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_int16 PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3876811Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_isposinf_cuda_int8 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3876942Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_bool PASSED [0.0029s] [ 66%]
2025-08-14T23:40:03.3877082Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_complex128 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3877218Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_complex64 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3877352Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_float32 PASSED [0.0031s] [ 66%]
2025-08-14T23:40:03.3877486Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_float64 PASSED [0.0031s] [ 67%]
2025-08-14T23:40:03.3877617Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_int32 PASSED [0.0029s] [ 67%]
2025-08-14T23:40:03.3877750Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_log1p_cuda_uint8 PASSED [0.0030s] [ 67%]
2025-08-14T23:40:03.3877895Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_float32 PASSED [0.0769s] [ 67%]
2025-08-14T23:40:03.3878149Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int16 SKIPPED [0.0012s] (masked.amax does not support input with torch.sparse_csr layout and torch.int16 dtype) [ 67%]
2025-08-14T23:40:03.3878402Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int32 SKIPPED [0.0010s] (masked.amax does not support input with torch.sparse_csr layout and torch.int32 dtype) [ 67%]
2025-08-14T23:40:03.3878667Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amax_cuda_int64 SKIPPED [0.0011s] (masked.amax does not support input with torch.sparse_csr layout and torch.int64 dtype) [ 67%]
2025-08-14T23:40:03.3878812Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_float16 PASSED [0.0518s] [ 67%]
2025-08-14T23:40:03.3878954Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_float32 PASSED [0.0500s] [ 67%]
2025-08-14T23:40:03.3879213Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_int32 SKIPPED [0.0010s] (masked.amin does not support input with torch.sparse_csr layout and torch.int32 dtype) [ 67%]
2025-08-14T23:40:03.3879471Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_amin_cuda_int8 SKIPPED [0.0011s] (masked.amin does not support input with torch.sparse_csr layout and torch.int8 dtype) [ 67%]
2025-08-14T23:40:03.3879617Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_bfloat16 PASSED [0.0680s] [ 67%]
2025-08-14T23:40:03.3879881Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_complex128 SKIPPED [0.0012s] (masked.mean does not support input with torch.sparse_csr layout and torch.complex128 dtype) [ 67%]
2025-08-14T23:40:03.3880950Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_float16 PASSED [0.0521s] [ 67%]
2025-08-14T23:40:03.3881096Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_mean_cuda_float32 PASSED [0.0520s] [ 67%]
2025-08-14T23:40:03.3881240Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_bfloat16 PASSED [0.0525s] [ 67%]
2025-08-14T23:40:03.3881379Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_bool PASSED [0.0348s] [ 67%]
2025-08-14T23:40:03.3881528Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_complex128 PASSED [0.0351s] [ 67%]
2025-08-14T23:40:03.3881670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float16 PASSED [0.0523s] [ 67%]
2025-08-14T23:40:03.3881828Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float32 PASSED [0.0517s] [ 67%]
2025-08-14T23:40:03.3881968Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_float64 PASSED [0.0518s] [ 67%]
2025-08-14T23:40:03.3882109Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_int16 PASSED [0.0342s] [ 67%]
2025-08-14T23:40:03.3882250Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_prod_cuda_uint8 PASSED [0.0342s] [ 67%]
2025-08-14T23:40:03.3882392Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_bfloat16 PASSED [0.0517s] [ 68%]
2025-08-14T23:40:03.3882528Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_bool PASSED [0.0352s] [ 68%]
2025-08-14T23:40:03.3882671Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_complex64 PASSED [0.0355s] [ 68%]
2025-08-14T23:40:03.3882814Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_float16 PASSED [0.0524s] [ 68%]
2025-08-14T23:40:03.3883000Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_float32 PASSED [0.0522s] [ 68%]
2025-08-14T23:40:03.3883140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_masked_sum_cuda_int64 PASSED [0.0346s] [ 68%]
2025-08-14T23:40:03.3883269Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_bool PASSED [0.0137s] [ 68%]
2025-08-14T23:40:03.3883402Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_complex64 PASSED [0.0072s] [ 68%]
2025-08-14T23:40:03.3883555Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_int16 PASSED [0.0065s] [ 68%]
2025-08-14T23:40:03.3883683Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_int8 PASSED [0.0066s] [ 68%]
2025-08-14T23:40:03.3883813Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_mul_cuda_uint8 PASSED [0.0065s] [ 68%]
2025-08-14T23:40:03.3884886Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_float16 PASSED [0.0031s] [ 68%]
2025-08-14T23:40:03.3885039Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_int16 PASSED [0.0029s] [ 68%]
2025-08-14T23:40:03.3885167Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_neg_cuda_int32 PASSED [0.0029s] [ 68%]
2025-08-14T23:40:03.3885337Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_float16 PASSED [0.0040s] [ 68%]
2025-08-14T23:40:03.3885539Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_float64 PASSED [0.0041s] [ 68%]
2025-08-14T23:40:03.3885690Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_int32 PASSED [0.0038s] [ 68%]
2025-08-14T23:40:03.3885840Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_int64 PASSED [0.0038s] [ 68%]
2025-08-14T23:40:03.3885991Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_nn_functional_relu_cuda_uint8 PASSED [0.0038s] [ 68%]
2025-08-14T23:40:03.3886132Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_bfloat16 PASSED [0.0029s] [ 68%]
2025-08-14T23:40:03.3886277Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_complex128 PASSED [0.0033s] [ 68%]
2025-08-14T23:40:03.3886417Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_positive_cuda_int32 PASSED [0.0029s] [ 68%]
2025-08-14T23:40:03.3886553Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float16 PASSED [0.0030s] [ 68%]
2025-08-14T23:40:03.3886689Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float32 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3886841Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_float64 PASSED [0.0029s] [ 69%]
2025-08-14T23:40:03.3886975Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_int64 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3887108Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_rad2deg_cuda_int8 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3887251Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_complex32 PASSED [0.0049s] [ 69%]
2025-08-14T23:40:03.3887397Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_complex64 PASSED [0.0048s] [ 69%]
2025-08-14T23:40:03.3887538Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_randn_like_cuda_float32 PASSED [0.0047s] [ 69%]
2025-08-14T23:40:03.3887673Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_bfloat16 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3887809Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_float16 PASSED [0.0031s] [ 69%]
2025-08-14T23:40:03.3889321Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_float32 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3889457Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_round_cuda_int16 PASSED [0.0029s] [ 69%]
2025-08-14T23:40:03.3889588Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_bfloat16 PASSED [0.0033s] [ 69%]
2025-08-14T23:40:03.3889736Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_bool PASSED [0.0028s] [ 69%]
2025-08-14T23:40:03.3889871Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_complex128 PASSED [0.0031s] [ 69%]
2025-08-14T23:40:03.3890007Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_complex32 PASSED [0.0031s] [ 69%]
2025-08-14T23:40:03.3890140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_float16 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3890283Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_float32 PASSED [0.0031s] [ 69%]
2025-08-14T23:40:03.3890411Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_int32 PASSED [0.0029s] [ 69%]
2025-08-14T23:40:03.3890540Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_int64 PASSED [0.0028s] [ 69%]
2025-08-14T23:40:03.3890679Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sgn_cuda_uint8 PASSED [0.0029s] [ 69%]
2025-08-14T23:40:03.3890809Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_bool PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3890940Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_float64 PASSED [0.0030s] [ 69%]
2025-08-14T23:40:03.3891071Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_int16 PASSED [0.0029s] [ 70%]
2025-08-14T23:40:03.3891201Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sign_cuda_int64 PASSED [0.0028s] [ 70%]
2025-08-14T23:40:03.3891343Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_signbit_cuda_bfloat16 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3891481Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_signbit_cuda_float16 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3891619Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_signbit_cuda_float32 PASSED [0.0032s] [ 70%]
2025-08-14T23:40:03.3891758Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_signbit_cuda_float64 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3891892Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_signbit_cuda_int64 PASSED [0.0029s] [ 70%]
2025-08-14T23:40:03.3892037Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_bfloat16 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3893200Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_complex128 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3893338Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_complex64 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3893470Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_float16 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3893602Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_float32 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3893730Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int16 PASSED [0.0029s] [ 70%]
2025-08-14T23:40:03.3893857Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int64 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3893987Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_int8 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3894114Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sin_cuda_uint8 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3894249Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_bfloat16 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3894384Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_complex32 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3894516Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_float16 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3894673Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_float64 PASSED [0.0032s] [ 70%]
2025-08-14T23:40:03.3894804Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_int16 PASSED [0.0031s] [ 70%]
2025-08-14T23:40:03.3894935Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_int64 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3895065Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sinh_cuda_uint8 PASSED [0.0030s] [ 70%]
2025-08-14T23:40:03.3895211Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_bool PASSED [0.0029s] [ 71%]
2025-08-14T23:40:03.3895351Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_complex128 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3895504Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_complex32 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3895642Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_complex64 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3895774Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_float16 PASSED [0.0030s] [ 71%]
2025-08-14T23:40:03.3895907Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_float32 PASSED [0.0030s] [ 71%]
2025-08-14T23:40:03.3896969Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_int64 PASSED [0.0029s] [ 71%]
2025-08-14T23:40:03.3897101Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_int8 PASSED [0.0030s] [ 71%]
2025-08-14T23:40:03.3897231Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sqrt_cuda_uint8 PASSED [0.0032s] [ 71%]
2025-08-14T23:40:03.3897361Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_bool PASSED [0.0072s] [ 71%]
2025-08-14T23:40:03.3897497Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_complex32 PASSED [0.0077s] [ 71%]
2025-08-14T23:40:03.3897630Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_complex64 PASSED [0.0084s] [ 71%]
2025-08-14T23:40:03.3897759Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float16 PASSED [0.0083s] [ 71%]
2025-08-14T23:40:03.3897916Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float32 PASSED [0.0083s] [ 71%]
2025-08-14T23:40:03.3898047Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_float64 PASSED [0.0083s] [ 71%]
2025-08-14T23:40:03.3898175Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int16 PASSED [0.0080s] [ 71%]
2025-08-14T23:40:03.3898303Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int32 PASSED [0.0079s] [ 71%]
2025-08-14T23:40:03.3898433Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_sum_cuda_int8 PASSED [0.0078s] [ 71%]
2025-08-14T23:40:03.3898566Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_bfloat16 PASSED [0.0029s] [ 71%]
2025-08-14T23:40:03.3898693Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_bool PASSED [0.0030s] [ 71%]
2025-08-14T23:40:03.3898830Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_complex128 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3898963Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_complex32 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3899092Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_int16 PASSED [0.0031s] [ 71%]
2025-08-14T23:40:03.3899220Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_int64 PASSED [0.0031s] [ 72%]
2025-08-14T23:40:03.3899348Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_int8 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3899488Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tan_cuda_uint8 PASSED [0.0031s] [ 72%]
2025-08-14T23:40:03.3899623Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_complex32 PASSED [0.0031s] [ 72%]
2025-08-14T23:40:03.3899754Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_int16 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3900816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_int64 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3900958Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_int8 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3901088Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_tanh_cuda_uint8 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3901242Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_bfloat16 PASSED [0.0036s] [ 72%]
2025-08-14T23:40:03.3901379Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_bool PASSED [0.0033s] [ 72%]
2025-08-14T23:40:03.3901523Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_complex128 PASSED [0.0036s] [ 72%]
2025-08-14T23:40:03.3901668Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_float16 PASSED [0.0035s] [ 72%]
2025-08-14T23:40:03.3901802Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_int8 PASSED [0.0034s] [ 72%]
2025-08-14T23:40:03.3901940Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_to_sparse_cuda_uint8 PASSED [0.0033s] [ 72%]
2025-08-14T23:40:03.3902073Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_bfloat16 PASSED [0.0030s] [ 72%]
2025-08-14T23:40:03.3902207Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_float64 PASSED [0.0033s] [ 72%]
2025-08-14T23:40:03.3902338Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int32 PASSED [0.0029s] [ 72%]
2025-08-14T23:40:03.3902470Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int64 PASSED [0.0029s] [ 72%]
2025-08-14T23:40:03.3902612Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_trunc_cuda_int8 PASSED [0.0028s] [ 72%]
2025-08-14T23:40:03.3902755Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_bfloat16 PASSED [0.0041s] [ 72%]
2025-08-14T23:40:03.3902958Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex128 PASSED [0.0041s] [ 72%]
2025-08-14T23:40:03.3903102Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex32 PASSED [0.0042s] [ 72%]
2025-08-14T23:40:03.3903245Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_complex64 PASSED [0.0042s] [ 72%]
2025-08-14T23:40:03.3903388Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_float64 PASSED [0.0041s] [ 73%]
2025-08-14T23:40:03.3903526Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int16 PASSED [0.0038s] [ 73%]
2025-08-14T23:40:03.3903664Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int64 PASSED [0.0039s] [ 73%]
2025-08-14T23:40:03.3904734Z test_sparse_csr.py::TestSparseCompressedCUDA::test_consistency_SparseCSR_zeros_like_cuda_int8 PASSED [0.0039s] [ 73%]
2025-08-14T23:40:03.3904857Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_bfloat16 PASSED [0.0074s] [ 73%]
2025-08-14T23:40:03.3904975Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_float16 PASSED [0.0071s] [ 73%]
2025-08-14T23:40:03.3905091Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_float32 PASSED [0.0073s] [ 73%]
2025-08-14T23:40:03.3905235Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int16 PASSED [0.0068s] [ 73%]
2025-08-14T23:40:03.3905347Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int32 PASSED [0.0068s] [ 73%]
2025-08-14T23:40:03.3905462Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_int8 PASSED [0.0070s] [ 73%]
2025-08-14T23:40:03.3905573Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSC_cuda_uint8 PASSED [0.0067s] [ 73%]
2025-08-14T23:40:03.3905709Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_bfloat16 PASSED [0.0070s] [ 73%]
2025-08-14T23:40:03.3905824Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_float16 PASSED [0.0071s] [ 73%]
2025-08-14T23:40:03.3905938Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_float32 PASSED [0.0072s] [ 73%]
2025-08-14T23:40:03.3906065Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseBSR_cuda_uint8 PASSED [0.0069s] [ 73%]
2025-08-14T23:40:03.3906184Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_bfloat16 PASSED [0.0153s] [ 73%]
2025-08-14T23:40:03.3906303Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_complex64 PASSED [0.0155s] [ 73%]
2025-08-14T23:40:03.3906418Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_float32 PASSED [0.0156s] [ 73%]
2025-08-14T23:40:03.3906532Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_float64 PASSED [0.0157s] [ 73%]
2025-08-14T23:40:03.3906646Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int16 PASSED [0.0149s] [ 73%]
2025-08-14T23:40:03.3906757Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int32 PASSED [0.0150s] [ 73%]
2025-08-14T23:40:03.3906870Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int64 PASSED [0.0152s] [ 73%]
2025-08-14T23:40:03.3906985Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSC_cuda_int8 PASSED [0.0150s] [ 73%]
2025-08-14T23:40:03.3907103Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_bfloat16 PASSED [0.0153s] [ 74%]
2025-08-14T23:40:03.3908139Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_bool PASSED [0.0150s] [ 74%]
2025-08-14T23:40:03.3908275Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_float16 PASSED [0.0152s] [ 74%]
2025-08-14T23:40:03.3908387Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int16 PASSED [0.0150s] [ 74%]
2025-08-14T23:40:03.3908501Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int32 PASSED [0.0149s] [ 74%]
2025-08-14T23:40:03.3908612Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_SparseCSR_cuda_int8 PASSED [0.0149s] [ 74%]
2025-08-14T23:40:03.3908736Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_bool PASSED [0.0076s] [ 74%]
2025-08-14T23:40:03.3908866Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_complex64 PASSED [0.0073s] [ 74%]
2025-08-14T23:40:03.3908990Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_float16 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3909112Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int16 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3909236Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int32 PASSED [0.0072s] [ 74%]
2025-08-14T23:40:03.3909356Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int64 PASSED [0.0072s] [ 74%]
2025-08-14T23:40:03.3909477Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_int8 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3909596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSC_cuda_uint8 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3909723Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_bfloat16 PASSED [0.0075s] [ 74%]
2025-08-14T23:40:03.3909866Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_complex128 PASSED [0.0076s] [ 74%]
2025-08-14T23:40:03.3909991Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_float16 PASSED [0.0072s] [ 74%]
2025-08-14T23:40:03.3910115Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_float64 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3910234Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int16 PASSED [0.0073s] [ 74%]
2025-08-14T23:40:03.3910366Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int64 PASSED [0.0072s] [ 74%]
2025-08-14T23:40:03.3910486Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_int8 PASSED [0.0070s] [ 74%]
2025-08-14T23:40:03.3910619Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseBSR_cuda_uint8 PASSED [0.0071s] [ 74%]
2025-08-14T23:40:03.3911672Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_complex64 PASSED [0.0034s] [ 74%]
2025-08-14T23:40:03.3911796Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_float16 PASSED [0.0037s] [ 75%]
2025-08-14T23:40:03.3911916Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int32 PASSED [0.0034s] [ 75%]
2025-08-14T23:40:03.3912036Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int64 PASSED [0.0033s] [ 75%]
2025-08-14T23:40:03.3912158Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_int8 PASSED [0.0033s] [ 75%]
2025-08-14T23:40:03.3912277Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSC_cuda_uint8 PASSED [0.0033s] [ 75%]
2025-08-14T23:40:03.3912403Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_bfloat16 PASSED [0.0031s] [ 75%]
2025-08-14T23:40:03.3912524Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_bool PASSED [0.0032s] [ 75%]
2025-08-14T23:40:03.3912653Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_complex128 PASSED [0.0031s] [ 75%]
2025-08-14T23:40:03.3912780Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_complex64 PASSED [0.0031s] [ 75%]
2025-08-14T23:40:03.3912972Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_float16 PASSED [0.0031s] [ 75%]
2025-08-14T23:40:03.3913097Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_float64 PASSED [0.0031s] [ 75%]
2025-08-14T23:40:03.3913218Z test_sparse_csr.py::TestSparseCompressedCUDA::test_copy_errors_SparseCSR_cuda_int64 PASSED [0.0032s] [ 75%]
2025-08-14T23:40:03.3913338Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_SparseCSC_cuda_complex128 PASSED [0.0066s] [ 75%]
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2025-08-14T23:40:03.3920385Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSC_SparseCSC_cuda_float32 PASSED [0.0062s] [ 77%]
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2025-08-14T23:40:03.3920663Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSC_SparseCSC_cuda_uint8 PASSED [0.0061s] [ 77%]
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2025-08-14T23:40:03.3923011Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSR_SparseBSC_cuda_complex128 PASSED [0.0061s] [ 77%]
2025-08-14T23:40:03.3923147Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSR_SparseBSC_cuda_float16 PASSED [0.0061s] [ 77%]
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2025-08-14T23:40:03.3923450Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSR_SparseBSR_cuda_bfloat16 PASSED [0.0076s] [ 77%]
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2025-08-14T23:40:03.3926782Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseBSR_SparseCSR_cuda_uint8 PASSED [0.0061s] [ 78%]
2025-08-14T23:40:03.3926923Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseBSC_cuda_bfloat16 PASSED [0.0056s] [ 78%]
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2025-08-14T23:40:03.3927466Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseBSC_cuda_int16 PASSED [0.0056s] [ 78%]
2025-08-14T23:40:03.3927608Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseBSR_cuda_complex128 PASSED [0.0056s] [ 78%]
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2025-08-14T23:40:03.3927891Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseBSR_cuda_float16 PASSED [0.0056s] [ 79%]
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2025-08-14T23:40:03.3928168Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseBSR_cuda_int8 PASSED [0.0056s] [ 79%]
2025-08-14T23:40:03.3928299Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseCSC_cuda_bool PASSED [0.0071s] [ 79%]
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2025-08-14T23:40:03.3928576Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseCSC_cuda_float32 PASSED [0.0071s] [ 79%]
2025-08-14T23:40:03.3928709Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseCSC_cuda_int32 PASSED [0.0071s] [ 79%]
2025-08-14T23:40:03.3928842Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseCSC_cuda_int64 PASSED [0.0071s] [ 79%]
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2025-08-14T23:40:03.3929107Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSC_SparseCSC_cuda_uint8 PASSED [0.0071s] [ 79%]
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2025-08-14T23:40:03.3931128Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseBSC_cuda_float16 PASSED [0.0056s] [ 79%]
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2025-08-14T23:40:03.3931553Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseBSC_cuda_uint8 PASSED [0.0056s] [ 79%]
2025-08-14T23:40:03.3931693Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseBSR_cuda_bfloat16 PASSED [0.0058s] [ 79%]
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2025-08-14T23:40:03.3932504Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_complex64 PASSED [0.0056s] [ 80%]
2025-08-14T23:40:03.3932641Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_float16 PASSED [0.0056s] [ 80%]
2025-08-14T23:40:03.3932778Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_float32 PASSED [0.0056s] [ 80%]
2025-08-14T23:40:03.3932979Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_float64 PASSED [0.0056s] [ 80%]
2025-08-14T23:40:03.3933112Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_int64 PASSED [0.0057s] [ 80%]
2025-08-14T23:40:03.3933246Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSC_cuda_uint8 PASSED [0.0057s] [ 80%]
2025-08-14T23:40:03.3933377Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_bool PASSED [0.0071s] [ 80%]
2025-08-14T23:40:03.3934457Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_complex128 PASSED [0.0071s] [ 80%]
2025-08-14T23:40:03.3934596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_float64 PASSED [0.0071s] [ 80%]
2025-08-14T23:40:03.3934728Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_int16 PASSED [0.0072s] [ 80%]
2025-08-14T23:40:03.3934862Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_int32 PASSED [0.0072s] [ 80%]
2025-08-14T23:40:03.3934994Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_int64 PASSED [0.0072s] [ 80%]
2025-08-14T23:40:03.3935127Z test_sparse_csr.py::TestSparseCompressedCUDA::test_empty_like_SparseCSR_SparseCSR_cuda_uint8 PASSED [0.0071s] [ 80%]
2025-08-14T23:40:03.3935290Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseBSC_target_sparse_compressed_tensor_cuda PASSED [0.0022s] [ 80%]
2025-08-14T23:40:03.3935470Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseBSC_target_validate_sparse_compressed_tensor_args_cuda PASSED [0.0015s] [ 80%]
2025-08-14T23:40:03.3935670Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseBSR_target_validate_sparse_compressed_tensor_args_cuda PASSED [0.0019s] [ 80%]
2025-08-14T23:40:03.3935830Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSC_target_sparse_compressed_tensor_cuda PASSED [0.0016s] [ 80%]
2025-08-14T23:40:03.3935999Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSC_target_sparse_compressed_tensor_no_size_cuda PASSED [0.0017s] [ 80%]
2025-08-14T23:40:03.3936194Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSC_target_validate_sparse_compressed_tensor_args_cuda PASSED [0.0014s] [ 81%]
2025-08-14T23:40:03.3936352Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSR_target_sparse_compressed_tensor_cuda PASSED [0.0015s] [ 81%]
2025-08-14T23:40:03.3936537Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSR_target_sparse_compressed_tensor_no_size_cuda PASSED [0.0015s] [ 81%]
2025-08-14T23:40:03.3936714Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_SparseCSR_target_validate_sparse_compressed_tensor_args_cuda PASSED [0.0014s] [ 81%]
2025-08-14T23:40:03.3936860Z test_sparse_csr.py::TestSparseCompressedCUDA::test_invalid_input_csr_large_cuda SKIPPED [0.0005s] (Only runs on cpu) [ 81%]
2025-08-14T23:40:03.3936995Z test_sparse_csr.py::TestSparseCompressedCUDA::test_layout_SparseBSC_cuda SKIPPED [0.0004s] (Only runs on cpu) [ 81%]
2025-08-14T23:40:03.3937131Z test_sparse_csr.py::TestSparseCompressedCUDA::test_layout_SparseCSC_cuda SKIPPED [0.0004s] (Only runs on cpu) [ 81%]
2025-08-14T23:40:03.3937252Z test_sparse_csr.py::TestSparseCompressedCUDA::test_pickle_SparseCSC_cuda_float64 PASSED [0.0492s] [ 81%]
2025-08-14T23:40:03.3937367Z test_sparse_csr.py::TestSparseCompressedCUDA::test_print_SparseBSC_cuda PASSED [0.3311s] [ 81%]
2025-08-14T23:40:03.3937479Z test_sparse_csr.py::TestSparseCompressedCUDA::test_print_SparseCSC_cuda PASSED [0.2649s] [ 81%]
2025-08-14T23:40:03.3938537Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int32_cuda_bfloat16 PASSED [0.1275s] [ 81%]
2025-08-14T23:40:03.3938671Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int32_cuda_float16 PASSED [0.1268s] [ 81%]
2025-08-14T23:40:03.3938820Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int32_cuda_int64 PASSED [0.0865s] [ 81%]
2025-08-14T23:40:03.3938953Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int64_cuda_bfloat16 PASSED [0.1262s] [ 81%]
2025-08-14T23:40:03.3939091Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int64_cuda_complex128 PASSED [0.1345s] [ 81%]
2025-08-14T23:40:03.3939223Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int64_cuda_float32 PASSED [0.1259s] [ 81%]
2025-08-14T23:40:03.3939353Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int64_cuda_int64 PASSED [0.0844s] [ 81%]
2025-08-14T23:40:03.3939483Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSC_int64_cuda_uint8 PASSED [0.0847s] [ 81%]
2025-08-14T23:40:03.3939615Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_bfloat16 PASSED [0.1277s] [ 81%]
2025-08-14T23:40:03.3939745Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_bool PASSED [0.0860s] [ 81%]
2025-08-14T23:40:03.3939880Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_complex128 PASSED [0.1345s] [ 81%]
2025-08-14T23:40:03.3940012Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_float32 PASSED [0.1270s] [ 81%]
2025-08-14T23:40:03.3940144Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_float64 PASSED [0.1265s] [ 81%]
2025-08-14T23:40:03.3940275Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_int64 PASSED [0.0856s] [ 82%]
2025-08-14T23:40:03.3940415Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int32_cuda_int8 PASSED [0.0865s] [ 82%]
2025-08-14T23:40:03.3940547Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_bfloat16 PASSED [0.1247s] [ 82%]
2025-08-14T23:40:03.3940675Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_bool PASSED [0.0847s] [ 82%]
2025-08-14T23:40:03.3940810Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_complex64 PASSED [0.1328s] [ 82%]
2025-08-14T23:40:03.3940954Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_float32 PASSED [0.1257s] [ 82%]
2025-08-14T23:40:03.3941082Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_int16 PASSED [0.0853s] [ 82%]
2025-08-14T23:40:03.3941228Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_int64 PASSED [0.0844s] [ 82%]
2025-08-14T23:40:03.3942282Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseBSR_int64_cuda_int8 PASSED [0.0847s] [ 82%]
2025-08-14T23:40:03.3942416Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_bfloat16 PASSED [0.1249s] [ 82%]
2025-08-14T23:40:03.3942546Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_bool PASSED [0.0844s] [ 82%]
2025-08-14T23:40:03.3942680Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_complex128 PASSED [0.1317s] [ 82%]
2025-08-14T23:40:03.3942816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_complex64 PASSED [0.1317s] [ 82%]
2025-08-14T23:40:03.3943007Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_float16 PASSED [0.1243s] [ 82%]
2025-08-14T23:40:03.3943139Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_float64 PASSED [0.1249s] [ 82%]
2025-08-14T23:40:03.3943268Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_int16 PASSED [0.0844s] [ 82%]
2025-08-14T23:40:03.3943397Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int32_cuda_int64 PASSED [0.0845s] [ 82%]
2025-08-14T23:40:03.3943550Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_bfloat16 PASSED [0.1242s] [ 82%]
2025-08-14T23:40:03.3943678Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_bool PASSED [0.0835s] [ 82%]
2025-08-14T23:40:03.3943815Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_complex128 PASSED [0.1290s] [ 82%]
2025-08-14T23:40:03.3943950Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_complex64 PASSED [0.1300s] [ 82%]
2025-08-14T23:40:03.3944081Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_float32 PASSED [0.1229s] [ 82%]
2025-08-14T23:40:03.3944213Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_float64 PASSED [0.1227s] [ 82%]
2025-08-14T23:40:03.3944341Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSC_int64_cuda_int16 PASSED [0.0834s] [ 83%]
2025-08-14T23:40:03.3944475Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_bfloat16 PASSED [0.1249s] [ 83%]
2025-08-14T23:40:03.3944603Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_bool PASSED [0.0845s] [ 83%]
2025-08-14T23:40:03.3944740Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_complex128 PASSED [0.1312s] [ 83%]
2025-08-14T23:40:03.3944874Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_complex64 PASSED [0.1315s] [ 83%]
2025-08-14T23:40:03.3945009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_float32 PASSED [0.1246s] [ 83%]
2025-08-14T23:40:03.3946094Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_float64 PASSED [0.1242s] [ 83%]
2025-08-14T23:40:03.3946223Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_int32 PASSED [0.0842s] [ 83%]
2025-08-14T23:40:03.3946354Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_int64 PASSED [0.0839s] [ 83%]
2025-08-14T23:40:03.3946481Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_int8 PASSED [0.0843s] [ 83%]
2025-08-14T23:40:03.3946627Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int32_cuda_uint8 PASSED [0.0846s] [ 83%]
2025-08-14T23:40:03.3946757Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int64_cuda_float16 PASSED [0.1226s] [ 83%]
2025-08-14T23:40:03.3946908Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int64_cuda_int64 PASSED [0.0825s] [ 83%]
2025-08-14T23:40:03.3947037Z test_sparse_csr.py::TestSparseCompressedCUDA::test_select_copy_SparseCSR_int64_cuda_uint8 PASSED [0.0831s] [ 83%]
2025-08-14T23:40:03.3947206Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_bfloat16 PASSED [0.0518s] [ 83%]
2025-08-14T23:40:03.3947370Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_bool PASSED [0.0256s] [ 83%]
2025-08-14T23:40:03.3947540Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_complex64 PASSED [0.0538s] [ 83%]
2025-08-14T23:40:03.3947706Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_float16 PASSED [0.0519s] [ 83%]
2025-08-14T23:40:03.3947871Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_float32 PASSED [0.0511s] [ 83%]
2025-08-14T23:40:03.3948035Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_float64 PASSED [0.0506s] [ 83%]
2025-08-14T23:40:03.3948197Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_int16 PASSED [0.0256s] [ 83%]
2025-08-14T23:40:03.3948372Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_int32 PASSED [0.0255s] [ 83%]
2025-08-14T23:40:03.3948532Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_int8 PASSED [0.0255s] [ 83%]
2025-08-14T23:40:03.3948695Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSC_cuda_uint8 PASSED [0.0253s] [ 84%]
2025-08-14T23:40:03.3948860Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_bfloat16 PASSED [0.0516s] [ 84%]
2025-08-14T23:40:03.3949021Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_bool PASSED [0.0255s] [ 84%]
2025-08-14T23:40:03.3949190Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_complex128 PASSED [0.0538s] [ 84%]
2025-08-14T23:40:03.3949357Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_complex64 PASSED [0.0531s] [ 84%]
2025-08-14T23:40:03.3950448Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_float32 PASSED [0.0511s] [ 84%]
2025-08-14T23:40:03.3950610Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_int16 PASSED [0.0257s] [ 84%]
2025-08-14T23:40:03.3950770Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseBSR_cuda_int8 PASSED [0.0255s] [ 84%]
2025-08-14T23:40:03.3950951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_bfloat16 PASSED [0.0494s] [ 84%]
2025-08-14T23:40:03.3951110Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_bool PASSED [0.0249s] [ 84%]
2025-08-14T23:40:03.3951280Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_complex128 PASSED [0.0517s] [ 84%]
2025-08-14T23:40:03.3951445Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_complex64 PASSED [0.0517s] [ 84%]
2025-08-14T23:40:03.3951620Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_float16 PASSED [0.0497s] [ 84%]
2025-08-14T23:40:03.3951796Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_float64 PASSED [0.0501s] [ 84%]
2025-08-14T23:40:03.3951958Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSC_cuda_int8 PASSED [0.0249s] [ 84%]
2025-08-14T23:40:03.3952117Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_bool PASSED [0.0247s] [ 84%]
2025-08-14T23:40:03.3952285Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_complex64 PASSED [0.0536s] [ 84%]
2025-08-14T23:40:03.3952444Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_int16 PASSED [0.0250s] [ 84%]
2025-08-14T23:40:03.3952605Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_int32 PASSED [0.0249s] [ 84%]
2025-08-14T23:40:03.3952768Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_int64 PASSED [0.0245s] [ 84%]
2025-08-14T23:40:03.3952979Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_int8 PASSED [0.0248s] [ 84%]
2025-08-14T23:40:03.3953141Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_list_SparseCSR_cuda_uint8 PASSED [0.0253s] [ 84%]
2025-08-14T23:40:03.3953329Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_bfloat16 PASSED [0.0875s] [ 84%]
2025-08-14T23:40:03.3953500Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_complex128 PASSED [0.0934s] [ 85%]
2025-08-14T23:40:03.3953668Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_float64 PASSED [0.0874s] [ 85%]
2025-08-14T23:40:03.3953831Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_int32 PASSED [0.0390s] [ 85%]
2025-08-14T23:40:03.3954912Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_int64 PASSED [0.0395s] [ 85%]
2025-08-14T23:40:03.3955074Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSC_cuda_uint8 PASSED [0.0392s] [ 85%]
2025-08-14T23:40:03.3955247Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_complex128 PASSED [0.0925s] [ 85%]
2025-08-14T23:40:03.3955417Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_complex64 PASSED [0.0924s] [ 85%]
2025-08-14T23:40:03.3955582Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_float16 PASSED [0.0877s] [ 85%]
2025-08-14T23:40:03.3955747Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_float32 PASSED [0.0873s] [ 85%]
2025-08-14T23:40:03.3955930Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_int32 PASSED [0.0387s] [ 85%]
2025-08-14T23:40:03.3956091Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_int8 PASSED [0.0389s] [ 85%]
2025-08-14T23:40:03.3956255Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseBSR_cuda_uint8 PASSED [0.0387s] [ 85%]
2025-08-14T23:40:03.3956440Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSC_cuda_complex64 PASSED [0.0891s] [ 85%]
2025-08-14T23:40:03.3956605Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSC_cuda_float32 PASSED [0.0856s] [ 85%]
2025-08-14T23:40:03.3956786Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSC_cuda_float64 PASSED [0.0845s] [ 85%]
2025-08-14T23:40:03.3956949Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSC_cuda_int64 PASSED [0.0376s] [ 85%]
2025-08-14T23:40:03.3957111Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSC_cuda_uint8 PASSED [0.0378s] [ 85%]
2025-08-14T23:40:03.3957279Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSR_cuda_bfloat16 PASSED [0.0847s] [ 85%]
2025-08-14T23:40:03.3957450Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSR_cuda_complex128 PASSED [0.0890s] [ 85%]
2025-08-14T23:40:03.3957612Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSR_cuda_int16 PASSED [0.0375s] [ 85%]
2025-08-14T23:40:03.3957775Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSR_cuda_int32 PASSED [0.0374s] [ 85%]
2025-08-14T23:40:03.3957936Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_____from_tensor_SparseCSR_cuda_int8 PASSED [0.0375s] [ 85%]
2025-08-14T23:40:03.3958106Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSC_cuda_bool PASSED [0.0259s] [ 85%]
2025-08-14T23:40:03.3958292Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSC_cuda_float16 PASSED [0.0505s] [ 86%]
2025-08-14T23:40:03.3959398Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSC_cuda_int8 PASSED [0.0252s] [ 86%]
2025-08-14T23:40:03.3959568Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSC_cuda_uint8 PASSED [0.0253s] [ 86%]
2025-08-14T23:40:03.3959745Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSR_cuda_complex64 PASSED [0.0535s] [ 86%]
2025-08-14T23:40:03.3959920Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSR_cuda_float32 PASSED [0.0510s] [ 86%]
2025-08-14T23:40:03.3960088Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSR_cuda_int64 PASSED [0.0261s] [ 86%]
2025-08-14T23:40:03.3960258Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSR_cuda_int8 PASSED [0.0269s] [ 86%]
2025-08-14T23:40:03.3960427Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseBSR_cuda_uint8 PASSED [0.0256s] [ 86%]
2025-08-14T23:40:03.3960596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_bool PASSED [0.0255s] [ 86%]
2025-08-14T23:40:03.3960773Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_complex64 PASSED [0.0526s] [ 86%]
2025-08-14T23:40:03.3960963Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_float16 PASSED [0.0493s] [ 86%]
2025-08-14T23:40:03.3961136Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_float32 PASSED [0.0498s] [ 86%]
2025-08-14T23:40:03.3961309Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_float64 PASSED [0.0493s] [ 86%]
2025-08-14T23:40:03.3961497Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_int16 PASSED [0.0247s] [ 86%]
2025-08-14T23:40:03.3961667Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_int32 PASSED [0.0247s] [ 86%]
2025-08-14T23:40:03.3961849Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSC_cuda_int64 PASSED [0.0246s] [ 86%]
2025-08-14T23:40:03.3962025Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_bfloat16 PASSED [0.0501s] [ 86%]
2025-08-14T23:40:03.3962202Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_complex128 PASSED [0.0518s] [ 86%]
2025-08-14T23:40:03.3962376Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_float64 PASSED [0.0495s] [ 86%]
2025-08-14T23:40:03.3962547Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_int16 PASSED [0.0248s] [ 86%]
2025-08-14T23:40:03.3962716Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_int32 PASSED [0.0247s] [ 86%]
2025-08-14T23:40:03.3963872Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_int64 PASSED [0.0245s] [ 86%]
2025-08-14T23:40:03.3964041Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_list_SparseCSR_cuda_int8 PASSED [0.0248s] [ 86%]
2025-08-14T23:40:03.3964241Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_bfloat16 PASSED [0.0888s] [ 87%]
2025-08-14T23:40:03.3964422Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_complex128 PASSED [0.0917s] [ 87%]
2025-08-14T23:40:03.3964604Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_complex64 PASSED [0.0930s] [ 87%]
2025-08-14T23:40:03.3964780Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_float32 PASSED [0.0881s] [ 87%]
2025-08-14T23:40:03.3964956Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_int16 PASSED [0.0388s] [ 87%]
2025-08-14T23:40:03.3965128Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_int32 PASSED [0.0386s] [ 87%]
2025-08-14T23:40:03.3965302Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_int64 PASSED [0.0388s] [ 87%]
2025-08-14T23:40:03.3965520Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSC_cuda_uint8 PASSED [0.0387s] [ 87%]
2025-08-14T23:40:03.3965697Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_bfloat16 PASSED [0.0892s] [ 87%]
2025-08-14T23:40:03.3965872Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_float32 PASSED [0.0884s] [ 87%]
2025-08-14T23:40:03.3966063Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_int16 PASSED [0.0388s] [ 87%]
2025-08-14T23:40:03.3966235Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_int32 PASSED [0.0387s] [ 87%]
2025-08-14T23:40:03.3966410Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_int64 PASSED [0.0387s] [ 87%]
2025-08-14T23:40:03.3966597Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseBSR_cuda_uint8 PASSED [0.0389s] [ 87%]
2025-08-14T23:40:03.3966774Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_bfloat16 PASSED [0.0855s] [ 87%]
2025-08-14T23:40:03.3966960Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_bool PASSED [0.0379s] [ 87%]
2025-08-14T23:40:03.3967143Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_complex128 PASSED [0.0891s] [ 87%]
2025-08-14T23:40:03.3967324Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_complex64 PASSED [0.0892s] [ 87%]
2025-08-14T23:40:03.3967500Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_float32 PASSED [0.0853s] [ 87%]
2025-08-14T23:40:03.3968610Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_float64 PASSED [0.0847s] [ 87%]
2025-08-14T23:40:03.3968783Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_int16 PASSED [0.0376s] [ 87%]
2025-08-14T23:40:03.3968959Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_int64 PASSED [0.0376s] [ 87%]
2025-08-14T23:40:03.3969130Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_int8 PASSED [0.0378s] [ 87%]
2025-08-14T23:40:03.3969318Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSC_cuda_uint8 PASSED [0.0382s] [ 88%]
2025-08-14T23:40:03.3969494Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_bfloat16 PASSED [0.0853s] [ 88%]
2025-08-14T23:40:03.3969666Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_bool PASSED [0.0380s] [ 88%]
2025-08-14T23:40:03.3969839Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_int16 PASSED [0.0377s] [ 88%]
2025-08-14T23:40:03.3970012Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_int32 PASSED [0.0375s] [ 88%]
2025-08-14T23:40:03.3970185Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_int64 PASSED [0.0377s] [ 88%]
2025-08-14T23:40:03.3970361Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor___factory_from_tensor_SparseCSR_cuda_int8 PASSED [0.0376s] [ 88%]
2025-08-14T23:40:03.3970592Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_complex128 SKIPPED [0.0005s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3970818Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_complex64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3971040Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_float16 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3971271Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3971491Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_int32 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3971719Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSC_cuda_int64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3971951Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cuda_float32 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3972170Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3972388Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cuda_int16 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3972607Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cuda_int32 SKIPPED [0.0005s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3973823Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseBSR_cuda_int64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3974046Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_float16 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3974265Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_float32 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3974482Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3974724Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_int16 SKIPPED [0.0005s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3974942Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_int32 SKIPPED [0.0004s] (nothing to test) [ 88%]
2025-08-14T23:40:03.3975163Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_int64 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3975380Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_int8 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3975600Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSC_cuda_uint8 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3975816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cuda_bool SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3976038Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cuda_complex64 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3976258Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cuda_float16 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3976494Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3976712Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_list_SparseCSR_cuda_int16 SKIPPED [0.0004s] (nothing to test) [ 89%]
2025-08-14T23:40:03.3976928Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_float16 PASSED [0.0849s] [ 89%]
2025-08-14T23:40:03.3977128Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_float32 PASSED [0.0840s] [ 89%]
2025-08-14T23:40:03.3977342Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_float64 PASSED [0.0843s] [ 89%]
2025-08-14T23:40:03.3977540Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_int16 PASSED [0.0383s] [ 89%]
2025-08-14T23:40:03.3977740Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_int32 PASSED [0.0381s] [ 89%]
2025-08-14T23:40:03.3977936Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSC_cuda_int64 PASSED [0.0383s] [ 89%]
2025-08-14T23:40:03.3978140Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_complex128 PASSED [0.0891s] [ 89%]
2025-08-14T23:40:03.3979273Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_float16 PASSED [0.0849s] [ 89%]
2025-08-14T23:40:03.3979474Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_float32 PASSED [0.0844s] [ 89%]
2025-08-14T23:40:03.3979671Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_int16 PASSED [0.0381s] [ 89%]
2025-08-14T23:40:03.3979883Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_int64 PASSED [0.0380s] [ 89%]
2025-08-14T23:40:03.3980079Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_int8 PASSED [0.0378s] [ 89%]
2025-08-14T23:40:03.3980275Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseBSR_cuda_uint8 PASSED [0.0381s] [ 89%]
2025-08-14T23:40:03.3980477Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_bfloat16 PASSED [0.0837s] [ 89%]
2025-08-14T23:40:03.3980672Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_bool PASSED [0.0371s] [ 90%]
2025-08-14T23:40:03.3980872Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_float64 PASSED [0.0831s] [ 90%]
2025-08-14T23:40:03.3981068Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_int16 PASSED [0.0371s] [ 90%]
2025-08-14T23:40:03.3981263Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_int32 PASSED [0.0371s] [ 90%]
2025-08-14T23:40:03.3981476Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_int64 PASSED [0.0369s] [ 90%]
2025-08-14T23:40:03.3981669Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSC_cuda_int8 PASSED [0.0370s] [ 90%]
2025-08-14T23:40:03.3981864Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_bool PASSED [0.0369s] [ 90%]
2025-08-14T23:40:03.3982079Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_float16 PASSED [0.0831s] [ 90%]
2025-08-14T23:40:03.3982276Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_float64 PASSED [0.0833s] [ 90%]
2025-08-14T23:40:03.3982483Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_int16 PASSED [0.0369s] [ 90%]
2025-08-14T23:40:03.3982678Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_int32 PASSED [0.0370s] [ 90%]
2025-08-14T23:40:03.3982876Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_int64 PASSED [0.0370s] [ 90%]
2025-08-14T23:40:03.3983132Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference___from_tensor_SparseCSR_cuda_int8 PASSED [0.0371s] [ 90%]
2025-08-14T23:40:03.3983365Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_bfloat16 SKIPPED [0.0005s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3984538Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_bool SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3984774Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_complex128 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3985028Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_float16 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3985256Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3985484Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_int32 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3985708Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_int8 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3985933Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSC_cuda_uint8 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3986167Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_bfloat16 SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3986391Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_bool SKIPPED [0.0004s] (nothing to test) [ 90%]
2025-08-14T23:40:03.3986624Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_complex128 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3986864Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int16 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3987090Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int32 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3987329Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseBSR_cuda_int64 SKIPPED [0.0005s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3987552Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_bool SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3987799Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_complex128 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3988024Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_int16 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3988249Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSC_cuda_int32 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3988479Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_bfloat16 SKIPPED [0.0005s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3988709Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_bool SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3988942Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_complex128 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3990104Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_complex64 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3990349Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_float16 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3990578Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_float64 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3990803Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_list_SparseCSR_cuda_int8 SKIPPED [0.0004s] (nothing to test) [ 91%]
2025-08-14T23:40:03.3991009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_bool PASSED [0.0385s] [ 91%]
2025-08-14T23:40:03.3991223Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_complex128 PASSED [0.0912s] [ 91%]
2025-08-14T23:40:03.3991434Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_complex64 PASSED [0.0898s] [ 91%]
2025-08-14T23:40:03.3991642Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_float32 PASSED [0.0850s] [ 91%]
2025-08-14T23:40:03.3991848Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_int16 PASSED [0.0386s] [ 91%]
2025-08-14T23:40:03.3992066Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_int64 PASSED [0.0382s] [ 91%]
2025-08-14T23:40:03.3992271Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSC_cuda_uint8 PASSED [0.0382s] [ 91%]
2025-08-14T23:40:03.3992494Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_bfloat16 PASSED [0.0855s] [ 91%]
2025-08-14T23:40:03.3992697Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_bool PASSED [0.0382s] [ 92%]
2025-08-14T23:40:03.3992975Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_complex128 PASSED [0.0894s] [ 92%]
2025-08-14T23:40:03.3993183Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_float32 PASSED [0.0861s] [ 92%]
2025-08-14T23:40:03.3993391Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_float64 PASSED [0.0850s] [ 92%]
2025-08-14T23:40:03.3993596Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_int16 PASSED [0.0381s] [ 92%]
2025-08-14T23:40:03.3993799Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseBSR_cuda_int32 PASSED [0.0379s] [ 92%]
2025-08-14T23:40:03.3994009Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_complex128 PASSED [0.0873s] [ 92%]
2025-08-14T23:40:03.3994221Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_complex64 PASSED [0.0882s] [ 92%]
2025-08-14T23:40:03.3994445Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_float32 PASSED [0.0852s] [ 92%]
2025-08-14T23:40:03.3995585Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_float64 PASSED [0.0838s] [ 92%]
2025-08-14T23:40:03.3995790Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_int16 PASSED [0.0371s] [ 92%]
2025-08-14T23:40:03.3995994Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSC_cuda_int8 PASSED [0.0371s] [ 92%]
2025-08-14T23:40:03.3996201Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float16 PASSED [0.0833s] [ 92%]
2025-08-14T23:40:03.3996408Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float32 PASSED [0.0833s] [ 92%]
2025-08-14T23:40:03.3996615Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_float64 PASSED [0.0856s] [ 92%]
2025-08-14T23:40:03.3996819Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int16 PASSED [0.0370s] [ 92%]
2025-08-14T23:40:03.3997023Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int32 PASSED [0.0372s] [ 92%]
2025-08-14T23:40:03.3997246Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_constructor_shape_and_device_inference_factory_from_tensor_SparseCSR_cuda_int64 PASSED [0.0367s] [ 92%]
2025-08-14T23:40:03.3997407Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_bfloat16 PASSED [0.0496s] [ 92%]
2025-08-14T23:40:03.3997568Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_complex64 PASSED [0.0507s] [ 92%]
2025-08-14T23:40:03.3997745Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_float16 PASSED [0.0495s] [ 92%]
2025-08-14T23:40:03.3997899Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_float32 PASSED [0.0499s] [ 92%]
2025-08-14T23:40:03.3998066Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSC_cuda_int64 PASSED [0.0437s] [ 92%]
2025-08-14T23:40:03.3998220Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_bool PASSED [0.0439s] [ 93%]
2025-08-14T23:40:03.3998377Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_complex64 PASSED [0.0509s] [ 93%]
2025-08-14T23:40:03.3998531Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_float16 PASSED [0.0497s] [ 93%]
2025-08-14T23:40:03.3998684Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_int16 PASSED [0.0437s] [ 93%]
2025-08-14T23:40:03.3998837Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseBSR_cuda_int64 PASSED [0.0471s] [ 93%]
2025-08-14T23:40:03.3998988Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_bool PASSED [0.0420s] [ 93%]
2025-08-14T23:40:03.3999146Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_complex64 PASSED [0.0483s] [ 93%]
2025-08-14T23:40:03.4000225Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_float16 PASSED [0.0470s] [ 93%]
2025-08-14T23:40:03.4000401Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_float64 PASSED [0.0468s] [ 93%]
2025-08-14T23:40:03.4000552Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int16 PASSED [0.0411s] [ 93%]
2025-08-14T23:40:03.4000705Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int32 PASSED [0.0411s] [ 93%]
2025-08-14T23:40:03.4000856Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_int64 PASSED [0.0423s] [ 93%]
2025-08-14T23:40:03.4001008Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSC_cuda_uint8 PASSED [0.0411s] [ 93%]
2025-08-14T23:40:03.4001167Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_complex128 PASSED [0.0495s] [ 93%]
2025-08-14T23:40:03.4001320Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_float16 PASSED [0.0473s] [ 93%]
2025-08-14T23:40:03.4001473Z test_sparse_csr.py::TestSparseCompressedCUDA::test_sparse_compressed_tensor_with_dims_SparseCSR_cuda_int8 PASSED [0.0422s] [ 93%]
2025-08-14T23:40:03.4001593Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_bool PASSED [0.1031s] [ 93%]
2025-08-14T23:40:03.4001718Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_complex64 PASSED [0.1031s] [ 93%]
2025-08-14T23:40:03.4001840Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_float16 PASSED [0.1028s] [ 93%]
2025-08-14T23:40:03.4001973Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_float32 PASSED [0.1021s] [ 93%]
2025-08-14T23:40:03.4002091Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_int64 PASSED [0.1007s] [ 93%]
2025-08-14T23:40:03.4002207Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_int8 PASSED [0.1013s] [ 93%]
2025-08-14T23:40:03.4002325Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSC_cuda_uint8 PASSED [0.1040s] [ 93%]
2025-08-14T23:40:03.4002447Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_bfloat16 PASSED [0.1007s] [ 94%]
2025-08-14T23:40:03.4002577Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_bool PASSED [0.1036s] [ 94%]
2025-08-14T23:40:03.4002697Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_float16 PASSED [0.1019s] [ 94%]
2025-08-14T23:40:03.4002828Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_float32 PASSED [0.1022s] [ 94%]
2025-08-14T23:40:03.4002998Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int16 PASSED [0.1008s] [ 94%]
2025-08-14T23:40:03.4004046Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int32 PASSED [0.0999s] [ 94%]
2025-08-14T23:40:03.4004165Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseBSR_cuda_int64 PASSED [0.1016s] [ 94%]
2025-08-14T23:40:03.4004289Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_complex128 PASSED [0.1064s] [ 94%]
2025-08-14T23:40:03.4004410Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_float16 PASSED [0.1003s] [ 94%]
2025-08-14T23:40:03.4004528Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_float64 PASSED [0.1020s] [ 94%]
2025-08-14T23:40:03.4004645Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_int64 PASSED [0.1001s] [ 94%]
2025-08-14T23:40:03.4004763Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSC_cuda_uint8 PASSED [0.1003s] [ 94%]
2025-08-14T23:40:03.4004887Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_complex64 PASSED [0.1018s] [ 94%]
2025-08-14T23:40:03.4005004Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_float32 PASSED [0.1011s] [ 94%]
2025-08-14T23:40:03.4005144Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_float64 PASSED [0.1016s] [ 94%]
2025-08-14T23:40:03.4005259Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_int16 PASSED [0.1006s] [ 94%]
2025-08-14T23:40:03.4005375Z test_sparse_csr.py::TestSparseCompressedCUDA::test_to_dtype_SparseCSR_cuda_int32 PASSED [0.0855s] [ 94%]
2025-08-14T23:40:03.4005531Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_bfloat16 PASSED [0.0206s] [ 94%]
2025-08-14T23:40:03.4005650Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_bool PASSED [0.0162s] [ 94%]
2025-08-14T23:40:03.4005778Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_complex128 PASSED [0.0211s] [ 94%]
2025-08-14T23:40:03.4005904Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_complex64 PASSED [0.0209s] [ 94%]
2025-08-14T23:40:03.4006023Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_int64 PASSED [0.0162s] [ 94%]
2025-08-14T23:40:03.4006141Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSC_cuda_uint8 PASSED [0.0162s] [ 94%]
2025-08-14T23:40:03.4006264Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_bfloat16 PASSED [0.0202s] [ 95%]
2025-08-14T23:40:03.4006391Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_complex128 PASSED [0.0210s] [ 95%]
2025-08-14T23:40:03.4006515Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_complex64 PASSED [0.0209s] [ 95%]
2025-08-14T23:40:03.4007584Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_float16 PASSED [0.0202s] [ 95%]
2025-08-14T23:40:03.4007704Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_float64 PASSED [0.0203s] [ 95%]
2025-08-14T23:40:03.4007821Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int16 PASSED [0.0162s] [ 95%]
2025-08-14T23:40:03.4007939Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int32 PASSED [0.0162s] [ 95%]
2025-08-14T23:40:03.4008057Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseBSR_cuda_int8 PASSED [0.0162s] [ 95%]
2025-08-14T23:40:03.4008196Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_bool PASSED [0.0152s] [ 95%]
2025-08-14T23:40:03.4008323Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_complex128 PASSED [0.0199s] [ 95%]
2025-08-14T23:40:03.4008461Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_complex64 PASSED [0.0201s] [ 95%]
2025-08-14T23:40:03.4008583Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_float32 PASSED [0.0192s] [ 95%]
2025-08-14T23:40:03.4008699Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_int32 PASSED [0.0153s] [ 95%]
2025-08-14T23:40:03.4008816Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_int8 PASSED [0.0153s] [ 95%]
2025-08-14T23:40:03.4008936Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSC_cuda_uint8 PASSED [0.0153s] [ 95%]
2025-08-14T23:40:03.4009062Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_bfloat16 PASSED [0.0191s] [ 95%]
2025-08-14T23:40:03.4009179Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_bool PASSED [0.0152s] [ 95%]
2025-08-14T23:40:03.4009305Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_complex128 PASSED [0.0253s] [ 95%]
2025-08-14T23:40:03.4009426Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_float32 PASSED [0.0223s] [ 95%]
2025-08-14T23:40:03.4009547Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_float64 PASSED [0.0221s] [ 95%]
2025-08-14T23:40:03.4009664Z test_sparse_csr.py::TestSparseCompressedCUDA::test_validate_SparseCSR_cuda_uint8 PASSED [0.0168s] [ 95%]
2025-08-14T23:40:03.4009807Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_TensorAsKey_cuda PASSED [0.0073s] [ 95%]
2025-08-14T23:40:03.4009957Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_addmm_meta_cuda PASSED [0.0010s] [ 95%]
2025-08-14T23:40:03.4010186Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_bfloat16 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4010409Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_float16 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4011560Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_16_int32_cuda_float32 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4011780Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_32_int64_cuda_float32 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4012000Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int32_cuda_float16 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4012225Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int64_cuda_bfloat16 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4012446Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_block_size_64_int64_cuda_float16 SKIPPED [0.0001s] (Skipped for internal with remote GPUs) [ 96%]
2025-08-14T23:40:03.4012629Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_dense_bmm_error_messages_cuda_float16 PASSED [0.0024s] [ 96%]
2025-08-14T23:40:03.4012798Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16_cuda_bfloat16 PASSED [14.6165s] [ 96%]
2025-08-14T23:40:03.4013021Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16_cuda_float16 PASSED [13.9913s] [ 96%]
2025-08-14T23:40:03.4013210Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_bfloat16 PASSED [13.7060s] [ 96%]
2025-08-14T23:40:03.4013380Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_float16 PASSED [13.9331s] [ 96%]
2025-08-14T23:40:03.4013563Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_16x32_cuda_float32 PASSED [13.4503s] [ 96%]
2025-08-14T23:40:03.4013728Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2_cuda_float16 PASSED [1.7507s] [ 96%]
2025-08-14T23:40:03.4013895Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2x3_cuda_bfloat16 PASSED [1.8711s] [ 96%]
2025-08-14T23:40:03.4014061Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_2x3_cuda_float16 PASSED [1.8682s] [ 96%]
2025-08-14T23:40:03.4014228Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_32_cuda_bfloat16 PASSED [23.7417s] [ 96%]
2025-08-14T23:40:03.4014391Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_32_cuda_float16 PASSED [23.6006s] [ 96%]
2025-08-14T23:40:03.4014636Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_bfloat16 SKIPPED [0.0003s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 96%]
2025-08-14T23:40:03.4014876Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_float16 SKIPPED [0.0002s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 96%]
2025-08-14T23:40:03.4015127Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_bsr_scatter_mm_blocksize_64_cuda_float32 SKIPPED [0.0002s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 96%]
2025-08-14T23:40:03.4015367Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_bfloat16 SKIPPED [0.0007s] (out dtype not implemented) [ 96%]
2025-08-14T23:40:03.4016540Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_float16 SKIPPED [0.0005s] (out dtype not implemented) [ 96%]
2025-08-14T23:40:03.4016777Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_float32 SKIPPED [0.0062s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4017009Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_int8 SKIPPED [0.0004s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4017306Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_bfloat16 SKIPPED [0.0004s] (Redundant test: _int_bsr_dense_addmm on torch.bfloat16 tensors) [ 97%]
2025-08-14T23:40:03.4017593Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float16 SKIPPED [0.0004s] (Redundant test: _int_bsr_dense_addmm on torch.float16 tensors) [ 97%]
2025-08-14T23:40:03.4017879Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float32 SKIPPED [0.0004s] (Redundant test: _int_bsr_dense_addmm on torch.float32 tensors) [ 97%]
2025-08-14T23:40:03.4018138Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_float32 SKIPPED [0.0005s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4018373Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_int8 SKIPPED [0.0004s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4018617Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float16 SKIPPED [0.0004s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4018873Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float32 SKIPPED [0.0004s] (out dtype not implemented) [ 97%]
2025-08-14T23:40:03.4019159Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_bfloat16 SKIPPED [0.0004s] (Redundant test: _int_bsr_dense_addmm on torch.bfloat16 tensors) [ 97%]
2025-08-14T23:40:03.4019443Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op__int_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_float32 SKIPPED [0.0004s] (Redundant test: _int_bsr_dense_addmm on torch.float32 tensors) [ 97%]
2025-08-14T23:40:03.4019724Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_int32_cuda_int8 SKIPPED [0.0005s] (triton kernel does not support support int8 blocks smaller than 32) [ 97%]
2025-08-14T23:40:03.4019932Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_bfloat16 PASSED [21.4790s] [ 97%]
2025-08-14T23:40:03.4020139Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float16 PASSED [17.7625s] [ 97%]
2025-08-14T23:40:03.4020343Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_float32 PASSED [17.8262s] [ 97%]
2025-08-14T23:40:03.4020640Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16_out_dtype_unspecified_cuda_int8 SKIPPED [0.0008s] (triton kernel does not support support int8 blocks smaller than 32) [ 97%]
2025-08-14T23:40:03.4020918Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_int32_cuda_int8 SKIPPED [0.0005s] (triton kernel does not support support int8 blocks smaller than 32) [ 97%]
2025-08-14T23:40:03.4021129Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_bfloat16 PASSED [18.6066s] [ 97%]
2025-08-14T23:40:03.4021336Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_float16 PASSED [17.9882s] [ 97%]
2025-08-14T23:40:03.4021543Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_float32 PASSED [17.8874s] [ 97%]
2025-08-14T23:40:03.4022764Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_16x32_out_dtype_unspecified_cuda_int8 SKIPPED [0.0008s] (triton kernel does not support support int8 blocks smaller than 32) [ 97%]
2025-08-14T23:40:03.4023049Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_int32_cuda_float32 SKIPPED [0.0006s] (incompatible out dtype) [ 97%]
2025-08-14T23:40:03.4023277Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_bfloat16 PASSED [21.1201s] [ 97%]
2025-08-14T23:40:03.4023481Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_addmm_blocksize_32_out_dtype_unspecified_cuda_float32 PASSED [26.8694s] [ 98%]
2025-08-14T23:40:03.4023718Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_int32_cuda_float16 SKIPPED [0.0008s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4023966Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_int32_cuda_float32 SKIPPED [0.0005s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4024185Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16_out_dtype_unspecified_cuda_float16 PASSED [0.0521s] [ 98%]
2025-08-14T23:40:03.4024423Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_int32_cuda_float16 SKIPPED [0.0005s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4024656Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_int32_cuda_int8 SKIPPED [0.0005s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4024935Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_bfloat16 SKIPPED [0.0008s] (bsr_dense_linear does not support non-square blocks) [ 98%]
2025-08-14T23:40:03.4025215Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_float16 SKIPPED [0.0004s] (bsr_dense_linear does not support non-square blocks) [ 98%]
2025-08-14T23:40:03.4025487Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_16x32_out_dtype_unspecified_cuda_int8 SKIPPED [0.0004s] (bsr_dense_linear does not support non-square blocks) [ 98%]
2025-08-14T23:40:03.4025722Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_bfloat16 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4025972Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_float32 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4026199Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_int32_cuda_int8 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4026404Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_unspecified_cuda_float32 PASSED [0.0501s] [ 98%]
2025-08-14T23:40:03.4026655Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_linear_blocksize_32_out_dtype_unspecified_cuda_int8 SKIPPED [0.0005s] (bsr_dense_linear does not support int8) [ 98%]
2025-08-14T23:40:03.4026882Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_int32_cuda_float32 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4027083Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_bfloat16 PASSED [0.0529s] [ 98%]
2025-08-14T23:40:03.4027282Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_float16 PASSED [0.0527s] [ 98%]
2025-08-14T23:40:03.4027562Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16_out_dtype_unspecified_cuda_int8 SKIPPED [0.0005s] (triton kernel does not support support int8 blocks smaller than 32) [ 98%]
2025-08-14T23:40:03.4028744Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_bfloat16 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4028979Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_float16 SKIPPED [0.0005s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4029229Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_int32_cuda_float32 SKIPPED [0.0004s] (out dtype not implemented) [ 98%]
2025-08-14T23:40:03.4029446Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_unspecified_cuda_float32 PASSED [0.0527s] [ 98%]
2025-08-14T23:40:03.4029732Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_16x32_out_dtype_unspecified_cuda_int8 SKIPPED [0.0005s] (triton kernel does not support support int8 blocks smaller than 32) [ 98%]
2025-08-14T23:40:03.4029958Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_int32_cuda_float32 SKIPPED [0.0005s] (out dtype not implemented) [ 99%]
2025-08-14T23:40:03.4030182Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_int32_cuda_int8 SKIPPED [0.0004s] (out dtype not implemented) [ 99%]
2025-08-14T23:40:03.4030381Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_unspecified_cuda_bfloat16 PASSED [0.0530s] [ 99%]
2025-08-14T23:40:03.4030580Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_kernel_op_bsr_dense_mm_blocksize_32_out_dtype_unspecified_cuda_float16 PASSED [0.7112s] [ 99%]
2025-08-14T23:40:03.4030749Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_bfloat16 PASSED [15.4536s] [ 99%]
2025-08-14T23:40:03.4030915Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_float16 PASSED [15.0007s] [ 99%]
2025-08-14T23:40:03.4031095Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_16_cuda_float32 PASSED [14.5394s] [ 99%]
2025-08-14T23:40:03.4031262Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_bfloat16 PASSED [14.7094s] [ 99%]
2025-08-14T23:40:03.4031427Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_float16 PASSED [14.7986s] [ 99%]
2025-08-14T23:40:03.4031592Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_sampled_addmm_block_size_32_cuda_float32 PASSED [14.2403s] [ 99%]
2025-08-14T23:40:03.4031855Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_16_cuda_float16 SKIPPED [0.0008s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 99%]
2025-08-14T23:40:03.4032114Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_16_cuda_float32 SKIPPED [0.0005s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 99%]
2025-08-14T23:40:03.4032376Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_32_cuda_float32 SKIPPED [0.0004s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 99%]
2025-08-14T23:40:03.4032633Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scaled_dot_product_attention_block_size_64_cuda_float16 SKIPPED [0.0004s] (skipIfRocm: test doesn't currently work on the ROCm stack) [ 99%]
2025-08-14T23:40:03.4032797Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scatter_mm_cuda_bfloat16 PASSED [0.2980s] [ 99%]
2025-08-14T23:40:03.4033929Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_scatter_mm_cuda_float32 PASSED [0.4070s] [ 99%]
2025-08-14T23:40:03.4034125Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_bfloat16 PASSED [4.2827s] [ 99%]
2025-08-14T23:40:03.4034320Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_float32 PASSED [3.3917s] [ 99%]
2025-08-14T23:40:03.4034529Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op__int_bsr_dense_addmm_out_dtype_unspecified_cuda_int8 PASSED [5.6993s] [ 99%]
2025-08-14T23:40:03.4034758Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_int32_cuda_float16 SKIPPED [0.0009s] (incompatible out dtype) [ 99%]
2025-08-14T23:40:03.4034971Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_int32_cuda_float32 SKIPPED [0.0005s] (incompatible out dtype) [ 99%]
2025-08-14T23:40:03.4035157Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_unspecified_cuda_float16 PASSED [4.2906s] [ 99%]
2025-08-14T23:40:03.4035342Z test_sparse_csr.py::TestSparseCompressedTritonKernelsCUDA::test_triton_tune_op_bsr_dense_addmm_out_dtype_unspecified_cuda_float32 PASSED [7.0199s] [100%]
2025-08-14T23:40:03.4035348Z 
2025-08-14T23:40:03.4035536Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/test_sparse_csr/test_sparse_csr-41127c0396c7a2d0.xml -
2025-08-14T23:40:03.4035618Z ======== 2021 passed, 269 skipped, 251 deselected in 441.12s (0:07:21) =========
2025-08-14T23:40:03.4035808Z The following tests failed and then succeeded when run in a new process['test/test_sparse_csr.py::TestSparseCSRCUDA::test_select_SparseBSC_int32_cuda_bfloat16']
2025-08-14T23:40:03.4035812Z 
2025-08-14T23:40:03.4035941Z FINISHED PRINTING LOG FILE of test_sparse_csr 1/2 (test/test-reports/test_sparse_csr_1.2_172afec2faee053e_.log)
2025-08-14T23:40:03.4035944Z 
2025-08-14T23:40:03.4036035Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading
2025-08-14T23:40:03.4036104Z Uploading artifacts took 0.00 seconds
2025-08-14T23:40:03.4036200Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2025-08-14 23:40:03.252343]
2025-08-14T23:40:03.4036247Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:03.4036564Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:40:03.252573]
2025-08-14T23:40:05.6723551Z 
2025-08-14T23:40:05.6725238Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_b8256e1f0e9ad342_.log
2025-08-14T23:40:05.6729098Z Running 9 items in this shard: test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float2, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float3, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_odd_pad_amount, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_pad_2d, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_zeros, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_large_integers, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_pad_empty_dimension
2025-08-14T23:40:05.6730551Z 
2025-08-14T23:40:05.6730671Z Running torch_np/numpy_tests/lib/test_function_base 1/1 ... [2025-08-14 23:40:05.672468]
2025-08-14T23:40:05.6730858Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:05.6731270Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_function_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'] ... [2025-08-14 23:40:05.672698]
2025-08-14T23:40:09.1468840Z 
2025-08-14T23:40:09.1480280Z torch_np/numpy_tests/lib/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_338f3afa1b3439ec_.log
2025-08-14T23:40:09.1545946Z Running 505 items in this shard: test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_rotation_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_4d, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_lr, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_ud, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_default_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_multiple_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_order, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_average_class_without_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x0_axis0_expected_avg0_weights0_expected_wavg0_expected_wsum0, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x1_axis_0_expected_avg1_weights1_expected_wavg1_expected_wsum1, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_returned, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_upcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_weights, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_broadcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_deprecated_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_many_arguments, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_non_bool_deprecation, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_return_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_array_copied, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_-4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_multidim, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmax::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmin::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestPtp::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumsum::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestProd::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumprod::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_append, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_n, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_prepend, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_array_order_preserve, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_fancy, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_[1], test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_array([1]), test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_non_int, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_hazen_expected_27_5, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nan_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nan_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_no_p_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_out_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_empty_dim, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_list, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_no_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_scalar_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_scalar_q_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_sequence, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_correct_quantile_value, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_fraction, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_max_ulp, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_no_p_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_hypo, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_averaged_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_closest_observation, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_hazen, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_higher, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_interpolated_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_linear, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_lower, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_median_unbiased, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_midpoint, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_nearest, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_normal_unbiased, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_weibull, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_scalar_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_array_like, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_axis_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis_invalid, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis4, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_overwrite_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_B_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_H_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_b_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_g_type_out_G, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_h_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_l_type_out_D
2025-08-14T23:40:09.1610482Z 
2025-08-14T23:40:09.1610589Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2025-08-14 23:40:09.147173]
2025-08-14T23:40:09.1610779Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:09.1611188Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:40:09.147392]
2025-08-14T23:40:11.5670072Z 
2025-08-14T23:40:11.5671665Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_c2d6d6c8b6a95e11_.log
2025-08-14T23:40:11.5684544Z Running 47 items in this shard: test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_big_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_clipmodes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_dtypes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_clip, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_raise, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_unravel, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_writeability, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_longdouble, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npcomplexfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_linspace_equivalence, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start0_stop_10_step0_expected0, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start_-10_stop_20_step1_expected1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_nd, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_sparse, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_1d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_2d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_complex_step, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_more_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdenumerate::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_simple_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_1d_only, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_bool, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_repeated_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_shape_and_dtype, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestC::test_c_, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_hetero_shape_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_low_dim_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_operate_4d_array, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_wide_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndices::test_diag_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_diag_indices_from, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_shape_mismatch, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_small_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdIndex::test_ndindex
2025-08-14T23:40:11.5697884Z 
2025-08-14T23:40:11.5698018Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2025-08-14 23:40:11.567107]
2025-08-14T23:40:11.5698240Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:11.5698721Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/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'] ... [2025-08-14 23:40:11.567387]
2025-08-14T23:40:14.1875061Z 
2025-08-14T23:40:14.1877007Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_f00aae018fe7b46c_.log
2025-08-14T23:40:14.1889751Z Running 73 items in this shard: test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_argequivalent, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_invalid, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_replace_max, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_0d_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_3d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion_ma, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_scalar_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple101, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_tuple_func1d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_with_iterable_object, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyOverAxes::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_out_of_range, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_tuple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_functionality, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_repeated_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_high_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_low_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_0_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_cols, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_default, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows_greater_max_int32, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_equal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_unequal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_1D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_2D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_3D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic_2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis_handling, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_contiguous, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_type, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a0_shape_b0, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a1_shape_b1, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a2_shape_b2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a3_shape_b3, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a4_shape_b4, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a5_shape_b5, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_kroncompare, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_tile_one_repetition_on_array_gh4679, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestMayShareMemory::test_basic
2025-08-14T23:40:14.1903666Z 
2025-08-14T23:40:14.1903784Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2025-08-14 23:40:14.187327]
2025-08-14T23:40:14.1903983Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:14.1904396Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_twodim_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'] ... [2025-08-14 23:40:14.187543]
2025-08-14T23:40:16.6082330Z 
2025-08-14T23:40:16.6083825Z torch_np/numpy_tests/lib/test_twodim_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_117303c3991c82d7_.log
2025-08-14T23:40:16.6088450Z Running 34 items in this shard: test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_bool, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_eye_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_diag_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_failure, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_fortran_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_matrix, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_vector, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFliplr::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFlipud::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_all_outliers, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_asym, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_10_y_len_11, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_20_y_len_19, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_binparameter_combination, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_density, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_empty, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_simple, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_mask_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim2, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim3, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_with_inf, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndices::test_triu_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTrilIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_dtypes
2025-08-14T23:40:16.6092303Z 
2025-08-14T23:40:16.6092422Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2025-08-14 23:40:16.608276]
2025-08-14T23:40:16.6097665Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:16.6098097Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:40:16.608526]
2025-08-14T23:40:19.0785708Z 
2025-08-14T23:40:19.0787949Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_fa98f88b62702e6d_.log
2025-08-14T23:40:19.0802065Z Running 50 items in this shard: test/torch_np/numpy_tests/lib/test_type_check.py::TestCommonType::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_1, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_2, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_3, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsscalar::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_isreal_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_list, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsrealobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsposinf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsneginf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_array, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad2, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_good, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_do_not_rewrite_previous_keyword, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_float, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestRealIfClose::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestArrayConversion::test_asfarray
2025-08-14T23:40:19.0810191Z 
2025-08-14T23:40:19.0815732Z Running torch_np/test_basic 1/1 ... [2025-08-14 23:40:19.078534]
2025-08-14T23:40:19.0815950Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:19.0816421Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_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'] ... [2025-08-14 23:40:19.078844]
2025-08-14T23:40:24.3044449Z 
2025-08-14T23:40:24.3046253Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_3d887b750dbb01a8_.log
2025-08-14T23:40:24.3093099Z Running 453 items in this shard: test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func20, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func21, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func22, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func23, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func24, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func25, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func26, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func27, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func28, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func29, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func3, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func30, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func31, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func32, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func33, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func34, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func35, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func36, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func37, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func38, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func39, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func4, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func40, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func41, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func42, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func43, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func44, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func45, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func46, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func47, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func48, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func49, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func5, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func50, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func51, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func52, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func53, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func54, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func55, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func56, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func57, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func58, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func59, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func6, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func60, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func61, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func62, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func63, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func64, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func65, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func66, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func67, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func68, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func69, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func7, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func70, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func71, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func72, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func73, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func74, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func8, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func9, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func20, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func21, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func22, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func23, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func24, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func25, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func26, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func27, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func28, 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test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func4, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func2_np_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func0, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func1, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func4, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func5, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func6, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func0_args0, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func1_args1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func2_args2, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func3_args3, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func4_args4, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func5_args5, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func6_args6, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func7_args7, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func8_args8, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func9_args9, test/torch_np/test_basic.py::TestNormalizations::test_too_few_args_positional, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args_with_defaults, test/torch_np/test_basic.py::TestCopyTo::test_copyto_basic, test/torch_np/test_basic.py::TestCopyTo::test_copyto_typecast, test/torch_np/test_basic.py::TestCopyTo::test_copytobcast, test/torch_np/test_basic.py::TestDivmod::test_divmod_no_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_both_pos_and_kw, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_list, test/torch_np/test_basic.py::TestDivmod::test_divmod_pos_only, test/torch_np/test_basic.py::TestSmokeNotImpl::test_nimpl_basic, test/torch_np/test_basic.py::TestDefaultDtype::test_defaultdtype_defaults, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_float32, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_pytorch, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_float32, test/torch_np/test_basic.py::TestExport::test_exported_objects, test/torch_np/test_basic.py::TestCtorNested::test_arrays_in_lists, test/torch_np/test_basic.py::TestMisc::test_f16_on_cuda, test/torch_np/test_basic.py::TestMisc::test_ndarrays_to_tensors
2025-08-14T23:40:24.3139398Z 
2025-08-14T23:40:24.3139491Z Running torch_np/test_ndarray_methods 1/1 ... [2025-08-14 23:40:24.304656]
2025-08-14T23:40:24.3139662Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:24.3140087Z 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'] ... [2025-08-14 23:40:24.304924]
2025-08-14T23:40:27.8757457Z 
2025-08-14T23:40:27.8758243Z 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_e49a3ea9133be2f3_.log
2025-08-14T23:40:27.8809902Z 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, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_onedim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_trivial, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_twodim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_sparse, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_max, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_min, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size26_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size26_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size27_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size27_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size28_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size28_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size29_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size29_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size2_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size2_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size30_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size30_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size31_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size31_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size32_axis32_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size32_axis32_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size33_axis_-4_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size33_axis_-4_method1, 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2025-08-14T23:40:27.8854820Z 
2025-08-14T23:40:27.8854921Z Running torch_np/test_nep50_examples 1/1 ... [2025-08-14 23:40:27.875913]
2025-08-14T23:40:27.8855092Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:27.8855483Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_nep50_examples.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:40:27.876117]
2025-08-14T23:40:32.2481753Z 
2025-08-14T23:40:32.2482767Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_7593fd7b2806731c_.log
2025-08-14T23:40:32.2768507Z Running 1897 items in this shard: test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_3j + array(3, complex64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_True + uint8(2), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array(1_0, float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([0_1], float32) == float64(0_1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([100], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + array(1, int64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + int64(1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_0], float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + 3, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + array(1_, float64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + float64(1_), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + int64(3), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_bool_(True) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 1j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 3e100, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(5) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(2) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(4) + 4j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int32(1) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(100) + 200, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array236_dtype236, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array237_dtype237, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array238_dtype238, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array239_dtype239, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array240_dtype240, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array241_dtype241, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array242_dtype242, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array243_dtype243, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array244_dtype244, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array245_dtype245, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array246_dtype246, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array247_dtype247, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array248_dtype248, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array249_dtype249, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array250_dtype250, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array251_dtype251, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array252_dtype252, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array253_dtype253, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array254_dtype254, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array255_dtype255, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array256_dtype256, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array257_dtype257, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array258_dtype258, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array259_dtype259, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array260_dtype260, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array261_dtype261, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array262_dtype262, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array263_dtype263, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array264_dtype264, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array265_dtype265, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array266_dtype266, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array267_dtype267, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array268_dtype268, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array269_dtype269, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array0_dtype0, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array10_dtype10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array11_dtype11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array12_dtype12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array13_dtype13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array14_dtype14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array15_dtype15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array16_dtype16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array17_dtype17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array18_dtype18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array19_dtype19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array1_dtype1, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array20_dtype20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array21_dtype21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array22_dtype22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array23_dtype23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array24_dtype24, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array25_dtype25, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array26_dtype26, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array27_dtype27, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array28_dtype28, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array29_dtype29, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array2_dtype2, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array30_dtype30, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array31_dtype31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array32_dtype32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array33_dtype33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array34_dtype34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array35_dtype35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array36_dtype36, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array37_dtype37, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array38_dtype38, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array39_dtype39, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array3_dtype3, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array40_dtype40, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array41_dtype41, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array42_dtype42, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array43_dtype43, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array44_dtype44, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array45_dtype45, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array46_dtype46, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array47_dtype47, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array48_dtype48, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array49_dtype49, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array4_dtype4, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array50_dtype50, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array51_dtype51, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array52_dtype52, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array53_dtype53, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array54_dtype54, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array55_dtype55, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array56_dtype56, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array57_dtype57, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array58_dtype58, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array59_dtype59, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array5_dtype5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array60_dtype60, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array61_dtype61, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array62_dtype62, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array63_dtype63, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array64_dtype64, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array65_dtype65, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array66_dtype66, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array67_dtype67, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array68_dtype68, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array77_dtype77, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array78_dtype78, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array79_dtype79, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array7_dtype7, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array80_dtype80, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array81_dtype81, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array82_dtype82, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array83_dtype83, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array84_dtype84, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array85_dtype85, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array86_dtype86, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array87_dtype87, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array88_dtype88, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array89_dtype89, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array8_dtype8, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array9_dtype9
2025-08-14T23:40:32.3041153Z 
2025-08-14T23:40:32.3045984Z Running xpu/test_gemm 1/1 ... [2025-08-14 23:40:32.249809]
2025-08-14T23:40:32.3046167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set
2025-08-14T23:40:32.3046550Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_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'] ... [2025-08-14 23:40:32.250018]
2025-08-14T23:40:34.6075015Z 
2025-08-14T23:40:34.6075789Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_554ca1d06c008707_.log
2025-08-14T23:40:34.6076497Z Running 0 items in this shard:
2025-08-14T23:40:34.6077043Z 
2025-08-14T23:40:36.1089720Z /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.
2025-08-14T23:40:36.1091034Z   import pkg_resources
2025-08-14T23:40:36.9250866Z Running test batch 'tests to run' cost 8411.58 seconds
2025-08-14T23:40:37.8185479Z 
2025-08-14T23:40:37.8185897Z real	140m14.837s
2025-08-14T23:40:37.8186121Z user	382m28.311s
2025-08-14T23:40:37.8187470Z sys	50m3.921s
2025-08-14T23:40:37.8187792Z + assert_git_not_dirty
2025-08-14T23:40:37.8188017Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]]
2025-08-14T23:40:37.8188218Z + sccache_epilogue
2025-08-14T23:40:37.8188392Z + echo '::group::Sccache Compilation Log'
2025-08-14T23:40:37.8188868Z ##[group]Sccache Compilation Log
2025-08-14T23:40:37.8189058Z + echo '=================== sccache compilation log ==================='
2025-08-14T23:40:37.8189294Z =================== sccache compilation log ===================
2025-08-14T23:40:37.8189590Z + python /var/lib/jenkins/pytorch/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log
2025-08-14T23:40:37.8264531Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ==========='
2025-08-14T23:40:37.8264846Z =========== If your build fails, please take a look at the log above for possible reasons ===========
2025-08-14T23:40:37.8265072Z + sccache --show-stats
2025-08-14T23:40:37.8285805Z Compile requests                   7991
2025-08-14T23:40:37.8286321Z Compile requests executed           547
2025-08-14T23:40:37.8286529Z Cache hits                           88
2025-08-14T23:40:37.8286729Z Cache hits (C/C++)                   88
2025-08-14T23:40:37.8286925Z Cache misses                        438
2025-08-14T23:40:37.8287131Z Cache misses (C/C++)                432
2025-08-14T23:40:37.8287331Z Cache misses (HIP)                    6
2025-08-14T23:40:37.8287540Z Cache hits rate                   16.73 %
2025-08-14T23:40:37.8287748Z Cache hits rate (C/C++)           16.92 %
2025-08-14T23:40:37.8287994Z Cache hits rate (HIP)              0.00 %
2025-08-14T23:40:37.8288214Z Cache timeouts                        0
2025-08-14T23:40:37.8288547Z Cache read errors                     0
2025-08-14T23:40:37.8288739Z Forced recaches                       0
2025-08-14T23:40:37.8288940Z Cache write errors                    0
2025-08-14T23:40:37.8289132Z Cache errors                          0
2025-08-14T23:40:37.8289330Z Compilations                        438
2025-08-14T23:40:37.8289647Z Compilation failures                 21
2025-08-14T23:40:37.8289849Z Non-cacheable compilations            0
2025-08-14T23:40:37.8290053Z Non-cacheable calls                 254
2025-08-14T23:40:37.8290254Z Non-compilation calls              7190
2025-08-14T23:40:37.8290461Z Unsupported compiler calls            0
2025-08-14T23:40:37.8290710Z Average cache write               0.000 s
2025-08-14T23:40:37.8290926Z Average compiler                  1.701 s
2025-08-14T23:40:37.8291148Z Average cache read hit            0.000 s
2025-08-14T23:40:37.8291387Z Failed distributed compilations       0
2025-08-14T23:40:37.8291531Z 
2025-08-14T23:40:37.8291609Z Non-cacheable reasons:
2025-08-14T23:40:37.8291797Z unknown source language             214
2025-08-14T23:40:37.8291999Z -E                                   40
2025-08-14T23:40:37.8292137Z 
2025-08-14T23:40:37.8292264Z Cache location                  Local disk: "/var/lib/jenkins/.cache/sccache"
2025-08-14T23:40:37.8292556Z Use direct/preprocessor mode?   yes
2025-08-14T23:40:37.8292769Z Version (client)                0.10.0
2025-08-14T23:40:37.8293250Z Cache size                           28 MiB
2025-08-14T23:40:37.8293481Z Max cache size                       10 GiB
2025-08-14T23:40:37.8295697Z + sccache --stop-server
2025-08-14T23:40:37.8306001Z Stopping sccache server...
2025-08-14T23:40:37.8309092Z Compile requests                   7991
2025-08-14T23:40:37.8309309Z Compile requests executed           547
2025-08-14T23:40:37.8309496Z Cache hits                           88
2025-08-14T23:40:37.8309666Z Cache hits (C/C++)                   88
2025-08-14T23:40:37.8309803Z Cache misses                        438
2025-08-14T23:40:37.8309922Z Cache misses (C/C++)                432
2025-08-14T23:40:37.8310043Z Cache misses (HIP)                    6
2025-08-14T23:40:37.8310164Z Cache hits rate                   16.73 %
2025-08-14T23:40:37.8310284Z Cache hits rate (C/C++)           16.92 %
2025-08-14T23:40:37.8310402Z Cache hits rate (HIP)              0.00 %
2025-08-14T23:40:37.8310521Z Cache timeouts                        0
2025-08-14T23:40:37.8315717Z Cache read errors                     0
2025-08-14T23:40:37.8315868Z Forced recaches                       0
2025-08-14T23:40:37.8315995Z Cache write errors                    0
2025-08-14T23:40:37.8316119Z Cache errors                          0
2025-08-14T23:40:37.8316235Z Compilations                        438
2025-08-14T23:40:37.8316352Z Compilation failures                 21
2025-08-14T23:40:37.8316471Z Non-cacheable compilations            0
2025-08-14T23:40:37.8316588Z Non-cacheable calls                 254
2025-08-14T23:40:37.8316704Z Non-compilation calls              7190
2025-08-14T23:40:37.8316819Z Unsupported compiler calls            0
2025-08-14T23:40:37.8316934Z Average cache write               0.000 s
2025-08-14T23:40:37.8318359Z Average compiler                  1.701 s
2025-08-14T23:40:37.8318481Z Average cache read hit            0.000 s
2025-08-14T23:40:37.8318601Z Failed distributed compilations       0
2025-08-14T23:40:37.8318757Z 
2025-08-14T23:40:37.8318800Z Non-cacheable reasons:
2025-08-14T23:40:37.8318901Z unknown source language             214
2025-08-14T23:40:37.8319012Z -E                                   40
2025-08-14T23:40:37.8319083Z 
2025-08-14T23:40:37.8319157Z Cache location                  Local disk: "/var/lib/jenkins/.cache/sccache"
2025-08-14T23:40:37.8319310Z Use direct/preprocessor mode?   yes
2025-08-14T23:40:37.8319426Z Version (client)                0.10.0
2025-08-14T23:40:37.8319543Z Cache size                           28 MiB
2025-08-14T23:40:37.8319660Z Max cache size                       10 GiB
2025-08-14T23:40:37.8320799Z + echo ::endgroup::
2025-08-14T23:40:37.8320987Z ##[endgroup]
2025-08-14T23:40:37.8408149Z ##[group]Run # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct
2025-08-14T23:40:37.8408629Z # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct
2025-08-14T23:40:37.8409191Z docker exec -t "4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49" sh -c "cd ../pytorch && sudo cp -R test/test-reports ../workspace/test"
2025-08-14T23:40:37.8415253Z shell: /usr/bin/bash -e {0}
2025-08-14T23:40:37.8415428Z env:
2025-08-14T23:40:37.8415564Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:37.8415749Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:37.8415935Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:37.8416106Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:37.8416523Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:37.8416895Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:37.8417020Z   AWS_REGION: us-east-1
2025-08-14T23:40:37.8417185Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:37.8417345Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:37.8419474Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:37.8419659Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:37.8419852Z ##[endgroup]
2025-08-14T23:40:37.9079323Z ##[group]Run docker exec -t "4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49" sh -c "sudo chown -R 1001:1001 test"
2025-08-14T23:40:37.9079859Z docker exec -t "4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49" sh -c "sudo chown -R 1001:1001 test"
2025-08-14T23:40:37.9085994Z shell: /usr/bin/bash -e {0}
2025-08-14T23:40:37.9086113Z env:
2025-08-14T23:40:37.9086213Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:37.9086353Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:37.9086533Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:37.9086707Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:37.9087104Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:37.9087513Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:37.9087635Z   AWS_REGION: us-east-1
2025-08-14T23:40:37.9087808Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:37.9087967Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:37.9089976Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:37.9090149Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:37.9090328Z ##[endgroup]
2025-08-14T23:40:37.9858734Z ##[group]Run cat test/**/*_toprint.log || true
2025-08-14T23:40:37.9858919Z cat test/**/*_toprint.log || true
2025-08-14T23:40:37.9864636Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:37.9864802Z env:
2025-08-14T23:40:37.9864909Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:37.9865148Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:37.9865332Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:37.9865506Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:37.9865884Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:37.9866285Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:37.9866413Z   AWS_REGION: us-east-1
2025-08-14T23:40:37.9866578Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:37.9875070Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:37.9877103Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:37.9877284Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:37.9877630Z ##[endgroup]
2025-08-14T23:40:37.9946531Z cat: 'test/**/*_toprint.log': No such file or directory
2025-08-14T23:40:38.0012372Z Prepare all required actions
2025-08-14T23:40:38.0012699Z Getting action download info
2025-08-14T23:40:38.2057410Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-08-14T23:40:38.6257970Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02)
2025-08-14T23:40:39.1294554Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-08-14T23:40:39.1294694Z with:
2025-08-14T23:40:39.1294780Z   use-gha: true
2025-08-14T23:40:39.1294922Z   file-suffix: test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093
2025-08-14T23:40:39.1295083Z   s3-bucket: gha-artifacts
2025-08-14T23:40:39.1295183Z env:
2025-08-14T23:40:39.1295266Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:39.1295391Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:39.1295558Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:39.1295748Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:39.1296130Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:39.1296489Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:39.1296699Z   AWS_REGION: us-east-1
2025-08-14T23:40:39.1296844Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:39.1296991Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:39.1299154Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:39.1299317Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:39.1299490Z ##[endgroup]
2025-08-14T23:40:39.1359015Z ##[group]Run actions/upload-artifact@v4
2025-08-14T23:40:39.1359136Z with:
2025-08-14T23:40:39.1359306Z   name: test-jsons-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip
2025-08-14T23:40:39.1359501Z   retention-days: 14
2025-08-14T23:40:39.1359609Z   if-no-files-found: warn
2025-08-14T23:40:39.1359713Z   path: test/**/*.json
2025-08-14T23:40:39.1359809Z   compression-level: 6
2025-08-14T23:40:39.1359903Z   overwrite: false
2025-08-14T23:40:39.1361723Z   include-hidden-files: false
2025-08-14T23:40:39.1361832Z env:
2025-08-14T23:40:39.1361918Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:39.1362051Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:39.1362220Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:39.1362377Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:39.1362752Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:39.1363208Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:39.1363317Z   AWS_REGION: us-east-1
2025-08-14T23:40:39.1363440Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:39.1363728Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:39.1367632Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:39.1367802Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:39.1367978Z ##[endgroup]
2025-08-14T23:40:39.5839674Z With the provided path, there will be 6 files uploaded
2025-08-14T23:40:39.5843362Z Artifact name is valid!
2025-08-14T23:40:39.5843685Z Root directory input is valid!
2025-08-14T23:40:39.6924776Z Beginning upload of artifact content to blob storage
2025-08-14T23:40:39.8589353Z Uploaded bytes 45533
2025-08-14T23:40:39.8914282Z Finished uploading artifact content to blob storage!
2025-08-14T23:40:39.8915264Z SHA256 digest of uploaded artifact zip is 1e4b612f6d01cda018eef34f8369222763e80c563aa3e1f2489d2b8946f49b90
2025-08-14T23:40:39.8916025Z Finalizing artifact upload
2025-08-14T23:40:39.9810451Z Artifact test-jsons-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip.zip successfully finalized. Artifact ID 3770199962
2025-08-14T23:40:39.9811795Z Artifact test-jsons-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip has been successfully uploaded! Final size is 45533 bytes. Artifact ID is 3770199962
2025-08-14T23:40:39.9813850Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16976255041/artifacts/3770199962
2025-08-14T23:40:39.9950786Z ##[group]Run actions/upload-artifact@v4
2025-08-14T23:40:39.9950929Z with:
2025-08-14T23:40:39.9951121Z   name: test-reports-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip
2025-08-14T23:40:39.9951331Z   retention-days: 14
2025-08-14T23:40:39.9951455Z   if-no-files-found: ignore
2025-08-14T23:40:39.9951577Z   path: test/**/*.xml
test/**/*.csv

2025-08-14T23:40:39.9951707Z   compression-level: 6
2025-08-14T23:40:39.9951814Z   overwrite: false
2025-08-14T23:40:39.9954407Z   include-hidden-files: false
2025-08-14T23:40:39.9954521Z env:
2025-08-14T23:40:39.9954608Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:39.9954751Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:39.9954928Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:39.9955088Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:39.9955468Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:39.9955833Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:39.9955945Z   AWS_REGION: us-east-1
2025-08-14T23:40:39.9956102Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:39.9957910Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:39.9959944Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:39.9960112Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:39.9960289Z ##[endgroup]
2025-08-14T23:40:40.4333421Z With the provided path, there will be 85 files uploaded
2025-08-14T23:40:40.4333722Z Artifact name is valid!
2025-08-14T23:40:40.4333846Z Root directory input is valid!
2025-08-14T23:40:40.5513358Z Beginning upload of artifact content to blob storage
2025-08-14T23:40:40.8399878Z Uploaded bytes 600628
2025-08-14T23:40:40.8706381Z Finished uploading artifact content to blob storage!
2025-08-14T23:40:40.8707541Z SHA256 digest of uploaded artifact zip is 595fd151f6c8aff0e1b7931b53bd8af6019c09b4da9466ae018a9e967295ad62
2025-08-14T23:40:40.8707952Z Finalizing artifact upload
2025-08-14T23:40:40.9543088Z Artifact test-reports-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip.zip successfully finalized. Artifact ID 3770200025
2025-08-14T23:40:40.9543636Z Artifact test-reports-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip has been successfully uploaded! Final size is 600628 bytes. Artifact ID is 3770200025
2025-08-14T23:40:40.9546640Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16976255041/artifacts/3770200025
2025-08-14T23:40:40.9690279Z ##[group]Run actions/upload-artifact@v4
2025-08-14T23:40:40.9690434Z with:
2025-08-14T23:40:40.9690618Z   name: logs-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip
2025-08-14T23:40:40.9690829Z   retention-days: 14
2025-08-14T23:40:40.9690952Z   if-no-files-found: ignore
2025-08-14T23:40:40.9691088Z   path: usage_log.txt
test/**/*.log

2025-08-14T23:40:40.9691233Z   compression-level: 6
2025-08-14T23:40:40.9691351Z   overwrite: false
2025-08-14T23:40:40.9691470Z   include-hidden-files: false
2025-08-14T23:40:40.9691599Z env:
2025-08-14T23:40:40.9691700Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:40.9691952Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:40.9692147Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:40.9692325Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:40.9692835Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:40.9693399Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:40.9693533Z   AWS_REGION: us-east-1
2025-08-14T23:40:40.9693713Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:40.9693890Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:40.9696052Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:40.9696245Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:40.9696444Z ##[endgroup]
2025-08-14T23:40:41.4181466Z Multiple search paths detected. Calculating the least common ancestor of all paths
2025-08-14T23:40:41.4186729Z The least common ancestor is /home/runner/_work/pytorch/pytorch. This will be the root directory of the artifact
2025-08-14T23:40:41.4186992Z With the provided path, there will be 82 files uploaded
2025-08-14T23:40:41.4187206Z Artifact name is valid!
2025-08-14T23:40:41.4187320Z Root directory input is valid!
2025-08-14T23:40:41.5226133Z Beginning upload of artifact content to blob storage
2025-08-14T23:40:42.0463746Z Uploaded bytes 651894
2025-08-14T23:40:42.1002394Z Finished uploading artifact content to blob storage!
2025-08-14T23:40:42.1003601Z SHA256 digest of uploaded artifact zip is ff87fe934897d4a01fb903bb7aee5d12d49e7086a01981ed877cc97e610be0e0
2025-08-14T23:40:42.1004308Z Finalizing artifact upload
2025-08-14T23:40:42.1798462Z Artifact logs-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip.zip successfully finalized. Artifact ID 3770200127
2025-08-14T23:40:42.1799733Z Artifact logs-runattempt1-test-default-4-6-linux.rocm.gpu.gfx942.1_48127863093.zip has been successfully uploaded! Final size is 651894 bytes. Artifact ID is 3770200127
2025-08-14T23:40:42.1809373Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16976255041/artifacts/3770200127
2025-08-14T23:40:42.1937401Z ##[group]Run # shellcheck disable=SC2156
2025-08-14T23:40:42.1937620Z # shellcheck disable=SC2156
2025-08-14T23:40:42.1938012Z find . -iname "core.[1-9]*" -exec docker exec "${CONTAINER_NAME}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-08-14T23:40:42.1943706Z shell: /usr/bin/bash -e {0}
2025-08-14T23:40:42.1943828Z env:
2025-08-14T23:40:42.1943924Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:42.1944067Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:42.1944254Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:42.1944427Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:42.1944815Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:42.1945190Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:42.1945324Z   AWS_REGION: us-east-1
2025-08-14T23:40:42.1945485Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:42.1945646Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:42.1947779Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:42.1947958Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:42.1948143Z ##[endgroup]
2025-08-14T23:40:42.3612075Z ##[group]Run aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722
2025-08-14T23:40:42.3614371Z with:
2025-08-14T23:40:42.3614540Z   role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_upload-benchmark-results
2025-08-14T23:40:42.3614731Z   role-duration-seconds: 18000
2025-08-14T23:40:42.3614856Z   aws-region: us-east-1
2025-08-14T23:40:42.3615062Z   audience: sts.amazonaws.com
2025-08-14T23:40:42.3615176Z env:
2025-08-14T23:40:42.3615268Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:42.3615407Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:42.3615588Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:42.3615756Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:42.3616170Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:42.3616536Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:42.3616659Z   AWS_REGION: us-east-1
2025-08-14T23:40:42.3616833Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:42.3618655Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:42.3620670Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:42.3620851Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:42.3621037Z ##[endgroup]
2025-08-14T23:40:42.5603096Z Assuming role with OIDC
2025-08-14T23:40:42.6692359Z Authenticated as assumedRoleId AROAUPVRELQNA5GQHA6IA:GitHubActions
2025-08-14T23:40:42.7132833Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-08-14T23:40:42.7133194Z with:
2025-08-14T23:40:42.7133308Z   benchmark-results-dir: test/test-reports
2025-08-14T23:40:42.7133448Z   dry-run: false
2025-08-14T23:40:42.7133558Z   schema-version: v3
2025-08-14T23:40:42.7133856Z   github-token: ***
2025-08-14T23:40:42.7135931Z env:
2025-08-14T23:40:42.7136035Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:42.7136187Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:42.7136377Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:42.7136550Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:42.7136940Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:42.7137329Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:42.7137455Z   AWS_REGION: us-east-1
2025-08-14T23:40:42.7139355Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:42.7139521Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:42.7141592Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:42.7141774Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:42.7141962Z ##[endgroup]
2025-08-14T23:40:42.7154929Z ##[group]Run set -eux
2025-08-14T23:40:42.7155064Z set -eux
2025-08-14T23:40:42.7155221Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0
2025-08-14T23:40:42.7155393Z 
2025-08-14T23:40:42.7155492Z DEVICE_NAME=""
2025-08-14T23:40:42.7155610Z DEVICE_TYPE=""
2025-08-14T23:40:42.7155717Z 
2025-08-14T23:40:42.7155824Z if command -v nvidia-smi; then
2025-08-14T23:40:42.7156011Z   # NB: I'm using PyTorch here to get the device name, however, it needs to
2025-08-14T23:40:42.7158189Z   # install the correct version of PyTorch manually for now. Any PyTorch
2025-08-14T23:40:42.7158410Z   # version is fine, I just use 2.7.1 to satify PYPIDEP linter
2025-08-14T23:40:42.7158585Z   python3 -mpip install torch==2.7.1
2025-08-14T23:40:42.7158814Z elif command -v rocminfo; then
2025-08-14T23:40:42.7158990Z   # NB: Installing torch on ROCm runner with pip here causes CI to fail
2025-08-14T23:40:42.7159203Z   # with a memoryview is too large error only on MI300 runners. Is pip
2025-08-14T23:40:42.7159415Z   # version on ROCm runner there too old? As a workaround, let's use the
2025-08-14T23:40:42.7159610Z   # GPU device name coming from rocminfo instead
2025-08-14T23:40:42.7159761Z   DEVICE_NAME=rocm
2025-08-14T23:40:42.7161781Z   DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs)
2025-08-14T23:40:42.7161972Z fi
2025-08-14T23:40:42.7162068Z 
2025-08-14T23:40:42.7162193Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV
2025-08-14T23:40:42.7162362Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV
2025-08-14T23:40:42.7168035Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:42.7168192Z env:
2025-08-14T23:40:42.7168293Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:42.7170488Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:42.7170685Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:42.7170859Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:42.7171249Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:42.7171625Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:42.7171752Z   AWS_REGION: us-east-1
2025-08-14T23:40:42.7171891Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:42.7172155Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:42.7176263Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:42.7176456Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:42.7176647Z ##[endgroup]
2025-08-14T23:40:42.7197049Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0
2025-08-14T23:40:42.8603558Z Defaulting to user installation because normal site-packages is not writeable
2025-08-14T23:40:43.3209560Z Collecting boto3==1.35.33
2025-08-14T23:40:43.4220972Z   Downloading boto3-1.35.33-py3-none-any.whl (139 kB)
2025-08-14T23:40:43.4800632Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 139.1/139.1 KB 2.4 MB/s eta 0:00:00
2025-08-14T23:40:43.5710715Z Collecting psutil==7.0.0
2025-08-14T23:40:43.5978829Z   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)
2025-08-14T23:40:43.6302207Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 278.0/278.0 KB 8.6 MB/s eta 0:00:00
2025-08-14T23:40:43.6638212Z Collecting pynvml==12.0.0
2025-08-14T23:40:43.6920195Z   Downloading pynvml-12.0.0-py3-none-any.whl (26 kB)
2025-08-14T23:40:44.0359136Z Collecting botocore<1.36.0,>=1.35.33
2025-08-14T23:40:44.0645325Z   Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB)
2025-08-14T23:40:44.4500177Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.3/13.3 MB 32.9 MB/s eta 0:00:00
2025-08-14T23:40:44.4999880Z Collecting s3transfer<0.11.0,>=0.10.0
2025-08-14T23:40:44.5266620Z   Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB)
2025-08-14T23:40:44.5298965Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 83.2/83.2 KB 37.0 MB/s eta 0:00:00
2025-08-14T23:40:44.5654355Z Collecting jmespath<2.0.0,>=0.7.1
2025-08-14T23:40:44.5918256Z   Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)
2025-08-14T23:40:44.6372031Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0
2025-08-14T23:40:44.6639200Z   Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB)
2025-08-14T23:40:44.6661384Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 47.5/47.5 KB 28.3 MB/s eta 0:00:00
2025-08-14T23:40:44.6695578Z 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)
2025-08-14T23:40:44.7066264Z Collecting python-dateutil<3.0.0,>=2.1
2025-08-14T23:40:44.7331517Z   Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)
2025-08-14T23:40:44.7361130Z      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 229.9/229.9 KB 108.2 MB/s eta 0:00:00
2025-08-14T23:40:44.8223960Z 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)
2025-08-14T23:40:44.8868597Z Installing collected packages: nvidia-ml-py, python-dateutil, pynvml, psutil, jmespath, botocore, s3transfer, boto3
2025-08-14T23:40:45.2345345Z 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
2025-08-14T23:40:45.3051319Z + DEVICE_NAME=
2025-08-14T23:40:45.3052380Z + DEVICE_TYPE=
2025-08-14T23:40:45.3052775Z + command -v nvidia-smi
2025-08-14T23:40:45.3053279Z + command -v rocminfo
2025-08-14T23:40:45.3053619Z + DEVICE_NAME=rocm
2025-08-14T23:40:45.3053922Z /usr/bin/rocminfo
2025-08-14T23:40:45.3069563Z ++ rocminfo
2025-08-14T23:40:45.3069782Z ++ grep 'Marketing Name'
2025-08-14T23:40:45.3069962Z ++ tail -n1
2025-08-14T23:40:45.3070090Z ++ awk -F: '{print $2}'
2025-08-14T23:40:45.3070228Z ++ xargs
2025-08-14T23:40:45.3753818Z + DEVICE_TYPE='AMD Instinct Mi325X VF'
2025-08-14T23:40:45.3754250Z + echo DEVICE_NAME=rocm
2025-08-14T23:40:45.3754681Z + echo 'DEVICE_TYPE=AMD Instinct Mi325X VF'
2025-08-14T23:40:45.3796713Z ##[group]Run set -eux
2025-08-14T23:40:45.3796904Z set -eux
2025-08-14T23:40:45.3797035Z 
2025-08-14T23:40:45.3797174Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-08-14T23:40:45.3797497Z   echo "Missing github-token input"
2025-08-14T23:40:45.3797663Z   exit 1
2025-08-14T23:40:45.3799712Z fi
2025-08-14T23:40:45.3804841Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.3805009Z env:
2025-08-14T23:40:45.3805111Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.3805259Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.3805531Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.3805706Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.3806100Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.3808351Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.3808478Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.3808640Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.3808812Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.3811000Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.3811184Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.3811375Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.3811490Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.3813620Z   GITHUB_TOKEN: ***
2025-08-14T23:40:45.3813736Z ##[endgroup]
2025-08-14T23:40:45.3839577Z + [[ -z *** ]]
2025-08-14T23:40:45.3874678Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-08-14T23:40:45.3874855Z with:
2025-08-14T23:40:45.3875030Z   github-token: ***
2025-08-14T23:40:45.3875140Z env:
2025-08-14T23:40:45.3875246Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.3875399Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.3875596Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.3875772Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.3876174Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.3876631Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.3879365Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.3879511Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.3879676Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.3881741Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.3881926Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.3882113Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.3882228Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.3882358Z ##[endgroup]
2025-08-14T23:40:45.3894661Z ##[group]Run set -eux
2025-08-14T23:40:45.3894852Z set -eux
2025-08-14T23:40:45.3894952Z 
2025-08-14T23:40:45.3895152Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-08-14T23:40:45.3900608Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.3900760Z env:
2025-08-14T23:40:45.3900862Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.3901013Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.3901202Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.3901377Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.3901762Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.3902136Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.3902266Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.3902408Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.3902579Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.3904729Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.3905034Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.3905229Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.3907918Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.3908112Z   GITHUB_TOKEN: ***
2025-08-14T23:40:45.3908219Z ##[endgroup]
2025-08-14T23:40:45.3934720Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 16976255041 linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d
2025-08-14T23:40:45.9249754Z setting job-id=48127863093
2025-08-14T23:40:45.9250409Z setting job-name=linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T23:40:45.9328760Z ##[group]Run set -eux
2025-08-14T23:40:45.9328935Z set -eux
2025-08-14T23:40:45.9329066Z 
2025-08-14T23:40:45.9329268Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-08-14T23:40:45.9329488Z   --schema-version "${SCHEMA_VERSION}" \
2025-08-14T23:40:45.9332084Z   --repo "${REPO}" \
2025-08-14T23:40:45.9332225Z   --head-branch "${HEAD_BRANCH}" \
2025-08-14T23:40:45.9332367Z   --head-sha "${HEAD_SHA}" \
2025-08-14T23:40:45.9332520Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-08-14T23:40:45.9332666Z   --run-attempt "${RUN_ATTEMPT}" \
2025-08-14T23:40:45.9332799Z   --job-id "${JOB_ID}" \
2025-08-14T23:40:45.9333157Z   --job-name "${JOB_NAME}"
2025-08-14T23:40:45.9338615Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.9340948Z env:
2025-08-14T23:40:45.9341053Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.9341200Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.9341385Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.9341560Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.9341947Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.9342423Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.9342547Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.9342718Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.9344969Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.9347052Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.9347234Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.9347421Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.9347535Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.9347666Z   SCHEMA_VERSION: v3
2025-08-14T23:40:45.9347775Z   REPO: pytorch/pytorch
2025-08-14T23:40:45.9347953Z   HEAD_BRANCH: refs/heads/main
2025-08-14T23:40:45.9349898Z   HEAD_SHA: 1fc683cf17c8c673044538d10266c00f92987be2
2025-08-14T23:40:45.9350051Z   WORKFLOW_RUN_ID: 16976255041
2025-08-14T23:40:45.9350171Z   RUN_ATTEMPT: 1
2025-08-14T23:40:45.9350272Z   JOB_ID: 48127863093
2025-08-14T23:40:45.9350449Z   JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)
2025-08-14T23:40:45.9350643Z ##[endgroup]
2025-08-14T23:40:45.9382797Z + 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 1fc683cf17c8c673044538d10266c00f92987be2 --workflow-id 16976255041 --run-attempt 1 --job-id 48127863093 --job-name 'linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)'
2025-08-14T23:40:45.9553308Z ##[group]Run set -eux
2025-08-14T23:40:45.9553437Z set -eux
2025-08-14T23:40:45.9553545Z 
2025-08-14T23:40:45.9553711Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py"
2025-08-14T23:40:45.9559423Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.9559588Z env:
2025-08-14T23:40:45.9559691Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.9559840Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.9560030Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.9560204Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.9560595Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.9560966Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.9561092Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.9562839Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.9563137Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.9565321Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.9565580Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.9565774Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.9565891Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.9566023Z ##[endgroup]
2025-08-14T23:40:45.9593417Z + python3 /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py
2025-08-14T23:40:45.9801769Z INFO:root:Fail to import torch to get the device name
2025-08-14T23:40:45.9868306Z ##[group]Run set -eux
2025-08-14T23:40:45.9868485Z set -eux
2025-08-14T23:40:45.9868618Z 
2025-08-14T23:40:45.9868761Z # TODO (huydhn): Implement this part
2025-08-14T23:40:45.9868955Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-08-14T23:40:45.9874898Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.9875098Z env:
2025-08-14T23:40:45.9875231Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.9878835Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.9879040Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.9879220Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.9879616Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.9880127Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.9880259Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.9880448Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.9880619Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.9884948Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.9885146Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.9885335Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.9885601Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.9885736Z ##[endgroup]
2025-08-14T23:40:45.9906403Z + echo 'dependencies={}'
2025-08-14T23:40:45.9929176Z ##[group]Run set -eux
2025-08-14T23:40:45.9929335Z set -eux
2025-08-14T23:40:45.9929457Z 
2025-08-14T23:40:45.9929603Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-08-14T23:40:45.9929821Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-08-14T23:40:45.9930055Z   # We don't want the job to fail if the directory doesn't exist
2025-08-14T23:40:45.9930240Z   exit 0
2025-08-14T23:40:45.9930363Z fi
2025-08-14T23:40:45.9930473Z 
2025-08-14T23:40:45.9930595Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-08-14T23:40:45.9930830Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-08-14T23:40:45.9931090Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-08-14T23:40:45.9931304Z     --metadata "${BENCHMARK_METADATA}" \
2025-08-14T23:40:45.9931494Z     --runners "${RUNNER_INFO}" \
2025-08-14T23:40:45.9931829Z     --dependencies "${DEPENDENCIES}" \
2025-08-14T23:40:45.9932008Z     --dry-run
2025-08-14T23:40:45.9932157Z else
2025-08-14T23:40:45.9932350Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-08-14T23:40:45.9932621Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-08-14T23:40:45.9932832Z     --metadata "${BENCHMARK_METADATA}" \
2025-08-14T23:40:45.9933164Z     --runners "${RUNNER_INFO}" \
2025-08-14T23:40:45.9933351Z     --dependencies "${DEPENDENCIES}"
2025-08-14T23:40:45.9933525Z fi
2025-08-14T23:40:45.9938179Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:45.9940666Z env:
2025-08-14T23:40:45.9940773Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:45.9940923Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:45.9941108Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:45.9941285Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:45.9941674Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:45.9942051Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:45.9942174Z   AWS_REGION: us-east-1
2025-08-14T23:40:45.9942320Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:45.9944473Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:45.9946644Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:45.9946826Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:45.9947012Z   DEVICE_NAME: rocm
2025-08-14T23:40:45.9947126Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:45.9947272Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-08-14T23:40:45.9947402Z   DRY_RUN: false
2025-08-14T23:40:45.9947874Z   BENCHMARK_METADATA: {"timestamp": 1755214845, "schema_version": "v3", "name": "linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "1fc683cf17c8c673044538d10266c00f92987be2", "workflow_id": 16976255041, "run_attempt": 1, "job_id": 48127863093}
2025-08-14T23:40:45.9948613Z   RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 160, "avail_mem_in_gb": 1255, "extra_info": {"hostname": "linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d"}, "name": "rocm", "type": "AMD Instinct Mi325X VF"}]
2025-08-14T23:40:45.9950667Z   DEPENDENCIES: {}
2025-08-14T23:40:45.9950772Z ##[endgroup]
2025-08-14T23:40:45.9973222Z + [[ ! -d test/test-reports ]]
2025-08-14T23:40:45.9973487Z + [[ false == \t\r\u\e ]]
2025-08-14T23:40:45.9975422Z + 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": 1755214845, "schema_version": "v3", "name": "linux-noble-rocm-py3.12-mi300 / test (default, 4, 6, linux.rocm.gpu.gfx942.1)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "1fc683cf17c8c673044538d10266c00f92987be2", "workflow_id": 16976255041, "run_attempt": 1, "job_id": 48127863093}' --runners '[{"cpu_info": "x86_64", "cpu_count": 160, "avail_mem_in_gb": 1255, "extra_info": {"hostname": "linux.rocm.gpu.gfx942.1-ccq8d-runner-24g2d"}, "name": "rocm", "type": "AMD Instinct Mi325X VF"}]' --dependencies '{}'
2025-08-14T23:40:46.0619262Z /home/runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'test_public_bindings'}, {'test_file': 'inductor/test_aot_inductor'}, {'test_file': 'inductor/test_torchinductor'}, {'test_file': 'inductor/test_max_autotune'}, {'test_file': 'inductor/test_torchinductor_dynamic_shapes'}, {'test_file': 'inductor/test_torchinductor_codegen_dynamic_shapes'}, {'test_file': 'inductor/test_torchinductor_opinfo'}, {'test_file': 'inductor/test_cpu_repro'}, {'test_file': 'inductor/test_cuda_repro'}, {'test_file': 'dynamo/test_unspec'}, {'test_file': 'dynamo/test_repros'}, {'test_file': 'inductor/test_kernel_benchmark'}, {'test_file': 'dynamo/test_dynamic_shapes'}, {'test_file': 'inductor/test_cudagraph_trees'}, {'test_file': 'inductor/test_mkldnn_pattern_matcher'}, {'test_file': 'inductor/test_perf'}, {'test_file': 'inductor/test_pattern_matcher'}, {'test_file': 'inductor/test_fused_attention'}, {'test_file': 'inductor/test_inductor_freezing'}, {'test_file': 'dynamo/test_misc'}, {'test_file': 'dynamo/test_higher_order_ops'}, {'test_file': 'dynamo/test_modules'}, {'test_file': 'dynamo/test_backends'}, {'test_file': 'dynamo/test_activation_checkpointing'}, {'test_file': 'dynamo/test_logging'}, {'test_file': 'inductor/test_select_algorithm'}, {'test_file': 'inductor/test_dependencies'}, {'test_file': 'inductor/test_compiled_optimizers'}, {'test_file': 'inductor/test_compiled_autograd'}, {'test_file': 'dynamo/test_ctx_manager'}, {'test_file': 'inductor/test_snode_runtime'}, {'test_file': 'inductor/test_coordinate_descent_tuner'}, {'test_file': 'inductor/test_foreach'}, {'test_file': 'inductor/test_extension_backend'}, {'test_file': 'dynamo/test_after_aot'}, {'test_file': 'dynamo/test_aot_autograd'}, {'test_file': 'dynamo/test_cudagraphs'}, {'test_file': 'dynamo/test_exc'}, {'test_file': 'inductor/test_binary_folding'}, {'test_file': 'inductor/test_config'}, {'test_file': 'inductor/test_custom_lowering'}, {'test_file': 'inductor/test_group_batch_fusion'}, {'test_file': 'inductor/test_layout_optim'}, {'test_file': 'inductor/test_minifier'}, {'test_file': 'inductor/test_mmdecomp'}, {'test_file': 'inductor/test_smoke'}, {'test_file': 'inductor/test_split_cat_fx_passes'}, {'test_file': 'inductor/test_triton_wrapper'}, {'test_file': 'inductor/test_flex_attention'}, {'test_file': 'inductor/test_control_flow'}, {'test_file': 'inductor/test_padding'}, {'test_file': 'inductor/test_aot_inductor_arrayref'}, {'test_file': 'inductor/test_halide'}, {'test_file': 'inductor/test_unbacked_symints'}, {'test_file': 'inductor/test_triton_kernels'}, {'test_file': 'inductor/test_torchinductor_strided_blocks'}, {'test_file': 'inductor/test_cpu_select_algorithm'}, {'test_file': 'inductor/test_aot_inductor_custom_ops'}, {'test_file': 'inductor/test_triton_cpu_backend'}, {'test_file': 'inductor/test_alignment'}, {'test_file': 'inductor/test_flex_decoding'}, {'test_file': 'inductor/test_torchbind'}, {'test_file': 'export/test_export'}, {'test_file': 'inductor/test_memory'}, {'test_file': 'inductor/test_benchmark_fusion'}, {'test_file': 'inductor/test_multi_kernel'}, {'test_file': 'inductor/test_inplace_padding'}, {'test_file': 'dynamo/test_functions'}, {'test_file': 'inductor/test_provenance_tracing'}, {'test_file': 'inductor/test_online_softmax'}, {'test_file': 'inductor/test_subgraph_choice'}, {'test_file': 'export/test_torchbind'}, {'test_file': 'inductor/test_cutlass_backend'}, {'test_file': 'dynamo/test_einops'}, {'test_file': 'inductor/test_external_callables'}, {'test_file': 'inductor/test_memory_planning'}, {'test_file': 'inductor/test_loop_ordering'}, {'test_file': 'inductor/test_fp8'}, {'test_file': 'inductor/test_combo_kernels'}, {'test_file': 'inductor/test_cpu_cpp_wrapper'}, {'test_file': 'inductor/test_fxir_backend'}, {'test_file': 'functorch/test_eager_transforms'}, {'test_file': 'inductor/test_cooperative_reductions'}, {'test_file': 'dynamo/test_decorators'}, {'test_file': 'inductor/test_triton_syntax'}, {'test_file': 'inductor/test_codecache'}, {'test_file': 'inductor/test_debug_trace'}, {'test_file': 'inductor/test_op_dtype_prop'}, {'test_file': 'inductor/test_pad_mm'}, {'test_file': 'export/test_nativert'}, {'test_file': 'test_custom_ops'}, {'test_file': 'inductor/test_triton_heuristics'}, {'test_file': 'inductor/test_fuzzer'}, {'test_file': 'dynamo/test_autograd_function'}, {'test_file': 'inductor/test_cpp_wrapper_hipify'}, {'test_file': 'inductor/test_profiler'}, {'test_file': 'export/test_serdes'}, {'test_file': 'inductor/test_ck_backend'}, {'test_file': 'dynamo/test_graph_deduplication'}, {'test_file': 'export/test_serialize'}, {'test_file': 'inductor/test_mps_basic'}, {'test_file': 'inductor/test_compile_subprocess'}, {'test_file': 'test_testing'}, {'test_file': 'inductor/test_cutlass_evt'}, {'test_file': 'export/test_retraceability'}, {'test_file': 'test_content_store'}, {'test_file': 'export/test_cpp_serdes'}, {'test_file': 'export/test_export_training_ir_to_run_decomp'}, {'test_file': 'inductor/test_aot_inductor_package'}, {'test_file': 'inductor/test_analysis'}, {'test_file': 'export/test_unflatten'}, {'test_file': 'dynamo/test_interop'}, {'test_file': 'inductor/test_quantization'}, {'test_file': 'dynamo/test_fake_distributed'}, {'test_file': 'inductor/test_gpu_cpp_wrapper'}, {'test_file': 'dynamo/test_export'}, {'test_file': 'dynamo/test_subclasses'}, {'test_file': 'export/test_export_strict'}, {'test_file': 'export/test_export_with_inline_and_install'}, {'test_file': 'inductor/test_compile_worker'}, {'test_file': 'export/test_unflatten_training_ir'}, {'test_file': 'test_model_exports_to_core_aten'}, {'test_file': 'test_quantization'}, {'test_file': 'inductor/test_async_compile'}, {'test_file': 'inductor/test_static_cuda_launcher'}, {'test_file': 'dynamo/test_error_messages'}, {'test_file': 'dynamo/test_fx_graph_runnable'}, {'test_file': 'inductor/test_remote_cache'}, {'test_file': 'dynamo/test_aot_autograd_cache'}, {'test_file': 'dynamo/test_backward_higher_order_ops'}, {'test_file': 'dynamo/test_base_hop'}, {'test_file': 'dynamo/test_base_output'}, {'test_file': 'dynamo/test_buffers_override'}, {'test_file': 'dynamo/test_bytecode_utils'}, {'test_file': 'dynamo/test_callback'}, {'test_file': 'dynamo/test_compile'}, {'test_file': 'dynamo/test_compiler_bisector'}, {'test_file': 'dynamo/test_comptime'}, {'test_file': 'dynamo/test_config'}, {'test_file': 'dynamo/test_cudagraphs_expandable_segments'}, {'test_file': 'dynamo/test_debug_utils'}, {'test_file': 'dynamo/test_deque_reconstruct'}, {'test_file': 'dynamo/test_deviceguard'}, {'test_file': 'dynamo/test_dicts'}, {'test_file': 'dynamo/test_exceptions'}, {'test_file': 'dynamo/test_export_mutations'}, {'test_file': 'dynamo/test_flat_apply'}, {'test_file': 'dynamo/test_frame_init'}, {'test_file': 'dynamo/test_fx_passes_pre_grad'}, {'test_file': 'dynamo/test_generator'}, {'test_file': 'dynamo/test_global'}, {'test_file': 'dynamo/test_graph_region_tracker'}, {'test_file': 'dynamo/test_guard_manager'}, {'test_file': 'dynamo/test_guard_serialization'}, {'test_file': 'dynamo/test_hooks'}, {'test_file': 'dynamo/test_inline_and_install'}, {'test_file': 'dynamo/test_input_attr_tracking'}, {'test_file': 'dynamo/test_install_free_tensors'}, {'test_file': 'dynamo/test_list'}, {'test_file': 'dynamo/test_metrics_context'}, {'test_file': 'dynamo/test_minifier'}, {'test_file': 'dynamo/test_model_output'}, {'test_file': 'dynamo/test_modes'}, {'test_file': 'dynamo/test_nops'}, {'test_file': 'dynamo/test_optimizers'}, {'test_file': 'dynamo/test_package'}, {'test_file': 'dynamo/test_pgo'}, {'test_file': 'dynamo/test_pre_dispatch'}, {'test_file': 'dynamo/test_precompile_context'}, {'test_file': 'dynamo/test_profiler'}, {'test_file': 'dynamo/test_python_autograd'}, {'test_file': 'dynamo/test_python_dispatcher'}, {'test_file': 'dynamo/test_recompile_ux'}, {'test_file': 'dynamo/test_recompiles'}, {'test_file': 'dynamo/test_reconstruct'}, {'test_file': 'dynamo/test_reorder_logs'}, {'test_file': 'dynamo/test_resume'}, {'test_file': 'dynamo/test_sdpa'}, {'test_file': 'dynamo/test_sets'}, {'test_file': 'dynamo/test_skip_guard_eval_unsafe'}, {'test_file': 'dynamo/test_skip_non_tensor'}, {'test_file': 'dynamo/test_sources'}, {'test_file': 'dynamo/test_structured_trace'}, {'test_file': 'dynamo/test_subgraphs'}, {'test_file': 'dynamo/test_torchrec'}, {'test_file': 'dynamo/test_trace_rules'}, {'test_file': 'dynamo/test_unittest'}, {'test_file': 'dynamo/test_utils'}, {'test_file': 'dynamo/test_verify_correctness'}, {'test_file': 'dynamo/test_view'}, {'test_file': 'export/test_converter'}, {'test_file': 'export/test_db'}, {'test_file': 'export/test_draft_export'}, {'test_file': 'export/test_experimental'}, {'test_file': 'export/test_functionalized_assertions'}, {'test_file': 'export/test_hop'}, {'test_file': 'export/test_lift_unlift'}, {'test_file': 'export/test_package'}, {'test_file': 'export/test_pass_infra'}, {'test_file': 'export/test_passes'}, {'test_file': 'export/test_schema'}, {'test_file': 'export/test_sparse'}, {'test_file': 'export/test_swap'}, {'test_file': 'export/test_tools'}, {'test_file': 'export/test_tree_utils'}, {'test_file': 'export/test_upgrader'}, {'test_file': 'export/test_verifier'}, {'test_file': 'inductor/test_aot_inductor_utils'}, {'test_file': 'inductor/test_auto_functionalize'}, {'test_file': 'inductor/test_autoheuristic'}, {'test_file': 'inductor/test_b2b_gemm'}, {'test_file': 'inductor/test_benchmarking'}, {'test_file': 'inductor/test_best_config'}, {'test_file': 'inductor/test_block_analysis'}, {'test_file': 'inductor/test_codegen_triton'}, {'test_file': 'inductor/test_compile'}, {'test_file': 'inductor/test_cudacodecache'}, {'test_file': 'inductor/test_cudagraph_trees_expandable_segments'}, {'test_file': 'inductor/test_custom_post_grad_passes'}, {'test_file': 'inductor/test_decompose_mem_bound_mm'}, {'test_file': 'inductor/test_distributed_patterns'}, {'test_file': 'inductor/test_efficient_conv_bn_eval'}, {'test_file': 'inductor/test_fx_fusion'}, {'test_file': 'inductor/test_graph_transform_observer'}, {'test_file': 'inductor/test_helion_kernels'}, {'test_file': 'inductor/test_indexing'}, {'test_file': 'inductor/test_inductor_annotations'}, {'test_file': 'inductor/test_inductor_scheduler'}, {'test_file': 'inductor/test_inductor_utils'}, {'test_file': 'inductor/test_inplacing_pass'}, {'test_file': 'inductor/test_kernel_optimization'}, {'test_file': 'inductor/test_metrics'}, {'test_file': 'inductor/test_minifier_isolate'}, {'test_file': 'inductor/test_minifier_utils'}, {'test_file': 'inductor/test_move_constructors_to_cuda'}, {'test_file': 'inductor/test_needs_exact_strides'}, {'test_file': 'inductor/test_op_completeness'}, {'test_file': 'inductor/test_ordered_set'}, {'test_file': 'inductor/test_scatter_optimization'}, {'test_file': 'inductor/test_split_cat_fx_aten_passes'}, {'test_file': 'inductor/test_torchinductor_codegen_config_overrides'}, {'test_file': 'inductor/test_triton_extension_backend'}, {'test_file': 'inductor/test_utils'}, {'test_file': 'inductor/test_xpu_basic'}, {'test_file': 'test_functionalization_of_rng_ops'}, {'test_file': 'test_sparse_semi_structured'}, {'test_file': 'test_dynamic_shapes'}, {'test_file': 'higher_order_ops/test_invoke_subgraph'}, {'test_file': 'functorch/test_control_flow'}, {'test_file': 'test_torch'}, {'test_file': 'test_reductions'}, {'test_file': 'test_fake_tensor'}, {'test_file': 'test_ops'}, {'test_file': 'test_matmul_cuda'}, {'test_file': 'test_linalg'}, {'test_file': 'test_nestedtensor'}, {'test_file': 'test_modules'}, {'test_file': 'test_proxy_tensor'}, {'test_file': 'test_hop_infra'}, {'test_file': 'test_fx'}, {'test_file': 'test_foreach'}, {'test_file': 'functorch/test_aotdispatch'}, {'test_file': 'benchmark_utils/test_benchmark_utils'}, {'test_file': 'test_decomp'}, {'test_file': 'test_expanded_weights'}, {'test_file': 'distributions/test_distributions'}, {'test_file': 'doctests'}, {'test_file': 'test_cpp_api_parity'}, {'test_file': 'test_ops_gradients'}, {'test_file': 'profiler/test_cpp_thread'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_nn'}, {'test_file': 'test_tensorboard'}, {'test_file': 'test_transformers_privateuse1'}, {'test_file': 'test_cpp_extensions_mtia_backend'}, {'test_file': 'test_autograd'}, {'test_file': 'test_autograd_fallback'}, {'test_file': 'test_jit'}, {'test_file': 'profiler/test_memory_profiler'}, {'test_file': 'functorch/test_ops'}, {'test_file': 'nn/test_parametrization'}, {'test_file': 'profiler/test_kineto'}, {'test_file': 'profiler/test_profiler'}, {'test_file': 'test_ci_sanity_check_fail'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_dataloader'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_ops_jit'}, {'test_file': 'test_overrides'}, {'test_file': 'test_type_hints'}, {'test_file': 'test_sparse'}, {'test_file': 'functorch/test_ac_logging'}, {'test_file': 'test_numa_binding'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}, {'test_file': 'test_datapipe'}, {'test_file': 'backends/xeon/test_launch'}, {'test_file': 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic'}, {'test_file': 'cpp_extensions/python_agnostic_extension/test/test_python_agnostic'}, {'test_file': 'distributions/test_constraints'}, {'test_file': 'functorch/test_ac'}, {'test_file': 'functorch/test_ac_knapsack'}, {'test_file': 'functorch/test_aot_joint_with_descriptors'}, {'test_file': 'functorch/test_logging'}, {'test_file': 'functorch/test_minifier'}, {'test_file': 'functorch/test_vmap'}, {'test_file': 'functorch/test_vmap_registrations'}, {'test_file': 'higher_order_ops/test_invoke_quant'}, {'test_file': 'higher_order_ops/test_with_effects'}, {'test_file': 'lazy/test_bindings'}, {'test_file': 'lazy/test_debug_util'}, {'test_file': 'lazy/test_functionalization'}, {'test_file': 'lazy/test_generator'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'lazy/test_step_closures'}, {'test_file': 'lazy/test_ts_opinfo'}, {'test_file': 'nn/test_convolution'}, {'test_file': 'nn/test_dropout'}, {'test_file': 'nn/test_embedding'}, {'test_file': 'nn/test_init'}, {'test_file': 'nn/test_lazy_modules'}, {'test_file': 'nn/test_load_state_dict'}, {'test_file': 'nn/test_module_hooks'}, {'test_file': 'nn/test_multihead_attention'}, {'test_file': 'nn/test_packed_sequence'}, {'test_file': 'nn/test_pooling'}, {'test_file': 'nn/test_pruning'}, {'test_file': 'optim/test_lrscheduler'}, {'test_file': 'optim/test_optim'}, {'test_file': 'optim/test_swa_utils'}, {'test_file': 'profiler/test_execution_trace'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'profiler/test_python_tracer'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'test_accelerator'}, {'test_file': 'test_ao_sparsity'}, {'test_file': 'test_appending_byte_serializer'}, {'test_file': 'test_autocast'}, {'test_file': 'test_autoload'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_binary_ufuncs'}, {'test_file': 'test_bundled_inputs'}, {'test_file': 'test_comparison_utils'}, {'test_file': 'test_compile_benchmark_util'}, {'test_file': 'test_complex'}, {'test_file': 'test_cpp_extensions_aot_ninja'}, {'test_file': 'test_cpp_extensions_jit'}, {'test_file': 'test_cpp_extensions_stream_and_event'}, {'test_file': 'test_cuda'}, {'test_file': 'test_cuda_expandable_segments'}, {'test_file': 'test_cuda_primary_ctx'}, {'test_file': 'test_cuda_sanitizer'}, {'test_file': 'test_cuda_trace'}, {'test_file': 'test_dispatch'}, {'test_file': 'test_dlpack'}, {'test_file': 'test_extension_utils'}, {'test_file': 'test_file_check'}, {'test_file': 'test_flop_counter'}, {'test_file': 'test_function_schema'}, {'test_file': 'test_functional_autograd_benchmark'}, {'test_file': 'test_functional_optim'}, {'test_file': 'test_functionalization'}, {'test_file': 'test_futures'}, {'test_file': 'test_fx_experimental'}, {'test_file': 'test_fx_passes'}, {'test_file': 'test_fx_reinplace_pass'}, {'test_file': 'test_hub'}, {'test_file': 'test_import_stats'}, {'test_file': 'test_indexing'}, {'test_file': 'test_itt'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'test_jit_disabled'}, {'test_file': 'test_jit_llga_fuser'}, {'test_file': 'test_jiterator'}, {'test_file': 'test_legacy_vmap'}, {'test_file': 'test_license'}, {'test_file': 'test_logging'}, {'test_file': 'test_masked'}, {'test_file': 'test_maskedtensor'}, {'test_file': 'test_meta'}, {'test_file': 'test_mkl_verbose'}, {'test_file': 'test_mkldnn'}, {'test_file': 'test_mkldnn_fusion'}, {'test_file': 'test_mkldnn_verbose'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'test_module_tracker'}, {'test_file': 'test_monitor'}, {'test_file': 'test_multiprocessing'}, {'test_file': 'test_multiprocessing_spawn'}, {'test_file': 'test_namedtensor'}, {'test_file': 'test_namedtuple_return_api'}, {'test_file': 'test_native_functions'}, {'test_file': 'test_native_mha'}, {'test_file': 'test_numba_integration'}, {'test_file': 'test_numpy_interop'}, {'test_file': 'test_openmp'}, {'test_file': 'test_openreg'}, {'test_file': 'test_ops_fwd_gradients'}, {'test_file': 'test_optim'}, {'test_file': 'test_out_dtype_op'}, {'test_file': 'test_package'}, {'test_file': 'test_per_overload_api'}, {'test_file': 'test_prims'}, {'test_file': 'test_pruning_op'}, {'test_file': 'test_python_dispatch'}, {'test_file': 'test_pytree'}, {'test_file': 'test_rename_privateuse1_to_existing_device'}, {'test_file': 'test_scatter_gather_ops'}, {'test_file': 'test_schema_check'}, {'test_file': 'test_segment_reductions'}, {'test_file': 'test_serialization'}, {'test_file': 'test_set_default_mobile_cpu_allocator'}, {'test_file': 'test_shape_ops'}, {'test_file': 'test_show_pickle'}, {'test_file': 'test_sort_and_select'}, {'test_file': 'test_sparse_csr'}, {'test_file': 'test_spectral_ops'}, {'test_file': 'test_stateless'}, {'test_file': 'test_subclass'}, {'test_file': 'test_sympy_utils'}, {'test_file': 'test_tensor_creation_ops'}, {'test_file': 'test_tensorexpr'}, {'test_file': 'test_tensorexpr_pybind'}, {'test_file': 'test_transformers'}, {'test_file': 'test_type_info'}, {'test_file': 'test_type_promotion'}, {'test_file': 'test_typing'}, {'test_file': 'test_unary_ufuncs'}, {'test_file': 'test_utils'}, {'test_file': 'test_utils_config_module'}, {'test_file': 'test_utils_filelock'}, {'test_file': 'test_view_ops'}, {'test_file': 'test_vulkan'}, {'test_file': 'test_weak'}, {'test_file': 'test_xnnpack_integration'}, {'test_file': 'torch_np/numpy_tests/core/test_dlpack'}, {'test_file': 'torch_np/numpy_tests/core/test_dtype'}, {'test_file': 'torch_np/numpy_tests/core/test_einsum'}, {'test_file': 'torch_np/numpy_tests/core/test_getlimits'}, {'test_file': 'torch_np/numpy_tests/core/test_indexing'}, {'test_file': 'torch_np/numpy_tests/core/test_numeric'}, {'test_file': 'torch_np/numpy_tests/core/test_numerictypes'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_methods'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarinherit'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarmath'}, {'test_file': 'torch_np/numpy_tests/core/test_shape_base'}, {'test_file': 'torch_np/numpy_tests/fft/test_helper'}, {'test_file': 'torch_np/numpy_tests/fft/test_pocketfft'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraypad'}, {'test_file': '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-298fd8a6f38019bea388.json is not a benchmark record, skipping
2025-08-14T23:40:46.0641774Z   warn(f"{result} from {filepath} is not a benchmark record, skipping")
2025-08-14T23:40:46.0704901Z Prepare all required actions
2025-08-14T23:40:46.0705122Z Getting action download info
2025-08-14T23:40:46.0716576Z ##[group]Run ./.github/actions/teardown-rocm
2025-08-14T23:40:46.0716714Z env:
2025-08-14T23:40:46.0716828Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:46.0716987Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:46.0717180Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:46.0717371Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:46.0717771Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:46.0718152Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:46.0718286Z   AWS_REGION: us-east-1
2025-08-14T23:40:46.0718433Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:46.0718615Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:46.0720709Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:46.0720895Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:46.0721093Z   DEVICE_NAME: rocm
2025-08-14T23:40:46.0721219Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:46.0721358Z ##[endgroup]
2025-08-14T23:40:46.0727903Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-08-14T23:40:46.0728132Z # ignore expansion of "docker ps -q" since it could be empty
2025-08-14T23:40:46.0728308Z # shellcheck disable=SC2046
2025-08-14T23:40:46.0728464Z docker stop $(docker ps -q) || true
2025-08-14T23:40:46.0728612Z # Prune all stopped containers.
2025-08-14T23:40:46.0728759Z docker container prune -f
2025-08-14T23:40:46.0732323Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:40:46.0732486Z env:
2025-08-14T23:40:46.0732592Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:40:46.0732743Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:40:46.0733036Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:40:46.0733221Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:40:46.0733608Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:40:46.0734030Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:40:46.0734163Z   AWS_REGION: us-east-1
2025-08-14T23:40:46.0734304Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:40:46.0734473Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:40:46.0736537Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:40:46.0736719Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:40:46.0740852Z   DEVICE_NAME: rocm
2025-08-14T23:40:46.0740984Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:40:46.0741115Z ##[endgroup]
2025-08-14T23:40:56.6908997Z 4ec848794e9f
2025-08-14T23:41:01.1482151Z Deleted Containers:
2025-08-14T23:41:01.1482748Z 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:41:01.1492291Z 
2025-08-14T23:41:01.1492449Z Total reclaimed space: 11.95GB
2025-08-14T23:41:01.1536847Z Prepare all required actions
2025-08-14T23:41:01.1560508Z ##[group]Run ./.github/actions/diskspace-cleanup
2025-08-14T23:41:01.1560666Z with:
2025-08-14T23:41:01.1560772Z   diskspace-cutoff: 70
2025-08-14T23:41:01.1560884Z env:
2025-08-14T23:41:01.1560985Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:41:01.1561136Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:41:01.1563007Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:41:01.1563186Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:41:01.1563777Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:41:01.1564202Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:41:01.1564328Z   AWS_REGION: us-east-1
2025-08-14T23:41:01.1564508Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:41:01.1564680Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:41:01.1566931Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:41:01.1568637Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:41:01.1568841Z   DEVICE_NAME: rocm
2025-08-14T23:41:01.1568960Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:41:01.1569090Z ##[endgroup]
2025-08-14T23:41:01.1584230Z ##[group]Run set -ex
2025-08-14T23:41:01.1584373Z set -ex
2025-08-14T23:41:01.1584486Z diskspace_cutoff=70
2025-08-14T23:41:01.1584652Z docker_root_dir=$(docker info -f '{{.DockerRootDir}}')
2025-08-14T23:41:01.1584826Z if [ ! -d "$docker_root_dir" ]; then
2025-08-14T23:41:01.1585044Z     echo "Docker root directory ($docker_root_dir) does not exist. Skipping disk space check."
2025-08-14T23:41:01.1585242Z     exit 0
2025-08-14T23:41:01.1585350Z fi
2025-08-14T23:41:01.1587134Z diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
2025-08-14T23:41:01.1587483Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified"
2025-08-14T23:41:01.1587784Z if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then
2025-08-14T23:41:01.1587948Z     docker system prune -af
2025-08-14T23:41:01.1588156Z     diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
2025-08-14T23:41:01.1588385Z     if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then
2025-08-14T23:41:01.1588621Z         echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace."
2025-08-14T23:41:01.1588832Z         echo "$msg"
2025-08-14T23:41:01.1588953Z         exit 1
2025-08-14T23:41:01.1590550Z     else
2025-08-14T23:41:01.1590682Z         difference=$((diskspace - diskspace_new))
2025-08-14T23:41:01.1590854Z         echo "Diskspace saved: $difference percent"
2025-08-14T23:41:01.1590995Z     fi
2025-08-14T23:41:01.1591092Z fi
2025-08-14T23:41:01.1596501Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-08-14T23:41:01.1596653Z env:
2025-08-14T23:41:01.1596754Z   GIT_DEFAULT_BRANCH: main
2025-08-14T23:41:01.1598740Z   RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts
2025-08-14T23:41:01.1598928Z   RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results
2025-08-14T23:41:01.1599103Z   RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs
2025-08-14T23:41:01.1599494Z   GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 992 --device /dev/dri/renderD169 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host
2025-08-14T23:41:01.1599942Z   AWS_DEFAULT_REGION: us-east-1
2025-08-14T23:41:01.1600068Z   AWS_REGION: us-east-1
2025-08-14T23:41:01.1600211Z   AWS_ACCESS_KEY_ID: ***
2025-08-14T23:41:01.1600378Z   AWS_SECRET_ACCESS_KEY: ***
2025-08-14T23:41:01.1605161Z   AWS_SESSION_TOKEN: ***
2025-08-14T23:41:01.1605403Z   CONTAINER_NAME: 4ec848794e9f7df6f240d304aad084ccae3ed5b5724aca290b3cada16d5a4a49
2025-08-14T23:41:01.1605672Z   DEVICE_NAME: rocm
2025-08-14T23:41:01.1605790Z   DEVICE_TYPE: AMD Instinct Mi325X VF
2025-08-14T23:41:01.1605921Z ##[endgroup]
2025-08-14T23:41:01.1628098Z + diskspace_cutoff=70
2025-08-14T23:41:01.1631655Z ++ docker info -f '{{.DockerRootDir}}'
2025-08-14T23:41:01.2052581Z + docker_root_dir=/home/runner/docker-data
2025-08-14T23:41:01.2052827Z + '[' '!' -d /home/runner/docker-data ']'
2025-08-14T23:41:01.2058445Z ++ df -H --output=pcent /home/runner/docker-data
2025-08-14T23:41:01.2060404Z ++ sed -n 2p
2025-08-14T23:41:01.2061038Z ++ sed s/%//
2025-08-14T23:41:01.2062494Z ++ sed 's/ //'
2025-08-14T23:41:01.2078801Z + diskspace=' 8'
2025-08-14T23:41:01.2079506Z + 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'
2025-08-14T23:41:01.2080209Z + [[  8 -ge 70 ]]
2025-08-14T23:41:01.2123914Z Post job cleanup.
2025-08-14T23:41:01.2156225Z Post job cleanup.
2025-08-14T23:41:01.2786496Z Post job cleanup.
2025-08-14T23:41:01.2966519Z Logging out of registry 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-08-14T23:41:01.3167821Z Post job cleanup.
2025-08-14T23:41:01.3767042Z Post job cleanup.
2025-08-14T23:41:01.3802858Z Post job cleanup.
2025-08-14T23:41:01.4288662Z [command]/usr/bin/git version
2025-08-14T23:41:01.4313335Z git version 2.50.1
2025-08-14T23:41:01.4329355Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/10e67529-1cea-4507-8187-ebdc00d8213d/.gitconfig'
2025-08-14T23:41:01.4337681Z Temporarily overriding HOME='/home/runner/_work/_temp/10e67529-1cea-4507-8187-ebdc00d8213d' before making global git config changes
2025-08-14T23:41:01.4338069Z Adding repository directory to the temporary git global config as a safe directory
2025-08-14T23:41:01.4339941Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch
2025-08-14T23:41:01.4370559Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-08-14T23:41:01.4395613Z [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' || :"
2025-08-14T23:41:01.4647310Z Entering 'android/libs/fbjni'
2025-08-14T23:41:01.4682135Z Entering 'third_party/FP16'
2025-08-14T23:41:01.4722736Z Entering 'third_party/FXdiv'
2025-08-14T23:41:01.4764139Z Entering 'third_party/NNPACK'
2025-08-14T23:41:01.4803204Z Entering 'third_party/NVTX'
2025-08-14T23:41:01.4855621Z Entering 'third_party/VulkanMemoryAllocator'
2025-08-14T23:41:01.4888149Z Entering 'third_party/XNNPACK'
2025-08-14T23:41:01.4942497Z Entering 'third_party/aiter'
2025-08-14T23:41:01.4986508Z Entering 'third_party/aiter/3rdparty/composable_kernel'
2025-08-14T23:41:01.5035905Z Entering 'third_party/benchmark'
2025-08-14T23:41:01.5089280Z Entering 'third_party/composable_kernel'
2025-08-14T23:41:01.5134474Z Entering 'third_party/cpp-httplib'
2025-08-14T23:41:01.5173055Z Entering 'third_party/cpuinfo'
2025-08-14T23:41:01.5202493Z Entering 'third_party/cudnn_frontend'
2025-08-14T23:41:01.5242046Z Entering 'third_party/cutlass'
2025-08-14T23:41:01.5286789Z Entering 'third_party/fbgemm'
2025-08-14T23:41:01.5329515Z Entering 'third_party/fbgemm/external/asmjit'
2025-08-14T23:41:01.5369988Z Entering 'third_party/fbgemm/external/composable_kernel'
2025-08-14T23:41:01.5417892Z Entering 'third_party/fbgemm/external/cpuinfo'
2025-08-14T23:41:01.5450834Z Entering 'third_party/fbgemm/external/cutlass'
2025-08-14T23:41:01.5490195Z Entering 'third_party/fbgemm/external/googletest'
2025-08-14T23:41:01.5530142Z Entering 'third_party/fbgemm/external/hipify_torch'
2025-08-14T23:41:01.5561373Z Entering 'third_party/fbgemm/external/json'
2025-08-14T23:41:01.5593258Z Entering 'third_party/flash-attention'
2025-08-14T23:41:01.5630330Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-08-14T23:41:01.5659035Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-08-14T23:41:01.5689769Z Entering 'third_party/flatbuffers'
2025-08-14T23:41:01.5720705Z Entering 'third_party/fmt'
2025-08-14T23:41:01.5750383Z Entering 'third_party/gemmlowp/gemmlowp'
2025-08-14T23:41:01.5789927Z Entering 'third_party/gloo'
2025-08-14T23:41:01.5827667Z Entering 'third_party/googletest'
2025-08-14T23:41:01.5859248Z Entering 'third_party/ideep'
2025-08-14T23:41:01.5890417Z Entering 'third_party/ideep/mkl-dnn'
2025-08-14T23:41:01.5925528Z Entering 'third_party/ittapi'
2025-08-14T23:41:01.5970403Z Entering 'third_party/kineto'
2025-08-14T23:41:01.6018472Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-08-14T23:41:01.6059044Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-08-14T23:41:01.6095646Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-08-14T23:41:01.6128993Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-08-14T23:41:01.6171007Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-08-14T23:41:01.6201406Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-08-14T23:41:01.6250643Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-08-14T23:41:01.6275424Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-08-14T23:41:01.6310312Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T23:41:01.6344069Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-08-14T23:41:01.6382826Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-08-14T23:41:01.6415527Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-08-14T23:41:01.6460532Z Entering 'third_party/kleidiai'
2025-08-14T23:41:01.6507709Z Entering 'third_party/mimalloc'
2025-08-14T23:41:01.6531065Z Entering 'third_party/nlohmann'
2025-08-14T23:41:01.6560644Z Entering 'third_party/onnx'
2025-08-14T23:41:01.6604169Z Entering 'third_party/onnx/third_party/pybind11'
2025-08-14T23:41:01.6647401Z Entering 'third_party/opentelemetry-cpp'
2025-08-14T23:41:01.6680260Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-08-14T23:41:01.6714740Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-08-14T23:41:01.6752543Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-08-14T23:41:01.6786148Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-08-14T23:41:01.6814427Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-08-14T23:41:01.6849264Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-08-14T23:41:01.6882374Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-08-14T23:41:01.6920088Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-08-14T23:41:01.6968157Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-08-14T23:41:01.7002599Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-08-14T23:41:01.7052710Z Entering 'third_party/pocketfft'
2025-08-14T23:41:01.7084032Z Entering 'third_party/protobuf'
2025-08-14T23:41:01.7123361Z Entering 'third_party/protobuf/third_party/benchmark'
2025-08-14T23:41:01.7162471Z Entering 'third_party/protobuf/third_party/googletest'
2025-08-14T23:41:01.7197949Z Entering 'third_party/psimd'
2025-08-14T23:41:01.7229601Z Entering 'third_party/pthreadpool'
2025-08-14T23:41:01.7268292Z Entering 'third_party/pybind11'
2025-08-14T23:41:01.7302820Z Entering 'third_party/python-peachpy'
2025-08-14T23:41:01.7341527Z Entering 'third_party/sleef'
2025-08-14T23:41:01.7372420Z Entering 'third_party/tensorpipe'
2025-08-14T23:41:01.7410857Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-08-14T23:41:01.7447500Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-08-14T23:41:01.7491335Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-08-14T23:41:01.7528567Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-08-14T23:41:01.7559863Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-08-14T23:41:01.7615198Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-08-14T23:41:01.7638176Z http.https://github.com/.extraheader
2025-08-14T23:41:01.7638909Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-08-14T23:41:01.7663509Z [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' || :"
2025-08-14T23:41:01.7878731Z Entering 'android/libs/fbjni'
2025-08-14T23:41:01.7901791Z http.https://github.com/.extraheader
2025-08-14T23:41:01.7918880Z Entering 'third_party/FP16'
2025-08-14T23:41:01.7947758Z http.https://github.com/.extraheader
2025-08-14T23:41:01.7970732Z Entering 'third_party/FXdiv'
2025-08-14T23:41:01.8000589Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8022641Z Entering 'third_party/NNPACK'
2025-08-14T23:41:01.8056380Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8080614Z Entering 'third_party/NVTX'
2025-08-14T23:41:01.8094945Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8118326Z Entering 'third_party/VulkanMemoryAllocator'
2025-08-14T23:41:01.8137340Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8158766Z Entering 'third_party/XNNPACK'
2025-08-14T23:41:01.8178076Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8217321Z Entering 'third_party/aiter'
2025-08-14T23:41:01.8238994Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8269303Z Entering 'third_party/aiter/3rdparty/composable_kernel'
2025-08-14T23:41:01.8296803Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8328973Z Entering 'third_party/benchmark'
2025-08-14T23:41:01.8348490Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8367979Z Entering 'third_party/composable_kernel'
2025-08-14T23:41:01.8384013Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8412130Z Entering 'third_party/cpp-httplib'
2025-08-14T23:41:01.8426540Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8446392Z Entering 'third_party/cpuinfo'
2025-08-14T23:41:01.8468485Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8486807Z Entering 'third_party/cudnn_frontend'
2025-08-14T23:41:01.8502058Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8520303Z Entering 'third_party/cutlass'
2025-08-14T23:41:01.8542361Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8565570Z Entering 'third_party/fbgemm'
2025-08-14T23:41:01.8587280Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8607285Z Entering 'third_party/fbgemm/external/asmjit'
2025-08-14T23:41:01.8620713Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8650733Z Entering 'third_party/fbgemm/external/composable_kernel'
2025-08-14T23:41:01.8670708Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8693312Z Entering 'third_party/fbgemm/external/cpuinfo'
2025-08-14T23:41:01.8709118Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8735994Z Entering 'third_party/fbgemm/external/cutlass'
2025-08-14T23:41:01.8756209Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8786171Z Entering 'third_party/fbgemm/external/googletest'
2025-08-14T23:41:01.8812133Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8839157Z Entering 'third_party/fbgemm/external/hipify_torch'
2025-08-14T23:41:01.8858931Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8879998Z Entering 'third_party/fbgemm/external/json'
2025-08-14T23:41:01.8901927Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8919753Z Entering 'third_party/flash-attention'
2025-08-14T23:41:01.8942297Z http.https://github.com/.extraheader
2025-08-14T23:41:01.8968463Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-08-14T23:41:01.8988181Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9010292Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-08-14T23:41:01.9036405Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9063214Z Entering 'third_party/flatbuffers'
2025-08-14T23:41:01.9077341Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9097895Z Entering 'third_party/fmt'
2025-08-14T23:41:01.9115983Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9136058Z Entering 'third_party/gemmlowp/gemmlowp'
2025-08-14T23:41:01.9150903Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9176002Z Entering 'third_party/gloo'
2025-08-14T23:41:01.9195395Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9223007Z Entering 'third_party/googletest'
2025-08-14T23:41:01.9244938Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9271028Z Entering 'third_party/ideep'
2025-08-14T23:41:01.9297482Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9316049Z Entering 'third_party/ideep/mkl-dnn'
2025-08-14T23:41:01.9328774Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9357196Z Entering 'third_party/ittapi'
2025-08-14T23:41:01.9376176Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9397252Z Entering 'third_party/kineto'
2025-08-14T23:41:01.9426325Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9443900Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-08-14T23:41:01.9457051Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9473994Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-08-14T23:41:01.9488103Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9506818Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-08-14T23:41:01.9530510Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9559381Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-08-14T23:41:01.9572808Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9591571Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-08-14T23:41:01.9607518Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9624528Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-08-14T23:41:01.9648773Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9671095Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-08-14T23:41:01.9701612Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9738685Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-08-14T23:41:01.9759920Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9781395Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-08-14T23:41:01.9799402Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9819677Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-08-14T23:41:01.9837810Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9860333Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-08-14T23:41:01.9875110Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9900379Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-08-14T23:41:01.9927797Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9955631Z Entering 'third_party/kleidiai'
2025-08-14T23:41:01.9973724Z http.https://github.com/.extraheader
2025-08-14T23:41:01.9992143Z Entering 'third_party/mimalloc'
2025-08-14T23:41:02.0012799Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0040081Z Entering 'third_party/nlohmann'
2025-08-14T23:41:02.0059325Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0080650Z Entering 'third_party/onnx'
2025-08-14T23:41:02.0096830Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0122046Z Entering 'third_party/onnx/third_party/pybind11'
2025-08-14T23:41:02.0140683Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0171380Z Entering 'third_party/opentelemetry-cpp'
2025-08-14T23:41:02.0186289Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0205859Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-08-14T23:41:02.0220751Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0239545Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-08-14T23:41:02.0259418Z http.https://github.com/.extraheader
2025-08-14T23:41:02.0275524Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
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